https://twitter.com/GeorgeSilverman/status/1533887228602200064?t=6RxmxTTSS9NfZx1IX78ssA&s=19

about ai

from https://developsense.com/blog/2024/01/a-super-quick-guide-to-evaluating-ai-claims

If you’d like to probe a little deeper, you can ask other questions too:

What parts of the product actually involve any kind of “AI” or machine learning algorithm, and what parts don’t? Is “AI” being used as a marketing term for “reasonably sophisticated software as usual”?
What flavour(s) of machine learning were applied? Evolutionary algorithms? Bayseian analysis? Connectionism? Symbolic manipulation?
What was the training data and where did it come from? What biases exist in it? (Don’t bother asking “Is the training data biased?” It’s all biased in some sense. For instance: everything in the training data has been converted into machine-readable strings of bits. A machine learning model designed to select images of dogs from a large set of images has no experience of dogs in the world. It only processess bit patterns that it has been fed, and ulitimately that’s based on some human’s choices. The model only “recognizes” images that are consistent with those that some human has evaluated as being consistent with dogs, or with models of dogs.)
To what degree is training data from the past relevant to what happens, or to what might happen, in the future? (All of the model’s training data comes from the past. How far in the past?)
Who will be affected by this product? Who stands to gain, and who might lose?
How might this product help to enhance expertise? To erode it?
To what degree might this pleasing demonstration be insufficent evidence of reliable behaviour?
Do we have to give the product the correct answer beforehand? To what degree? How much? How correct?
How much damage might we have to repair?

Asking the right questions

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