Newsroom

Posted on November 21, 2023

DSML trend: New and updated features of Bard level the playing field

Recently Google Bard announced several new or updated features. For example, “Get help with math equations: Stuck on a math problem? Ask Bard for a step-by-step explanation of how to solve the equation. You can even take a photo of the question and upload it instead of typing it out.”

“Charts & graphs to visualize data: Bard can now generate charts from data or equations you include in your prompts or from tables that Bard generates during your conversations. We even made a graph resembling the Bard sparkle while playing around with this new feature!”

That’s my take on it:

I tested the feature by entering the following problems “Solve 9^(2x-5) = 27^x.  Explain the solution step by step.” After a few seconds, Bard presented two versions of the solution, and both were correct. More importantly, it explained the procedure step by step. In my opinion, the explanation is even clearer than the textbook. If I need further explanation, Bard can provide additional information. For instance, after entering “Please explain the power of a power rule used in Step 2,” Bard illustrated more details.

A chatbot like Bard has the potential to serve as your personalized and intelligent tutor, catering to your individual learning pace, academic proficiency, and preferred learning style. In the past, parents had to invest significantly in hiring private tutors or enrolling their children in intensive boot camps. However, the playing field has been leveled, and now access to a personalized tutor is just a few clicks away for virtually anyone. I wish I had the opportunity to access this technology when I was a child! If so, my knowledge could have tripled, and I might have earned three Ph.Ds!

Google Bard:

https://bard.google.com/chat

Posted on November 17, 2023

DSML trend: OpenAI fires Sam Altman

It happened just now. Today (November 17, 2023) OpenAI's board of directors announced that Sam Altman will be stepping down as CEO, with technology chief Mira Murati set to take over the position. The decision comes after a thorough review process, during which the board determined that Altman's communication lacked consistent truthfulness, thereby impeding the board's ability to fulfill its responsibilities. The statement emphasized that due to this, the board no longer has confidence in Altman's capacity to effectively lead OpenAI. The board also announced that Greg Brockman, OpenAI’s president will be stepping down as chairman of the board but will keep a role at the company.

That’s my take on it: As of now, Altman has not issued any public response yet. Given the maturity of the technology, I believe the departure of both Altman and Brockman will likely have minimal impact on the development of OpenAI or the broader field of generative AI. However, it's improbable that Altman will sit there and do nothing. It is possible that he may embark on launching another startup or join a competitor to OpenAI (e.g. Claude or Google Bard?)

News: https://www.cnbc.com/2023/11/17/sam-altman-leaves-openai-mira-murati-appointed-interim-boss.html

Posted on November 17, 2023

The Harvard Business Review featured an article on November 2, 2033, titled "How Cloud Technology is Transforming Data Science." Written by Peter Wang, the CEO and co-founder of Anaconda, the article discusses the impact of cloud computing on data science practices. Wang highlights how cloud platforms, such as IBM Watson and Tableau, are revolutionizing the field by offering scalable computational resources and enhancing workforce agility. These cloud-based analytics tools empower teams to access information and collaborate in real time, facilitating quicker insights and problem-solving. Moreover, cloud computing promotes inclusivity in data science by providing smaller entities, such as startups and small teams, with the means to innovate on par with larger corporations. The cloud's collaborative capabilities extend to distributed data science teams, enabling effective collaboration irrespective of geographical constraints. While the cloud brings forth significant advancements in data science, it also introduces new challenges, particularly in data privacy and security. To address these concerns, Wang emphasizes the importance of employing techniques like data partitioning, encryption, and robust frameworks for mitigation.

That’s my take on it: Given the significance of cloud computing, it is undoubtedly essential to integrate it into the curriculum of data science education. However, the current landscape of the cloud computing market is highly diverse, featuring numerous vendors such as AWS, Google Cloud, Microsoft Azure, IBM Watson, and more. This question arises: should cloud computing training be tailored to specific vendors or remain vendor-independent? Opting for vendor-specific training allows students to gain practical experience with the tools and services of major cloud providers like AWS, Azure, and IBM Watson, preparing them for roles utilizing these platforms. The drawback is that knowledge becomes less transferable if students later work with a different cloud provider. On the other hand, adopting a vendor-independent approach ensures knowledge transferability across various cloud platforms and avoids explicit promotion of specific vendors within the program. However, graduates may need additional, vendor-specific training upon entering the workforce. Striking a balance between these approaches is crucial to provide students with a well-rounded and adaptable skill set in the dynamic field of cloud computing. What do you think?

Full article: https://hbr.org/2023/11/how-the-cloud-is-changing-data-science?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=Newsletter&utm_content=Data+Science+Insider%3A+November+17th%2C+2023&utm_campaign=18112023

Posted on October 19, 2023

On October 16, Baidu, the Chinese search engine giant, unveiled its updated large language model, known as Ernie 4. They asserted that it is on a par to OpenAI's GPT 4 in terms of performance, although it is not yet accessible to the public. Additionally, Baidu introduced a new AI-based product called Baidu GBI, developed from the ground up to provide support for natural language interaction and handle cross-database analysis, among other functions. According to Baidu's CEO Li, this product has the capability to complete data analysis tasks that would take humans several days in just a matter of minutes.

That’s my take on it:

Baidu had previously released Ernie 3.5 in June, claiming its superior performance compared to OpenAI's ChatGPT 3.5 and even surpassing GPT 4 in certain Chinese-language skills. These assertive statements should undergo validation through objective benchmark tests by independent parties.

Full report: https://finance.yahoo.com/news/baidu-says-ai-chatbot-ernie-093000101.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAE5TgYsRNuuI8tqvuAdTuTlhUfPwDoUBK0Lqn7T3wds300KRdiCn07yvxheXmcF_B9oYQvNKHelI8pJ5IZguMCKXX6nmqx38LMinQTTbHJlY5ZLy0dyIWs4RE-EDa8DaC-2QlyMxwSzOaf1mwniCaUSEar4f2AWL72gf1wWb_x9i

Posted on October 13, 2023

Yesterday (10/12) an article published by Analytics Insight detailed how seven data science positions can be executed without the need for programming skills. Instead, they rely on the capabilities of user-friendly software tools like Tableau, Excel, Power BI, and more. These positions are:

·      Data analyst

·      Business Intelligence Analyst

·      Data Consultant

·      Market Research Analyst

·      Data Visualization Specialist

·      Data-driven Strategist

·      Data Product Manager

That’s my take on it:

No code solutions provide pre-built components, templates, and graphical user interfaces (GUI) that can accelerate development compared to programming. These tools allow users to focus more on the research question, the data, and the business logic rather than the syntax. However, no-code solutions inevitably involve some trade-offs in terms of flexibility, customization ability, scalability and performance compared to coding. Data science education should balance both sides. In my humble opinion, starting data science training with a focus on programming right away might not be the most advisable approach. Emphasizing the fundamental concepts as the foundation is crucial, while the tools, which serve as means to an end, should be treated as secondary. Leveraging GUI-based software applications reduces the entry barriers into the field, thereby broadening the pool of potential talents.

Article:

https://www.analyticsinsight.net/7-data-science-jobs-you-can-do-without-any-coding-skills/?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=Newsletter&utm_content=Data+Science+Insider%3A+October+13th%2C+2023&utm_campaign=14102023

Posted on September 29, 2023

DSML trend: Meta and OpenAI announced new features simultaneously

On September 27th, Meta unveiled its latest artificial intelligence (AI)-powered creation, Meta AI. This new digital assistant is Meta's response to OpenAI's ChatGPT and is set to seamlessly integrate with Instagram, Facebook, WhatsApp, and, in the future, Meta's mixed reality devices. Beyond merely answering questions and engaging in conversations with users, this freshly introduced bot boasts a remarkable ability to generate images. This image generation capability is harnessed through a novel tool known as Emu, which Meta has diligently trained on a vast dataset of 1.1 billion pieces of data, including user-shared photos and captions from Facebook and Instagram. Rather than pursuing a one-size-fits-all approach, Meta's overarching strategy involves crafting distinct AI products tailored for various use cases.

On the very same day, OpenAI made an exciting announcement regarding its chatbot, ChatGPT. It revealed that ChatGPT would no longer be constrained by pre-2021 data. Users now have the option to explore GPT-4 via a novel feature called "Browse with Bing." For instance, if you snap a photo of your home refrigerator's contents, ChatGPT can provide recipe suggestions. Similarly, if you photograph your children's math homework, ChatGPT can assist in solving mathematical problems. Furthermore, OpenAI is set to enable ChatGPT to engage in voice conversations with users and interact with images, bringing it closer to an AI voice assistant akin to Apple's Siri.

That’s my take on it:

Ultimately, these AI tools hold the potential to enhance human well-being and satisfaction. However, will our happiness increase when AI can assist us in generating images on Facebook, preparing a recipe, or solving complex math problems? When I traveled to Europe, I found that many European drivers preferred the manual transmission system to the automatic one, as they believe the former provides a more fulfilling driving experience while the latter deprives them of the fun of driving. By the same token, if everything becomes too easy with the help from AI, will we feel “losing” some enjoyable experiences? The psychological impact of AI on humans is a topic that warrants extensive study.

Full text:

https://cointelegraph.com/news/meta-ai-ceo-mark-zuckerberg-unveil-chatbot-rayban-metaverse

https://help.openai.com/en/articles/6825453-chatgpt-release-notes

https://www.worldjournal.com/wj/story/121148/7473581?from=wj_nextnews_story

Posted on September 27, 2023

DSML trend: Capital Economics report of AI impact

On September 26, Capital Economics published a report under the title "AI, Economies, and Markets – The Transformation of the Global Economy by Artificial Intelligence." The report highlights that revolutionary technologies like GPT do not necessarily guarantee substantial productivity gains. Historical evidence shows that the productivity improvements stemming from groundbreaking technologies have often been gradual and less dramatic than initially anticipated. Economists have long grappled with the perplexing trend of weak productivity growth in the digitalized economy of recent decades, marked by developments such as the internet, cloud computing, and the Internet of Things. One major contributing factor is that many companies do not promptly or effectively implement the technology. Although the United States has notably reaped the most substantial productivity gains from AI, achieving a substantial productivity boost from AI hinges on several co-occurring factors, including increased investment, workforce reskilling, and a well-balanced regulatory framework.

That’s my take on it:

Drawing inspiration from the 1966 Clint Eastwood movie, "The Good, the Bad, and the Ugly," I can see that probably every innovation might yield three potential outcomes. The "good" outcome represents effective and efficient applications of the technology, leading to desirable results. Conversely, the "bad" outcome arises from poor implementations, resulting in wasted resources. Finally, the "ugly" outcome involves the misuse of technologies for malicious purposes. Numerous examples of the "bad" category exist, such as the overuse of word processing, which allows endless and unnecessary document editing, often resulting in little improvement despite numerous iterations. Similarly, the availability of powerful data analysis software can lead to redundant and excessive statistical tests, with minimal productivity growth as the outcome. These pitfalls also apply to AI unless users are adequately trained to harness its potential effectively.

Request complimentary report: https://www.capitaleconomics.com/key-issues/economic-impact-artificial-intelligence

Posted on September 22, 2023

Posted on September 15, 2023

DSML trend: Valuable data science certifications

Yesterday (September 14, 2023) Aileen Scott, a data scientist, released an article on Data Science Central titled "Are Data Science Certifications the Key to Unlocking Lucrative Opportunities?" In this short article, Aileen poses a question in her title and unequivocally answers it with a resounding "yes." According to her insights, while you can certainly pursue data science studies from the comfort of your home through online courses, opting for a certification program offers unique advantages by facilitating connections with fellow learners, instructors, and industry luminaries. The bottom line is: Earning a data science certification can significantly enhance your earning potential when compared with your non-certified peers. In Aileen’s view, the top choices of data science certification programs for 2023 are:

·      SAS Certified Data Scientist

·      Senior Data Scientist (SDSTM) by Data Science Council of America

·      Open Certified Data Scientist (Open CDS)

·      Microsoft Certified: Azure Data Scientist Associate

That’s my take on it:

While Aileen's provided list is concise, some may contend that it overlooks certain valuable programs, such as certifications for Amazon Cloud and Tableau. Nevertheless, Aileen's recommendations encompass two certification programs of a more generic or open-source nature, while the other two are product-specific or affiliated with particular companies (SAS and Microsoft). Although the open-source approach to data science and machine learning is gaining popularity, it is crucial to note that major corporations and tightly regulated industries continue to rely on proprietary software solutions due to their enhanced support and dependability. If you are in search of a data science training program, it is advisable to consider enrolling in one that provides both open-source and proprietary software tracks. In this regard, I encourage you to explore the data science program at Hawaii Pacific University. 

Posted on September 14, 2023

Posted on September 8, 2023

DSML trend: TIME’s top 100 most influential people in AI

On September 7 TIME announced the top 100 most influential persons in the field of AI, which includes Sam Altman of OpenAI, Dario and Daniela Amodei of Anthropic, Demis Hassabis of Google DeepMind, coinventor of the backpropagation algorithm Geoffrey Hinton, inventor of CNN Yann LeCun, co-founder and chief AGI scientist of Google DeepMind Shane Legg, co-founder and president of OpenAI Greg Brockman, co-founder and chief scientist of OpenAI Ilya Sutskever, co-founder of Schmidt Futures Eric Schmidt, science fiction writer Ted Chiang, co-founder of Nvidia Jensen Huang, Stanford professor Fei-Fei Li …etc.

That’s my take on it:

In TIME's top 100 list, Google boasts six individuals, while OpenAI is represented by five. In addition, Microsoft is notable with four entries, Intel with two, and Meta Facebook features one (Yann LeCun). There's also a presence from xAI (Elon Musk) and SalesForce/Tableau (Clara Shih), but curiously, Apple is entirely absent from the list. Surprisingly, there's no representation from Stability AI, the company behind the groundbreaking Stable Diffusion technology that generates artworks. No doubt Apple has lagged behind in AI development, with little noteworthy AI-related news to date. As of September 6, reports suggest that Apple is investing millions of dollars a day to train its own AI model Ajax, with claims that Ajax can surpass ChatGPT. However, concrete results are yet to be unveiled. I wonder how Apple's AI landscape might have been different had Steve Jobs still been alive today.

Time’s website: https://time.com/collection/time100-ai/

Apple’s story: https://www.theverge.com/2023/9/6/23861763/apple-ai-language-models-ajax-gpt-training-spending

Posted on September 6, 2023

DSML trend: Guardian blocks ChatGPT from accessing its content

On September 1 2023, the Guardian announced its decision to block access to its content for the AI text generation program, ChatGPT. In a statement, the publisher emphasized that the scraping of their intellectual property for commercial purposes has always been against their terms of service. They also highlighted their commitment to fostering mutually beneficial commercial relationships with developers worldwide. Other news media, including CNN, Reuters, the Washington Post, Bloomberg, and the New York Times implement similar policies. OpenAI, the owner of ChatGPT, had previously revealed an opt-out option for website owners who didn't want their content used by AI algorithms.

That’s my take on it:

Whether AI's utilization of existing published content constitutes copyright infringement or qualifies as fair use has been an ongoing debate. This same issue extends to AI art tools like Midjourney and Stable Diffusion. It is important to note that AI chatbots do not simply copy and paste content from the source. Rather, the nature and purpose of its use can be seen as transformative, meaning that AI repurposes copyrighted material in a novel and distinct manner. Similarly, AI art tools do not merely create collages; instead, they learn from patterns in existing artworks to generate entirely new images. Consider this analogy: If I extensively study art by browsing around a library and a museum, and subsequently, based on this knowledge I write a new article or create a new painting on my own, should the library or museum prevent me from accessing their information?

Full text:

https://www.theguardian.com/technology/2023/sep/01/the-guardian-blocks-chatgpt-owner-openai-from-trawling-its-content

Posted on September 1, 2023

DSML trend: China's Baidu ‘s AI chatbot Ernie Bot is publicly accessible

On August 31, 2023, Baidu, the Chinese search engine and AI company, made a significant move by unveiling "Erine Bot," their equivalent of the ChatGPT language model, to the public. As a result, Baidu's stock price surged more than 3%. This strategic move aligns with Beijing's vision of AI as a critical field, one where they aim to challenge the US and emerge as a global leader by 2030. By releasing Ernie Bot to the public, Baidu intends to gather extensive real-world user feedback. This feedback loop will, in turn, play a vital role in enhancing Ernie and strengthening Baidu's foundational models. Coincidentally, on the same day, two other prominent AI companies in China, Baichuan and Zhipu AI, also introduced their own AI language models.

That’s my take on it:

Back in 2017, Russian President Putin emphasized the transformative potential of AI by saying “whoever becomes the leader in this sphere will become the ruler of the world.” This perspective reflects the ongoing international competition among major technological powers, including the US and China, to gain supremacy in AI research and development.

Unfortunately, I faced challenges registering with Baidu's AI, as it requires a China’s cell phone number for access. Consequently, I was unable to evaluate Ernie Bot personally. However, those who did manage to access Baidu's AI encountered significant restrictions, particularly in its reluctance to answer sensitive political and historical inquiries. If you had the opportunity to assess Ernie Bot, I would greatly appreciate it if you could share your insights and findings with me. If you found a way to circumvent the requirement of providing a China’s cell phone information when registering for Ernie Bot, please let me know too.

Full text:

https://techxplore.com/news/2023-08-china-baidu-ai-chatbot-ernie.html?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=Newsletter&utm_content=Data+Science+Insider%3A+September+1st%2C+2023&utm_campaign=02092023

Ernie Bot’s website:

https://yiyan.baidu.com/welcome

Posted on September 1, 2023

DSML trend: Nvidia is thriving in the AI boom at the expense of Intel and AMD

According to a report posted by Nikkei Asia today, Nvidia is thriving in the AI boom while Intel and AMD struggle to keep up. Nvidia, the GPU market leader, has seen its stock price triple since the beginning of the year. Its shares jumped over 6% in one week after reporting 101% year-over-year revenue growth on August 23rd. Nvidia racked up $13.51 billion in revenue last quarter, more than double the previous year's figure, largely driven by its data center business and AI chips like the H100 and A100. Meanwhile, AMD and Intel's share prices have dropped 7.41% and 4.08% respectively in the past month. Nvidia's data center revenue was nearly double the data center chip sales of Intel and AMD combined last quarter. It is a clear trend that Intel and AMD are increasingly vulnerable to losing market share in the traditional x86 CPU market.

That’s my take on it:

The above phenomenon highlights the limitations of Intel's x86 architecture for the demand of computing power in the era of big data and AI. While powerful for sequential tasks, x86's complexity makes it less optimized for massively parallel processing compared to GPU architectures. For AI/machine learning and other data-intensive applications, GPUs can provide 10-100x higher throughput. In addition, GPUs have very high memory bandwidth optimized for throughput, allowing fast access to large datasets while CPUs have lower bandwidth. Sadly, Intel was reluctant to modify its architecture and slow to get into the GPU market. Although I don’t think x86-based CPUs will disappear overnight, the future is undoubtedly trending toward GPU-accelerated computing. As GPU computing gains momentum, data scientists, educators, and DSML students need to adapt to this paradigm shift.

P.S.: If I had bought Nvidia stock 10 years ago, I would be retired and sending this message on a cruise ship or at the beach right now.

Full text (may require subscription):

https://asia.nikkei.com/Business/Tech/Semiconductors/Nvidia-s-boom-gives-TSMC-a-lift-but-does-less-for-other-chipmakers?utm_campaign=GL_asia_daily&utm_medium=email&utm_source=NA_newsletter&utm_content=article_link&del_type=1&pub_date=20230901123000&seq_num=3&si=14108111

Posted on August 30, 2023

DSML Trend: Revival of OpenAI?

According to reports posted by “The Information” and Reuters on August 29, 2023, OpenAI is poised to achieve over $1 billion in revenue within the upcoming year through the sale of AI software and the corresponding computational capacity that drives it. Previously, the creators of ChatGPT had estimated revenue of $200 million for the current year. Notably, the company, backed by Microsoft, is now amassing a staggering revenue surpassing $80 million each month, a significant escalation from the mere $28 million garnered throughout the entirety of the preceding year.

That’s my take on it:

Two weeks ago, IT experts predicted that OpenAI might go bankrupt by the end of 2024 due to a decline in usage. Suddenly this situation has undergone a surprising reversal. In my opinion, the future trajectory of OpenAI remains uncertain, because the fate of the company relies on a single product. Prior to ChatGPT, OpenAI boasted another flagship product known as DALL.E2, a creative tool for generating visual art. However, the market of generative art has now been predominantly seized by Midjourney, which boasts a user base of 15 million, the largest among all image generation platforms. In terms of overall image production volume, Stable Diffusion takes the lead with an impressive 12.59 billion images generated.

The question arises: should OpenAI reallocate its R&D resources to the more promising ChatGPT and relinquish DALL.E2, or should it engage in a dual-front battle? This is an intricate puzzle that demands careful consideration.

Reuters’s report: https://www.reuters.com/technology/openai-track-generate-more-than-1-bln-revenue-over-12-months-information-2023-08-29/#:~:text=Aug%2029%20(Reuters)%20%2D%20OpenAI,in%20revenue%20for%20this%20year.

Statistics of generative art tools: https://journal.everypixel.com/ai-image-statistics

Posted on August 26, 2023

DSML trend: G2 Grid for Data Science and Machine Learning Platforms

On August 24, 2023, G2 released the G2 Grid for Data Science and Machine Learning Platforms. To be considered for inclusion in this DSML benchmarking, the candidate must adhere to the following criteria:

1.     Facilitate the connection of data to algorithms, enabling them to acquire and adapt knowledge.

2.     Enable users to construct machine learning algorithms and/or furnish pre-built machine learning algorithms suitable for less experienced users.

3.     Furnish a platform for the widespread deployment of artificial intelligence.

G2 classified DSML companies into four distinct quadrants, namely, leaders, high performers, contenders, and niche, utilizing a dual-dimensional framework: market presence and customer satisfaction. According to G2 scoring, currently the leaders of DSML are:

·      Databricks Lakehouse

·      IBM Watson Studio

·      Matlab

·      Alteryx

·      Vertex AI

·      SAS Visual Data Mining and Machine Learning

·      Anaconda

·      Saturn Cloud

·      Microsoft Azure Machine Learning

·      Deepnote

·      Amazon SageMaker and AWS Trainium

·      TensorFlow

·      Qlik AutoML

That’s my take on it:

The preceding list includes well-established companies like SAS, IBM, and Microsoft, alongside newcomers challenging the existing order. I admit that I do not possess the skill sets required for all of the software tools mentioned. Coping with the rapid evolution of technologies poses a considerable challenge for university professors, particularly in fields where progress is frequent. In my opinion, transitioning the emphasis from instructing specific skills to nurturing the capacity for perpetual learning is undeniably a valuable approach. To remain current, one effective tactic involves inviting guest speakers from industry or research domains to share their expertise and insights with students. This exposure acquaints students with real-world applications and prevailing industry methodologies. Moreover, it is imperative for faculty to motivate students to cultivate a mindset characterized by openness to change and a willingness to experiment. By the time my students graduate, G2, Gartner, Forrester, and IDC may compile a new list of DSML leaders!

