4 ms·

Extraordinary claims require extraordinary evidence. Otherwise, it is a clickbait (if done intentionally) or delusion (if not). Well, I was shocked to see LLMs

by stared 3y ago

Extraordinary claims require extraordinary evidence. Otherwise, it is a clickbait (if done intentionally) or delusion (if not). Well, I was shocked to see LLMs (rather than something intrinsically related to Reinforcement Learning) reach the level of GPT-3.5, not even to mention GPT-4. For starters, he should define what AGI means. By some criteria, it does not exist (no free lunch theorem and stuff). Some others say that GPT-4 already fulfils that. So, the question to the author is: can he say which AGI he means, and would he actually bet money on this claim?

xanderlewis 3y ago
I was shocked to see what ‘pure’ Transformer-based language models can do too, until I realised the sheer scale of training data. I don’t think we have a good enough intuitive sense of just how much is in there. The purported examples of generalisation seem to have turned out to be mostly just a result of having trained something approximating everything ever written. As a human it’s almost impossible to imagine how knowledge that is. I reckon even a Markov chain model of similar magnitude could wow us in various ways.
- stared 3y ago
  > I reckon even a Markov chain model of similar magnitude could wow us in various ways. I bet $100 it cannot.
  - xanderlewis 3y ago
    It’s completely subjective, so it’s hard to have such a bet without further specification. But I definitely feel like a sufficiently large Markov model could output sentences in response to input that would amaze/freak out at least some users. It would just be parroting its training data pretty much verbatim (at the level of short n-grams, at least), but due to the sheer amount of the data the exact source would be obscure and hence more easily attributed to the model ‘thinking’ for itself and being creative. That’s all I was in fact saying — not that it would wow all of us all of the time. Blake Lemoine was fired from Google for (/in relation to his) expressing that a language model was sentient. We all have different thresholds for thinking ‘oh my god this thing has to be conscious!’.
- gwern 3y ago
  I think if you go check the n-gram (Markov chain) models which were trained by IBM and Google and others (eg. 'unreasonable effectiveness of data'), you'll find them remarkably chonky sample-wise. (This was possible because processing data for fitting n-grams was cheaper than training any neural net on them, so even in the '80s or '90s you could fit n-grams to billions of tokens.) But their scaling was much, much worse. An example: https://gwern.net/doc/ai/scaling/2020-chrisdyer-aacl2020-machinetranslationscaling-ngramsvsrnns.jpg https://gwern.net/doc/ai/scaling/2020-chrisdyer-aacl2020-mac... Imagine how far to the right you would have to draw out that n-gram curve before it reached the seq2seq RNN trained on a fixed dataset size - and where that RNN is considered garbage by current standards. (Note the x-axis is in "millions of tokens".)

by stared 3y ago

xanderlewis 3y ago

I was shocked to see what ‘pure’ Transformer-based language models can do too, until I realised the sheer scale of training data. I don’t think we have a good enough intuitive sense of just how much is in there. The purported examples of generalisation seem to have turned out to be mostly just a result of having trained something approximating everything ever written. As a human it’s almost impossible to imagine how knowledge that is. I reckon even a Markov chain model of similar magnitude could wow us in various ways.

stared 3y ago
> I reckon even a Markov chain model of similar magnitude could wow us in various ways. I bet $100 it cannot.
- xanderlewis 3y ago
  It’s completely subjective, so it’s hard to have such a bet without further specification. But I definitely feel like a sufficiently large Markov model could output sentences in response to input that would amaze/freak out at least some users. It would just be parroting its training data pretty much verbatim (at the level of short n-grams, at least), but due to the sheer amount of the data the exact source would be obscure and hence more easily attributed to the model ‘thinking’ for itself and being creative. That’s all I was in fact saying — not that it would wow all of us all of the time. Blake Lemoine was fired from Google for (/in relation to his) expressing that a language model was sentient. We all have different thresholds for thinking ‘oh my god this thing has to be conscious!’.
gwern 3y ago
I think if you go check the n-gram (Markov chain) models which were trained by IBM and Google and others (eg. 'unreasonable effectiveness of data'), you'll find them remarkably chonky sample-wise. (This was possible because processing data for fitting n-grams was cheaper than training any neural net on them, so even in the '80s or '90s you could fit n-grams to billions of tokens.) But their scaling was much, much worse. An example: https://gwern.net/doc/ai/scaling/2020-chrisdyer-aacl2020-machinetranslationscaling-ngramsvsrnns.jpg https://gwern.net/doc/ai/scaling/2020-chrisdyer-aacl2020-mac... Imagine how far to the right you would have to draw out that n-gram curve before it reached the seq2seq RNN trained on a fixed dataset size - and where that RNN is considered garbage by current standards. (Note the x-axis is in "millions of tokens".)