The data deluge exponentially increases the number of coincidental, useless statistical patterns — so the probability of useful patterns approaches zero.
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The data-mining algorithms may recognize that a statistical pattern has vanished but won’t know why because they do not understand it as a logical relationship.
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Recent studies indicate that members of Gen Z are dividing politically according to sex, with men leaning more conservative and women going more liberal.
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Real science is currently under siege, pummeled by conspiracy nuts and undermined internally by a replication crisis created by sloppy science.
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Gary Smith describes the problems with today’s science in his new book Distrust: Big Data, Data-Torturing, and the Assault on Science. He recounts endless examples of disinformation, data torture, and data mining, much of which we already knew. Taken together, however, and as I described in this review, they are mind-blowing. He argues that many of these problems come from things scientists do such as p-hacking during statistical analysis, too little emphasis on “impact” in statistical analyses, outright data falsification, and the creation of the Internet, which can be a huge disinformation machine in addition to a valuable resource. In the last chapter, he also offers some solutions such as ending the artificial thresholds for p-values such as 0.05, requiring Read More ›
Walter Bradley Center Senior Fellow Gary Smith’s book Distrust: Big Data, Data-Torturing, and the Assault on Science was reviewed in The Washington Post today. Smith is a frequent contributor to Mind Matters and teaches economics at Pomona College. In his new book, Smith argues that science itself is being “undermined” by the tools scientists use. Reviewer Abby Ohlheister writes, Smith, an economist whose work often examines the misuse of data and statistics in a variety of disciplines, argues that the current crisis of trust in science falls at the intersection of three forces: disinformation, data torturing and data mining. Disinformation, as Smith writes, is “as old as the human race,” but accelerated in speed and reach alongside social media. Data Read More ›
ChatGPT is a direct descendent of GPT-3, and is a fancy form of a fancy machine learning algorithm called a neural network. For an overview of all of ChatGPT’s neural network complexity, here is a fun article. However, all that is beside the point. The important thing about a neural network: It can only generate what is in its training data. Therefore, ChatGPT can only produce what is in its training data. ChatGPT’s training data does not include the conversation you or I are having with ChatGPT. Therefore, if something novel occurs in the conversation, ChatGPT cannot reproduce it. That is, if ChatGPT is a neural network. Conversely, if ChatGPT reproduces novel text from the conversation, then ipso facto ChatGPT is not a Read More ›
Debra J. Saunders, a fellow at Discovery Institute’s Chapman Center for Citizen Leadership, wrote an article for The Daily Caller this week on the mounting problem of privacy regarding cell phone usage. Saunders expresses concern about the ease in which government and Big Tech companies can mine data and track people through their cell phones. Saunders thinks the COVID pandemic sped up the process of privacy violations. She recalls a 2021 incident in which the state of Massachusetts purportedly worked with Google to download a COVID tracker app without users’ notice or consent. Now, the state is facing a lawsuit. Saunders reports, The New Civil Liberties Alliance filed a class-action lawsuit this month against the Massachusetts Department of Public Health to end Read More ›
Until a few years ago, Brian Wansink (pictured in 2007) was a Professor of Marketing at Cornell and the Director of the Cornell Food and Brand Lab. He authored (or co-authored) more than 200 peer-reviewed papers and wrote two popular books, Mindless Eating and Slim by Design, which have been translated into more than 25 languages. In one of his most famous studies, 54 volunteers were served tomato soup. Half were served from normal bowls and half from “bottomless bowls” which had hidden tubes that imperceptibly refilled the bowls. Those with the bottomless bowls ate, on average, 73 percent more soup but they did not report feeling any fuller than the people who ate from normal bowls. Eating is evidently Read More ›
What are some assumptions about artificial intelligence? How does artificial intelligence affect the stock market? George Gilder and Robert J. Marks discuss assumptions about artificial intelligence, the stock market, and George Gilder’s new book Gaming AI: Why AI Can’t Think but Can Transform Jobs (which you can get for free here). Show Notes Additional Resources
I told my students that I had a model that predicted the popular vote for the the last ten presidential elections (1980–2016) perfectly.
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Random data shows geographic clusters. Thus some locations will inevitably have higher COVID-19 rates than others. As my example shows, associating these clusters with personal characteristics after the fact is not convincing scientific evidence. That’s where randomized, controlled trials are needed.
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Findings patterns in data is easy. Finding meaningful patterns that have a logical basis and can be used to make accurate predictions is elusive. We can see this from 18th-century attempts to cure scurvy through 21st century claims about the stock market or history.
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If Alphabet’s Deep Mind or Microsoft had successfully data mined the 29,000 papers and found useful coronavirus information, that would be pretty impressive. But they appear to be giving others a chance to try instead, raising issues once again about the value of data mining in medicine.
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The explosion of data has vastly increased the number of coincidental patterns that can be discovered by tenacious researchers. If there are a relatively fixed number of useful patterns and the number of coincidental patterns is growing exponentially, then the ratio of useful patterns to useless patterns must necessarily be getting closer to zero every day.
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Bob Marks and Gary Smith offer a range of startling examples of how the pressure to publish drives a lack of rigor — and sometimes honesty — in analyzing and presenting experimental data. The result is a never ending parade of headlines in health and medicine that are unwarranted and often reversed or impossible to replicate. Shownotes 01:00 | Data Read More ›