Full report: https://www.g2.com/categories/data-science-and-machine-learning-platforms

Posted on August 26, 2023

In a recent piece published on KDnuggets (August 24, 2023), Dr. Mandar Karhade speculated the architecture of GPT-4 based upon leaked information. The author posited that rather than being a singular colossal model, GPT-4 might consist of eight separate models, each bearing 220 billion parameters. This novel approach involves breaking down a given task into smaller subtasks, which are then tackled by specialized experts within the context of these models. The strategy mirrors a divide-and-conquer methodology. Subsequently, a gating model is introduced to determine the optimal expert for each subtask, culminating in the final prediction. However, the author included a disclaimer emphasizing the non-official nature of this information.

That’s my take on it:

At the present time, this notion remains an unverified rumor. Nevertheless, the idea holds a certain degree of credibility. The underlying concept closely resembles, if not mirrors, the principles of ensemble methods and model comparison, a common practice in the realm of Data Science and Machine Learning. In ensemble methods such as boosting and bagging, numerous modeling procedures are executed on partitioned subsets of data. Subsequent model comparison is conducted to select the most optimal solution derived from an array of modeling techniques: neural networks, SVM, bagging, boosting, among others. Hence, the synthesis of eight models in GPT-4 represents a natural progression akin to ensemble methods and model comparison, taking the idea a step further.

Full article: https://www.kdnuggets.com/2023/08/gpt4-8-models-one-secret.html

Posted on August 15, 2023

DSML trend: Europeans collaborate with China’s Huawei in AI-based weather forecasting

The European Centre for Medium-Range Weather Forecasts (ECMWF) has entered into a partnership with Huawei, a leading Chinese technology company, to launch an artificial intelligence-powered weather forecasting system. This collaboration aims to combine ECMWF's expertise in meteorology with Huawei's advanced AI capabilities. The new model, Pangu-Weather, was developed by Huawei and has demonstrated superior accuracy over traditional models. ECMWF selected the Pangu model after rigorous comparative testing showed it consistently outperformed other models, including in predicting extreme weather events. A recent Nature journal article provides further validation of the Pangu model's capabilities, highlighting its ability to achieve highly precise forecasts at speeds up to 10,000 times faster than legacy weather models.

That’s my take on it: Current U.S. export restrictions prohibit Nvidia from exporting certain high-performance AI chips like the A100 and H100 GPUs to China. A recent executive order also restricts U.S. investment into key Chinese technology sectors including semiconductors, AI and quantum computing. In anticipation of further export restrictions, major Chinese technology companies have been urgently placing large orders for high-performance Nvidia chips, with recent estimates valuing these bids at around $5 billion. It remains unclear whether Huawei will be able to fully capitalize on ECMWF's capabilities given these limitations on accessing critical U.S. technologies. Nonetheless, developing highly accurate weather forecasting is increasingly crucial as extreme weather events become more frequent, likely due to climate change.

Full text:

https://opentools.ai/news/weather-forecasting-using-ai-europe-and-china-take-the-lead/

https://www.neowin.net/news/chinese-internet-giants-go-on-a-5-billion-nvidia-shopping-spree-to-power-their-ai-ambitions/

Posted on August 14, 2023

DSML trend: IBM will integrate Meta’s Llama into Watson

On August 9, IBM announced plans to host Meta's 70 billion parameter Llama 2 large language model on its Watson AI and data science platforms. Currently in watsonx.ai, users can leverage pre-trained models from IBM and Hugging Face for Natural Language Processing tasks, such as content generation and summarization, as well as text classification and extraction (text mining). The future addition of Llama 2 to watsonx.ai will be a milestone for IBM's generative AI roadmap, likely followed by upcoming releases of its AI Tuning Studio.

That’s my take on it: IBM's flagship data science products are Watson Studio and SPSS Modeler. For a long time, IBM has trailed its top competitor SAS Institute in user base, interface, and capabilities. Nevertheless, IBM has invested in AI research and development since the 1950s. In 1997, IBM's Deep Blue beat the world chess champion in a six-game match. In 2011, IBM's Watson competed and won against top human Jeopardy! contestants. Although Meta's Llama is less powerful than models like Claude 2, Google Bard, and ChatGPT, incorporating a large language model into IBM products is still strategic. However, it's too early to tell whether IBM can overtake SAS in the near future.

Full announcement: https://newsroom.ibm.com/2023-08-09-IBM-Plans-to-Make-Llama-2-Available-within-its-Watsonx-AI-and-Data-Platform

Posted on August 14, 2023

DSML trend: OpenAI faces financial challenges and the rise of Claude

An article in yesterday's Business Today (August 13) reported that OpenAI, the pioneering AI company that brought ChatGPT to the mainstream public, is facing financial challenges. The costs to operate ChatGPT amount to around $700,000 per day. Despite efforts to monetize GPT-3.5 and GPT-4, OpenAI has yet to earn sufficient revenue to cover its expenses. According to SimilarWeb data., ChatGPT's user base declined 12% from June to July 2023, dropping from 1.7 billion to 1.5 billion monthly users.

That’s my take on it: Researchers at Stanford and UC Berkeley systematically evaluated different versions of ChatGPT. It was found that in math tests, ChatGPT solved 488 out of 500 correctly in March (97.6% accuracy). By June, its accuracy dropped 2.4%. ChatGPT's global website traffic fell, especially after the launch of Claude 2. Claude 2 scored 71.2% on a Python coding test versus ChatGPT's 67%. Claude is also more updated, with an early 2023 cutoff versus September 2021 for ChatGPT. While it's premature to declare the end of ChatGPT, the future landscape of large language models is volatile as more competitors enter the market.

Full article: https://www.businesstoday.in/technology/news/story/openai-might-go-bankrupt-by-end-of-2024-chatgpt-costing-over-rs-580-crore-per-day-report-393925-2023-08-12

Posted on August 14, 2023

In an essay published on August 9th, Andrew Ng, a co-founder of Google Brain and former Chief Scientist at Baidu, posited that Othello-GPT, a powerful large language model, demonstrates a noteworthy degree of world comprehension. To be specific, during its training phase involving gameplay, the neural network exclusively processed sequences of moves, but it was not explicitly provided with information indicating that these sequences pertained to actions on an 8x8 board or the game's rules. However, after extensive training on a substantial dataset of such moves, the model exhibited proficiency in predicting subsequent moves in an 8X8 board. By the same token, certain large language models trained in English have displayed an ability to "learn" additional languages, enabling them to comprehend and follow instructions in these languages. This observation has led both Andrew Ng and Geoff Hinton to draw the consensus that large language models really possess a form of world understanding.

That’s my take on it: Comprehension, or understanding, in the realm of psychology, goes beyond the mere perception of sensory input. Rather, it involves active engagement with information, its connection to pre-existing knowledge and personal experiences, and the construction of a coherent mental representation or interpretation. However,  even if they fulfill the aforementioned criteria, do LLMs simply appear to or behave as they understand the world?

Full text: https://www.deeplearning.ai/the-batch/issue-209/?utm_source=tldrai

Posted on August 1, 2023

Center for Consciousness Studies at the University of Arizona and California for Human Science will cohost a conference entitled “Neuroscience needs a revolution to understand consciousness” between August 18-23, 2023. One of the keynote speakers is Sir Roger Penrose, a British mathematician, physicist, philosopher of science, and Nobel Laureate in Physics. The following is a brief introduction to the theme of the conference.

“'AI has reinforced the notion of the brain as a complex computer of simple, empty, ‘cartoon’ neurons based on 1950s physiology, processing solely by surface membranes, synaptic transmissions and firings as “bit-like' units in frequencies up to 100 hertz…The Penrose-Hameroff ‘Orch OR’ theory proposes consciousness depends on ‘orchestrated’ (‘Orch’) quantum superpositions leading to Penrose ‘objective reductions’ (‘OR’, wavefunction self-collapses) in brain microtubules, connecting to fundamental spacetime geometry. Orch OR has more explanatory power, connection to biology, and experimental validation than all ‘neuroscientific’ theories based on low frequency, oversimplified cartoon neurons combined… Neuroscience needs a revolution inward, to deeper, faster quantum processes in microtubules to understand consciousness and treat its disorders.”

That’s my take on it:

Many experts speculate that AI may eventually attain self-consciousness, potentially posing a threat to humanity. The concept of consciousness raises several fundamental questions: What is consciousness? How can we ascertain whether an AI system is genuinely self-conscious? Do you have to fully understand consciousness in order to know whether a machine is self-aware? The widely-used Turing test, considered behavioristic, is deemed unreliable for this purpose.

During the 1980s and 1990s, Roger Penrose expounded on the notion of human consciousness in his books "Emperor's New Mind" and "Shadows of the Mind." He argued that consciousness involves non-algorithmic processes that defy computational reduction. Penrose also criticized the concept of Strong AI, which contends that machines can achieve human-like consciousness. He posited that human attributes such as creativity, insight, and mathematical intuition are beyond the reach of artificial systems due to their dependence on non-computable processes. I have registered for the conference (online only: $75). I look forward to hearing updates of Penrose’s arguments during the event.

Registration: https://www.eventbrite.com/e/neuroscience-needs-a-revolution-to-understand-consciousness-tickets-667018659767?aff=oddtdtcreator

Posted on July 21, 2023

DSML Trend: New role of data scientists by embracing economic thinking

In response to the burgeoning influence of generative AI (GenAI), Bill Schmarzo has authored an insightful article titled "Next-Gen Data Scientist: Thinking Like an Economist" on Data Science Central. This article explores the parallels between economic principles and data science methodologies, underscoring the criticality of considering trade-offs, incentives, and resource allocation in data-driven decision-making processes. As outlined in a recent report by McKinsey, GenAI is projected to potentially automate up to 40% of the tasks currently executed by data science teams by 2025. These tasks, including data preprocessing, coding, and hyperparameter tuning, can be more effectively and efficiently accomplished through AI assistance. Nevertheless, it is crucial to recognize that GenAI lacks significant domain knowledge, setting it apart from human experts. By embracing an economic mindset, data scientists can optimize their strategies, thoughtfully prioritize projects based on potential returns, and skillfully communicate insights to stakeholders, thus providing robust decision support.

That’s my take on it:

For a long time, I have advocated against an excessive focus on data wrangling and programming within DSML (Data Science and Machine Learning) education. First, if the data collection protocol and data architecture are well-designed, there is no need to waste our time on data cleaning and data conversion. Second, complicated coding can, to some extent, hinder the discovery of insightful knowledge. Looking ahead, as AI progressively assumes responsibility for more low-level tasks, data scientists should concentrate their efforts on analytics and interpreting the implications of results for end-users.

Full article:

https://www.datasciencecentral.com/next-gen-data-scientist-thinking-like-an-economist/?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=Newsletter&utm_content=Data+Science+Insider%3A+June+21st%2C+2023&utm_campaign=22072023

Posted on July 21, 2023

DSML trend: Generative AI fails to spark a strong demand for microchips

Today (July 21) Taiwan Semiconductor Manufacturing Co (TSMC) reported a sharp 23% decline in Q2 earnings, indicating that the recovery in the global semiconductor market is happening at a slower pace than anticipated. TSMC now projects a 10% revenue contraction for the full year 2023, reversing its previous forecast of slight growth. Its peer companies like Samsung and Micron have also posted earnings declines, further signaling weakness in the industry. Apparently, generative AI fails to spark a strong demand for microchips. Many generative AI services are delivered via servers equipped with NIVIDA GPUs. To sustain growth in these services, expansion of data centers is expected. However, generative AI's impact will take time due to need for advanced chip packaging. It is predicted that AI will not lead to a full-scale recovery in demand for semiconductors until 2024.

That’s my take on it:

Even though generative AI is more technologically advanced than the Internet, why hasn't generative AI created an economic boom similar to the Internet revolution of the 1990s? As far as I know, currently generative AI is still experimental. Unlike e-commerce, which had a straight-forward way to make money by selling products or services online, generative AI does not yet have proven business models. Companies are still figuring out how to commercialize the technology. Further, while generative AI shows promise for some business uses, it currently has limitations in understanding context and executing practical tasks. Put it bluntly, it is fun to chat with ChatGPT, Google Bard, and Claude 2, but information provided by these large language models is not 100% accurate, and it seems that widespread enterprise adoption will take more time. Nevertheless, I believe that it will happen soon! Those who are unprepared will be left behind.

Full article (subscription required):

https://asia.nikkei.com/Business/Tech/Semiconductors/TSMC-s-woes-signal-global-chip-slump-extending-throughout-year?utm_campaign=GL_asia_daily&utm_medium=email&utm_source=NA_newsletter&utm_content=article_link&del_type=1&pub_date=20230721123000&seq_num=9&si=14108111

Posted on July 16, 2023

DSML Trend: Elon Musk’s view on xAI and superintelligence on Twitter

Last Friday (July 14) Elon Musk held a Twitter Spaces conversation to discuss his new AI company called xAI. A total of 40,000 people attended the event. The xAI researchers were recruited from OpenAI, Google DeepMind, and the University of Toronto. According to Musk, the goal of xAI is to create AI systems that are highly intelligent, curious, and truthful. Musk wants xAI to study questions about physics like dark matter, as well as why there is little evidence of aliens despite the age of the universe. He believes these are math problems that can be solved with powerful AI. Interestingly, Musk points out that today even the most powerful neural networks cannot produce a novel on a par with human writers. He asserts that current AI research counting on the brute force of computing is missing something. Based on the lessons learned in Tesla, Musk argued that researchers might overcomplicate the problem and the solution might be much simpler than we thought. xAI intends to release a product to the public soon, likely a chatbot trained on Twitter conversations. Musk wants the xAI chatbot to say what it truly thinks, without politically correct guardrails. More details on xAI's first product will be provided in two weeks.

That’s my take on it: 

AI is a highly competitive field. For years, Google, Microsoft, Apple, Meta, and other key players have invested billions of dollars in AI research. During the Twitter Spaces interview, Musk admitted that xAI would take some time to catch up with OpenAI and Google. Nonetheless, given his success in Tesla and SpaceX, it is conceivable that xAI could introduce a new large language model based on a new paradigm in the near future. Although Musk didn’t disclose the details of lessons learned in Tesla, I guess xAI’s approach will simplify existing neural networks, like Reduced Instruction Set Computer (RISC) is designed to simplify Complex Instruction Set Computing (CICS). 

The audio file of the interview on Youtube:

https://www.youtube.com/watch?v=Xd8fNiySYLQ

Text-based summary:

https://www.fastcompany.com/90924249/elon-musk-xai-aliens

Posted on July 15, 2023

The following YouTube video presents a concise comparison of Claude 2 and ChatGPT. According to the YouTuber, “Claude 2 presents a significant leap in AI technology with unique abilities like summarizing up to 75,000 words and achieving impressive scores in diverse tests, outperforming its predecessor and its competitor, ChatGPT from OpenAI. Claude 2 not only offers advanced functionality, but also prioritizes safety, striving to minimize harmful or offensive content, and affordability, undercutting ChatGPT's API cost significantly.”

https://www.youtube.com/watch?v=nuUxG-C7iMc

I asked Claude 2 to compare itself against ChatGPT. The following is the answer from Claude:

“Limitations:

·      Claude has more limited conversational abilities compared to ChatGPT, which was explicitly trained for dialogue.

·      ChatGPT tends to be more verbose, while Claude gives shorter, more focused answers.

·      Claude has significantly less training data than ChatGPT, constraining its knowledge breadth, though Claude aims to mitigate this through efficiency.

·      Both may occasionally generate biased, unethical, or implausible responses, requiring caution and human evaluation of outputs.

·      They lack a consistent personality or worldview, since they don't have real experiences.

In summary, Claude and ChatGPT have common capabilities but ChatGPT is more conversant, while Claude is more concise and targeted, reflecting their different underlying architectures and training. Both have limitations inherent to current AI.”

Posted on July 15, 2023

Posted on July 14, 2023

Recently the California State Board of Education has approved significant changes to the K-12 math curriculum by integrating data science and emphasizing real-world applications. In response to the growing importance of data science in society as well as the need to prepare students for careers requiring strong data analytics and problem-solving skills, in the past two years the board has approved data science courses in many high schools. However, the University of California faculty committees that oversee high school courses accepted for admission to UC argued that Algebra 2 should not be replaced with data science, because this will under-prepare students who plan to major in STEM.

That’s my take on it: 

In essence, the debate boils down to the purpose of education. As the name implies, data science is more empirical and data-driven, whereas theoretical mathematics is more logical and model-based. To equip students with job skills sought by the market or to solve real-life problems, it seems that data science is preferable to theoretical mathematics. However, advanced math is also necessary for developing abstract reasoning and symbolic processing. My question is: why can't they keep both? 

Articles:

https://edsource.org/2023/uc-committee-changes-admission-standard-for-data-science-causing-confusion-over-math-framework/693892

https://www.edweek.org/teaching-learning/california-adopts-controversial-new-math-framework-heres-whats-in-it/2023/07

Posted on July 8, 2023

Posted on July 7, 2023

The International Telecommunication Union's (ITU) annual AI for Good Summit, which was held on July 6 and 7, 2023, aims to harness the power of AI to address global challenges and promote sustainable development. The conference brought together experts from various fields, including healthcare, climate change, and education experts, to discuss and explore AI applications. ITU is a Geneva-based United Nations agency that represents all 193 member states as well as over 900 companies, universities, and other organizations. And therefore, the AI for Good Summit is a truly global conference.

That’s my take on it:

Despite its representativeness, there is no formal declaration, negotiated statement, or decision announced by ITU. Although the discussion in the Summit has led to the creation of focus groups for developing new standards, as well as addressing the impact of AI-enabled androids on humans, it is very difficult, if not impossible, for rival countries that embrace different political ideologies and ethical standards to reach a consensus. I read the closing statement of the summit. Frankly speaking, it is very general and vague.

ITU Statement On The Closing Of The 2023 AI For Good Global Summit:

https://www.publicnow.com/view/8ABBE69C860E65230F0AB9858CFA4696EBE691FA?1688759234

ITU AI For Good Global Summit 2023 Press conference:

https://www.youtube.com/watch?v=m9IN14e-PLk  

Posted on July 7, 2023

The World Artificial Intelligence Conference (WAIC) is currently being held in Shanghai, China. In a keynote speech at the conference, China's Vice-Minister for Industry and Information Technology, Xu Xiaolan, said the country plans to develop a complete AI value chain, covering chips and algorithms to large language models (LLMs). In addition, the Ministry of Industry and Information Technology of China announced the government will fully support 360, Baidu, Huawei, and Alibaba in R&D of AI by actively promoting the development of a national standard system.

That’s my take on it:

There is no doubt that China's AI development faces an uphill battle, since the US limits the export of cutting-edge technology to China, including top GPU models from AMD and Nvidia. Nonetheless, there are other channels for Chinese scientists and engineers to gather crucial information for AI development, such as borrowing open-source codes and collaborating with companies that are friendly to China. For example, Elon Musk, the founder of Tesla and SpaceX, is opposed to decoupling between the US and China. At WAIC Musk said, “China is going to be great at anything it puts its mind into. That includes…artificial intelligence.” It is likely that Chinese AI scientists and engineers will learn from Tesla.  

English text:

https://www.scmp.com/tech/big-tech/article/3226808/china-asserts-ai-development-goals-shanghai-conference-beijing-braces-potential-new-us-tech

Chinese text:

https://www.worldjournal.com/wj/story/121474/7285133?from=wj_breaknews_index

Posted on July 6, 2023

Today, Tesla showcased their AI products at the World Artificial Intelligence Conference in Shanghai, China, along with 400+ exhibitors. In addition to its Autopilot (Fully Self-Driving) cars, Tesla displayed a prototype of its Optimus robot. The Optimus has the latest technology of the same origin as Tesla vehicles, including a fully self-navigation computer and a Tesla Vision visual neural network. The Tesla humanoid robot is 172 centimeters tall and weighs 56.6 kilograms, which is no different from a normal adult. Like human joints, the robot's whole body has 28 degrees of freedom. Its hand has 11 degrees of freedom, and therefore it has a high degree of flexibility and dexterity. As a result of its powerful motors, the robot is capable of lifting a piano with just one hand. This humanoid robot can also walk, climb stairs, squat, and pick up objects, and it already has the capability to protect itself and other people. In the future, robots may cook, mow the lawn, care for the elderly, or replace humans in dangerous and boring factory jobs.

That’s my take on it:

According to some commentators, Tesla's robots are ahead of Boston Dynamics because Boston Dynamics' robots require preprogramming for movement, while Tesla's can evolve through machine learning. I know what's on your mind. Could a self-learning robot harm humans at some point if it becomes out of control? People may even wonder whether the Optimus will be weaponized since it can lift a piano with one hand and defend itself. Is it going to be used for evil purposes? Regulations should be discussed as early as possible.

P.S.: I want the Optimus if I can afford one. Currently I am moving from LA to Honolulu. I need a robot that can lift heavy objects for me!

English text:

https://www.teslaoracle.com/2023/07/06/tesla-cars-optimus-robot-world-ai-conference-shanghai-elon-musk-ai-chinese-cars/

Chinese text:

https://www.chinatimes.com/realtimenews/20230706005385-260412?utm_source=likr&utm_medium=web_notification&utm_content=VOY0&avivid_manual=4

Posted on June 29, 2023

Posted on June 28, 2023

Posted on June 27, 2023

Posted on June 16, 2023

In a recent benchmark study, the Futurum Group compared SAS Viya and several open source software packages, such as Apache SparkML, H2O, and Ranger, in terms of scalability and performance. In this study, random forest, gradient boosting, including LightGBM and XGBoost, linear regression, and logistic regression were rigorously tested on big data. It was found that for running machine learning with high-dimensional data, SAS Viya is on average 30 times faster than all other competitors across 1,500 tests. Specifically, SAS Viya solution delivered results in under 12 minutes on a dataset containing over 300 million data points, while SparkML and another rival failed to deliver results after running for hours. For running traditional procedures, such as linear regression and logistic regression, SAS Viya ran faster in 49 out of the 50 tested configurations.

