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Considering my former co founder works at OpenAI and we spent years working on and talking about related ML with and around some of the best people in the world

Considering my former co founder works at OpenAI and we spent years working on and talking about related ML with and around some of the best people in the world, considering I’ve implemented language models multiple times myself, please try and be more specific on where I’m wrong. I know many people who work in ML share this concern - people thinking it’s smart, it’s more than it is. It’s very useful no doubt, but your comment is an example of extrapolation from maladjusted priors. Your example proves my point, a very uninteresting looking game. I don’t even doubt GPT will find all sorts of interesting stuff by giving averaged out things. There’s plenty of gaps in the averages humans have missed. But nothing new, and until you can show that let’s just call it what it is.

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BulgarianIdiot 4y ago
Your goalpost moved from "nothing novel" to "it's novel, but it's not interesting to me". I'll let not the years ahead, but literally the DAYS ahead prove you wrong. Because things are changing quite rapidly lately.
ad404b8a372f2b9 4y ago
I've seen ChatGPT draw logical conclusions from (most-likely) previously unseen axioms. I've seen it invent ideas that (most-likely) were not in his training set. I've seen it fail too, but when it succeeds I can not substantially distinguish it from the novelty or logical reasoning of a human. On niche subjects, it fails often. But you can get it to correct itself by making it think logically by spelling out axioms, which seems to fit your definition of novelty. All the arguments I've read about it not being intelligent seem to stem from some form of essentialism. Right now it's dumb intelligent but I think giving it an adjustable recursive depth, similarly to what BulgarianIdiot suggests, will go a long way to improving it given its base capabilities.
- BulgarianIdiot 4y ago
  Indeed. And telling it to spell out the steps in math solution is essentially (ab)using the chat log as the memory for an iteration loop. This demonstrates then when you instruct it "break down the problem and solve it iteratively" it comes out with superior solutions. More accurate, more novel. Ergo, then if it was allowed to iterate in this way internally, it'd produce better answers by default. This thread sums up the same idea: https://twitter.com/ItIsFinch/status/1634730897520951296 https://twitter.com/ItIsFinch/status/1634730897520951296
  - ad404b8a372f2b9 4y ago
    Yeah, I think it's going to be a promising area of research. It's known as Chain-Of-Thought (CoT) in the literature and there are a few papers already out: https://openreview.net/forum?id=_VjQlMeSB_J https://openreview.net/forum?id=_VjQlMeSB_J https://openreview.net/forum?id=5NTt8GFjUHkr https://openreview.net/forum?id=5NTt8GFjUHkr https://openreview.net/forum?id=qFVVBzXxR2V https://openreview.net/forum?id=qFVVBzXxR2V I'm also looking forward to explicit long-term memory mechanisms that don't rely on the main architecture's weights nor on CoT.
  - fock 4y ago
    There's no memory or iteration log. You are just priming the (more or less deterministic even...) predictor with some history. You are doing a lot of work yourself, setting a factually correct context (and this has severe limits...). As for "we just need a way to include iterations": isn't this what attention is supposed to do (kind of dynamically updating weights). The usual way to really update weights are variations on gradient descent. Can you link a paper outlining how we integrate your proposal into the current framework (which took around 20years to mature)? Otherwise your statements are Sci-Fi (at the flying cars-level).
    - BulgarianIdiot 4y ago
      Oh so there's no memory, just recallable history. Oh so it's not learning by example, it's just primed by a sample. Oh so there's no iteration of thought, just a loop of prediction. OK. Also, the papers you asked about: https://news.ycombinator.com/item?id=35115563 https://news.ycombinator.com/item?id=35115563 BTW, people are already implementing recursive/iterative queries on the ChatGPT API, and getting promising results.
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BulgarianIdiot 4y ago

Your goalpost moved from "nothing novel" to "it's novel, but it's not interesting to me". I'll let not the years ahead, but literally the DAYS ahead prove you wrong. Because things are changing quite rapidly lately.

ad404b8a372f2b9 4y ago

I've seen ChatGPT draw logical conclusions from (most-likely) previously unseen axioms. I've seen it invent ideas that (most-likely) were not in his training set. I've seen it fail too, but when it succeeds I can not substantially distinguish it from the novelty or logical reasoning of a human. On niche subjects, it fails often. But you can get it to correct itself by making it think logically by spelling out axioms, which seems to fit your definition of novelty. All the arguments I've read about it not being intelligent seem to stem from some form of essentialism. Right now it's dumb intelligent but I think giving it an adjustable recursive depth, similarly to what BulgarianIdiot suggests, will go a long way to improving it given its base capabilities.

BulgarianIdiot 4y ago
Indeed. And telling it to spell out the steps in math solution is essentially (ab)using the chat log as the memory for an iteration loop. This demonstrates then when you instruct it "break down the problem and solve it iteratively" it comes out with superior solutions. More accurate, more novel. Ergo, then if it was allowed to iterate in this way internally, it'd produce better answers by default. This thread sums up the same idea: https://twitter.com/ItIsFinch/status/1634730897520951296 https://twitter.com/ItIsFinch/status/1634730897520951296
- ad404b8a372f2b9 4y ago
  Yeah, I think it's going to be a promising area of research. It's known as Chain-Of-Thought (CoT) in the literature and there are a few papers already out: https://openreview.net/forum?id=_VjQlMeSB_J https://openreview.net/forum?id=_VjQlMeSB_J https://openreview.net/forum?id=5NTt8GFjUHkr https://openreview.net/forum?id=5NTt8GFjUHkr https://openreview.net/forum?id=qFVVBzXxR2V https://openreview.net/forum?id=qFVVBzXxR2V I'm also looking forward to explicit long-term memory mechanisms that don't rely on the main architecture's weights nor on CoT.
- fock 4y ago
  There's no memory or iteration log. You are just priming the (more or less deterministic even...) predictor with some history. You are doing a lot of work yourself, setting a factually correct context (and this has severe limits...). As for "we just need a way to include iterations": isn't this what attention is supposed to do (kind of dynamically updating weights). The usual way to really update weights are variations on gradient descent. Can you link a paper outlining how we integrate your proposal into the current framework (which took around 20years to mature)? Otherwise your statements are Sci-Fi (at the flying cars-level).
  - BulgarianIdiot 4y ago
    Oh so there's no memory, just recallable history. Oh so it's not learning by example, it's just primed by a sample. Oh so there's no iteration of thought, just a loop of prediction. OK. Also, the papers you asked about: https://news.ycombinator.com/item?id=35115563 https://news.ycombinator.com/item?id=35115563 BTW, people are already implementing recursive/iterative queries on the ChatGPT API, and getting promising results.

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