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Literally the paragraph right before the one you quote is this: > I am generally outside the ML field, but I do talk with people in the field. One of the thing

by saghm 7mo ago

Literally the paragraph right before the one you quote is this: > I am generally outside the ML field, but I do talk with people in the field. One of the things they tell me is that we don’t really know why transformer models have been so successful, or how to make them better. This is my summary of discussions-over-drinks; take it with many grains of salt. I am certain that People in The Comments will drop a gazillion papers to tell you why this is wrong. As I understand it, this article is basically a conglomeration of several attempts at an article that the author has attempted to make over the past decade or so considering the impacts of AI on society. In their own words: > Some of these ideas felt prescient in the 2010s and are now obvious. Others may be more novel, or not yet widely-heard. Some predictions will pan out, but others are wild speculation. I hope that regardless of your background or feelings on the current generation of ML systems, you find something interesting to think about. As for the "Bitter Lesson" part, they pretty much directly said that it wasn't the Bitter Lesson exactly, saying it might be a variant of it. Honestly, it felt more like a way of throwing in a reference to something that also might provoke thought, which was done throughout the piece (which again, is the entire point). It's totally valid to say "this article didn't provoke much thought for me". I'm a bit confused at why you think a lack of specific domain knowledge in a domain that they literally state they are not an expert in would be disqualifying for that purpose though.

joefourier 7mo ago
The title of the article is “The Future of Everything is Lies, I Guess” and the first part is literally complaining about LLMs being bullshit machines, while the author proceeds to tell confabulations (or lies) of his own. Is there not a bit of irony in that? If you’re a non-expert in a field, I don’t think it’s a good sign if you’re writing a 10 part article about that field’s impact on society and getting basic facts wrong. How can I trust that the conclusions will be any more credible?
- otterley 7mo ago
  Might the conclusions be correct even if some of the facts are not? Even a stopped clock is right twice a day. And, "approximately correct" is still sometimes valuable.
- saghm 7mo ago
  > The title of the article is “The Future of Everything is Lies, I Guess” and the first part is literally complaining about LLMs being bullshit machines, while the author proceeds to tell confabulations (or lies) of his own. Is there not a bit of irony in that? Maybe some, but not that much given the disclaimers I cited above. There's value in a qualitative confidence level for a statement, and I'd argue that this is something that LLMs do not seem to produce in practice without someone explicitly asking for it. The human author's ability to anticipate potential mistakes in their logic and communicate those ahead of time is not equivalent to the type of fabrications that LLMs routinely make. > If you’re a non-expert in a field, I don’t think it’s a good sign if you’re writing a 10 part article about that field’s impact on society and getting basic facts wrong. How can I trust that the conclusions will be any more credible? I don't know why an expert in LLM implementation would be inherently more qualified to analyze the second-order effects of their product than anyone else. There's precedent for people who are "too close" to something having biases that make them less effective at recognizing how tools will get used by non-experts, and society as a whole is largely composed of people who are not experts in LLM implementations. If you wanted to understand what the net effect of everyone having access to LLMs, having an understanding of people is probably more important than knowing exactly what an LLM does under the hood.
imtringued 7mo ago
He is wrong about why transformers are popular. The most obvious reason is that transformers accept a sequence as an input and produce a sequence as an output. The vast majority of pre-transformer architectures only accepted a fixed input and output size. Before 2016 I was somewhat interested in ML, but my curiosity vanished because of the fixed input and output size limitations. RNNs including LSTMs at the time were pretty bad and difficult to train due to vanishing and exploding gradients at long sequence lengths and sequential training along the sequence length. Meanwhile transformers can be parallelized along the sequence length. Then there are theoretical limitations. Transformers re-read the entire sequence for every output. This leads to quadratic attention. There are plenty of papers that tell you why it is impossible to replicate the properties of quadratic attention with linear attention. The reason is blatantly obvious. If you want linear attention to have the same capability, you need to re-read the entire input sequence after every output. If you do this at the token level, then you have basically implemented quadratic attention. Transformers aren't a mystery success, they are using computational brute force, which is hard to beat with other architectures. If you go with a more efficient architecture, you are by definition giving up some non-zero capabilities. Nobody really cares about getting slightly worse results from a much more efficient architecture. In the current ML space, it's SOTA (state of the art) or go home.
- ainch 7mo ago
  Transformers do have a fixed input/output size though - that's what a context window is. It's just that, via scaling and algorithmic improvements, the length of usable context windows has increased to the point that they're much less of a bottleneck. I think your points around parallelisation and the flexibility of quadratic attention are spot-on though.
- vrighter 7mo ago
  transformers have a fixed input size (padding the unneeded context window with null tokens). Whether you put in a sequence of things or just random tokens is irrelevant. To the network it is just "one input" They also have a fixed output of one probability distribution for the next one token. running it in a loop does not mean it can work with sequences, by that definition, so can literally everything else