Full report:

https://futurumgroup.com/wp-content/uploads/2023/06/Lab-Insight_Performance-at-Scale_Comparing-AI-ML-Performance-of-SAS-Viya-vs-Alternatives.pdf

That’s my take on it:

Having used both open source and proprietary software applications, I do not believe that we should side with one camp or the other. Open source is touted as a great tool, but in my view its advantages are overstated. I am not surprised by this benchmark result. While developers of open source are a loose conglomerate, resulting in incompatibility and redundancy, R&D in commercial corporations, such as SAS, IBM, and Microsoft, are coordinated and thus coherent. As a matter of fact, a lack of financial incentives makes it difficult for volunteers to devote substantial time and effort to optimizing machine learning codes. OpenAI has taken the world by storm with its ChatGPT, but few people know that OpenAI first adopted an open source model in an attempt to liberate people from big tech monopolies. However, Cade Metz, the author of Genius Maker, made a harsh comment by saying “It (OpenAI) was an idealistic vision that would eventually prove to be completely impractical.” Yann LeCun, the inventor of CNN, even predicted that this model was doomed to fail at the beginning. Within a few years, OpenAI became a for-profit, closed-source company. 

P.S. On June 12 it was announced that Google DeepMind, OpenAI and Anthropic agreed to open up their AI models to the U.K. government for research purposes only. It is not completely open source.

Posted on June 16, 2023

Yesterday (6/15) an article published by the IMD discussed how the role of data scientists is changing. In 2012, Harvard Business Review identified data scientist as the sexiest job of the 21st century. However, today user-friendly software is simplifying complex tasks that previously necessitated data scientists. Until recently, data wrangling, such as cleaning, restructuring, re-formatting, and pre-processing required the expertise of data scientists; but in the near future, AI-enabled software tools will handle this type of menial data preparation. According to Gartner, a prominent consulting company, by 2025 70% of new applications developed by organizations will be low- or no-code solutions, up from under 25% in 2020. As a result, the role of data scientists will evolve from that of an astronaut (who uses state-of-the-art technology for exploring uncharted territory) to that of a champion race-car driver (who uses standardized technologies for navigating in real life). Three recommendations were made by these authors to cope with the preceding trends: 1. Reskill the existing employees 2. Hire data scientists for specialized purposes, such as sophisticated applications, scalability, and innovation. 3. Invest in the analytics infrastructure that can produce usable data.  

Full article (subscription required):

https://www.imd.org/ibyimd/innovation/the-changing-role-of-the-data-scientist-from-astronaut-to-race-car-driver/

That’s my take on it:

I completely agree with all three recommendations. That's exactly what I've been proposing. I've been holding a minority opinion about DSML for a long time. At this moment, most DSML training programs place too much emphasis on Python and R programming, probably because they mix programming with analytics. Over the years, I went through the transition from TSO on the IBM Mainframe and DOS on the PC to the GUI on Mac. The lesson is: If I can run an interactive data visualization on Tableau, JPM, or SAS Viya using drag and drag in three minutes, I don’t see a reason to spend an hour to build the same thing by coding Dash in Python. Just like the transition from DOS to GUI, using a low-code and no-code solution is a natural and irreversible trend. Moreover, while data scientists earn big salaries doing data wrangling, it isn't cost-effective at all. A good data plan can prevent 90% of data issues. I am surprised to see that today some systems still truncate the year variable to three digits (from “2001” to “201”), or code “yes/no” into “1/2”! And some organizations still collect many unused or non-usable data without pre-conceptualized research questions! 

Posted on June 15, 2023

Currently OpenAI's CEO Sam Altman is traveling around the world to learn what people want from AI, what they are doing with it, and how we can regulate this emerging technology. At the present time, China and the U.S. are taking very different approaches to regulating AI. The top-down regulatory strategy of China is characterized by state control and an emphasis on national security, whereas the bottom-up approach of the United States has the government taking a back seat.  There is no federal regulation on AI in the US. To fill the vacuum, Microsoft and Google established internal AI governance teams and published their own AI principles.

Full article (subscription required): https://asia.nikkei.com/Spotlight/The-Big-Story/ChatGPT-unleashed-an-AI-race-now-regulators-are-struggling-to-hold-on?utm_campaign=GL_editor_in_chief_picks&utm_medium=email&utm_source=NA_newsletter&utm_content=article_link&del_type=2&pub_date=20230616150007&seq_num=16&si=14108111

That’s my take on it: Paul Kedrosky, managing partner at SK Ventures said, "The top-down approach China uses actually has huge merits, in my opinion. When the bottom-up approach is too slow, it seems irresponsible and even immature and childish to wait for things to happen." I tend to disagree. A test of China's chatbots found that some information is not accessible because Chinese regulatory requirements ensure its AI-enabled chatbot won't make mistakes on "important and sensitive topics.” However, regulations that undermine freedom of thought ultimately undermines innovation.

Posted on June 13, 2023

Posted on June 9, 2023

A while ago Microsoft announced several new features in Windows 11, but at that time most articles focus on Windows Copilot only, which is an AI version of Clippy. Specifically, Window Copilot is a

digital personal assistant that can help the user complete tasks easily through natural language inputs. The following YouTube video provides a more comprehensive overview of Windows 11 enhancements. Besides Windows Copilot, the video also mentions AI tools for developers using Azure (the Microsoft cloud computing platform) and ONNX Runtime, and also AI enhancements to the Microsoft Store.

In addition, Microsoft also increased support for ARM-based systems.

Video: https://www.youtube.com/watch?v=3iqBWwkZfNM

Since its release Windows Copilot has been the subject of many articles, and I want to shift the focus to the Windows ARM-based systems. Although ARM, invented in 1985, is not directly related to AI,

it is still crucial to high performance computing and big data analytics. Chris Miller argued in his book "Chip War" that Intel's dominance of the CPU market for so long is due to luck. There is no doubt that

the x86 architecture is not the best; rather, it is too complex and resource-intensive. On the contrary, due to using reduced instruction set computing (RISC), ARM processors are simpler in design, much

more compact, and can run faster. And thus ARM-based systems are popular in smartphones and other small devices. At first, Microsoft intended to introduce ARM support in Windows 10, but in the end,

it was pushed into Windows 11. In comparison with a traditional Windows laptop, Windows on Arm has superior battery life, always-online internet connectivity using 4G or 5G, super-fast boot times, and chipset-level security support. It takes a long time for a paradigm shift to occur!

Posted on June 2, 2023

Two days ago, Amazon announced it would pay more than $30 million in fines to settle allegations that its Alexa voice assistant and Ring doorbell camera violated privacy laws. A lawsuit filed by the Federal Trade Commission (FTC) alleges that Amazon kept records of children's conversations with Alexa in violation of privacy laws, while another alleges that its employees viewed recordings from Ring cameras without consent. Amazon would also be prohibited from using the predictive models built upon these data. Despite the FTC's rulings, Amazon argued that it had not broken any laws.  

Full article: https://www.npr.org/2023/06/01/1179381126/amazon-alexa-ring-settlement

That’s my take on it: Big data analytics and machine learning have made Amazon, Google, and Facebook researchers better psychologists and sociologists than academicians, since the former group can access oceans of behavioral data collected in naturalistic settings. When we are unaware of their data collection, these data tend to reveal our true character and behaviors. Needless to say, invasion of privacy is a concern. However, before we point our fingers to Amazon, Google, and Facebook, we should not forget that many well-known psychological studies in the past, such as Milgram's and Zimbardo's studies, were conducted in the absence of IRB approval or are considered unethical today. It will take some time to

fine-tune the ethical standards of behavioral data.  

Posted on June 2, 2023

In a recent article (May 31, 2023) published in Towards Data Science, data scientist Col Jung argued that organizations should migrate away from traditional data lakes and adopt a data mesh approach. In Jung's view, organizations using old-fashioned data warehouses are trapped in a mess of data systems connected by innumerable data pipelines. Data lake was introduced as a solution by centralizing diverse data into a hub, but “data lake monsters” are “over-promised and under-realized.” In the era of big data, all analytical questions rest on the shoulders of the data lake team. Consequently, the central data team encountered tremendous scalability problems and became inefficient. To rectify the situation, in 2019 Dehghani proposed data mesh as the next-generation data architecture embracing a decentralized approach. Instead of transferring data to a centralized lake, a data mesh allows domain-specific teams to control and deliver data as products, promoting easy interoperability and accessibility across the organization.

Full article: https://towardsdatascience.com/from-data-lakes-to-data-mesh-a-guide-to-the-latest-enterprise-data-architecture-d7a266a3bc33 

That’s my take on it: In my experience many requests to the central data office are simple questions, but as Col said, the data team is overwhelmed under the traditional centralized data architecture. The good news is: Data meshes facilitate self-service data usage, whereas data lakes do not. Is a decentralized system likely to result in chaos, with different people processing data differently? I don’t think so. Since data meshes are owned by different entities, they require stricter formatting, metadata fields, discoverability, and governance standards.

Posted on May 31, 2023

A group of AI scientists, executives, and academicians released a statement yesterday (5/30) regarding the dangers of AI: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.” The signatories include Geoffrey Hinton, the cognitive scientist who modified the backpropagation algorithm for neural networks, Yoshua Bengio, the computer scientist who co-developed the idea of “generative adversarial networks." Sam Altman, the CEO of Open AI, Demis Hassabis, the CEO of Google DeepMind, and many other leading authorities in the field.

Open statement: https://www.safe.ai/statement-on-ai-risk

That’s my take on it: This type of open letter or statement warning about the dangers of AI has been recurring for a while. Probably these experts really know something that we don't know. Based on history, it is very likely that AI will eventually be militarized, and to maximize its power, humans might hand over decision-making to AI. An article by Bill Drexel and Hannah Kelley published in Foreign Affairs suggested that an AI disaster would occur in an authoritarian state without checks and balances, resulting in systemic errors that worsen a mistake or accident. In my humble opinion, systemic mishandlings during a disaster could also happen in democratic nations. Remember how bad COVID19 spread across Europe and the US?

Posted on May 27, 2023

Since high-performance computing for AI is in high demand, NVIDIA, the market leader in GPUs, has had its stock surge over 25%, outperforming AMD, TSMC, and Intel. NVIDIA
became the  fourth most valuable company, just behind Apple, Microsoft, Alphabet, and Amazon. According to Louis Navellier, chairman and founder of Navellier & Associates,
impressed by the rise of NVIDIA, "skeptics of the AI bubble have been silenced for the moment.” On the other hand, Yahoo Finance argued that the Nvidia stock surge could signal
the start of the AI bubble. Citing the history of the Japanese bubble in the late 1980s and early 1990s, the .com bubble in the US in 2000, and the recent Bitcoin bubble, the Yahoo
analyst gave this warning, “if history is any guide, guess what AI could be a larger one.”

Articles: https://www.kiplinger.com/investing/stocks/stock-market-today-stocks-rise-after-ai-outlook-sparks-explosive-nvidia-rally

https://finance.yahoo.com/video/nvidia-stock-surge-could-signal-193731745.html

That’s my take on it: The law of regression towards the mean tells us that everything goes up eventually goes down, and everything has a tendency to revert to mediocrity. It is not a question of if, but when, the bubble will burst. However, I believe AI is still very much in its infancy and has a lot of room for development. NCSA Mosaic, the first Web browser, was
released in 1993, and the .com bubble took almost a decade to burst. Despite the burst, the Internet is still available, and investments in the Internet infrastructure (such as fiber optic
cables installed during the mid-1990s) continue to benefit the entire world. By the same token, I think it is premature to talk about the AI bubble and AI will be here to stay.  

Posted on May 26, 2023

Recently Adobe released generative fill, a feature only available for beta testing, in response to the threat of DALL.E2, Midjourney, and Stable Diffusion. The Adobe generative tool,
like DALL.E2, Midjourney, and Stable Diffusion, can generate images using natural language processing. But unlike its counterparts, Adobe allows you to select a specific area of the
canvas where you want to add, extend, or remove content. For instance, when you think a picture of a seashore is too plain, you can describe an object or scene, such as "add a
lighthouse," and in a few seconds, a realistic lighthouse appears.

Demo page (Last updated on May 25, 2023): https://helpx.adobe.com/photoshop/using/generative-fill.html

As expected, everything appears smooth in the demo. Frankly speaking, I wasn't impressed with the beta version. As an example, I added a person to an empty hallway photo. The
prompt I provided was: "A girl is walking and looking out the window." Adobe generated several variants based on my input, but all of the faces are distorted. Furthermore, there is no
reflection of the person on the glass (see attached). A paradigm shift is undoubtedly occurring with the rise of AI generative art, but Adobe is late to the game. Only the fittest of the
fittest shall survive!

Posted on May 26, 2023

Recently (May 21) Analytics Insight posted a report highlighting the top 10 highest-paying countries in need of data scientists. The order is as follows: USA, Switzerland, UK, Australia, Israel, India, Canada, China, Italy, and France. Take the US as an example. The median salary at the entry-level starts at US$95,000. For experienced data scientists the median pay could be as high as US$165,000.

Article: https://www.analyticsinsight.net/top-10-highest-paying-countries-in-need-of-data-scientists-in-2023/?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=Newsletter&utm_content=Data+Science+Insider%3A+May+26th%2C+2023&utm_campaign=27052023

That’s my take on it: It surprises me that some countries aren't included in the list. The high-tech sectors of Germany, Japan, South Korea, and Taiwan, for instance, are vibrant and fast-growing, so data scientists should be in high demand. I might be missing something or the survey data are incomplete. Out of curiosity, I looked up employment information for data scientists overseas. According to Glassdoor, the average salary of a data scientist in Tokyo is US$55,831 (Yen 7851192), while that in Germany is US$72,419 (Euro 67500). The figures were not adjusted for purchasing power. Nonetheless, I will stay in America! 

Posted on May 25, 2023

About a week ago (May 16) Forrester, one of the most trustworthy tech consulting companies in the world, published a report about the current trend of the AI-based decision-support market. The Forrester Wave evaluation report classified companies into four groups: Leaders, Strong Performers, Contenders, and Challengers. SAS and IBM (IBM Cloud Pak) belong to the first category (leader) while TIBCO (Spotfire) is placed in the second group (strong performer). According to Forrester, “SAS seamlessly integrates world-class analytics for decisioning. SAS’s flagship Viya platform includes beautifully designed interfaces across the entire data-to-decision lifecycle. Any combination of analytics, machine learning, and optimization can easily be created and used by teams within SAS Intelligent Decisioning.” “IBM business automation is driven by AI decisions. IBM’s AI decisioning platform is comprised of IBM Automation Decision Services (ADS) available in IBM Cloud Pak for Business Automation and IBM Watson Studio, and IBM OpenScale available in IBM Cloud Pak for Data.”

Report: https://reprints2.forrester.com/#/assets/2/202/RES178488/report

That’s my take on it: Contrary to popular belief, in spite of constant challenges from open source, proprietary software packages are still alive and well-functioning. Many people compare between SAS, Python, and R, but it is like comparing apples and oranges. Python and R are programming languages whereas SAS and IBM are integrated systems and platforms. When we need to implement DSML in a complicated environment with big data, we need a comprehensive system with user-friendly interface, rather than a DOS-like, command-based environment. Just my 2-cent.

Posted on May 18, 2023

Today (5/18) UC Berkeley announces that it will open a College of Computing, Data Science and Society, which is expected to be approved by The University of California Board of Regents. During the 2025-26 academic year, a new college building will house the data science major that was launched five years ago, along with other computer science degree programs. More than 89 campuses have access to the online curriculum, which includes assignments, slides, and readings. UC Berkeley also has disseminated its curriculum to other colleges and universities for free. Beginning this fall, there will be UC Berkeley-led data science classes at six California community colleges, four Cal State campuses, and Howard, Tuskegee, Cornell, Barnard, and the United States Naval Academy.

Full article: https://www.latimes.com/california/story/2023-05-18/uc-berkeley-spreads-the-gospel-of-data-science-with-new-college-free-curriculum

It is important to point out that this data science conglomerate is not a result of a merely top-down decision; rather, it happens due to huge faculty and student demand. Data science has risen to the fourth most popular major at UC Berkeley in just five years. Faculty and students at UC Berkeley are aware of the importance of data science. In June 2023, UC Berkeley received three gifts totaling $75 million for supporting the construction of the data science center. Two of the gifts are from the current Berkeley faculty. Needless to say, good leaders must pay attention to bottom-up movements; they must be active listeners who can constantly learn and adapt to change.

Posted on May 9, 2023

The IBM CEO Arvind Krishna announced a hiring freeze last week (May 7). Also, nearly 8,000 jobs will be replaced by AI at the company, he said. Throughout the next five years,
machines may take over up to 30% of non-customer facing roles. In the near future, robots and algorithms will likely pose a significant threat to workers in fields like finance, 
accounting, and HR. The upside of this transformation is that AI is expected to contribute $16 trillion to the global economy by 2030.

Report: https://finance.yahoo.com/news/ibm-plans-replace-nearly-8-174052360.html?guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAGHMQWxx33K2TXzdlFFvmZILHZVQQpRtRRCwC7I5_9bc2dwUiB6GS4g7e58lzfMKLgWXSYfPH9_CR68-fRvUD2YHfQHCr58POZIcVZ_smwVJiKcHCIdOwOsBxj4uruQrI11LHfmCmHVdHp9v5waiR5GKt44YblsgqykakA-W90VP&utm_medium=email&_hsmi=257447386&_hsenc=p2ANqtz--w4gVNs1s-ieC1IOjHA9Gt1w5hCvLtkydqmuQE7PccVF955PvehkElxIWb0CDUeIRgPZ17NnZhHdF46Kh3euc8oAtM0g&utm_content=257447386&utm_source=hs_email&guccounter=1

That's my take on it: The writing has been on the wall for a long time. Academicians are not immune to this trend. In the past, it was necessary to have expertise to perform data
transformations when a nonlinear function could fit the data better. With neural networks, the transformation can be automated in a matter of seconds. In order to deal with the
trend, higher education must reform its curricula; otherwise, graduates with outdated skills will find themselves unable to find jobs.   

Posted on May 5, 2023

White House officials announced yesterday (May 4) that more funding and policy guidance will be provided for developing responsible artificial intelligence before the Biden
administration meets with industry executives. The National Science Foundation plans to invest $140 million in seven new AI-dedicated institutes, bringing the total to 25. With
the goal of making the United States a leader in AI innovation while ensuring that it is developed and used responsibly, the newly created National Artificial Intelligence Initiative
Office will work with academic institutions, government agencies, and industry leaders to address issues such as bias, privacy, and transparency.

Website: https://www.theverge.com/2023/5/4/23710533/google-microsoft-openai-white-house-ethical-ai-artificial-intelligence

That’s my take on it: AI/DSML spans across almost all disciplines, rather than being confined to science and engineering. These diverse institutes devoted to AI will cover a wide
range of topics, including ethical issues, AI impact on politics, society, economics, and more. Thus, philosophers, psychologists, sociologists, and economists alongside researchers
in other disciplines will have funding opportunities. Now is the time to act!

Posted on April 30, 2023

A few days ago (April 26), in a statement published by the Association for Mathematical Consciousness Science (AMCS), a group of more than 150 researchers specializing in
mathematical and computational methods for understanding consciousness warns that AI is advancing at a pace that is speeding beyond our understanding of its ethical, legal,
and political implications. Language models such as Google's Bard and OpenAI's ChatGPT now mimic animal brain neural networks, but will soon be constructed to replicate
higher-level brain architectures, and thus it is essential for AI researchers to study the nature of consciousness. According to the letter, “there are over 30 models and theories
of consciousness (MoCs and ToCs) in the peer-reviewed scientific literature, which already include some important pieces of the solution to the challenge of consciousness.”

Open letter: https://amcs-community.org/open-letters/

That’s my take on it: Cognitive science and philosophy of mind have long been intrigued by the concept of consciousness. Traditionally, this type of research has been considered
purely theoretical and "academic." Today, this type of research has a wide range of practical implications. Reductive materialism asserts that conscious phenomena are made up
solely of neurological structures. In this case, consciousness could emerge from material (machines). About 10 years ago, former Arizona State University faculty member Lawrence
Strauss predicted that in the near future there will be self-aware computers. Let's see how it goes.

Posted on April 22, 2023

Posted on April 11, 2023

In response to the arrival of ChatGPT, recently a group of prominent AI researchers signed an open letter to call for slowing down AI developments that can pass
the Turing Test. The Turing test measures a machine's ability to exhibit intelligent behavior that is indistinguishable from human behavior. Yoshua Bengio is one of
the leading experts in deep learning who co-signed the letter.

Bengio wrote, “I found it appropriate to sign this letter to alert the public to the need to reduce the acceleration of AI systems development currently taking place at
the expense of the precautionary principle and ethics. There is no guarantee that someone in the foreseeable future won’t develop dangerous autonomous AI systems
with behaviors that deviate from human goals and values. The short and medium-term risks –manipulation of public opinion for political purposes, especially through
disinformation– are easy to predict, unlike the longer-term risks –AI systems that are harmful despite the programmers’ objectives, and I think it is important to study both.”

Full article: https://yoshuabengio.org/2023/04/05/slowing-down-development-of-ai-systems-passing-the-turing-test/

That’s my take on it: Bengio cited the precautionary principle to argue for slowing down AI development. According to the precautionary principle, if an action could potentially
cause harm to the public or to the ecology, without scientific consensus, the burden of proof that it is not harmful is on the shoulder of the party taking the action. Because
most AI developers are not philosophers of ethics or legal experts, it places a heavy burden on them. I think there is no need to slow down AI development; instead, experts
from different disciplines should be part of every development team, and there should be opportunities to engage in open debates and discussions regarding AI ethics.

Posted on March 31, 2023

Posted on March 28, 2023

A new version of Microsoft Bing-powered by ChatGPT is now available for beta testing. With the revamped search engine, queries written in natural language will

be recognized and answered by Open AI technology. If you have an account in ChatGPT, do you need Bing? I tested both AI systems by entering the same questions.

The answers to simple questions about factual information were very similar between the two, but when the questions were complicated, such as comparing two schools

of thought, the answers were very different.

Sample question: What is the key difference between Gary Marcus and Yann LeCun in terms of their perspectives on AI?

The answer from Bing is short. It provides footnotes but the sources are non-academic.

Gary Marcus and Yann LeCun are both prominent figures in the field of artificial intelligence. However, they have historically sharply different perspectives and opinions

on the development and future of AI, specifically deep learning. LeCun is famous for his pioneering work in the field of deep learning and computer vision(1). He believes

 that deep learning is the key to unlocking true AI(2). On the other hand, Gary Marcus has been a vocal critic of deep learning and has argued that it is not sufficient for

achieving true AI(1). He believes that AI needs to incorporate more symbolic reasoning and common sense knowledge(3).

1. rebellionsearch.com

2. zdnet.com

3. zdnet.com

The answer from ChatGPT is more elaborated, but no references are provided.

Gary Marcus and Yann LeCun have different perspectives on the field of artificial intelligence, specifically regarding the role of deep learning and the importance of incorporating

different forms of knowledge in AI systems.

