5 ms·

Expecting the rate of progress to drop off so abruptly after realistically just a few years of serious work on the problem seems like the more unreasonable and

by nilkn 2y ago

Expecting the rate of progress to drop off so abruptly after realistically just a few years of serious work on the problem seems like the more unreasonable and grander prediction to me than expecting it to continue at its current pace for even just 5 more years.

hn_throwaway_99 2y ago
The problem is that the rate of progress over the past 5/10/15 years has not been linear at all, and it's been pretty easy to point out specific inflection points that have allowed that progress to occur. I.e. the real breakthrough that allowed such rapid progress was transformers in 2017. Since that time, the vast majority of the progress has simply been to throw more data at the problem, and to make the models bigger (and to emphasize, transformers really made that scale possible in the first place). I don't mean to denigrate this approach - if anything, OpenAI deserves tons of praise for really making that bet that spending hundreds of millions on model training would give discontinuous results. However, there are loads of reasons to believe that "more scale" is going to give diminishing returns, and a lot of very smart people in the field have been making this argument (at least quietly). Even more specifically, there are good reasons to believe that more scale is not going to go anywhere close to solving the types of problems that have become evident in LLMs since when they have had massive scale. So the big thing I'm questioning is that I see a sizable subset of both AI researchers (and more importantly VC types) believing that, essentially, more scale will lead to AGI. I think the smart money believes that there is something fundamentally different about how humans approach intelligence (and this difference leads to important capabilities that aren't possible from LLMs).
- calf 2y ago
  Could it be argued that transformers are only possible because of Moore's law and the amount of processing power that could do these computations in a reasonable time? How complex is the transformer network really, every lay explanation I've seen basically says it is about a kind of parallelized access to the input string. Which sounds like a hardware problem, because the algorithmic advances still need to run on reasonable hardware.
- svnt 2y ago
  Transformers in 2017 as the basis, but then the quantization-emergence link as a grad student project using spare time on ridiculously large A100 clusters in 2021/2022 is what finally brought about this present moment. I feel it is fair to say that neither of these were natural extrapolations from prior successful models directly. There is no indication we are anywhere near another nonlinearity, if we even knew how to look for that. Blind faith in extrapolation is a finance regime, not an engineering regime. Engineers encounter nonlinearities regularly. Financiers are used to compound interest.
janalsncm 2y ago
I don’t see why it’s unreasonable. Training a model that is an order of magnitude bigger requires (at least) an order of magnitude more data, an order of magnitude more time, hardware, energy, and money. Getting an order of magnitude more data isn’t easy anymore. From GPT2 to 3 we (only) had to scale up to the internet. Now? You can look at other sources like video and audio, but those are inherently more expensive. So your data acquisition costs aren’t linear anymore, they’re something like 50x or 100x. Your quality will also dip because most speech (for example) isn’t high-quality prose, it contains lots of fillers, rambling, and transcription inaccuracies. And this still doesn’t fix fundamental long-tail issues. If you have a concept that the model needs to see 10x to understand, you might think scaling your data 10x will fix it. But your data might not contain that concept 10x if it’s rare. It might contain 9 other one-time things. So your model won’t learn it.

by nilkn 2y ago

hn_throwaway_99 2y ago

The problem is that the rate of progress over the past 5/10/15 years has not been linear at all, and it's been pretty easy to point out specific inflection points that have allowed that progress to occur. I.e. the real breakthrough that allowed such rapid progress was transformers in 2017. Since that time, the vast majority of the progress has simply been to throw more data at the problem, and to make the models bigger (and to emphasize, transformers really made that scale possible in the first place). I don't mean to denigrate this approach - if anything, OpenAI deserves tons of praise for really making that bet that spending hundreds of millions on model training would give discontinuous results. However, there are loads of reasons to believe that "more scale" is going to give diminishing returns, and a lot of very smart people in the field have been making this argument (at least quietly). Even more specifically, there are good reasons to believe that more scale is not going to go anywhere close to solving the types of problems that have become evident in LLMs since when they have had massive scale. So the big thing I'm questioning is that I see a sizable subset of both AI researchers (and more importantly VC types) believing that, essentially, more scale will lead to AGI. I think the smart money believes that there is something fundamentally different about how humans approach intelligence (and this difference leads to important capabilities that aren't possible from LLMs).

calf 2y ago
Could it be argued that transformers are only possible because of Moore's law and the amount of processing power that could do these computations in a reasonable time? How complex is the transformer network really, every lay explanation I've seen basically says it is about a kind of parallelized access to the input string. Which sounds like a hardware problem, because the algorithmic advances still need to run on reasonable hardware.
svnt 2y ago
Transformers in 2017 as the basis, but then the quantization-emergence link as a grad student project using spare time on ridiculously large A100 clusters in 2021/2022 is what finally brought about this present moment. I feel it is fair to say that neither of these were natural extrapolations from prior successful models directly. There is no indication we are anywhere near another nonlinearity, if we even knew how to look for that. Blind faith in extrapolation is a finance regime, not an engineering regime. Engineers encounter nonlinearities regularly. Financiers are used to compound interest.

janalsncm 2y ago

I don’t see why it’s unreasonable. Training a model that is an order of magnitude bigger requires (at least) an order of magnitude more data, an order of magnitude more time, hardware, energy, and money. Getting an order of magnitude more data isn’t easy anymore. From GPT2 to 3 we (only) had to scale up to the internet. Now? You can look at other sources like video and audio, but those are inherently more expensive. So your data acquisition costs aren’t linear anymore, they’re something like 50x or 100x. Your quality will also dip because most speech (for example) isn’t high-quality prose, it contains lots of fillers, rambling, and transcription inaccuracies. And this still doesn’t fix fundamental long-tail issues. If you have a concept that the model needs to see 10x to understand, you might think scaling your data 10x will fix it. But your data might not contain that concept 10x if it’s rare. It might contain 9 other one-time things. So your model won’t learn it.