Oh man, I love crazy stuff like this on HN. For a community which espouses rationality and careful thought, somehow an article with "C ~ D^2" has floated to the top. No notes.
The Bitter Lesson Is Misunderstood
91–100 of 259 posts
Re: The Bitter Lesson Is Misunderstood
#92I am not an expert in AI by any means but I think I know enough about it to comment on one thing: there was an interesting paper not too long ago that showed if you train a randomly-initialized model from scratch on questions, like a bank of physics questions & answers, models will end up with much higher quality if you teach it the simple physics questions first, and then move up to more complex physics questions. This shows that in some ways, these large language models really do learn like we do.
I think the next steps will be more along this vain of thinking. Treating all training data the same is a mistake. Some data is significantly more valuable to developing an intelligent model than most other training data, even when you pass quality filters. I think we need to revisit how we 'train' these models in the first place, and come up with a more intelligent/interactive system of doing so
Re: The Bitter Lesson Is Misunderstood
#93I am not an expert in AI by any means but I think I know enough about it to comment on one thing: there was an interesting paper not too long ago that showed if you train a randomly-initialized model from scratch on questions, like a bank of physics questions & answers, models will end up with much higher quality if you teach it the simple physics questions first, and then move up to more complex physics questions. T…
A relevant paper: https://arxiv.org/abs/2306.11644 -- the Phi models (and many others too) are based on this idea.
Re: The Bitter Lesson Is Misunderstood
#94I am not an expert in AI by any means but I think I know enough about it to comment on one thing: there was an interesting paper not too long ago that showed if you train a randomly-initialized model from scratch on questions, like a bank of physics questions & answers, models will end up with much higher quality if you teach it the simple physics questions first, and then move up to more complex physics questions. T…
Wow. I really like this take. I've seen how time and time again nature follows the Pareto principle. It makes sense that training data would follow this principle as well.
Further that the order of training matters is novel to me and seems so obvious in hindsight.
Maybe both of these points are common knowledge/practice among current leading LLM builders. I don't build LLMs, I build on and with them, so I don't know.
Re: The Bitter Lesson Is Misunderstood
#95Earlier quoted context omitted.
10+ years ago I expected we would get AI that would impact blue collar work long before AI that impacted white collar work. Not sure exactly where I got the impression, but I remember some "rising tide of AI" analogy and graphic that had artists and scientists positioned on the high ground. Recently it doesn't seem to be playing out as such. The current best LLMs I find marvelously impressive (despite their flaws), a…
Robots are only harder because they have expensive hardware. We already have robots that can load dishwashers and do other manual work but humans are cheaper so there isn't much of a market for them. The rising tide idea came from a 1997 paper by Moravec. Here's a nice graphic and subsequent history https://lifearchitect.ai/flood/ Interestingly, Moravec also stated: "When the highest peaks are covered, there will be…
> Interestingly, Moravec also stated: "When the highest peaks are covered, there will be machines than can interact as intelligently as any human on any subject. The presence of minds in machines will then become self−evident
> We pretty much have those today so by 1997 standards, machines have minds, yet somehow we moved the goalposts and decided that doesn't count anymore
What you describe as "moving the goalposts" could also just be explained as simply not meeting the standard of "as intelligently as any human on any subject".
Even in the strongest possible example of LLM's strengths applying their encyclopedic knowledge and (more limited) ability to apply that knowledge for a given subject I don't think they meet that bar. Especially if we're comparing to a human over a time period greater than 30 minutes or so.
Re: The Bitter Lesson Is Misunderstood
#96Earlier quoted context omitted.
This is interesting to think about. It’s basically just birds and primates. Birds have an ancient evolutionary tree as they are dinosaurs, which did actually walk on two legs. But the gap between dinos and primates walking on two feet, I think, is tens of millions of years. So yea pretty long time.
This makes me think something else, though. Once we were able to reason about the physics behind the way things can move, we invented wheels. From there it's a few thousand years to steam engines and a couple hundred more years to jet planes and space travel. We may have needed a billion years of evolution from a cell swimming around to a bipedal organism. But we are no longer speed limited by evolution. Is there any…
Note that wheels, steam engines, jet planes, spaceships wouldn't survive on their own in nature. Compared to natural structures, they are very simple, very straightforward. And while biological organisms are adapted to survive or thrive in complicated, ever-changing ecosystems, our machines thrive in sanitized environments. Wheels thrive on flat surfaces like roads, jet planes thrive in empty air devoid of trees, and so on. We ensure these conditions are met, and so far, pretty much none of our technology would survive without us. All this to say, we're playing a completely different game from evolution. A much, much easier game. Apples and oranges.