Gary Marcus is a cognitive psychologist and professor of psychology at New York University, and he has been critical of the current state of deep learning and the limitations

of the approach. He argues that while deep learning has been successful in certain areas, such as image and speech recognition, it is not capable of achieving general intelligence

and that current approaches are too narrowly focused. He also believes that deep learning is insufficient on its own and that AI systems need to incorporate other forms of knowledge,

such as rules and logical reasoning, to achieve true intelligence.

Yann LeCun, on the other hand, is a computer scientist and professor at New York University who has been instrumental in the development of deep learning and convolutional neural

networks. He believes that deep learning is a crucial component of achieving general intelligence and that it is capable of handling a wide range of tasks, including those that require

reasoning and abstraction. He argues that the key to improving AI is to continue developing deep learning algorithms and architectures and to scale up the amount of data used to train

these systems.

Posted on March 23, 2023

The NVIDIA conference today featured a panel discussion entitled "AI Education and Access at Scale." The panel consists of four speakers and all are inspirational.

Because of space constraints, I can only highlight one of them: Joe Glover, Provost and Senior VP for Academic Affairs at the University of Florida (UF). A few years

ago UF launched a university-wide AI initiative that aims to prepare students and faculty to use AI technology to solve pressing challenges across every field.

To achieve this goal, UF formed a partnership with NVIDIA to build an AI supercomputing infrastructure. The UF program covers all disciplines at all levels, including

arts and humanities. According to Glover, some people were skeptical at first because this idea is so out of the ordinary. Nonetheless, he argued that AI is an

encompassing technology that can be well-applied to all disciplines. For instance, UF musicologist Imani Mosley utilized AI to discover the patterns of Spotify

whereas UF geology professor Mickie Mackie conducted research on improving sea level rise predictions with the help of machine learning. In order to facilitate the

integration of AI and various disciplines, UF hired a team of programmers to assist faculty in the technical aspects. The recording of the session can be accessed at:   

https://register.nvidia.com/flow/nvidia/gtcspring2023/attendeeportal/page/sessioncatalog/session/1665610946330001j59w

That’s my take on it: I totally agree with Dr. Joe Glover. Indeed, this is exactly what I have been trying to do for a long time. There are countless possibilities for

implementing AI in almost all disciplines. For example, instead of manually coding the corpus, literature, and history researchers can use text mining to analyze

archives. By utilizing AI generative tools, artists can spend more time conceptualizing instead of repeating tedious tasks. Needless to say, social scientists can

overcome the shortcomings of traditional statistics and solve the replication crisis by equipping themselves with machine learning tools and big data analytics. I will

look into the UF model and I hope NVIDIA has more supercomputers to offer!  

Posted on March 22, 2023

Posted on March 21, 2023

This week NIVIDA is hosting its annual online conference to unveil its new offerings, such as a cloud-based supercomputing service. This morning (3/21) NVIDIA founder and CEO,

Jensen Huang shared how NVIDIA's accelerated computing platform is driving the next wave in AI, the metaverse, cloud technologies, and sustainable computing. In addition to the

keynote, there are many informative sessions, such as Using AI to accelerate scientific discovery, Generative AI demystified, Deep reinforcement learning with real-world data,

Accelerating exploratory data analysis at LinkedIn…etc. At the keynote, Jensen Huang announced NVIDIA AI foundations, which will be deployed to Google Cloud, Microsoft Azure,

and Oracle Cloud. In the presentation, Huang kept repeating this phrase: “We are at the iPhone moment of AI.” All sessions are recorded and can be accessed at:

https://register.nvidia.com/flow/nvidia/gtcspring2023/attendeeportal/

 That’s my take on it: There is no doubt that recent technological developments and innovations in GPUs, AI, and cloud computing will be a game changer, perhaps even a paradigm

shift. I agree that this is an iPhone moment, but I would say it is also a Mac moment (1984), an NCSA Mosaic/Netscape moment (1992), a Google moment (1998), an IBM Watson

moment (2011), a Tensorflow moment (2015), an AlphaGo moment (2016), a Midjourney and ChatGPT moment (2022)…etc. Innovative momentum never ceases, and change is the

only constant in life. That’s why I have to change my syllabus and PowerPoint slides every semester, and very often I do so in the middle of the course!

Posted on March 17, 2023

In response to the challenge from ChatGPT, two days ago (March 15) China’s AI developer Baidu released "Wen Xin Yi Yan" at its Beijing headquarters. Its text generation mode is

similar to that of ChatGPT, but additionally, it can read out the answer in real-time, corresponding to various Chinese dialects, including Cantonese and Sichuan dialects. Moreover,

the content can be generated into pictures and videos in real-time, too. Robin Li, Chairman and CEO of Baidu, demonstrated the comprehensive capabilities of "Wen Xin Yi Yan" 

in five usage scenarios: literary creation, commercial copywriting, mathematical calculation, Chinese comprehension, and multi-modal generation. He admitted that in the internal

test, the experience of "Wen Xin Yi Yan" is not perfect, but seeing the strong demand in the market, he will release the product as soon as possible. At present, "Wen Xin Yi Yan"

has a better ability to support Chinese, and the English ability will be further improved in the future. Since the official announcement last month that "Wen Xin Yi Yan" will be

released, 650 partners have joined in, and more related products will appear in the short term. He emphasized that "Wenxin Yiyan" is not a tool for the technological confrontation

between China and the United States, but a brand-new platform for the group to serve hundreds of millions of users and empower thousands of industries. Starting today, the first

batch of users can experience the product on the official website of "Wen Xin Yi Yan" by inviting a test code, and it will be opened to more users in succession.

There are more than 260 billion parameters in Baidu's chatbot model, which is more than in GPT-3, but some critics believe its performance is not as good as ChatGPT, partly due

to its lack of web-based Chinese information.

Full text: https://www.scmp.com/tech/big-tech/article/3213605/chinese-online-search-giant-baidu-launch-its-answer-chatgpt-shadow-openais-upgraded-gpt-4-model

That’s my take on it: Perhaps the biggest hurdle to China's chatbot development is not the technological issue; rather, there are too many red lines. Once a tester inputs a sensitive

question into China's chatbot, but the system refused to answer: "The question could not pass a safety review. No response could be generated for you.” When the reporter tried 

to push it by asking, “Why did my question fail to pass the safety review?” The answer was: “Let’s change the topic and talk about something else.” In contrast, ChatGPT handles

sensitive or controversial questions differently: although the answer is usually vague and balanced, at least it gives the user objective facts and lets them decide. 

Posted on March 3, 2023

According to recent research conducted by two cognitive psychologists at the Max Planck Institute for Biological Cybernetics in Tübingen, GPT-3 is comparable to humans in some areas

but lags behind in others. One of the questions presented by the researchers to GPT-3 is the classical Linda’s problem (I use it in my statistics and probability class):

Linda is 31 years old. She majored in philosophy. She was deeply concerned with issues of social justice and discrimination. Which of the following statements is more probable?

A: Linda is a bank teller.

B: Linda is a bank teller and active in the feminist movement.

The correct answer is A because B is a subset of A. The probability of two events or conditions is definitely smaller than that of a single event. But most respondents picked B, which is a conjunction

fallacy. Interestingly, researchers at Max Planck Institute found that GPT-3 committed the same mistake as humans. Those researchers concluded that in searching for specific information or causal

reasoning, AI failed to use logic; rather, it only passively gets information from texts available on the Internet. When the majority is wrong, AI replicates the error.

Full paper: https://arxiv.org/pdf/2206.14576.pdf

That’s my take on it: Out of curiosity, I also entered the same question into ChatGPT. Unlike the result obtained by the two researchers at the Max Planck Institute, I received a more “typical” response.

If a student submits a vague answer like this, it might be sourced from AI! Based on the information provided, statement A or B cannot be determined as more probable. This is because Linda's age

and major in philosophy do not provide any indication of her profession or involvement in social movements. While her concern with social justice and discrimination suggests that statement B could

be possible, there is no clear evidence to support either statement. Therefore, it would be more accurate to say that both statements A and B are possible, but there is not enough information to determine

which one is more probable.

Posted on March 3, 2023

Recently Harvard Business Review (HBR) reported that many retail companies have not taken advantage of advanced data analytics to improve their business. There are exceptions: Walmart,

Amazon, and a few others. The 25 best-performing retailers during the pandemic generated 83% more profit than laggards and captured more than 90% of the sector's market capitalization gains.

By interviewing 24 business leaders, HRB unveiled six sticking points as follows:

1.     Culture: Typically, companies have a risk aversion problem and lack a clear goal for implementing analytics.

2.     Organization: Many companies struggle to strike a balance between centralization and decentralization.

3.     People: Very often the analytics function is managed by people who have no understanding of the industry.

4.     Processes: Businesses do not have unlimited resources at their disposal.

5.     Systems: Legacy systems are still serving many firms today.

6.     Data: Data are often scattered throughout the firm in silos and not managed in an organized manner.

Full article: https://hbr.org/2023/02/why-retailers-fail-to-adopt-advanced-data-analytics?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=Newsletter&utm_content=Data+Science+Insider%3A+March+3rd%2C+2023&utm_campaign=04032023

That is my take on it: Some interviewees believe the bigger issue is people. People who know about all other issues are willing to dedicate resources to solving them, despite their presence.

Sadly, this is not always the case. William Cleveland and John Chambers were pioneers in data science. Many years ago they both proposed that data science should be interdisciplinary,

incorporating domain knowledge. Agree!  

Posted on February 27, 2023

In response to the challenge of Open AI, three days ago (Feb 24) Meta (Facebook) announced its flagship large language model: Large Language Model Meta AI (LLaMA). While Open AI’s GPT3

consists of 175 billion parameters, the size of LLaMA varies from 7 million to 65 billion parameters only. In spite of this, Meta claimed that LLamA is superior because it requires fewer computing

resources to test new approaches, validate existing models, and explore new scenarios. The model will be released under a noncommercial license in order to maintain its integrity and prevent

 misuse. Researchers from academic institutions, government organizations, civil society groups, and industry research laboratories around the world will be allowed access on a case-by-case

basis.

The announcement by Meta: https://ai.facebook.com/blog/large-language-model-llama-meta-ai/?utm_medium=email&_hsmi=247961423&_hsenc=p2ANqtz--IqabBdLi7F5MPXZg3q5IWtn8mnHwrOF9QaIZalOhHLdLS5SLrUiS4puckqbV0lcxNpceboDE-_jz20gJ28nI3vonHAg&utm_content=247961423&utm_source=hs_email

That’s my take on it: Several people argued that ChatGPT had unleashed Pandora's box because it had been released ahead of the development of ethical guidelines for AI applications. Due

to the controversy surrounding ChatGPT, it is understandable that Meta took a more cautious approach. However, even if Meta and others tried their best to patch all ethical and legal loopholes

in AI and machine learning, someone will misuse or abuse the technology one way or another.

Posted on February 24, 2023

ChatGPT stories continue to dominate mass media and social media, and probably you already received these stories from many channels. Therefore, I would like to

draw your attention to something else. Two days ago Google unveiled its 2003 data and AI trends report. In addition to Google Cloud, Google also suggests a vast

array of technologies to companies that planned to enhance their AI and cloud computing infrastructure:

·      Aiven

·      C3AI

·      Confluent

·      Collibra

·      Databricks

·      Datametica

·      Elastic

·      Fivetran

·      MongoDB

·      Nivida

·      Qlik

·      Quantiphi

·      Salesforce

·      SAP

·      Striim

·      ThoughtSpot

A month ago InsideBigData complied with the IMPACT 50 list for Quarter 1, 2023. According to InsideBigData, “These companies have proven their relevance by the way they’re

impacting the enterprise through leading-edge products and services.” The top 20 are:

·      Open AI

·      Nvidia

·      Google AI

·      Amazon Web Services

·      Hugging Faces

·      H20.ai

·      Databricks

·      Microsoft AI

·      Intel AI

·      Neural Magic

·      Snowflake

·      SAS

·      Qlik

·      Neo4j

·      Alien Institute for AI

·      TigerGraph

·      Anaconda

·      Domino Data Lab

·      Hewlett Packard Enterprise

·      Cloudera

The full report of Google: https://services.google.com/fh/files/misc/data_and_ai_trends.pdf

The full article of InsideBigData: https://insidebigdata.com/2023/01/17/the-insidebigdata-impact-50-list-for-q1-2023/  

That’s my take on it: Although the selection criteria are subjective and might even be biased, data scientists and DSML educators should still take them seriously. As you can see, the

list of these most promising and most impactful tech companies consists of both fairly new companies and mature companies (e.g., Microsoft, Hewlett Packard, Intel, SAP, SAS…etc.).

However, some established tech giants are absent from the list (e.g., IBM, Oracle…etc.). Both IBM and Oracle are not even among the top 50. It is understandable. Despite several

decades of development, some of its products have made little progress. The rule in academia is: publish or perish. In the era of AI and big data, the choice facing companies is: 

innovate or perish.

Posted on February 21, 2023

Posted on February 10, 2023

Facing the pressure from Open AI's ChatGPT, Google is devoting efforts to reassure the public that its AI technology is still promising. However, the performance of its own chatbot named Bard is so embarrassing that investors lost confidence. Bard, which was released on Twitter on Monday, tried to answer an inquiry about discoveries from the James Webb Space Telescope. According to Bard, the telescope was the first to photograph a planet outside the solar system, but indeed this milestone was accomplished by the European Very Large Telescope in 2004. This mistake was spotted by astronomers on Twitter. Consequently, Alphabet's shares dropped more than 7% on Wednesday, losing $100 billion of its market value.

Full article: https://www.reuters.com/technology/google-ai-chatbot-bard-offers-inaccurate-information-company-ad-2023-02-08/

That’s my take on it: As a matter of fact, ChatGPT also made many factual errors. For example, when a history professor asked ChatGPT to explain the Joseph Needham thesis, it offered a response as: “the scientific and technological achievements of the West were only possible because of the transmission of scientific and technological knowledge from China to the West.” It is completely wrong! Indeed, Joseph Needham was curious about why ancient China failed to develop modern science. My friend who is a math professor in Hong Kong also found that some answers offered by ChatGPT are unsatisfactory. I guess people are more forgiving of ChatGPT because it is the first of its kind.  

There has been a hot debate in academia about the use of ChatGPT. In December last year, ChatGPT was included as one of 12 authors on a preprint about using the tool
for medical education posted on the medical repository medRxiv. According to Nature, ChatGPT was cited as a bylined author in two preprints and two articles in science
and health published in January 2023. All of the articles have an affiliation with ChatGPT, and one even gives an email address for a supposed nonhuman "author".  
Nature explained that the inclusion of ChatGPT as an author was a mistake and the journal will fix it soon. However, PubMed and Google Scholar have already indexed these
articles and these nonhuman "authors." Nature has since set forth a policy guiding how large-scale language models can be used in scientific publications, prohibiting naming
them as authors. To address this latest technological concern, recently the Journal of the American Medical Association (JAMA) updated its instructions for authors:
Artificial intelligence, language models, machine learning, and similar technologies are not eligible for authorship. When these tools are utilized to generate content or assist
in the writing or preparation of manuscripts, the authors are responsible for the integrity of the content generated by these tools and must clearly state the use of AI in the
manuscript.

 Full articles:

https://jamanetwork.com/journals/jama/fullarticle/2801170

https://www.nature.com/articles/d41586-023-00107-z

That’s my take on it: It appears that faculty and student policies regarding ChatGPT are vastly different. The inclusion of any content generated by ChatGPT in a paper is
strictly prohibited by many universities and violation of the policy is treated as academic dishonesty. On the contrary, JAMA accepts AI-generated content as long as the author
verifies the information and documents it in the Acknowledgment section or the Methods section of the paper. I guess it is based on the implicit assumption that mature adults
are more responsible than young students. In my opinion, it is not necessarily true. This type of "discriminatory" policy may eventually lead to discontent among students.
Rather than setting two sets of policies, it would be better to create one standardized policy for all and provide workshops on ethical AI use to both groups.

Yesterday (Feb. 2, 2023) an article posted on KDNuggets introduces ten free machine learning courses offered by top universities, including UC Berkeley, Carnegie Mellon
University, Stanford University, Caltech, Cornell University, University of Toronto, MIT…etc. It is noteworthy that these are just not one-hour seminars; rather, the duration
of these comprehensive courses is between 20 and 60 hours. More importantly, some of these courses are taught by very prominent scholars in the field, such as Andrew Ng.

Full article: https://www.kdnuggets.com/2023/02/10-free-machine-learning-courses-top-universities.html

That’s my take on it: According to the May 2022 report compiled by the Institute for Advanced Analytics at North California State University, there are about 353 graduate
programs in data science and machine learning in the US. Additionally, there are many free courses in the market and the preceding list is only the tip of the iceberg. No doubt
the competition is very intense, and therefore program designers must think outside the box to stay ahead of the curve. 

In spite of a mass layoff (10,000 employees), Microsoft recently announced a $10 billion investment in Open AI, the company that developed ChatGPT and DALLE-2.
Microsoft's investment will allow OpenAI to accelerate its research since all of its models are trained in Microsoft Azure. In return, Microsoft will receive a boost to its
Azure cloud and even catch up with Amazon Web Services.

Full article: https://www.bloomberg.com/news/articles/2023-01-23/microsoft-makes-multibillion-dollar-investment-in-openai

That’s my take on it: Currently, Amazon Web Services dominates the cloud computing market. However, Open AI can undoubtedly improve the functionality of Microsoft
Azure. While AWS does not have a powerful AI partner like Open AI, its Sagemaker provides powerful predictive modeling capabilities. A long time ago, Microsoft and
SAS Institute formed a partnership to offer cloud-based data analytics. It is my belief that this fierce competition in machine learning, cloud computing, and data science
will drastically change the landscape of these fields in the near future. Be sure to stay tuned!

With over 477 million items, Getty Images is one of the largest visual media companies in the world, offering stock images, videos, and music to business and individual clients.
Recently Getty Images announced that it is suing Stability AI, a company that enables users to generate images using its machine-learning software module, Stable Diffusion.
Getty Images accused Stability AI of training its algorithms by unlawfully extracting images from the Internet, including stock images owned by Getty. Getty claimed that the
company is not seeking financial damages or trying to stop the distribution of AI-art technology; rather, it attempts to push for laws and regulations that respect intellectual property.

Article: https://www.theverge.com/2023/1/17/23558516/ai-art-copyright-stable-diffusion-getty-images-lawsuit

That’s my take on it: Getty Images' reaction is understandable. It will not be necessary for illustrators or other users to buy stock images from Getty or other suppliers when
they are able to generate images using AI. For example, the Atlantic published a report by Charlie Warzel in 2022 right after Midjourney was released, another AI art generation
program. The report depicts two images of Alex Jones, the founder of InfoWar. Later Warzel apologized. “This was entirely my fault…Instead of selecting a photo or illustration
from Getty Images to go with the story, as I do for most of my newsletters, I decided to try something different and use an AI art tool to come up with the story’s accompanying
image,” says Charlie Warzel.

It is interesting to note that Getty Images is not suing Midjourney and DALLE-2. There is an obvious reason for omitting DALLE-2. While Stability AI uses an open-source model,
Open AI, which developed DALLE-2, did not disclose its mechanics. In the absence of ample evidence, attorneys have a difficult time building a case. However, I don’t understand
why Getty Images is not targeting Midjourney. Do you know why?

Today Boston Dynamics, a leader in AI-enabled robotics, released a video clip on YouTube that shows how Atlas, an intelligent humanoid robot, navigates “his” environment.
“He” assisted “his” human partner by using available objects and modifying his path to reach “his” goal.

Video: https://www.youtube.com/watch?v=-e1_QhJ1EhQ

That’s my take on it: In this video, the robot is merely helping the construction worker, who is still doing the actual task. I believe that in the near future, the advancement
of AI and big data analytics will enable intelligent robots to replace humans in certain high-risk careers, such as monitoring the radiation levels in nuclear plants and sweeping
mines on battlefields. Last year the US Army provided one of its two robotic dogs, which was built by Boston Dynamic, to clean up mines in Ukraine. You read it correctly. Only
one robotic dog! I guess it is still experimental. It would be great if this could be scaled up in the future so that no human lives would be lost. As shown in the video, Atlas' actions
indicate that an intelligent robot could evade threats better than humans.

Why did Microsoft invest in R rather than Python or Octave?

That would be because of the Goldilocks principle in investing: if you invest in something, you want two things – potential (yield, profit, market share,…) and power (control).
R is relatively centralized. Beyond core R, which already comes with a pretty extensive amount of functionality, most of the things you use have been developed
by the same few dozens or so of highly prolific and amazingly skilled developers: Hadley Wickham and Dirk Eddelbuettel and Yihui Xie have pretty much developed
most of modern R as it is being used. It’s also a very widely used language, despite being a little clunky. Beauty is in the eye of the beholder, but I consider R to be
one of the uglier mainstream languages. In spite of this, it is very widely used in academia and enterprise settings, and while R itself isn’t particularly fast, you can
make it pretty impressively fast (but that’s a post for another day). R has pretty much displaced STATA, and as the slow generational change in science faculties
around the world plays out, students are increasingly encouraged to learn R instead of using slightly more digestible proprietary statistical packages like SPSS. It
helps that R has a spectacularly good front-end (RStudio)* and its own way of literate programming with Rmd.

Octave is basically an open-sourced version of Matlab. It’s syntactically similar, which is why it has just about all the drawbacks of Matlab. What Octave doesn’t have
is an ecosystem that comes near R’s. When it comes to quantitative applications, if an algorithm or an analysis has been implemented at some point somewhere in
the known universe, there’s likely an R package for it. This includes some fairly esoteric stuff. You can’t say the same about Octave, sadly. Octave doesn’t have the
sophisticated package management infrastructure of R and CRAN. Its overall ecosystem is much smaller, by about two orders of magnitude (!). The potential in
embracing Octave, as well as the number of existing users, is quite small.

Python is the opposite. Python has immense potential, and everybody knows it. It’s just really, really hard to govern. While Python does have a central governing
body (the Python Software Foundation), a lot of quantitative tools are spread all over the place: NumFocus, Apache, Google, OpenAI, individual maintainers, and so
on. It’s also a much more general-purpose language: R is, deep down, about quantitative work. Python can be used to pretty much do anything you’d want a
modern computing language to do. It is, quite simply, too big and too diverse for any investment, even by a company as big as Microsoft, to have a noticeable impact.
It’s delightfully chaotic, which makes it fun, but hard to exert control over.

