zlacker

I recommend reading Norvig's thinking about the various cultures.

https://static.googleusercontent.com/media/research.google.c... and https://norvig.com/chomsky.html

In short, Norvig concludes there are several conceptual approaches to ML/AI/Stats/Scientific analysis. One is "top down": teach the system some high level principles that correspond to known general concepts, and the other is "bottom up": determine the structure from the data itself and use that to generate general concepts. He observes that while the former is attractive to many, the latter has continuously produced more and better results with less effort.

I've seen this play out over and over. I've concluded that Norvig is right: empirically based probabilistic models are a cheaper, faster way to answer important engineering and scientific problems, even if they are possibly less satisfying intellectually. Cheap approximations are often far better than hard to find analytic solutions.

>>dekhn+(OP)
this is the same concept as the bitter lesson, am I correct? I don't see a substantial difference yet.

>>golol+E3
I hadn't read that before, but yes. Sutton focuses mostly on "large amounts of compute" whereas I think his own employer has demonstrated that it's a combination of large amount of compute, large amounts of data, and really clever probabilistic algorithms, in combination, which really demonstrate the utility of the bitter lesson.

And speaking as a biologist for a moment, that minds are irredeemably complex and attemptng to understand them with linear, first-order rules and logic is unlikely to be fruitful.

>>dekhn+(OP)
> One is "top down": teach the system some high level principles that correspond to known general concepts, and the other is "bottom up": determine the structure from the data itself and use that to generate general concepts.

This is the same pattern explaining why bottom-up economic systems, i.e. lassaire faire free markets, flawed as they are, work better than top-down systems like central planning.

>>jyscao+ph
They don't work for a more specific reason than that; a central planning system that was "bottom up" (asking everyone what they want rather than dictating it) couldn't work either, because people aren't capable of expressing their preferences in a way you can calculate.

How much iron does a steel mill need this year? Well, that depends on how many customers they'll get, which depends on what price they sell steel at.

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

>>dekhn+(OP)
Empirically it's worked out this way.

It's true that it's less satisfying and less attractive, but these subjective adjectives are based on relevant objective truths, namely that LLMs are difficult or impossible to analyze from the outside, and at a coarse level they're the knowledge equivalent of pathological functions. Calling them "intelligent" is to privilege a very limited definition of the word, while ignoring all of the other things that we normally associate with it.

I don't want us to make an AGI or anything like it for both humanist and economic reasons, but if we make one, I think it's very likely that it has to have more internal structure than do LLMs, even if we do not explicitly force a given structure to be there.

(I am not an expert.)