Technology

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Jul 15 2023

What Generative AI Generates

Let us focus on what Generative AI does generate, speaking exclusively from personal experience. Generative AI is the generic term for systems like ChatGPT, Google’s Bard, DALL-E, or MidJourney, that generate text, images, or other media in response to prompts. All sorts of news sources report it as a revolutionary technology poised to dramatically change how we work and communicate.

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By • Information Technology • 4 • Tags: , , , , , ,

DALL·E 2023-05-13 15.03.43 - vaccine

May 13 2023

Effect of COVID-19 Vaccines on Excess Deaths

Even on LinkedIn, you still see posts and comments asserting that the COVID-19 vaccines aren’t “real” and alleging that they do more harm than good. This is usually based on articles of questionable value and the author’s brother-in-law catching COVID-19 while vaccinated. Public health, however, warrants serious research and is not a matter of anecdotes.

The real question is whether the administration of these vaccines to large populations was effective in curbing the pandemic and saving lives. Today, we can answer yes to some of these questions with simple methods applied to data from the US government. Specifically, we can analyze US CDC and census data on state-by-state Excess Deaths and fractions of the population vaccinated for 2021 and 2022. The results are obvious but, sometimes, the obvious needs belaboring. 

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By • Data science • 4 • Tags: ,

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Apr 10 2023

The Possession of Data

Since the turn of the 21st century, the possession of data has become a greater source of economic power than ever before, dwarfing, in particular, analytics. It’s data about us humans, not about the physical world: our demographic segment, where we live, what we buy, our opinions, and our relationships.

This is a development that manufacturing companies have not been leading. To survive this disruption, their managers need to think more deeply about data, how they can collect it, and what they can do with it to stay competitive.

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By • Information Technology • 2 • Tags: , , , , , ,

ChatGPT about "Michel Baudin"

Feb 26 2023

Wrong things ChatGPT says about me

What ChatGPT says about me is generally flattering but inaccurate and inconsistent. Ask multiple times and you get different wrong answers. I find it disturbing that it credits me for things I didn’t do. It also omits some I have done, which makes me wonder to whom ChatGPT attributes them. Here are a few mistakes I found in four successive versions:

  • “Baudin is the author of several books […], including […] Lean Work Design.” Sorry, I never wrote a book called “Lean Work Design.” Instead, I wrote Introduction to Manufacturing (2022), Working with Machines (2007), Lean Logistics (2005), Lean Assembly (2002), and Manufacturing Systems Analysis (1990).
  • “He has over 30 years of experience…” 42 years is the number.
  • “Baudin began his career at Hewlett-Packard…” Nope. I started out at Fairchild. Years later, Hewlett-Packard was a consulting client, but I never was an employee.
  • “his own consulting firm, Baudin Associates.” Wrong. I never had a firm by that name. It was MMTI, and then The Takt Times Group.
  • “He has also served as a professor of Lean manufacturing at the California State University, Chico.” I have never set foot in Chico. I have taught extension courses at UC Berkeley, the University of Dayton, the Hong Kong Productivity Council, and the University of Buckingham, but not Chico State.
  • “Baudin has taught courses […] at several universities, including […] MIT, and Stanford…” No, I never did.
  • “He is currently a professor at Institut Mines-Télécom Business School in France.” No.
  • “Baudin is a graduate of Ecole Centrale Paris.” No, it’s Mines-ParisTech, not Centrale.
  • “He holds a Ph.D. in Mechanical Engineering from the University of California, Berkeley.” I don’t have a Ph.D.
  • “He has worked as a researcher at several institutions, including the Lawrence Berkeley National Laboratory and the French National Institute for Industrial Environment and Risks.” I never worked at either of these places. I did research early in my career, at the Hahn-Meitner Institute in Berlin, Germany, Mines-ParisTech in Fontainebleau, France, and the University of Tokyo, Japan.
  • “Baudin has also served as a consultant for a variety of companies, including Toyota, Ford,…” No. I never did any work for either. In the auto industry, I did some work for Honda, Renault, and GM, but neither Toyota nor Ford.

In other words, #ChatGPT is looser with facts than any journalist I have ever read. 

 

By • Technology • 6 • Tags:

DinosaurPresentingHistogram.png

Nov 7 2022

Analyzing Variation with Histograms, KDE, and the Bootstrap

Assume you have a dataset that is a clean sample of a measured variable. It could be a critical dimension of a product, delivery lead times from a supplier, or environmental characteristics like temperature and humidity. How do you make it talk about the variable’s distribution? This post explores this challenge in the simple case of 1-dimensional data. I have used methods from histograms to KDE and the Bootstrap, varying in vintage from the 1890s to the 1980s:

Timeline of tools used in this post

Other methods were surely invented for the same purpose between 1895 and 1960 or since 1979, that I don’t know about or haven’t used. Readers are welcome to point them out.

The ones discussed here are not black boxes, automatically producing answers from a stream of data. All require a human to tune the settings of the tools. And this human needs to know the back story of the data.

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By • Data science • 2 • Tags: , , ,

Jakob-Bernoulli-stamp-Swiss-formula-graph-law-1713

Oct 12 2022

Musings on Large Numbers

Anyone who has taken an introductory course in probability, or even SPC, has heard of the law of large numbers. It’s a powerful result from probability theory, and, perhaps, the most widely used. Wikipedia starts the article on this topic with a statement that is free of any caveat or restrictions:

In probability theory, the law of large numbers is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value and tends to become closer to the expected value as more trials are performed.

This is how the literature describes it and most professionals understand it. Buried in the fine print within the Wikipedia article, however, you find conditions for this law to apply. First, we discuss the differences between sample averages and expected values, both of which we often call “mean.” Then we consider applications of the law of large numbers in cases ranging from SPC to statistical physics. Finally, we zoom in on a simple case, the Cauchy distribution. It easily emerges from experimental data, and the Law of Large Numbers does not apply to it.

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By • Laws of nature • 1

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