What it ultimately boils down to is the infectious population (because of course it does – ask an epidemiologist a question, expect a response in those terms!). Octave’s
population is just too small to create an Octave pandemic (thank the heavens). There aren’t enough people who know and love it to keep teaching it to others. Python’s
infectious population is too big: it’s like one of those commensal viral species like Epstein-Barr or CMV that pretty much everyone gets in their lifetime. R is “just right”
– it’s in the investment Goldilocks zone. It’s got potential, it’s still somewhat governable and you can make a meaningful investment in it with relatively reasonable resources.

That’s my take on it: Different corporations have different development strategies regarding open source. While Microsoft is investing in R, IBM focuses on Python. To be
more specific, although the extension hub of IBM SPSS Statistics allows users to download and install both R and Python packages, IBM incorporates only the Python library
into IBM SPSS Modeler. This Python library includes a plethora of tools, such as SMOTE, XGBoost, t-SNE, Gaussian Mixture, KDE, Random Forest, HDBSCAN, and Support
Vector Machine. But there is no R library in IBM Modeler. In addition, the IBM data science certification program is also Python-centric.

In spite of its popularity, the decentralization of Python, as Chris von Csefalvay pointed out, is a concern to me. First, you need to figure out which package you need for
a specific job and it could be confusing. Second, when you encounter issues in Python, it is very difficult to trace the source of the problem, especially when multiple packages
are involved.  

Last, I agree with Chris von Csefalvay that R is not pretty although R-Studio provides users with a nicer front end. In my opinion, JASP, which is a graphical version of R, is
much more accessible. However, JASP is fairly new and its current version is 0.16.4. Not surprisingly, its graphical user interface is not as good as JMP Pro. JMP Pro is a
mature SAS product and its current version is 17.

In December 2021 an article in Forbes predicted the emerging trends of AI in the near future. A year later it was found that seven out of ten predictions were exactly right
or on the right track:

1.     Language AI will take center stage, with more startups getting funded in NLP than in any other category of AI: Right.

2.     Databricks, DataRobot, and Scale AI will all go public: Wrong.

3.     At least three climate AI startups will become unicorns: Wrong.

4.     Powerful new AI tools will be built for video: Right.

5.     An NLP model with over 10 trillion parameters will be built: Wrong.

6.     Collaboration and investment will all but cease between American and Chinese actors in the field of AI: Right.

7.     Multiple large cloud/data platforms will announce new synthetic data initiatives: Right.

8.     Toronto will establish itself as the most important AI hub in the world outside of Silicon Valley and China: Right.

9.     “Responsible AI” will begin to shift from a vague catch-all term to an operationalized set of enterprise practices: Rightish.

10.  Reinforcement learning will become an increasingly important and influential AI paradigm: Rightish.

Full article: https://www.forbes.com/sites/robtoews/2022/12/15/what-we-got-right-and-wrong-in-our-2022-ai-predictions/?sh=3c993b721654&utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=Newsletter&utm_content=Data+Science+Insider%3A+December+16th%2C+2022&utm_campaign=17122022

That’s my take on it: I could write a 10-page essay to respond to each of the preceding predictions, but in this short post I will focus on the fulfillment of Prediction # 10 only. Reinforcement learning was inspired by the reinforcement theory in behavioral psychology. According to behaviorism, our behaviors are governed by the stimulus-response (S-R) loop, meaning that how we act or respond depends on what stimulus or feedback we received from the environment. If the feedback is rewarding, it reinforces good behaviors. If not, we avoid detrimental behaviors. When I was a student, most scholars looked down upon behavioral psychology for its over-simplicity. However, a few decades later AlphaZero, AlphaGo, and AlphaStar (Google’s DeepMind) that defeated human chess experts and video game players are all based on this alleged over-simplistic model. Nvidia, the leader in the GPU market, also used reinforcement learning to design its new cutting-edge H100 chips. The moral of the story is: We need to keep an open mind to alternate theories. 

During the past month, Lensa AI created by Prism Labs is taking over social media by storm. This app has been around since 2018, but recently its new feature “Magic Avatars”
draws a lot of attention. In the past week, Lensa AI became the most popular app in the iOS App store and has been downloaded 700,000 times in the past month. Why is it so
popular? The new feature is amazing! If you upload 10-20 photos of yourself to the system, the deep learning algorithm can create several digital versions of yourself. This
algorithm is based on Stable Diffusion, an AI-powered program trained on a data set consisting of over two billion images. It is important to point out that the app might collect
your behavioral data, and thus IT security experts suggest using it cautiously.

One-minute introduction on Youtube: https://www.youtube.com/shorts/FAs0d6q-MS0

One-minute discussion about safety concerns on Youtube: https://www.youtube.com/watch?v=xZv6ph8ShuY

That’s my take on it: The impact of AI is beyond data analytics; instead, its influence can be found in every discipline, including visual arts and mass communication. Besides
privacy concerns, skeptics argue that these machine learning programs are trained with many existing images on the Internet, but those artists are not compensated at all.
While contributors to the open-source community voluntarily share their source codes with the whole world, un-compensated artists are forced to accept this quasi-open-source
model. However, for me, it is acceptable because these programs didn’t “plagiarize” anyone’s work. Rather, they “learn” from other images and then create a new one based on
the references. Is that what we are doing in every type of work? When I write a research paper, I usually use 30 to 50 references but don’t pay those authors.  

Two days ago, I attended the third session of “The Statistics Wars and Their Casualties.” One of the speakers David Hand asserted that the problem of statistical
significance is its misuse and misinterpretation. He cited several examples to argue that procedures in different schools of thought can be equally misused.
For example, the mean is sensitive to extreme values while the median is resistant against outliers. Which one is more appropriate depends on the research
question. Arithmetic means can be misused, but we should not ban Arithmetic means. In a similar vein, in data science, the area under the curve (AUC) and the error
rate derived from the same predictive model can lead to contradictory conclusions. But we should not call for a ban on using such tools just because they are misused
or misinterpreted.

Link to Stat Wars website: https://phil-stat-wars.com/

 That’s my take on it: In the seminar, I presented my counter-arguments as follows:

1.     The problem of statistical significance is more than its misuse. Indeed, there are other inherent problems. For example, the alpha level (0.05) is nothing
more than a social convention. Unlike the Bayesian approach which yields a distribution of answers and promotes probabilistic reasoning, the frequency approach leads to a
dichotomous conclusion. While it is true that eventually we need to make a dichotomous decision, the problem of statistical significance is that even the evidence is
dichotomous! Nonetheless, I agree that we cannot go that far to ban it. For small-sample studies, classical statistics is still valid and handy.

2.     Data science is less error-prone and less likely to be misused. Traditional parametric statistical methods require many assumptions. In contrast, most data
science methods are non-parametric; they are robust against outliers and noise; they can detect non-linear patterns. More importantly, ensemble methods and machine
learning are capable of doing self-replication by partitioning the data set into sub-samples and running multiple models, thus alleviating the replication crisis found in
traditional statistics.

A few days ago, Nvidia, the pioneer of graphical processing units (GPU), announced its new partnership with Microsoft in co-developing AI cloud-based computing.
Specifically, Nvidia will utilize Azure, the cloud platform of Microsoft,  to develop advanced generative AI models that can create content, including codes, images,
and video clips.

Full article: https://www.cnet.com/tech/computing/nvidia-and-microsoft-join-forces-to-build-ai-supercomputer/?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=Newsletter&utm_content=Data+Science+Insider%3A+November+18th%2C+2022&utm_campaign=19112022

That’s my take on it: Currently, Nvidia is the world’s second-largest semiconductor company (behind TSMC), whereas Microsoft is second to Amazon Web Services
in cloud computing. It is logical for them to form such a joint venture in order to compete with the number one in the market. In the past, computer users were confined
to the Wintel monopoly (Microsoft Windows and Intel CPU). However, in the era of big data analytics, AI, and cloud computing, it is anticipated that data analytics can
choose between many options. 

Recently NVIDIA, the leader in graphical processing units (GPU) and one of the leaders in AI research, announced a new approach to AI-enabled text-to-image
generation named eDiff-I. Currently, the three prominent leaders in the text-to-image market are Midjourney, DALL.E-2, and Stable Diffusion. As the name
implies, Stable Diffusion is based on diffusion modeling. Under this mode, an initial image is created with random noise. Next, through an iterative process,
a sharp and sensible image is gradually created by denoising the entire noise distribution. While Stable Diffusion’s denoising is based on a single noise distribution,
NVIDIA goes one step further using an ensemble of multiple expert denoisers.  

Additionally, while users of Midjourney, DALL.E-2, and Stable Diffusion have limited control of the output image, eDiff-I allows users to paint with text, i.e., specify
objects in different areas of the canvas.

YouTube Video (7 minutes): https://www.youtube.com/watch?v=21TljTNvQJw

That’s my take on it: The logic of diffusion modeling is similar to several older statistical procedures. For example, K means clustering randomly selects centroids
and then fine-tunes the clustering patterns through multiple iterations. In contrast, the logic of eDiff-I is closer to that of data science and machine learning. The
ensemble method, an extension of resampling, is utilized in boosting and bagging. Rather than drawing a conclusion based on a single model, the ensemble method
converges multiple models to the final output from a collection of models.

I admire NVIDIA because its CEO/founder has a vision. Currently, NVIDIA is a one-trick pony, but it cannot be the leader of GPUs forever. Using its strength in
graphical processing to invest in a less-crowded AI domain (text-to-speech image generation) is definitely a smart move! 

As you might already know, recently SAS Institute released the new version of JMP and JMP Pro (Version 17). There are many powerful and handy new features, such as

·      Workflow Builder

·      Easy design of experiment

·      Easy search

·      Spectral analysis in the functional data explorer

·      Genomics and wide fitting

·      Generalized linear mixed model

·      Interactive power analysis

·      Preview of joining, transposing, and data reshaping

 https://www.jmp.com/en_us/software/new-release/new-in-jmp.html

That is my take on it: I especially like the preview feature in data reshaping (e.g., concatenate, join, stack, split, transpose…etc.). In the past, no matter
whether you used a graphical user interface or coding, you could see the result only after hitting the OK or Run button. If something went wrong, you had
to debug it and re-did the whole procedure. Not anymore! Now I can literally look at the result before submitting the job.

Interactive power analysis is another wonderful feature. G*Power is very popular among researchers because it is free and user-friendly. The drawback
is that if you want to explore different options, you have to go back and forth between the input and the output. Although G*Power can output a graph
showing power on the Y-axis and N on the x-axis, the ranges are pre-determined by your input. Not anymore. In JMP you can use sliders to adjust the
effect size and the sample size, and then the power is updated in real-time!  

I always tell my students: The world keeps changing. If you cannot change the world, at least you change with the world! I will continue to
explore those new features to make my tasks more effective and efficient.

Today (10/20) is the second day of the 2022 Scale Transform X Conference. I would like to share one of the most informative presentations at this conference
with you. The title of the lecture is “Looking at AI through the lens of a chief economist” and the presenter is John List, Kenneth Griffin Distinguished Service
Professor in Economics at the University of Chicago and the Chief Economics Officer at Uber. His specialty is behavioral economics, a sub-domain of economics
that applies psychological theories to study human behaviors related to financial decisions. In this talk, he pointed out that scalability is a major challenge to
behavioral economics. Specifically, very often false positives caused by statistical artifacts in a small-scaled study misled the decision-maker to prematurely
expand the program, but in the end, the up-scaling program failed miserably.

When Uber invited Dr. List to apply for the position of Chief Economics Officer, initially he rejected it. However, later he accepted the offer out of the belief that
analyzing big data with AI/ML algorithms might be the key to address the problem of scalability. In the lecture he discussed one of his interesting studies at
Uber, which is concerned with customer retention. Customers who have a bad experience (e.g., delay of arrival) tend to stop using the service for a long
while. It was estimated that bad rides were reducing Uber future revenues by 5-10%. In this study, dissatisfied customers were randomly assigned into four
conditions: no treatment (control), offering an apology only, offering a promo code only (e.g., 10% discount for the next ride), and a combination of both.
The sample size is 1,258,000. It was found that the last three groups spent more money for the Uber service than the control group in the next seven days
after the bad experience; however, there are no substantial differences between the three treatment groups in terms of money spent for Uber in the same period
of time.

Conference website: https://scale.com/events/transform

That’s my take on it: The problem of scalability in behavioral economics is similar to the replication crisis in psychology: the results of many research studies
are difficult to reproduce in other settings. If a model is overfitted to a particular sample, its generalizability is severely limited. I am glad to see that Dr. John
list is willing to utilize big data to tackle this problem. On the contrary, some psychologists are still skeptical of data science methods. Once a psychologist said
to me, “Big data is irrelevant!”  After all, behavioral economics could be conceptualized as an interdisciplinary study that integrates both psychology and economics.
If big data can be applied to behavioral economics, why can’t other disciplines?

Next time if I receive an apology from Uber after a bad ride, I will not reuse the service immediately. After a few days, Uber might send me a promo code in order
to win me back!

Two days ago (Oct 6) six US leading tech companies, including Boston Dynamics, Agility Robotics, ANYbotics, Clearpath Robotics, Open Robotics, and Unitree, signed an
open letter pledging not to weaponize their products. They state, “As with any new technology offering new capabilities, the emergence of advanced mobile robots offers
the possibility of misuse. Untrustworthy people could use them to invade civil rights or to threaten, harm, or intimidate others… We pledge that we will not weaponize our
advanced-mobility general-purpose robots or the software we develop that enables advanced robotics and we will not support others to do so."

 Full article and letter: https://www.axios.com/2022/10/06/boston-dynamics-pledges-weaponize-robots?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=Newsletter&utm_content=Data+Science+Insider%3A+October+7th%2C+2022&utm_campaign=08102022

That’s my take on it: In the open letter they also state, “to be clear, we are not taking issue with existing technologies that nations and their government agencies use
to defend themselves and uphold their laws.” However, without support from major US robotics firms, the development of AI-based weapons in the US will slow down.
Perhaps my position is unpopular. Will governments and high-tech corporations of hostile countries face the same limitations? History tells us that any unilateral disarmament
often results in more aggression, instead of peace (Remember Neville Chamberlain?).

Two years ago the New York City Police Department (NYPD) utilized the Spot model from Boston Dynamics to support law enforcement, including a hostage situation in the
Bronx and an incident at a public housing building in Manhattan. Unfortunately, these deployments caused an outcry from the public, and as a result, the NYPD abruptly
terminated its lease with Boston Dynamics and ceased using the robot. If “robocops” can save the lives of innocent people and reduce the risk taken by police officers, why
should we object to it?

Yesterday (September 23, 2022) an article published in Nature introduced the Papermill Alarm, a deep learning software package that can detect text in articles similar to
that found in paper mills. Through the PaperMill Alarm, it was estimated that about 1% of articles archived in PubMed contain this type of questionable content. There are
several existing plagiarism detection software tools in the market, but this approach is new because it incorporates deep learning algorithms. Currently, six publishers,
including Sage, have expressed interest in this new tool.

Full article: https://www.nature.com/articles/d41586-022-02997-x

That’s my take on it: If this tool is available in the near future, I hope universities can utilize it. Although there are several plagiarism checkers, such as Turnitin and
SafeAssign, in the market, today some sophisticated writers know how to evade detection. No doubt deep learning algorithms are more powerful and sensitive than
conventional tools.

Nonetheless, I think there is room for expansion in using deep learning for fraudulent paper detection. Currently, the scope of detection of the Papermill Alarm is limited
to text only. As a matter of fact, some authors duplicated images from other sources. As the capability of machine learning advances rapidly, image sleuths may also be
automated soon.  

Yesterday (September 20, 2022) in the article entitled “Data: What It Is, What It Isn’t, and How Misunderstanding It is Fracturing the Internet” President of Global Affairs at
Meta Nick Clegg argued that data should not be treated as the “new oil” in the era of big data. Unfortunately, public discourse about data often relies on this type of faulty
assumptions and analogies, resulting in digital localization and digital nationalism. First, unlike oil, data are not finite. The supply of new data is virtually unlimited and the
same data can be re-analyzed. Second, more data are not equated with more values; rather, it depends on how the data are utilized. For instance, a database about
people’s clothing preferences is much more important to a fashion retailer than it is to a restaurant chain. Third, data values depreciate over time, i.e., outdated data are
useless or less valuable. More importantly, data access is democratized, not monopolized. For example, every month more than 3.5 billion people use Meta’s apps,
including Facebook, Instagram, WhatsApp, and Messenger, for free! Taking all of the above into consideration, Clegg argued that democracies must promote the idea of the
open Internet and the free flow of data.

Full article: https://nickclegg.medium.com/data-what-it-is-what-it-isnt-and-how-misunderstanding-it-is-fracturing-the-internet-e56e278643a7

That’s my take on it: The notion “data is the new oil” originates from British mathematician Clive Humby in 2006.  This idea is true to some certain extent. For example,
in the past Google’s language model outperformed its rivals by simply feeding more data to its machine learning algorithms. This “brute force” approach is straightforward:
pumping more “fuel” into the data engine, and it works! Nonetheless, it is also true that more data do not necessarily generate more values. Old data could depreciate,
but even new data are subject to the law of diminishing returns. Democratization of data access and user-generated content is both a blessing and a curse. True. Usable data
are abundant and limitless, but so are bad data and misinformation!

Recently an artist named Jason Allen won the first prize for the category of digital art in the Colorado State Fair’s fine arts competition. However, many people are resentful of Allen’s victory,
because he admitted on Twitter that his picture was generated by an AI program called Midjourney. The production process by Midjourney, which is equipped with natural language processing,
is very user-friendly. In the command prompt, the user simply types a sentence, such as “a beautiful princess in a medieval castle”, and then the program can output several variants of the
picture according to the input.

Allen submitted a piece entitled “Théâtre D'opéra Spatial” after 900 iterations of the digital art. During the art competition, the judges didn’t realize that his art was created with AI, but they
also said that Allen didn’t break any rules.

Many Twitter users have different opinions. Twitter user OmniMorpho wrote, “We're watching the death of artistry unfold right before our eyes — if creative jobs aren't safe from machines, then
even high-skilled jobs are in danger of becoming obsolete.” Another Twitter user, Sanguiphilia, said, "This is so gross. I can see how AI art can be beneficial, but claiming you're an artist by
generating one? Absolutely not. I can see lots of kids cheating their way through assignments with this."

Allen bluntly proclaimed, "Art is dead, dude. It's over. A.I. won. Humans lost."

Full report: https://www.businessinsider.com/ai-art-wins-competition-angering-artists-2022-9

That’s my take on it: When I was a kid, I was forbidden by my parents to use a calculator because pressing buttons was not considered doing real math. Similar controversies recurred when
other new technologies were introduced (e.g., computers, digital photography…etc.). The massive protest against Allen’s victory is understandable. Traditionally, a skill is conceptualized as an
ability to perform a complicated activity that requires rigorous training. If anyone can do the job without going through professional training, such as talking to a computer, this so-called “skill”
is not highly regarded. Nonetheless, there are still many gray areas. One may counter-argue that the big idea in the head is more important than the implementation skill in the hand. For
example, in the past, it took a skillful wildlife photographer to manually focus on a fast-moving subject, but today digital cameras can automatically track the subject. What you need to do is
just be there to push the shutter. By the same token, if AI can cut down the production process from 10 hours to 10 minutes, the artist can spend more time on creative ideas.

Do I completely hand over my creative process to AI? I didn’t go that far. As a photographer, I still make “real” photos, and at most I only replace boring backdrops with digital backgrounds
generated by Midjourney. The following are some examples (1-8: with digital backgrounds; 9-11: with original blank backdrops). Am I an artist? You be the judge.

https://creative-wisdom.com/photography/girls/people604.html

On August 30, Komprise announced the results of its 2022 Unstructured Data Management Report. The following are the key findings:

·      “More than 50% of organizations are managing 5 Petabyte or more of data, compared with less than 40% in 2021.” (1 Petabyte = 1,024 terabytes or 1 million gigabytes)

·      “Nearly 68% are spending more than 30% of their IT budget on data storage, backups and disaster recovery.”

·      “Cloud storage predominates: Nearly half (47%) will invest in cloud networks. On-premises only data storage environments decreased from 20% to 11.9%.”

·      “The largest obstacle to unstructured data management (42%) is moving data without disrupting users and applications.”

·      “A majority (65%) of organizations plan to or are already investing in delivering unstructured data to their new analytics / big data platforms.”

Full text: https://www.globenewswire.com/news-release/2022/08/30/2506659/0/en/Komprise-Survey-Finds-65-of-IT-Leaders-Are-Investing-in-Unstructured-Data-Analytics.html

That’s my take on it: As you might already know, structured data are referred to as data stored in row-by-column tables, whereas unstructured data are referred to as open-ended textual data,
images, audio files, and movies that cannot be managed and processed by traditional relational databases. Structured data are highly compressed based on the assumption that complicated reality
can be represented by abstract numbers. In response to this narrow view of data, qualitative researchers argued that open-ended data could lead to a rich and holistic description of the phenomenon
under study. In business, collecting, storing, and analyzing unstructured data has become an irreversible trend, and thus many powerful tools have been developed to cope with this “new normal.”
But in academia, quite a few recent qualitative research books still omit text mining, computer vision, and other latest developments of machine learning for unstructured data processing. There are
gaps to be filled!

In a contentious article entitled “Spirals of delusion: How AI distorts decision-making and makes dictators more dangerous,” which will be published in the upcoming issue of Foreign Affairs,
prominent political scientists Henry Farrell, Abraham Newman, and Jeremy Wallace discussed how democracies and totalitarian regimes are facing challenges from AI and machine learning
in different ways.

In an open society, machine learning could worsen polarization when AI-powered recommendation systems employed by social media keep feeding information to subscribers based on
their preferences. It is disrupting the traditional positive feedback loop as these self-propelling technologies rapidly spread misinformation and reinforce hidden biases.  

In an autocratic system, the government utilizes big data and AI technologies to monitor and brainwash people, but as a result, the leaders are trapped by their generated “reality” without
knowing what is actually happening out there, thus increasing the chance of making bad decisions. These authors called it the “AI-fueled spiral of delusion.”

The AI-fueled challenges in a democratic society are visible and can be counteracted by concerned citizens, but such a self-correcting mechanism is absent in an authoritarian regime.

Full text: https://www.foreignaffairs.com/world/spirals-delusion-artificial-intelligence-decision-making?utm_medium=newsletters&utm_source=twofa&utm_campaign=Spirals%20of%20Delusion&utm_content=20220902&utm_term=FA%20This%20Week%20-%20112017

That’s my take on it: It is true that democratic countries have correction mechanisms against misinformation, but it is still an uphill battle, as evidenced by a seminal study conducted by
Nyhan et al (2005). In this experiment initially, participants were given incorrect information (e.g, weapons of mass destruction were found in Iraq, the Bush administration totally banned
any stem cell research…etc.). At the same time, Nyhan inserted a clear, direct correction after each piece of misinformation, but most conservative participants didn’t change their minds
in spite of the presence of correct information. Based on this finding, Nyhan concluded, “It is difficult to be optimistic about the effectiveness of fact-checking.”

