undefined | Better HN

0 pointsmajormajor3y ago0 comments

It's not just the future, though the examples I gave were future oriented.

But it's all very interpolation/summarization-focused.

A "song lyrics in the style of Taylor Swift" isn't an actual song by Taylor Swift.

A summary of the history of Texas isn't actually vetted by any historian to ensure accuracy.

The answer to a math problem may not be correct.

To me, those things don't qualify as "new data." They aren't suitable for future training as-is. Sometimes for a simple reason: they aren't facts, using the dictionary "facts and statistics collected together for reference or analysis" definition of data. So very simply "not new data."

Sometimes in a blurrier way - the song lyrics, for instance, could be touching, or poignant, or "true" in a Keats sense[0] - but if the internet gets full of GPT-dreams and future models are trained on that, you could slide down further and further into an uncanny valley, especially since most of the time you don't get one of those amazing poignant ones. Most of the time I've gotten something bland.

[0] "What the imagination seizes as beauty must be truth"

0 comments

PaulHoule3y ago

One way to think about prompting is as a conditional probability distribution. There is a particular song by Taylor Swift or the set of all songs by Taylor Swift but ChatGPT is particularly talented at sampling the "set of all songs in the style of Taylor Swift".

One of the worst problems in the "Expert Systems" age of A.I. was reasoning over uncertainty, for instance this system

https://en.wikipedia.org/wiki/Mycin

had a half-baked approach that worked well enough for a particular range of medical diagnosis. In general it is an awful problem because it involves sampling over a joint probability distribution. If you have 1000 variables you have to sample a 1000-dimensional space, to do it the brute force way you'd have sample the data in an outrageous number of hypercubes.

Insofar as machine learning is successful it is that we have algorithms that take a comparatively sparse sample and make a good guess of what the joint p.d. is. The success of deep learning is particularly miraculous in that respect.

j / k navigate · click thread line to collapse