As for limits, in my opinion, there are a few limits human intelligence has that evolution doesn't. For example, intent is a double-edged sword: it is extremely effective if the environment can be accurately modelled and predicted, but if it can't be, it's useless. Intelligence is limited by chaos and the real world is chaotic: every little variation will eventually snowball into large scale consequences. "Eventually" is the key word here, as it takes time, and different systems have different sensitivities, but the point is that every measure has a half-life of sorts. It doesn't matter if you know the fundamentals of how physics work, it's not like you can simulate physics, using physics, faster than physics. Every model must be approximate and therefore has a finite horizon in which its predictions are valid. The question is how long. The better we are at controlling the environment so that it stays in a specific regime, the more effective we can be, but I don't think it's likely we can do this indefinitely. Eventually, chaos overpowers everything and nothing can be done.
Evolution, of course, having no intent, just does whatever it does, including things no intelligence would ever do because it could never prove to its satisfaction that it would help realize its intent.
Re: The Bitter Lesson Is Misunderstood
#97Hey folks, OOP/original author and 20-year HN lurker here — a friend just told me about this and thought I'd chime in. Reading through the comments, I think there's one key point that might be getting lost: this isn't really about whether scaling is "dead" (it's not), but rather how we continue to scale for language models at the current LM frontier — 4-8h METR tasks. Someone commented below about verifiable rewards…
10+ years ago I expected we would get AI that would impact blue collar work long before AI that impacted white collar work. Not sure exactly where I got the impression, but I remember some "rising tide of AI" analogy and graphic that had artists and scientists positioned on the high ground. Recently it doesn't seem to be playing out as such. The current best LLMs I find marvelously impressive (despite their flaws), a…
Part of the answer to this puzzle is that your dishwasher itself is a robot that washes dishes, and has had enormous impact on blue collar jobs since its invention and widespread deployment. There are tons of labor saving devices out there doing blue collar work that we don't think of as robots or as AI.
Re: The Bitter Lesson Is Misunderstood
#98Hey folks, OOP/original author and 20-year HN lurker here — a friend just told me about this and thought I'd chime in. Reading through the comments, I think there's one key point that might be getting lost: this isn't really about whether scaling is "dead" (it's not), but rather how we continue to scale for language models at the current LM frontier — 4-8h METR tasks. Someone commented below about verifiable rewards…
> this isn't really about whether scaling is "dead"
> verifiable rewards
I'm in the recovery camp. Honestly I haven't heard a strong argument against it. Mostly "we just care that things work" which, frankly, isn't that the primary concern of all of us? I'm all for throwing shit at a wall and seeing what sticks, it can be a really efficient method sometimes (especially in early exploratory phases), but I doubt it is the most efficient way forward.
In my experience, having been a person who's created models that require magnitudes fewer resources for equivalent performance, I cannot stress enough the importance of quality over quantity. The tricky part is defining that quality.
[0] https://arxiv.org/abs/2407.05694
[1] Personally, I'm unconvinced. Despite success of our LLMs it's difficult to decouple other variables.
[2] The "a" is important here. There's not one physics per-say. There are different models. This is a level of metaphysics most people will not encounter and has many subtleties.
[3] I must stress that there's a huge difference between the universe being computable and the universe being a computation. The universe being computable does not mean we all live in a simulation.
Re: The Bitter Lesson Is Misunderstood
#99Hey folks, OOP/original author and 20-year HN lurker here — a friend just told me about this and thought I'd chime in. Reading through the comments, I think there's one key point that might be getting lost: this isn't really about whether scaling is "dead" (it's not), but rather how we continue to scale for language models at the current LM frontier — 4-8h METR tasks. Someone commented below about verifiable rewards…
10+ years ago I expected we would get AI that would impact blue collar work long before AI that impacted white collar work. Not sure exactly where I got the impression, but I remember some "rising tide of AI" analogy and graphic that had artists and scientists positioned on the high ground. Recently it doesn't seem to be playing out as such. The current best LLMs I find marvelously impressive (despite their flaws), a…
> 10+ years ago I expected we would get AI that would impact blue collar work long before AI that impacted white collar work. Not sure exactly where I got the impression, but I remember some "rising tide of AI" analogy and graphic that had artists and scientists positioned on the high ground.
The moment you strip away the magical thinking, the humanization (bugs not hallucinations) what you realize is that this is just progress. Ford in the 1960's putting in the first robot arms vs auto manufacturing today. The phone: from switch board operators, to mechanical switching to digital to... (I think phone is in some odd hybrid era with text but only time will tell). Draftsmen in the 1970's all replaced by autocad by the 90's. GO further back to 1920, 30 percent of Americans were farmers, today thats less than 2.
Humans, on very human scales are very good at finding all new ways of making ourselves "busy" and "productive".
Re: The Bitter Lesson Is Misunderstood
#100I don't understand why we need more data for training. Assuming we've already digitized every book, magazine, research paper, newspaper, and other forms of media, why do we need this "second internet?" Legal issues aside, don't we already have the totality of human knowledge available to us for training?
The totality of human knowledge is a rounding error to what’s needed for AGI