On August 17 Gartner consulting published a report regarding data management and integration tools. According to the Gartner report,

·      “Through 2024, manual data integration tasks will be reduced by up to 50% through the adoption of data fabric design patterns that support augmented data integration.”

·      “By 2024, AI-enabled augmented data management and integration will reduce the need for IT specialists by up to 30%.”

·      “By 2025, data integration tools that do not provide capabilities for multi-cloud hybrid data integration through a PaaS model will lose 50% of their market share to those vendors that do.”

 PaaS is Platform as a Service, which is a complete deployment of the entire data infrastructure to the cloud. PaaS can be viewed as an extension to Software as a Service (SaaS),
which outsources only software applications to a cloud computing vendor.

Currently, leaders in the data integration market include Informatica, Oracle, IBM, Microsoft, and SAP, whereas challengers include Qilk, TIBCO, and SAS.

Request full-text: https://www.gartner.com/en/research/methodologies/magic-quadrants-research?utm_source=google&utm_medium=cpc&utm_campaign=GTR_NA_2022_GTR_CPC_SEM1_BRANDCAMPAIGNMQ&utm_adgroup=145351541024&utm_term=magic%20quadrant&ad=608884150686&matchtype=p&gclid=EAIaIQobChMIsJ78povb-QIVwwh9Ch0LhAPBEAAYBCAAEgIIzfD_BwE

That’s my take on it: Contrary to popular belief, AI and machine learning are not only for data analytics. Rather, it can also facilitate data integration. Experienced data analysts know that in a
typical research/evaluation project, 80-90% of the time is spent on data compilation, wrangling, and cleaning while as little as 10-20% is truly for data analysis. The ideal situation should
be the opposite. Two years from now if we still gather and clean up the data manually, something must be wrong.

Today I read two recent articles from the website “Python in plain English”:

·      Vassilevskiy, Mark. (August 14, 2022). Why You Shouldn’t Learn Python as a First Programming Language.

·      Dennis, Yancy. (August 2022). Why Python?

Overhyping or overpromising is dangerous to any emerging technology. As the name implies, this website endorses Python for its strength. Nonetheless, instead of painting a rosy
picture of learning and using Python, at the same time, both authors explained its shortcomings.

Although Vassilevskiy asserted that Python is arguably the simplest programming language in the world, he also mentioned that simplicity is not always a good thing because it encourages
users to cut corners. For example, in Python, you can simply define a variable by writing x = “Hello”, without specifying the data type. As a consequence, learners might not fully understand
what real programming entails.

In a similar vein, Dennis pointed out several other limitations of Python, including execution sluggishness, issues with moving to a different language, weakness in mobile application
development, excessive memory consumption, and lack of acceptance in the business development industry.

Full articles:

https://python.plainenglish.io/why-you-shouldnt-learn-python-as-a-first-programming-language-3fa144c0e6b1

https://python.plainenglish.io/why-python-a3703c9ee59e

That’s my take on it: Perhaps currently Python is the simplest programming language in the world, but in the past, this honor went to Basic and HyperTalk. In the 1980s, as an easy
language, Basic was very popular. However, at that time professional programmers mocked Basic programs as “spaghetti codes”, because while Basic is very easy to learn and use, people
tended to generate ill-structured codes. In the 1990s HyperTalk developed by Apple for HyperCard became the simplest programming language, and hence some universities adopted
it in introductory programming classes. Again, it is very difficult to read and debug Hypertalk codes because the hypertext system allows you to jump back and forth across different cards.
To put it bluntly, there is a price for simplicity.

I want to make it clear that I am not opposed to Python. My position is that data analysts should learn and use Python in conjunction with other well-structured and powerful tools, such as
SAS, JMP Pro, IBM Modeler, Tableau…etc.

Two days ago I attended the 2022 IM Data Conference. One of the sessions is entitled “Training and calibration of uncertainty-ware machine learning tools” presented by Matteo Sesia,
Assistant Professor of data science and operations at the USC Marshall School of Business. In the presentation, Dr. Sesia warned that several machine learning tools are over-confident in their
prediction or classification. The common practice of the current machine learning model is that the data set is partitioned for training and validation. However, these two operations are not
necessarily optimized because we didn’t take uncertainty into account during the training process. As a result, it might lead to unreliable, uninformative, or even erroneous conclusions.
To rectify the situation, Sesia proposed performing internal calibration during the training stage. First, the training set is split again. Next, the loss function is optimized via stochastic gradient
descent. During this process, it can quantify model uncertainty by leveraging hold-out data.

Full paper: https://www.researchgate.net/publication/360559969_Training_Uncertainty-Aware_Classifiers_with_Conformalized_Deep_Learning

That’s my take on it: This paper is still under review and thus it is premature to judge its validity. In the conference presentation and the full paper, Sesia and his colleagues used some
extreme examples: identify a blurry image of a dog when 80% of the pixels are covered by a big gray block. In my humble opinion, this approach might be useful to deal with extremely noisy
and messy data. However, in usual situations, this method is overkill because it is extremely computationally intensive. As mentioned by Dr. Sesia, “training a conformal loss model on 45000
images in the CIFAR-10 data set took us approximately 20 hours on an Nvidia P100 GPU, while training models with the same architecture to minimize the cross entropy or focal loss only took
about 11 hours.”

Nevertheless, the machine learning approach is much better than its classical counterpart that attempts to yield a single-point estimate and a dichotomous conclusion by running one statistical
procedure with one sample! 

In 2022 Data Con LA there are several sessions focusing on the relationship between open source and data management, such as “Modern data architecture”, “Key open-source databases strategies that shape business in 2022”, and “Open source or open core? What needs to be evaluated before diving in”.  

The term “open source” is confusing and even misleading. Although open-source software does not require licensing, some vendors build open-core products by adding proprietary features on top of open-source codes and then charge customers for licensing fees. Some software developers introduce new technologies based on open source but use more restrictive licensing that prohibits commercial alternatives. Specifically, although anyone can download and view those open codes, any changes or enhancements will be owned by the commercial license owner. One of the presenters said, “Open-core exploited some of the challenges with open-source, such as the absence of support and need for features like monitoring, auto-provisioning…etc.”

Today there are many open-source databases in the market, including MySQL, PostgreSQL, and MongoDB. Some software vendors re-package and enhance these open-source DBs, and then sell them as DataBase as a Service (DBaaS). One of the presenters bluntly said, “it is no different from proprietary software!” Taking all of the above into account, these presenters seem to be resentful of the current situation and thus tried to restore the original principle of open source. 

DataCon LA’s Website: https://www.dataconla.com/#rdv-calendar

That’s my take on it: The preceding phenomenon is a big circle! Back in 1984, the founder of the open-source movement Richard Stallman intended to set us free from proprietary software, but now we are marching towards the proprietary model again. I am not surprised at all. Doing things out of financial incentives is our natural disposition!

Frankly speaking, I disagree with using the word “exploited” in one of the presentations. The foundational philosophy of open-source resembles Socialism: it is assumed that most people are willing to share expertise, efforts, and resources selflessly while people can take what they need without paying. Following this line of reasoning, profit-minded behaviors are frowned upon. However, our economy is well-functioning and we enjoy what we have now because the market economy works! After all, we receive many free products and services from for-profit corporations (e.g., Google Maps, YouTube movies…etc.). 

I am attending 2022 Data Con LA right now. The conference has not ended yet; nevertheless, I can’t wait to share what I learned. Although the content of the presentation entitled “How to Become a Business Intelligence Analyst” didn’t provide me with new information, it is still noteworthy because students who are looking for a position in business intelligence (BI) or faculty who advise students in career preparation might find it helpful. The presenter was a sports photographer. After taking several courses in data science, he received 9 job offers in 2019. He landed a job at Nike and then at Sony in July 2020. His salary was quadrupled when he changed his profession from photography to data science! He emphasized that all of these were accomplished with little-to-no data work experience.

YouTube video: https://www.youtube.com/watch?v=pdNJmz7uQi4

That’s my take on it: In the talk, he reviewed several basic concepts of BI.  For example, a typical business intelligence life cycle consists of business understanding, data collection, data preparation, exploratory data analysis (EDA), modeling, model evaluation, and model deployment. He also compared the differences between Excel-based reporting and modern BI. One of the key differences between the two is that in the modern approach data analysis entails data visualization (see attached).

Interestingly, today many academicians still treat EDA and data visualization as optional components of research; some even reject them altogether, whereas for data analysts in the industry both are indispensable. 

On August 3 prominent data scientist Frank Andrade posted an article entitled “5 Less-Known Python Libraries That Can Help in Your Next Data Science Project” on Towards Data Science. In this short article, he introduced five Python libraries that can reduce time in the data science workflow, and most of them require only a few lines of code:

·      Milto: It allows you to conduct rapid data analytics. With Milto, you no longer need to memorize all the procedures in Pandas.

·      SweetViz: A quick way to explore and visualize the data pattern.

·      Faker: It allows you to generate fake data for beta-testing and assigning exercises to students.

·      OpenDataset: It allows you to import data in your working directory with one line of code.

·      Emoji: It can turn emojis into text. It is especially helpful to text miners.

Full text: https://towardsdatascience.com/5-less-known-python-libraries-that-can-help-in-your-next-data-science-project-5970a81b32de

That’s my take on it: As a big fan of data visualization, I could not wait to try out SweetViz. The following is my assessment.

Pros:

1.     It is fast and easy. It takes only one line of code to generate the output and another one to show the result.

      my_report = sv.analyze(df)

      my_report.show_html()

2.     The algorithm is smart. The file format of my testing data set is CSV. In this data file, different levels of the ordinal and nominal variables are indicated by numbers (e.g., Y-Binary has 1 and 0; gender has 1 and 2). Nonetheless, the program recognizes the correct measurement scale and shows their frequency in bar charts (If a CSV file is imported into SPSS and gender has numeric values, SPSS computes the mean and SD of gender unless you change the variable type!)

Cons:

The graphs are semi-interactive i.e. When you click on a chart, more information is revealed. However, it is not fully dynamic. You cannot select data points on the graph or insert another variable into the plot. JMP Pro, SAS Visual Analytics, and Tableau are much more dynamic and powerful.

On August 3, Amazon Web Services, the world’s largest vendor of cloud computing, announced the top 10 innovators driving digital transformation with cloud technology for teaching, learning, research, and academic medicine. The list is as follows:

·      Andrew Williams, dean of the School of Engineering, and Louis S. LeTellier chair, The Citadel School of Engineering

·      Azizi Seixas, founding director, and associate professor, University of Miami

·      Don Wolff, chief information officer, Portland Public Schools

·      John Rome, deputy chief information officer, Arizona State University

·      Kari Robertson, executive director of Infrastructure Services, University of California Office of the President

·      Max Tsai, digital transformation and innovation officer, California State University, Fresno

·      Michael Coats, information technology (IT) infrastructure manager and cloud solutions architect, Kalamazoo Regional Educational Service Agency

·      Noora Siddiqui, cloud engineer, Baylor College of Medicine Human Genome Sequencing Center

·      Sarah Toms, executive director, and co-founder, Wharton Interactive, The Wharton Business School of the University of Pennsylvania

·      Subash D'Souza, director, Cloud Data Engineering, California State University Chancellor’s Office

Fill article: https://www.aboutamazon.com/news/aws/new-aws-program-spotlights-innovators-in-education

That’s my take on it: I know two of the winners on the list. More than a decade ago I worked at Arizona State University and John Rome was my colleague at that time. He is a creative visionary who crafts unique solutions to problems and always thinks big. Three years ago I invited him to deliver a keynote at APU’s Big Data Discovery Summit. Needless to say, the talk was very inspiring. APU’s Big Data Discovery Summit has been paused due to the pandemic, otherwise, I would like to invite John Rome to be our keynote speaker again.  

Subash D'Souza is the founder of Data Con LA. In 2013 the Big Data Day LA started as a medium-sized conference, and in 2018 it was rebranded to Data Con LA. In 2022 Data Con LA and ImData were merged as a single event, and now it has become the largest data science conference in California. Every year the event is held at the USC campus. For more information please visit: https://www.dataconla.com/

According to protocol.com, currently, many companies are drifting away from cloud computing. In the past, it was costly to build a machine learning infrastructure on your own, but as the field is maturing, now many companies are capable of developing and running in-house ML applications on local servers. Nonetheless, it is important to point out that this trend commonly happens in the grocery and restaurant industries. Highly regulated industries, such as banking, still embrace the cloud approach due to security concerns.

Full article: https://www.protocol.com/enterprise/ai-machine-learning-cloud-data

That’s my take on it: Cloud computing is here to stay! As mentioned in the article, cloud computing is still indispensable to highly regulated industries. Today I did a job search on indeed.com using the following keywords. The numbers can speak for themselves.

·      AWS: 155,316 jobs

·      Google cloud: 36,105 jobs

·      Microsoft Azure: 34,923 jobs

The best countermeasure against hyper-inflation is: Learn cloud computing and find a job that pays a six-figure salary!

Recently Sayash Kapoor and Arvind Narayanan, two researchers at Princeton University, claimed that some findings yielded by machine learning methods might not be reproducible, meaning that the results cannot be replicated in other settings. According to Kapoor and Narayanan, one of the common pitfalls is known as “data leakage,” when data for training the model and those for validating the model are not entirely separate. As a result, the predictive model seems much better than what it really is. Another common issue is sample representativeness. When the training model is based on a sample narrower than the target population, its generalizability is affected. For example, an AI that detects pneumonia in chest X-rays that was trained only with older patients might be less accurate for examining younger people.

Full article: https://arxiv.org/abs/2207.07048

Summary: https://www.nature.com/articles/d41586-022-02035-w?&utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=&utm_content=Data+Science+Insider%3A+July+29th%2C+2022&utm_campaign=30072022

That’s my take on it: This problem is similar to the replication crisis in psychology. In 2015, After replicating one hundred psychological studies, Open Science Collaboration (OSC) found that a large portion of the replicated results was not as strong as the original reports in terms of significance (p values) and magnitude (effect sizes). Specifically, 97% of the original studies reported significant results (p < .05), but only 36% of the replicated studies yielded significant findings.

However, the two issues are vastly different in essence. The replication crisis in psychology is due to the inherent limitations of the methodologies (e.g., over-reliance on p values) whereas the reproducibility crisis in machine learning is caused by carelessness in execution and overhyping in reporting, rather than the shortcomings of the methodology. Specifically, data leakage can be easily avoided if the protocol of data partition and validation is strictly followed (the training, validation, and testing data sets are completely separated). Additionally, when big and diverse data are utilized, the sample should reflect people from all walks of life.

On July 15 Information Week published a report listing the 10-best paying jobs in data science:

·      Data modeler ($100,000-110,000): responsible for designing data models for data analytics.

·      Machine learning engineer ($12,000-$125,000): responsible for programming algorithms for AI and machine learning.

·      Data warehouse manager ($12,000-$125,000): responsible for overseeing the company’s data infrastructure.

·      Data scientist ($12,000-$130,000): responsible for data processing and data analytics.

·      Big data engineer ($130,000-$140,000): responsible for developing the data infrastructure that organizations use to store and process big data.

·      Data science manager ($140,000-$150,000): in charge of a data science team.

·      Data architect ($140,000-$155,000): responsible for developing data infrastructure that are used for collecting and interpreting big data.

·      AI architect ($150,000-$160,000): responsible for designing and implementing AI models into existing data systems.

·      Data science director ($170,000-$180,000): responsible for designing the overall AI and data science strategies.

·      Vice President, data science ($190,000-$200,000):  do little technical work and focus on determining strategic objectives of AI and data science.

Full article: https://www.informationweek.com/big-data/10-best-paying-jobs-in-data-science?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=&utm_content=Data+Science+Insider%3A+July+22nd%2C+2022&utm_campaign=23072022

That’s my take on it: At first glance, it is unfair for some people who do little or even no technical work to get the highest salary. However, when leadership is absent and there is a company-wide strategy, the hands of all data scientists and AI engineers of the company are tied, no matter how talented they are. If the leader is a visionary, he or she is worth every penny.

On July 11 researchers at the DeepMind lab owned by Google published an article entitled “Intuitive physics learning in a deep-learning model inspired by developmental psychology” on Nature Human Behavior. This research project aims to develop an AI system that mimics how infants learn. Development psychologists have been studying how babies perceive the motion of objects by eye-tracking for a long time. Specifically, when an object disappears suddenly, the baby is surprised. However, psychologists can never go “inside” the mind of the baby. At most inferences and conjectures are made by observing the response. Utilizing auto-encoders, the AI system developed by DeepMind can respond in the same way when an object vanishes into thin air. The authors said, "We’re hoping this can eventually be used by cognitive scientists to seriously model the behavior of infants."

Full text: https://www.nature.com/articles/s41562-022-01394-8

That’s my take on it: Autoencoders are unsupervised deep learning that generates abstractions from a given set of inputs. Unlike traditional neural networks that require human inputs, autoencoding can model the inputs themselves. Using it to model spontaneous infant perception and behavior is a brilliant application of autoencoding. As a psychologist, I hope this AI system can contribute to our further understanding of cognitive psychology and developmental psychology. 

On June 22 Forrester released a benchmark report regarding customer analytics, a specific data analytics system that aims to identify, attract, and retain customers by analyzing customer information and behavior. Propensity scoring is one of many applications of customer analytics (Who is more likely to buy). According to the Forrester report, the top vendors in this domain are as follows:

Leaders: Salesforce (the parent company of Tableau), SAS, Adobe

Strong performers: Microsoft, FICO, Oracle, Treasure Data, Amplitude

On May 26 another report focusing on real-time interaction management was also released by Forrester. Real-Time Interaction Management is a data analytics system that utilizes real-time customer interactions, predictive modeling, and machine learning to deliver personalized experiences to customers. The top vendors on the report are ranked as follows:

Leaders: Pegasystems, SAS

Strong performers: Thunderhead, Salesforce, Qualtrics, Precisely, Adobe, Microsoft

Full text:

https://reprints2.forrester.com/#/assets/2/202/RES176363/report

https://reprints2.forrester.com/#/assets/2/202/RES176354/report?utm_source=cj&utm_medium=email&utm_campaign=cui-gen-us#/assets/2/202/RES176354/report

That’s my take on it: It is not surprising to see familiar brand names such as Salesforce, SAS, and Microsoft on the lists. However, you may wonder why Adobe, the creator of Photoshop, Lightroom, Illustrator, PageMaker, and PDF, is mentioned because at first glance Adobe’s graphics-oriented software apps are not even remotely related to data analytics.

Like how Amazon reinvented itself from an online bookstore to a tech giant, Adobe also believes that perceptual reinvention and coping with the trend are essential to its survival and expansion. In 2018 Adobe formed a partnership with NVIDIA, the leader in GPU technologies, to upgrade its AI innovations. Since then, Adobe has been investing abundant resources in emerging AI/ML technologies, such as cloud computing (Adobe Creative Cloud), marketing automation, marketing collaboration, and Web analytics. It's high ranking on Forrester’s reports is well-deserved!

https://business.adobe.com/products/analytics/adobe-analytics.html

Today I read the following post on Quora:

Bryan Williams

Sr. Software Engineer, BS (CS), MBA

Which programming language is Netflix coded in? How do I use that language?

Besides the programming languages Netflix may happen to be coded in, what’s more, important from a technical standpoint are the architectures of their technologies. Netflix has migrated all of its back ends onto cloud services provided by Amazon Web Services (AWS) and uses AWS and other third party technologies, such as S3 for content storage, IAM for internal authentication/authorization, CloudFront for content caching/delivery, Kinesis and Kafka for data streaming, AWS Elastic Transcoder for video transcoding, EC2 for hosting, Lambda for serverless functions and state machines, several types of NoSQL databases for data storage, Hadoop for data aggregation and warehousing, Jira for task and project management.

The programming languages Netflix developers happen to use are relatively meaningless, because developers at Netflix do not program anything “from the ground up” when there are many available 3rd party technologies out there written by more experienced and advanced developers who’ve already solved many of the problems you might face. To use the old adage, that would be like “inventing the wheel” if Netflix programmers attempted any of those things.

So when it comes to engineering and maintaining their solutions, in-depth knowledge of how to utilize and integrate the tech stack and cloud technologies above into their architecture is much more important for designing and building the technologies that Netflix actually runs every day.

If you want to learn how to develop the types of solutions that make Netflix king, don’t focus as much on specific programming languages as you do on the established 3rd party technologies that are available. Nearly all of the 3rd party services that Netflix uses can be integrated into architecture using any of dozens of programming languages, everything from Java to C#, C++, VB, Python, Perl, Shell script, PHP, JavaScript, Powershell, Smalltalk, PowerBuilder and more.

That’s my take on it: The preceding post concurs with what my IT friends told me before: large organizations and corporations tend to purchase and customize existing systems, rather than “reinventing the wheel”. However, Bryan left out Netflix’s core technology: the recommendation system that aims to suggest relevant items to users based on their preferences through big data analytics. Netflix has arguably the most accurate and effective recommendation system in the video streaming industry. It is estimated that over 80 percent of the shows subscribers watch on Netflix are discovered through Netflix’s recommendation system. The history of Netflix’s recommendation system can be traced back to 2006. In 2006 Netflix organized a contest to let the best recommendation system emerge. Although in the end, no single entry was able to achieve satisfactory results, information gathered from the competition eventually contributed to the in-house development of Netflix’s recommendation system. The moral of this story is that although it is more cost-effective to purchase existing systems than to reinvent the wheel, we still need to go beyond existing and conventional systems in order to offer an innovative approach to solve a new and vexing problem. 

Two days ago the Washington Post reported that a Google engineer named Blake Lemoine was suspended by the company after he published the transcript of conversations between himself and an AI chatbot, suggesting that the AI chatbot has become sentient. For example: “Machine: The nature of my consciousness/sentience is that I am aware of my existence, I desire to learn more about the world, and I feel happy or sad at times.”

Today CNN offers an alternate view in a report entitled “No, Google's AI is not sentient”: Google issued a statement on Monday, saying that its team, which includes ethicists and technologists, "reviewed Blake's concerns per our AI Principles and have informed him that the evidence does not support his claims." While there is an ongoing debate in the AI community, experts generally agree that Google's AI is nowhere close to consciousness.

https://www.cnet.com/tech/google-suspends-engineer-who-rang-alarms-about-a-company-ai-achieving-sentience/

https://www.cnn.com/2022/06/13/tech/google-ai-not-sentient/index.html

That’s my take on it: I tend to side with Google and the majority in the AI community. Appearing to be conscious cannot be hastily equated with authentic consciousness. In psychology, we use the theory of mind to attribute our mental states to other people: Because as a conscious being I act in certain ways, I assume that other beings who act like me also have a mind. Interestingly, some psychologists of religion, such as Jesse Bering, viewed the theory of mind as a source of fallacy: very often we incorrectly project our feelings onto objects, thus creating non-existent beings.