by saghm 7mo ago

joefourier 7mo ago

The title of the article is “The Future of Everything is Lies, I Guess” and the first part is literally complaining about LLMs being bullshit machines, while the author proceeds to tell confabulations (or lies) of his own. Is there not a bit of irony in that? If you’re a non-expert in a field, I don’t think it’s a good sign if you’re writing a 10 part article about that field’s impact on society and getting basic facts wrong. How can I trust that the conclusions will be any more credible?

otterley 7mo ago
Might the conclusions be correct even if some of the facts are not? Even a stopped clock is right twice a day. And, "approximately correct" is still sometimes valuable.
saghm 7mo ago
> The title of the article is “The Future of Everything is Lies, I Guess” and the first part is literally complaining about LLMs being bullshit machines, while the author proceeds to tell confabulations (or lies) of his own. Is there not a bit of irony in that? Maybe some, but not that much given the disclaimers I cited above. There's value in a qualitative confidence level for a statement, and I'd argue that this is something that LLMs do not seem to produce in practice without someone explicitly asking for it. The human author's ability to anticipate potential mistakes in their logic and communicate those ahead of time is not equivalent to the type of fabrications that LLMs routinely make. > If you’re a non-expert in a field, I don’t think it’s a good sign if you’re writing a 10 part article about that field’s impact on society and getting basic facts wrong. How can I trust that the conclusions will be any more credible? I don't know why an expert in LLM implementation would be inherently more qualified to analyze the second-order effects of their product than anyone else. There's precedent for people who are "too close" to something having biases that make them less effective at recognizing how tools will get used by non-experts, and society as a whole is largely composed of people who are not experts in LLM implementations. If you wanted to understand what the net effect of everyone having access to LLMs, having an understanding of people is probably more important than knowing exactly what an LLM does under the hood.

imtringued 7mo ago

He is wrong about why transformers are popular. The most obvious reason is that transformers accept a sequence as an input and produce a sequence as an output. The vast majority of pre-transformer architectures only accepted a fixed input and output size. Before 2016 I was somewhat interested in ML, but my curiosity vanished because of the fixed input and output size limitations. RNNs including LSTMs at the time were pretty bad and difficult to train due to vanishing and exploding gradients at long sequence lengths and sequential training along the sequence length. Meanwhile transformers can be parallelized along the sequence length. Then there are theoretical limitations. Transformers re-read the entire sequence for every output. This leads to quadratic attention. There are plenty of papers that tell you why it is impossible to replicate the properties of quadratic attention with linear attention. The reason is blatantly obvious. If you want linear attention to have the same capability, you need to re-read the entire input sequence after every output. If you do this at the token level, then you have basically implemented quadratic attention. Transformers aren't a mystery success, they are using computational brute force, which is hard to beat with other architectures. If you go with a more efficient architecture, you are by definition giving up some non-zero capabilities. Nobody really cares about getting slightly worse results from a much more efficient architecture. In the current ML space, it's SOTA (state of the art) or go home.

ainch 7mo ago
Transformers do have a fixed input/output size though - that's what a context window is. It's just that, via scaling and algorithmic improvements, the length of usable context windows has increased to the point that they're much less of a bottleneck. I think your points around parallelisation and the flexibility of quadratic attention are spot-on though.
vrighter 7mo ago
transformers have a fixed input size (padding the unneeded context window with null tokens). Whether you put in a sequence of things or just random tokens is irrelevant. To the network it is just "one input" They also have a fixed output of one probability distribution for the next one token. running it in a loop does not mean it can work with sequences, by that definition, so can literally everything else