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A conversation with Sussman on AI and asynchronous programming

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Re: A conversation with Sussman on AI and asynchronous programming

#2

Post by rurban »

This accountable argument is stone-old. You'll find it in an intro AI book. Of course only when implemented in lisp, so it needs be an older book.

It's the typical argument against neural nets, because they cannot explain their chain of reasoning, and so you are not able to train it to right way, or do not train into the wrong direction. when something goes wrong you got a problem.

Old AI had the same problem, that's why they added the chain to the backtracking to give better answers, and you are able to introspect answers.

Re: A conversation with Sussman on AI and asynchronous programming

#5

Post by paroneayea »

rurban wrote: post #2

This accountable argument is stone-old. You'll find it in an intro AI book. Of course only when implemented in lisp, so it needs be an older book. It's the typical argument against neural nets, because they cannot explain their chain of reasoning, and so you are not able to train it to right way, or do not train into the wrong direction. when something goes wrong you got a problem. Old AI had the same problem, that's…

Maybe true, but "stone-old" ideas don't mean "bad ideas". Neural networks were "stone old" until all this big data stuff went crazy and now suddenly they're on the frontpage of hacker news all the time again and people think they're the New Hotness (TM). Similar with many long-forgotten functional programming techniques which are being talked about as new stuff, much of it is refinements of old ideas, but finally hitting the mainstream.

The AI Winter is thawing... but whose lap will it fall into after the defrost? Big data mining organizations, or everyday hackers, or....?

Re: A conversation with Sussman on AI and asynchronous programming

#7

Post by rspeer »

paroneayea wrote: post #5
rurban wrote: post #2

This accountable argument is stone-old. You'll find it in an intro AI book. Of course only when implemented in lisp, so it needs be an older book. It's the typical argument against neural nets, because they cannot explain their chain of reasoning, and so you are not able to train it to right way, or do not train into the wrong direction. when something goes wrong you got a problem. Old AI had the same problem, that's…

Maybe true, but "stone-old" ideas don't mean "bad ideas". Neural networks were "stone old" until all this big data stuff went crazy and now suddenly they're on the frontpage of hacker news all the time again and people think they're the New Hotness (TM). Similar with many long-forgotten functional programming techniques which are being talked about as new stuff, much of it is refinements of old ideas, but finally hit…

My dream would be if Good Old-Fashioned AI (the symbolic, explainable kind Sussman is interested in) were to have the sudden redemption that neural nets had.

It was easier for neural nets, though, because they were close to a previously successful AI mechanism (machine learning with logistic regression), it's just that we had spent decades talking about them with different words for no good reason. There's a much larger gulf between GOFAI and what we do now.

Re: A conversation with Sussman on AI and asynchronous programming

#8

Post by kcorbitt »

There definitely exist "accountable" AI models. Things like decision trees and various types of regressions.

The thing is though... any sufficiently advanced AI is going to be unaccountable pretty much by definition. It's like, calculus is an extremely useful tool for predicting the temperature of a cooling object over time, but good luck explaining to a 3-year-old how to perform the necessary maths. The fact that they can't comprehend it doesn't mean that calculus isn't useful, it just means that it's beyond the 3-year-old's ability to intuitively grasp. In this smilie we're the 3-year-olds. :)

Re: A conversation with Sussman on AI and asynchronous programming

#10

Post by kruhft »

Whenever I'm out of ideas I always end up going back to the Propagator/Logic Simulator chapters of SICP and rewriting them hoping to glean a bit more about the process and usability of such an abstraction.

I usually end up writing a Propagating logic simulator (combining the chapters) and marveling at the working forward/backward ripple carry adder. This time I've derived all the way down to switches which infer unknown values based on their current inputs and used them to successfully create the reversible logic functions required by the Propagator model.

Fascinating stuff and highly recommended reading for anybody interested in alternative models of computation.

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