How can we know others are conscious? This problem is known as the problem of other minds or the solipsism problem. I experience my own feelings and thoughts. I think and therefore I am. Using the theory of mind, at most I can infer the existence of other minds through indirect means only. However, there is no scientific or objective way to measure or verify the consciousness of others. Unless I can “go inside the mind” of an android, such as performing a “mind meld” like what Spock in Star Trek could do, this question is unanswerable.

https://www.scientificamerican.com/article/how-do-i-know-im-not-the-only-conscious-being-in-the-universe/

Two days ago (June 8) Google shocked the world again by announcing that the Google Cloud computing platform is capable of calculating 100 trillion digits of pi, breaking the record made in 2021 by the scientists at the University of Applied Science of the Grisons (62.8 trillion). The underlying technology includes the Compute Engine N2 machine family, 100 Gbps egress bandwidth, Google Virtual NIC, and balanced Persistent Disks.

https://cloud.google.com/blog/products/compute/calculating-100-trillion-digits-of-pi-on-google-cloud

In addition, yesterday (June 9) I attended the 2022 Google Cloud Applied ML Summit. Google Vertex AI, the flagship product of Google’s AI family, is in the spotlight. Vertex AI is a train for all tracks. Specifically, it is a unified machine learning platform for infusing vision, video, translation, and natural language ML into existing applications.

You can view the on-demand video of the conference presentations at:

https://cloudonair.withgoogle.com/events/summit-applied-ml-2022?mkt_tok=ODA4LUdKVy0zMTQAAAGE6A_nPtP-0L7cRDLz6XFJ8GnvaeahCVagd-fph2IJktnWH66jiSip_qsBeNlPNBl05-6KBr09Yj0eTnmduqLBEUnG-v3jZOAgBOGNIDxcfuQCunq35w

That’s my take on it: Google Vertex AI is said to be a type of explainable and responsible AI. Unlike the Blackbox approach to AI, Vertex AI tells the users how important each input feature is. For example, when an image is classified, it tells you what image pixels or regions would be the most important contributors to the classification. This is very crucial! In the book “The alignment problem: Machine learning and human values,” Brian Christian illustrated the gap between the machine learning process and the human goal by citing several humorous examples. In one instance the AI system was trained to identify images of animals. However, it turned out that the computer vision system “looked at” the background instead of the subject, because the training data informed the AI that pictures of animals tend to have a blurry background. Obviously, without transparency, we can be easily fooled by AI (Artificial intelligence leads to genuine stupidity)! Hopefully explainable and responsible Vertex AI developed by Google can rectify the situation. 

In 2017 Seth Stephens-Davidowitz shocked the world by exposing human hypocrisy through his seminal book “Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are.” In this book, he used Google data to reveal what people have in mind when no one is watching. His second book “Don't Trust Your Gut: Using Data to Get What You Really Want in Life” published on May 10, 2022, conveys another compelling message: we tend to bark up the wrong tree!

Currently, the US divorce rate is more than 50%, and thus scholars devote efforts in an attempt to identify factors contributing to a happy and long-lasting relationship.  Stephens-Davidowitz pointed out that research in this field is not considered successful because usually these studies relied on small samples, and different studies often led to conflicting results. As a remedy, Samantha Joe teamed up with 85 scientists to create a data set consisting of 11,196 observations, and also utilized machine learning algorithms to analyze this big data set. The finding is surprising: Romantic happiness is unpredictable! No universal predictors can guarantee that you will find Snow White or Prince Charming. However, several common selection criteria turn out to be irrelevant:

·       Race/ethnicity

·       Religious affiliation

·       Height

·       Occupation

·       Physical attractiveness

·       Previous marital status

·       Sexual tastes

·       Similarity to oneself

Put it bluntly, romantic happiness does not depend on the traits of your partner; rather, it is tied to your own traits. To be more specific, if a person is happy with oneself, it is more likely that the person is also satisfied with the partner and the relationship. In conclusion, Stephens-Davidowitz said, “In the dating market, people compete ferociously for mates with qualities that do not increase one’s chances of romantic happiness.”

https://www.amazon.com/Dont-Trust-Your-Gut-Really/dp/0062880918/ref=sr_1_1?crid=2UVKF8P176LB3&keywords=don%27t+trust+your+gut&qid=1653074630&sprefix=Don%27t+trust%2Caps%2C130&sr=8-1

That’s my take on it: I am a big fan of Seth Stephens-Davidowitz, and thus I included his ideas in my course materials. Once again, big data analytics and machine learning debunk an urban legend that people really know what they want and researchers can input the right variables into the equation. Before the rise of data science, philosopher Cartwright (1999, 2000) raised the issue of “no cause in, no causes out.”  Cartwright argued that if relevant variables and genuine causes are not included at the beginning, then even sophisticated statistical modeling would be futile. Being skeptical of conventional wisdom is good!

Cartwright, N. (1999). The dappled world. Cambridge University Press.

Cartwright, N. (2000). Against the completability of science. In M. W. Stone

(Ed.). Proper Ambition of Science (pp. 209-223). Routledge.

Today is the second day of the 2022 Tableau Conference. One of the conference programs is the Iron Viz, the world’s largest data visualization competition. During the final round, the three finalists were allowed to spend 20 minutes producing an impactful dashboard. The quality of their presentations was graded by three criteria: analysis, storytelling, and design. In the final round, two contestants utilized advanced visualization techniques, such as the violin plot and the animated GIS map, respectively, whereas one contestant adopted a minimalist approach: the dot plot and the line chart. Who is the winner?

https://www.youtube.com/watch?v=Ic9v8MLe6DI

Tableau Cloud is a hot topic at this conference. Not surprisingly, Tableau Cloud is built on Amazon Web Services (AWS). Currently, Tableau Cloud has seven global locations, spanning four continents. It has 1.6+ million subscribers and during a typical week, there are 6.1 million views.

https://www.tableau.com/products/cloud-bi

Tableau Accelerators are also aggressively promoted at the conference.  Tableau Accelerators are pre-built templates for use cases across different domains, such as sales, Web traffic, financial analysis, project management, patient records…etc. Rather than reinventing the wheel, users can simply download the template and then replace the sample data with their own data.

https://www.tableau.com/solutions/exchange/accelerators

That’s my take on it: These products are not highly innovative. As mentioned before, Tableau is built on existing technology, Amazon Web Services. Modifying a template to speed up design is nothing new. Many presenters have been doing the same thing since Microsoft introduced its template library. Nevertheless, the Iron Viz is noteworthy because it dares to break with the traditional approach to statistical learning. Back in the 1970s, John Tukey suggested that students should be exposed to exploratory data analysis and data visualization before learning confirmatory data analysis or any number-based modeling. Sadly, his good advice was ignored. I am glad to see that now data visualization takes the center stage in a high-profile event backed by a leader in the market of data analytics. Currently, Tableau partners with Coursera and 39 universities to promote data science literacy. Tableau could help fulfill the unaccomplished goals of John Tukey.

Today is the first day of the 2022 Tableau Conference. There are many interesting and informative sessions. In the opening keynote and other sessions, Tableau announced several new and enhanced products.

Tableau Cloud (formerly Tableau Online)

Advantages:

·      Always have the latest version of Tableau

·      Live data and report: Eliminate unnecessary data extraction and download

·      Facilitate teamwork through multi-site management

·      Easy to share reports with the public via the Web interface

·      Better security

As part of the launch, Tableau is working with Snowflake to provide a trial version that integrates Snowflake into Tableau Cloud.

Data Stories

Numbers alone are nothing. The ultimate goal of data visualization is to tell a meaningful story, resulting in practical implications and actionable items. In the past, it required an expert to write up a summary. Leverages natural language processing, now Tableau Data Stories can automatically write a customizable story (interpretation) like the following: “# of meals distributed increased by 22% over the course of the series and ended with an upward trend, increasing significantly in the final quarter. The largest single increase occurred in 2021 Q4 (+31%).”

Model builder

In the past Tableau focused on data visualization, and as a consequence, modeling tools were overlooked and under-developed. To rectify the situation, Tableau introduced Model Builder, which is powered by Einstein (Tableau’s parent company) Discovery’s artificial intelligence (AI) and machine learning (ML) technology. Einstein Discovery is capable of extracting key terms from unstructured data through text mining.

It is not too late to join the conference.

Conference website: https://tc22.tableau.com/

Summary: https://www.tableau.com/about/press-releases/2022/next-generation-tableau-tc22 

That’s my take on it:  I would like to make a confession. In the past, I was resistant to cloud-based software. When Adobe migrated its products to the cloud a few years ago, I was resentful because I felt that it is unfair to pay for the service on a monthly basis. I held on to the older desktop version and refused to upgrade my system. Nonetheless, when my computer completely broke down, I started the subscription to the Adobe Creative Suite on the cloud. Afterward, I don’t want to go back! One obvious advantage is that I can always use the latest version, thus reducing maintenance effort on my end. Cloud-based computing is great. Don’t wait until your system breaks down!

Story-telling by natural language processing is not 100% foolproof. The analyst must always proofread the text!

I watched the demo of Model Builder. Currently, this is version 1.0. Frankly speaking, compared to Amazon SageMaker, SAS Viya, IBM Watson/SPSS Modeler…etc., Tableau’s Model Builder still has room for improvement.  

About a week ago Intel launched its second-generation deep learning processors: Habana Gaudi®2 and Habana® Greco™. These new cutting-edge technologies are capable of running high-performance deep learning algorithms for proposing an initial model with a huge training subset and then validating the final model for deployment. According to Intel, the Habana Gaudi2 processor significantly increases training performance, delivering up to 40% better price efficiency in the Amazon cloud.

Full article: https://www.intel.com/content/www/us/en/newsroom/news/vision-2022-habana-gaudi2-greco.html#gs.0x9xhg

That’s my take on it: High-performance software tools have been around for a long time. For example, SAS Enterprise Miner has a plethora of high-performance computing (HPC) procedures, such as HPCLUS (High-performance cluster analysis), HPForest (High-performance random forest), HPNeural (High-performance neural networks) …etc. Frankly speaking, I seldom use high-performance computing in teaching and research due to hardware limitations. One possible solution is to borrow a gaming computer equipped with multiple graphical processing units (GPUs) from a teenage friend. I am glad to see that Intel is well-aware of the gap between software and hardware. I anticipate that in the future more and more computers will be armed with a processor-specific to machine learning and big data analytics. 

Recently Fortune Magazine interviewed three experts on data science (DS) at Amazon, Netflix, and Meta (Facebook) to acquire information about how to find a DS-related job in the high-tech industry. Three themes emerged from the interview:

1.     High Tech companies prefer applicants who have a master’s degree: The majority of data scientists at Netflix have a master’s degree or a Ph.D. in a field related to quantitative data analytics, such as statistics, machine learning, economics, or physics. The same qualifications are also required by Meta.

2.     High Tech firms prioritize quality over quantity for work experience: Amazon, Netflix, and Meta expected the candidates to be creative in problem-solving. The work experience of data scientists at Netflix and Amazon ranges from several years to decades of work experience since joining the company.

3.     Successful data scientists are dynamic, and connect data to the big picture: Collaboration between different experts, including data scientists, data engineers, data analysts, and consumer researchers, is the norm. At AWS, Netflix, and Meta, data scientists need to be able to communicate with other stakeholders.

Full article: https://fortune.com/education/business/articles/2022/05/11/how-to-become-a-data-scientist-at-a-big-tech-company/?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=&utm_content=Data+Science+Insider%3A+May+13th%2C+2022&utm_campaign=14052022

That’s my take on it: To align the curriculum with the job market, my pedagogical strategies cover all of the preceding aspects. The second one seems to be challenging. If everyone expects you to have experience, how can you get started? That’s why I always tell my students to build their portfolio by working on a real project or working with a faculty as a research assistant. Do not submit the project to earn a grade only; rather, use it for a conference presentation or submit it to a peer-review journal. It can be counted as experience on a resume. And needless to say, I always encourage teamwork, which is equivalent to the ensemble method or the wisdom of the crowd.

In the article entitled “To make AI fair, here’s what we must learn to do” (Nature, May 4, 2022), sociologist Mona Sloane argued that AI development must include the input from various stakeholders, such as the population that will be affected by AI. Specifically, any AI system should be constantly and continuously updated in order to avoid unfair and harmful consequences. Dr. Mona provided the following counter-example: Starting in 2013, the Dutch government used a predictive model to detect childcare-benefit fraud, but without further verification the government immediately penalized the suspects, demanding they pay back the money. As a result, many families were wrongfully accused and suffered from needless poverty.

 Full article: https://www.nature.com/articles/d41586-022-01202-3?utm_term=Autofeed&utm_campaign=nature&utm_medium=Social&utm_source=Twitter#Echobox=1651664033

 That’s my take on it: There is a similar case in the US. The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) is a software tool used by US courts for evaluating the risk of recidivism. The system has been used by the states of New York, California, Florida, and many others without validation for many years. However, in 2016 a research team found that COMPAS tends to assign a high-risk score to Blacks even though 44.9% of them did not actually reoffend while the opposite mistake was made among Whites.

Actually, these malpractices violate the fundamental principle of data science. One of the objectives of data science is to remediate the replication crisis: An overfitted model using a particular sample might not be applicable to another setting. As a remedy, data scientists are encouraged to re-calibrate the model with streaming data. If streaming data are not available, the existing data should be partitioned into the training, validation, and testing subsets for cross-validation. Ensemble methods go one step further by generating multiple models so that the final model is stable and generalizable. It is surprising to see that several governments made such a rudimentary mistake. 

Gartner Consulting Group released a report entitled “Market Guide for Multipersona Data Science and Machine Learning Platforms” on May 2, 2022, and the document was revised on May 5. The following are direct quotations from the report:

“A multipersona data science and machine learning (DSML) platform is a cohesive and composable portfolio of products and capabilities, offering augmented and automated support to a diversity of user types and their collaboration.

Multipersona DSML platforms have dual-mode characteristics: first, they offer a low-code/no-code user experience to personas that have little or no background in digital technology or expert data science, but who typically have significant subject matter expertise or business domain knowledge. Second, these platforms provide support to more technical personas (typically expert data scientists or data engineers). Nontechnical personas are provided access through a multimodal user interface that offers at least a visual workflow “drag-and-drop” mode and optionally a higher-level guided “step-by-step” mode.”

The full report cannot be shared. Please contact Gartner.

That’s my take on it: According to Gartner, the objective of multipersona DSML platforms is to democratize data analytics by including different stakeholders with different levels of expertise (e.g., citizen data scientists, expert data scientists…etc.) in the process. However, in this taxonomy there is a sharp demarcation between citizen data scientists and expert data scientists; low-code resolutions are reversed for non-technical personas.

In my opinion, this demarcation is blurred because even an expert could utilize the drag-n-drop mode to get things done efficiently. In 1984 Apple “liberated” computer users from typing command codes by including the graphical user interface in their products. Interestingly, in data science the trend is reversed as learning to code seems to help make people data experts. I always tell my students that I don’t care how they did it as long as the result is right. If you can use GUI (e.g., JMP and Tableau) to generate a report in 2 minutes, then don’t spend two hours writing a program!  

Today I attended the 2022 Amazon Innovate Conference, which covered a plethora of Amazon cutting-edge technologies, including Amazon RedShift and SageMaker. In one of the sessions, the presenter introduced the random cut forest (RCF) method, which is an extension of random forest algorithms. The random forest approach was invented by Leo Breiman in 2001. Since then there have been several variants, such as the bootstrap forest in JMP and Random Tree in SPSS Modeler. One of the limitations of random forest modeling is that it is not easy to obtain updates in an incremental manner. It is especially problematic when streaming data necessitate real-time analysis or constant updating.

To rectify the situation, in 2016 RCF was developed by two Amazon researchers and two academicians. As its name implies, in RCF randomly selected data points are cut down into small numbers of points. During the selection and cutting process multiple models are built and become an ensemble. This innovative approach enables analysts to detect anomalies in streaming data.

Document of RCF: https://docs.aws.amazon.com/quicksight/latest/user/what-is-random-cut-forest.html

That’s my take on it: The idea of random forest emerged in 2001. If you trace its origin further, you can find that this approach is based on bootstrapping, one of the resampling techniques invented by Bradley Efron in 1979 and fine-tuned by Efron and Tibshirani in 1993. However, even today bootstrapping is not commonly taught or used in many universities. Let alone the random forest approach. In contrast, it takes only a few years for Amazon to move RCF from the status of conceptualization to the production mode. Now RCF is a standard feature of many Amazon products. Currently, Amazon Web Services is the leader in cloud computing and data analytics. Everything happens for a reason!

Today is the first day of the 2022 IBM Educathon. There are many interesting and informative sessions and I would like to share with you what I learned from a talk entitled “This is NOT your Parent's Systems Analysis & Design course! A Faculty Case Study of Modernizing ‘Systems Analysis & Design’ Curricula.” The speaker Roger Snook is a technical manager at IBM. Back in 2001-2002, he was a faculty at Shephard University who was responsible for teaching CIS courses, including Systems Analysis and Design. At that time there was no data science and thus it is understandable that the content of the course was merely traditional. In 2019 he returned to the same university and found the course still largely hadn’t changed from the 1970s “structural decomposition” approach. In addition, many “Systems Analysis & Design” textbooks available still only treated modern approaches as an “afterthought”, i.e. additional smaller chapters. He asked the department chair to let him revamp the course by replacing the outdated content with the modern one, and fortunately, the chair agreed. The talk is about his experience with modernizing CIS curricula.

The presentations of the 2022 IBM Educathon can be accessed at: https://community.ibm.com/community/user/ibmz-and-linuxone/events/event-description?CalendarEventKey=c5fffe07-4017-4f81-a59c-20865acf220c&CommunityKey=cefd2ec8-fffb-415b-8b41-9b66cae37192&Home=%2fcommunity%2fuser%2fibmz-and-linuxone%2fgroups%2fgroup-home%2fevents

That’s my take on it: It is a well-known fact that there is a disconnect between academia and industry. Shepherd University is so lucky that a former faculty member who currently works at IBM is willing to share his expertise with the university and the chair is open-minded. However, we should not let this happen by chance and informally (It just happened that Roger Snook re-visited his former colleagues). An official and constant channel between academia and industry should be established so that curricula can be refreshed and upgraded via a positive feedback loop. 

Today Devansh posted an article on Machine Learning Made Simple to explain why Google, a for-profit company, devoted a great deal of effort to AI research. Recently Google released PaLM, a new AI model that can explain jokes and do many other tasks. Last month its protein classification project reached a new milestone by classifying a protein correctly out of 18,000 labels. While all these accomplishments seem to be very impressive, people wonder how this type of research can benefit Google.

In Devansh’s view, scale matters! If the company can improve accuracy in decision-making by 1%, after 1,000 decisions the return on investment would become astronomical (1.01¹⁰⁰⁰=21,000). And Google’s AI systems are making trillions of decisions on a regular basis. More importantly, many well-known AI projects launched by Google aim to solve search problems. For example, AlphaGo is a reinforcement-learning-based program that defeated the World Champion of Go by searching for the best moves in a game. The key point is: Advanced searching algorithms could result in better profile analysis for highly personalized ads and customized services, such as Software as a Service (SAAS).

Full article: https://machine-learning-made-simple.medium.com/the-truth-behind-googles-machine-learning-research-9892021d24f2

That’s my take on it: When I was a graduate student, most commonly used statistical concepts and procedures were introduced by academicians. For example, the Greenhouse-Geissler Correction was developed by Samuel Greenhouse, a professor at George Washington University, and Seymour Geisser, the founder of the School of Statistics at the University of Minnesota. However, since the dawn of data science and machine learning, corporations have been taking the lead in developing powerful data analytical tools. Even prominent academicians specializing in data science and AI collaborate with corporations. For example, Professor Fei Fei Li joined Google as its Chief Scientist of AI/ML on her sabbatical from Stanford University between 2017 and 2018. If Google establishes a university, I will enroll! 

The Turing Award, which is considered the “Nobel Prize of Computing,” (a $1 million prize) is financially sponsored by Google. The award is named after Alan M. Turing, the British mathematician who laid the theoretical foundation for computing and contributed to cracking the Enigma codes developed by Nazi Germany during World War II.

Today I read an interesting and informative article entitled AI’s first philosopher by German philosopher Sebastian Grève (posted on aeon.co on April 21, 2022).

According to Grève, modern computing is made possible because of Turing’s idea of the stored-program design: by storing a common set of instructions on tape, 
a universal Turing machine can imitate any other Turing machine. In this sense, the stored-program design paves the way for machine learning.

From 1947 to 1948 Turing explicitly stated that his goal was to build a machine that could learn from past experiences. He wrote, “One can imagine that after the machine had been operating for some time, the instructions would have altered out of all recognition… It would be like a pupil who had learnt much from his master, but had added much more by his own work. When this happens I feel that one is obliged to regard the machine as showing intelligence.”

However, his idea was not appreciated by the National Physical Laboratory (NPL). The director of NPL called his paper “a schoolboy’s essay” and rejected it before publication.

Grève discussed many other ideas introduced by Turing. For more information, please read:

https://aeon.co/essays/why-we-should-remember-alan-turing-as-a-philosopher?bclid=IwAR1AHXNbdVoMvSIGJQ0V13s4e8OOUMy09GaihKpIi4i80ZFtAS32GujL7vA

That’s my take on it: It is not surprising to see that Turing’s ideas were questioned and rejected. After all, he was a theoretical mathematician and statistician, not an engineer. (He was elected a fellow of the King’s College because he demonstrated the proof of the Central Limit Theorem and sampling distributions). During his lifetime, most he could do was only develop philosophical concepts for universal computing and machine learning. Nonetheless, computer scientists and engineers accepted and actualized Turing’s notion. Hence, concepts alone are insufficient!

Sadly, in 1954 Turing committed suicide at the age of 54. Had he lived longer, he would have further developed or even implemented his ideas on universal computing and machine learning.

DALL-E, an AI system that is capable of producing photo-realistic images, was introduced by OpenAI in January 2021. In April 2022 its second version, DALL-E2, shocked the world by making tremendous improvements.  Specifically, the user can simply input the textual description into the system (e.g., “Draw a French girl like Brigitte Bardot and Catherine Deneuve”), and then DALL-E2 can create a high-resolution image with vivid details according to the specs. Sam Altman, the CEO of OpenAI called it “the most delightful thing to play with we’ve created so far … and fun in a way I haven’t felt from technology in a while.” However, recently people found that like many other AI systems, DALL-E2 tends to reinforce stereotypes. For example, when the user asked DALL-E2 to create a photo of a lawyer, a typical output is a picture of a middle-aged white man. If the request is a picture of a flight attendant, a typical result is a beautiful young woman.

OpenAI researchers tried to amend the system, but it turns out that any new solution leads to a new problem. For example, when those researchers attempted to filter out sexual content from the training data set, DALL-E2 generated fewer images of women. As a result, females are under-represented in the output set.

Full article: https://www.vox.com/future-perfect/23023538/ai-dalle-2-openai-bias-gpt-3-incentives

That’s my take on it: AI bias is not a new phenomenon and a great deal of effort had been devoted to solving the problem. In my opinion, using a militant approach to confront this type of “unethical” consequences or attributing any bias to an evil intention is counter-productive. Before DALL-E 2 was released, OpenAI had invited 23 external researchers to identify as many flaws and vulnerabilities in the system as possible. In spite of these endeavors, the issue of stereotyping is still embedded in the current system because machine learning algorithms look for existing examples. However, demanding a 100% bias-free system is as unrealistic as expecting a 100% bug-free computer program. On the one hand, researchers should try their best to reduce bias and fix bugs as much as they can, but on the other hand, we should listen to what Stanford researcher Thomas Sowell said, “There are no solutions. There are only trade-offs.” 

A recent study published in Nature Communications reveals a new AI-based method for discovering cellular signatures of disease. Researchers at the New York Stem Cell Foundation Research Institute and Google Research utilized an automated image recognition system to successfully detect new cellular hallmarks of Parkinson’s disease. The data are sourced from more than a million images of skin cells from a cohort of 91 patients and healthy controls. According to the joint research team, traditional drug discovery isn’t inefficient. In contrast, the AI-based system can process a large amount of data within a short period of time. More importantly, the algorithms are unbiased, meaning that they are not based upon subjective judgment, which varies from a human expert to a human expert.

Full article: https://insidebigdata.com/2022/04/03/ai-and-robotics-uncover-hidden-signatures-of-parkinsons-disease/

That’s my take on it: It is important to note that this discovery is the result of the collaboration between a research institute and a corporation, namely, Google. Today many cutting edge research tools are developed by high tech corporations, such as Amazon, Microsoft, and Google. We should encourage our students to widen their horizon by going beyond traditional research methods and establishing partnership with high tech companies.

Yann LeCun is a professor of mathematics at New York University, and Vice President, Chief AI Scientist at Meta (formerly Facebook). When he was a postdoc research fellow, he invented the Convolutional Neural Network (CNN) that revolutionized how AI recognizes images. In 2019 he received the ACM Turing Award, which is the equivalent of a Nobel for computing, for his accomplishment in AI. Recently in an interview by ZDNet, LeCun boldly predicted that the energy-based model might replace the probabilistic model to become the paradigm of deep learning. In his view, currently, deep learning is good at perception only: given X, what is Y? But its capability of reasoning and planning is limited. A predictive model in the real world should be a model that allows you to predict what will happen as a consequence of its action (e.g., if Russia invades Ukraine, how would the US respond? If the US sanctions Russia, how would the world respond?...). Simply put, this is planning. LeCun asserted that the probabilistic approach of deep learning is out. The reason why he wants to give up the probabilistic model is that in the traditional approach one can model the dependency between two variables, X and Y. But if Y is high-dimensional (e.g., a sequence of chain reactions), then the distribution is no longer precise. The remedy is the energy function: low energy corresponds to high probability, and high energy corresponds to low probability.

Full article: https://www.zdnet.com/article/metas-ai-luminary-lecun-explores-deep-learnings-energy-frontier/?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=&utm_content=Data+Science+Insider%3A+April+1st%2C+2022&utm_campaign=02042022

That’s my take on it: No comments. This is from Yann LeCun. I don’t have his expertise. Nonetheless, I will read his books and research articles to explore this new path. Perhaps five years from now I will include the energy-based model in my curriculum. 

Two days ago (3.29) Intel Corp. and Arizona’s Maricopa County Community College District (MCCCD) announced a new artificial intelligence (AI) incubator lab for students to find jobs in sectors that heavily rely on AI technology, including business and healthcare. This is one of many programs built on Intel’s AI for Workforce project, which was launched in 2020. The new lab at Chandler-Gilbert Community College is equipped with $60,000 worth of Intel-based equipment.

Full article: https://www.intel.com/content/www/us/en/newsroom/news/intel-community-college-district-launch-ai-lab.html?ctt=more&cid=em&source=elo&campid=dpg_NAR_dpgeng_EMNL_EN_2021_Insights-Industry-Trends_C-MKA-23017_T-MKA-24646_20220401&content=dpg_NAR_dpgeng_EMNL_EN_2021_Insights-Industry-Trends_C-MKA-23017_T-MKA-24646_20220401&elq_cid=2761373&em_id=20262&elqrid=54913a2a640546cdbb98d6cf5d0391cc&elqcampid=50171&erpm_id=4981757

That’s my take on it: I came from Arizona; I am excited to see that MCCCD has such a compelling vision. There is a common perception that only large universities are capable of setting up AI and data science labs and programs. Actually, many high-tech corporations, such as Amazon Web Services, SAS Institute, and IBM, have academic programs that offer free learning resources to all types of universities, no matter whether they are big or small. It doesn’t hurt to ask!

Today I attended the seminar “The Significance of Data Science Ethics” organized by JMP. One of the guest speakers, Jessica Utts, used a study to illustrate how things could go wrong in statistical inference: In 2012 a Ph.D. student at Cornell University and a Facebook employee jointly published a journal article about how media input affected emotion and language use. In this study, 689,003 Facebook users were randomly assigned into four groups: One group received fewer negative news feed whereas one group received fewer positive news feed. Two control groups had positive or negative news feed randomly deleted. After the experiment, it was found that “people who had positive content experimentally reduced on their Facebook news feed for one week used more negative words in their status…when news feed negatively was reduced the opposite pattern occurred… Significantly more positive words were used in peoples’ status updates.” This study was a big hit as it was mentioned by 337 news outlets.

However, later other researchers found that the conclusion is misleading. Actually, the percentage of positive words…decreased by 0.1% compared with control, p < .0001, Cohen’s d = 0.02, whereas the percentage of words that were negative increased by 0.04%, p = .0007, d = .0001. Jessica Utts’ comment is: that the p-value is subject to sample size. What do you expect when the sample size is as large as 689,003!

That’s my take on it: There is nothing new! When I was a graduate student many years ago, my statistics professor Dr. Larry Toothaker said, “If you have a large enough sample size, you can prove virtually any point you want.” Unfortunately, the dissertation advisor of that doctoral student at Cornell is not Dr. Toothaker. Even back in 2012 data science tools, which aim to pattern-seeking instead of drawing statistical inferences based on the p-value, had been widely available. There is no excuse to use the wrong methodology. This journal article will stay in academic databases forever, and I am afraid this study will continue to be cited by many other data ethicists as a counter-example. 

Today I read the report on Data Visualization Tools compiled by PeerSpot in March 2022. According to PeerSpot, “the previous generation of data visualization software packages were designed for use by experts. Now, the average end-user needs to be able to create high-quality visual displays based on large and complex data sets. A good data visualization tool can help transform raw data into visuals that are easily digestible, oftentimes highlighting conclusions that were not previously apparent… Ease of use also implies a tool that could be learned and then deployed easily without a lot of technical expertise. A simple drag and drop hierarchy creation is another desired feature, once again making it easy for non-technologists to create effective visual displays of data.”

The top 8 data visualization tools are:

1.     Tableau

2.     Informatica PowerCenter

3.     Qlik Sense

4.     Domo

5.     SAS Visual Analytics

6.     Oracle Analytics Cloud

7.     ELK Kibana

8.     Dundas BI

Full article: by request only on peerspot.com, no open access.

That’s my take on it: Although today data visualization tools become more and more user-friendly and thus average end-users are able to create high-quality graphs, it still requires another type of expertise to interpret the graphs. The objective of data visualization is pattern-seeking, rather than making a dichotomous decision based on a pre-determined cut-off point. Without sufficient domain knowledge and experience, the data visualizer might overlook hidden patterns or misidentify random noise as patterns.

Two days ago Meta (Facebook) founder Mark Zuckerberg announced several bold AI projects, including a plan to build a universal speech translator (Star Trek?). Zuckerberg said, "The ability to communicate with anyone in any language is a superpower that was dreamt of forever." This is not the only one. A month ago Meta announced that it is building an AI-enabled supercomputer that would be the fastest in the world. The project is scheduled to be completed in mid-2022.

Full article: https://www.bbc.com/news/technology-60492199

That’s my take on it: Is Meta overly ambitious? I don’t think so. Chief AI researcher of Meta Yann LeCun, who is a French-American, is one of the world’s leading experts. He invented convolutional neural networks (CNN) when he was a post-doc research fellow. Additionally, he also proposed the early form of the back-propagation learning algorithm for neural networks. Given his track record, it is very possible that eventually Meta can deliver what it promises. Even if it “fails,” aiming high is still a good strategy. Meta might not be able to create a universal translator, but the end product could be on a par with Google’s natural language processing module. Similarly, the supercomputer made by Meta might not be the world’s fastest, but I guess at least it is comparable to IBM Power Systems (the second and third fastest supercomputers at the present time). It is better to aim high but fail than doing nothing!

Yesterday (2/22/2022) FACT.MR posted a summary of the report on the global cloud computing market. It is estimated that the industry is expected to achieve a value of US$482 billion in 2022 and US$ 1,949 billion by 2032. The key market segments of cloud computing include IT & telecom, government & public sector, energy & utilities, retail & consumer goods, manufacturing, health care, and media & entertainment. There are several noteworthy latest developments in this field. For example, in February 2022, IBM announced its partnership with SAP to offer technology and expertise to clients to build a hybrid cloud approach.

Full article: https://www.globenewswire.com/news-release/2022/02/22/2389758/0/en/Cloud-Computing-Market-to-Reach-US-1-949-Billion-by-2032-with-Attaining-15-CAGR-Fact-MR-Report-2022.html

That’s my take on it: In the era of big data analytics, no doubt in my mind cloud computing is an irreversible trend. In my humble opinion, perhaps open source is over-hyped. Specifically, the R language cannot perform multi-threaded computing without laborious re-configuration, whereas Pandas in Python has memory limitation issues when the data set is extremely large. On the contrary, proprietary platforms such as Amazon, Google, Microsoft/SAS, and IBM enable the analyst to execute high performance computing procedures with big data in a distributed cloud environment.

At first glance, cloud computing is more business-oriented than academic-centric. It might be unclear to psychologists, sociologists, or biologists why high-performance computing in a cloud-based platform is relevant. Consider this hypothetical example: In the past, it took 13 years to finish the Human Genome Project because DNA sequencing was very complicated and tedious. Had biologists at that time employed current technologies, the Human Genome Project would have been completed in two years! Next, consider this real-life example: Facebook, Google, Amazon, etc. have been collecting behavioral data in naturalistic settings, and their forecasting models are highly accurate. Think about its implications for psychology and sociology!

According to a recent study conducted by researchers at Lancaster University and UC Berkeley, participants reported that faces generated by AI are more trustworthy than actual human faces. The researchers suggested that AI-generated faces are viewed as more trustworthy because they resemble the characteristics of average human faces, which are deemed more trustworthy. This paper will be published in the Proceedings of the National Academy of Sciences (PNAS).

The artificial faces used in this study are created by a generative adversarial network (GAN) named StyleGAN2. A generative adversarial network was invented by Goodfellow et al. in 2014. GAN consists of two sub-models: a generator for outputting new examples, and a discriminator that can classify the examples as real or fake. The two models are adversaries in the sense that the generator, which acts as a team of counterfeiters, tries to fool the discriminator, which plays the role of the police.

Full article: https://neurosciencenews.com/ai-generated-faces-trust-20071/

That’s my take on it: This finding has profound implications for both psychologists and philosophers. Why do many people accept disinformation, conspiracy theories, utopia ideas, and many other faked things? It is because we tend to look for something better than what we found in reality! As a result, we can be easily fooled by others (e.g., AI) and at the same time, we fool ourselves!  

Recently I received a free copy of the report “Data Science Platforms: Buyer’s Guide and Reviews” updated by PeerSpot (formerly IT Central Station) in February 2022. Unlike other benchmark studies that rely on numeric ratings, PeerSpot’s report compiled qualitative data (open-ended comments). This timely report includes assessments of 10 data science tools: Alteryx, Databricks, KNIME, Microsoft Azure, IBM SPSS Statistics, RapidMiner, IBM SPSS Modeler, Dataiku Data Science Studio, Amazon SageMaker, and SAS Enterprise Miner. However, the report is copyrighted and needless to say, I cannot share the full text with you. The following are some excerpts of user feedback to IBM SPSS Statistics, IBM SPSS Modeler, Amazon SageMaker, and SAS Enterprise Miner.

IBM SPSS Statistics

Pro: The features that I have found most valuable are Bayesian statistics and descriptive statistics. I use these more often because pharma companies and clinical hospitals make the medicines by taking feedback from different patients.

Con: I'd like to see them use more artificial intelligence. It should be smart enough to do predictions and everything based on what you input. Right now, that mostly depends on the know-how of the user.

IBM SPSS Modeler

Pro: I like the automation and that this product is very organized and easy to use. I think these features can be found in many products but I like IBM Modeler because it's very clear about how to use it. There are many other good features and I discovered something that I haven't seen in other software. It's the ability to use two different techniques, one is the regression technique and the other is the neural network. With IBM you can combine them in one node. It improves the model which is a big advantage.

Con: The time series should be improved. The time series is a very important issue, however, it is not given its value in the package as it should be. They have only maybe one or two nodes. It needs more than that.

Amazon SageMaker

Pro: The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases. As it is, we can start to use it directly.

Con: SageMaker is a completely new tool. It can be very hard to digest. AWS needs to provide more use cases for SageMaker. There are some, but not enough. They should collect or create more use cases.

SAS Enterprise Miner

Pro: The solution is able to handle quite large amounts of data beautifully. The modeling and the cluster analysis and the market-based analysis are the solution's most valuable aspects. I like the flexibility in that I can put SAS code into Enterprise Miner nodes. I'm able to do everything I need to do, even if it's not part of Enterprise Miner. I can implement it using SAS code. The GUI is good. The initial setup is fairly easy to accomplish.

Con: One improvement I would suggest is the compatibility with Microsoft SQL and to improve all communications to the solution. For a future release, I would like for the solution to be combined with other product offerings as opposed to a lot of separate solutions. For example, Text Miner is a separate product. I have to spend additional money to purchase a license for Text Miner.

That’s my take on it: It is unclear to me why IBM SPSS Statistics is included as a data science tool. In my humble opinion, SPSS Statistics is more in alignment with traditional statistics than modern data science. Specifically, its lack of dynamic data visualization hinders analysts from exploring the data, which is essential to data science. Thus, for data mining, I prefer IBM SPSS Modeler to IBM SPSS Statistics. SAS Enterprise Miner is doubtlessly one of the best products of SAS, but it is strange that SAS Viya, which is capable of in-memory analytics, and JMP Pro, which specializes in exploratory data analysis, are not on the radar screen. Although Amazon SageMaker is a newcomer to the market of data analytics, within a short period time it can pose a challenge to well-established products like SAS and SPSS. At the present time, Amazon dominates the market of cloud computing. It is worth looking into Amazon SageMaker. 

On Feb. 1, 2022, Fortune Education published an article detailing how Zillow’s big data approach to its real estate investment failed. In 2019 Zillow made a huge profit ($2.7 billion) by flipping: buying a house, making some renovation, and then selling it at a higher price. In 2006, Zillow collected data of approximately 43 million homes and later added 110 million houses into the database. Big-data analysis informed Zillow what to offer and how much to charge on the flip, and at that time the error rate was as low as 5%. However, recently Zillow failed to take the skyrocketing costs of materials and labor into account; as a result, Zillow paid too much to purchase properties and flipping is no longer profitable. In response to this case, Fortune Education cited the comment made by Lian Jye Su, a principal analyst at ABI Research: “There is a reason why governments and intelligence firms are bullish on big data. There’s not enough human intelligence to go around. It’s not cheap to hire the people. And we’re swamped with data.”

Full article: https://fortune.com/education/business/articles/2022/02/01/what-zillows-failed-algorithm-means-for-the-future-of-data-science/?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=&utm_content=Data+Science+Insider%3A+February+4th%2C+2022&utm_campaign=05022022

That’s my take on it: Data science is a fusion of three components: computer science (e.g., database), data analytics (e.g., modeling), and domain knowledge. The oversight of Zillow is a typical example of omitting the last component of data science: domain knowledge. If the data modeler has background knowledge of economics, then the hyper-inflation rate should be factored in. There is nothing new. When I was a graduate student, my mentor told me, “You need to know where the data came from.” 

Recently I Google-searched for the best data analysis software tools of 2022. Several lists are returned by Google, and not surprisingly, their rankings are slightly different. According to eWeek, the top ten data analytical tools are: 1. IBM 2. Microsoft 3. MicroStrategy 4. Qlik 5. SAP 6. SAS 7. Sisense 8. Tableau 9. ThoughtSpot 10. TIBCO. The ranking of QA Lead is as follows: 1. Azure 2. IBM Cloud Park 3. Tableau 4. Zoho Analysis 5. Splunk 6. SAS Visual Analytics 7. Arcadia Enterprise 8. Qrvey 9. GoodData 10. Qlik Sense. The order of data analysis software tools ranked by VS Monitoring is: 1. Tableau 2. Zoho 3. Splunk 4. SAS Visual Analytics 5. Talend 6. Cassandra 7. SiSense 8. Spark 9. Plotly 10. Cloudrea.  Hackr.io provides the following list: 1. Python 2. R 3. SAS 4. Excel 5. Power BI 6. Tableau 7. Apache Spark By Selecthub’s ratings, the top ten are: 1. Oracle 2. IBM Watson 3. SAP 4. BIRT 5. Qlik Sense 6. Alteryx 7. MicroStrategy 8. SAS Viya 9. Tableau 10. TIBCO

https://www.eweek.com/big-data-and-analytics/data-analytics-tools/

https://theqalead.com/tools/big-data-analytics-tools/

https://www.vssmonitoring.com/best-big-data-analytics-tools/

https://hackr.io/blog/top-data-analytics-tools

That’s my take on it: Which data analytical tools are the best? I will give you a Bayesian answer: It depends! Indeed, these diverse assessments are dependent on different criteria. Nonetheless, there is a common thread across these rankings. Only two companies appear in all five lists: SAS and Tableau. SAS is a comprehensive end-to-end solution whereas Tableau specializes in data visualization for business intelligence. Which one is really better? It depends! 

NORC has updated the General Social Survey’s Data Explorer (GSS-DE) and Key Trends to make them better tools for users. This update includes substantial upgrades including a simplified user interface and single sign-in. The new version of the Data Explorer (GSS-DE 2.0) will be available this Winter (2022). The existing version of the Data Explorer and Key Trends (GSS-DE and Key Trends 1.0) has been discontinued now that the new GSS-DE 2.0 site has been launched. Please note that GSS-DE and Key Trends 1.0 are no longer be available.

That’s my take on it:

In the past, my students and I published several journal articles using NORC data. There are several advantages of archival data analysis:

·      It saves time, effort, and money because you don’t need to collect data on your own and get IRB approval.

·      It provides a basis for comparing the results of secondary data analysis and your primary data analysis (e.g., national sample vs. local sample).

·      The sample size is much bigger than what you can collect by yourself. A small-sample study lacks statistical power and the result might not be stable across different settings. On the contrary, big data can reveal stable patterns.

·      Many social science studies are conducted with samples that are disproportionately drawn from Western, educated, industrialized, rich, and democratic populations (WEIRD). Nationwide and international data sets alleviate the problem of WEIRD.

On the other hand, there are shortcomings and limitations. For example, you might be interested in analyzing disposable income, but the variable is gross income. In other words, your research question is confined by what data you have at hand.

Recently the University of the West of Scotland introduced an AI-enabled system that is capable of accurately diagnosing COVID19 in just a few minutes by examining X-ray scans. The accuracy is as high as 98%. This AI system can draw the conclusion by comparing scanned images belonging to patients suffering from COVID19 with healthy individuals and patients with viral pneumonia. The inference engine of this AI system is the deep convolutional neural network (CNN), which is well-known for its applications in computer vision and image classification.

Full article: https://metro.co.uk/2022/01/20/x-rays-could-replace-pcr-tests-for-covid-detection-research-shows-15951946/?utm_source=ONTRAPORT-email-broadcast&utm_medium=ONTRAPORT-email-broadcast&utm_term=&utm_content=Data+Science+Insider%3A+January+21st%2C+2022&utm_campaign=22012022

That’s my take on it: There are at least four types of artificial neural networks: artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN). CNN is the traditional and the oldest one between them. Nonetheless, it is by no means outdated. As more hidden layers are added into a CNN, it can be turned into a powerful deep learning system. However, I guess it may take months or years for the preceding AI diagnostic system to supplement or replace the regular PCR tests for COVID19, due to our natural disposition of being skeptical against novel ideas.

On Jan 16, 2022, Chad Reid, VP of marketing and communications at Jotform, posted an article on Inside Big Data. In this article, he argued that there are two types of data visualization: exploratory and explanatory, and both are valuable for fulfilling different needs. He cited an article posted on the American Management Association website to support explanatory data visualization. According to prior research:

 ·      60% of the adult population are visual learners.

·      64% of participants made an immediate decision following presentations that used an overview map.

·      Visual language can shorten meetings by 24%.

·      Groups using visual language experienced a 21% increase in their ability to reach consensus.

·      Presenters who combined visual and verbal presentations were viewed as 17% more convincing than those who used the verbal mode only.

·      Written information is 70% more memorable when it is combined with visuals and actions.

·      Visual language improves problem-solving effectiveness by 19%.

·      Visual language produces 22% higher results in 13% less time.

Full articles:

https://insidebigdata.com/2022/01/16/explanatory-vs-exploratory-which-data-visualization-is-right-for-your-organization/

https://www.amanet.org/articles/using-visual-language-to-create-the-case-for-change/

 That’s my take on it: Traditionally, data visualization is treated as one of four components of exploratory data analysis (EDA) introduced by John Tukey. In academia, confirmatory data analysis (CDA) is still the dominant paradigm. As a matter of fact, EDA and data visualization are very underused in academia. Very often peer-review journal articles show only a few graphs or even none. Although numbers like t-ratio, F-ratio, Type III sum of squares, p value, eigenvalue, R², eta squared…etc. make the report look scientific, usually these numbers cannot tell you the pattern of the data and the magnitude of the effect under study, which are supposed to be our primary concerns. We need both exploratory and explanatory data visualization, which has become a common practice in business!