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The Bitter Lesson Is Misunderstood

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Re: The Bitter Lesson Is Misunderstood

#71
post #24

I 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 goal/theory behind the LLM investment explosion is that we can get to AGI by feeding them all the data. And to be clear, by AGI I don't mean "superhuman singularity", just "intelligent enough to replace most humans" (and, by extension, hoover up all the money we're spending on their salaries today).

But if we've already fed them all the data, and we don't have AGI (which we manifestly don't), then there's no way to get to AGI with LLMs and the tech/VC industry is about to have a massive, massive problem justifying all this investment.

Re: The Bitter Lesson Is Misunderstood

#72
post #66
post #36

Earlier quoted context omitted.

The point is that current methods are unable to get more than the current state-of-the-art models' degree of intelligence out of training on the totality of human knowledge. Previously, the amount of compute needed to process that much data was a limit, but not anymore. So now, in order to progress further, we either have to improve the methods, or synthetically generate more training data, or both.

What does synthetic training data actually mean? Just saying the same things in different ways? It seems like we're training in a way that's just not sustainable.

One example: when we want to increase performance on a task which can be automatically verified, we can often generate synthetic training data by having the current, imperfect models attempt the task lots of times, then pick out the first attempt that works. For instance, given a programming problem, we might write a program skeleton and unit tests for the expected behavior. GPT-5 might take 100 attempts to produce a working program; the hope is that GPT-6 would train on the working attempt and therefore take much less attempts to solve similar problems.

As you suggest, this costs lots of time and compute. But it's produced breakthroughs in the past (see AlphaGo Zero self-play) and is now supposedly a standard part of model post-training at the big labs.

Re: The Bitter Lesson Is Misunderstood

#73
post #38

Earlier quoted context omitted.

Interesting! Could you give an example with a bit more specific detail here? I take it there's some kind of work output, like a report, in a semi-structured format, and the goal is to automate creation of these. And you would provide a UX that lets them explain what they want the system to create?

Yes, essentially. There are multiple long-form text inputs, one set is provided by User A, and another set by User B. User A inputs act as a prompt for User B, and then User A analyzes User B's input according to the original User A inputs, producing an output. My system takes User A and B inputs and produces the output with more accuracy and precision than User As do, but a wide margin. Instead of trying to train a…

Thanks a lot for the detailed reply! Makes a lot of sense now. I'm working on similar problems, and have dabbled with this kind of approach.

Re: The Bitter Lesson Is Misunderstood

#74
post #49

Hey 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…

> So it makes me wonder, is embodiment (advanced robotics) 1000x harder than LLMs from an information processing perspective?

Essentially, yes, but I would go further in saying that embodiment is harder than intelligence in and of itself.

I would argue that intelligence is a very simple and primitive mechanism compared to the evolved animal body, and the effectiveness of our own intelligence is circumstantial. We manage to dominate the world mainly by using brute force to simplify our environment and then maintaining and building systems on top of that simplified environment. If we didn't have the proper tools to selectively ablate our environment's complexity, the combinatorial explosion of factors would be too much to model and our intelligence would be of limited usefulness.

And that's what we see with LLMs: I think they model relatively faithfully what, say, separates humans from chimps, but it lacks the animal library of innate world understanding which is supposed to ground intellect and stop it from hallucinating nonsense. It's trained on human language, which is basically the shadows in Plato's cave. It's very good at tasks that operate in that shadow world, like writing emails, or programming, or writing trite stories, but most of our understanding of the world isn't encoded in language, except very very implicitly, which is not enough.

What trips us up here is that we find language-related tasks difficult, but that's likely because the ability evolved recently, not because they are intrinsically difficult (likewise, we find mental arithmetic difficult, but it not intrinsically so). As it turns out, language is simple. Programming is simple. I expect that logic and reasoning are also simple. The evolved animal primitives that actually interface with the real world, on the other hand, appear to be much more complicated (but time will tell).

Re: The Bitter Lesson Is Misunderstood

#75
post #49

Hey 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…

The big robot AI issue is: no data!

There is a lot of high quality text from diverse domains, there's a lot of audio or images or videos around. The largest robotics datasets are absolutely pathetic in size compared to that. We didn't collect or stockpile the right data in advance. Embodiment may be hard by itself, but doing embodiment in this data-barren wasteland is living hell.

So you throw everything but the kitchen sink at the problem. You pre-train on non-robotics data to squeeze transfer learning for all its worth, you run hard sims, a hundred flavors of data augmentation, you get hardware and set up actual warehouses with test benches where robots try their hand at specific tasks to collect more data.

And all of that combined only gets you to "meh" real world performance - slow, flaky, fairly brittle, and on relatively narrow tasks. Often good enough for an impressive demo, but not good enough to replace human workers yet.

There's a reason why a lot of those bleeding edge AI powered robots are designed for and ship with either teleoperation capabilities, or demonstration-replay capabilities. Companies that are doing this hope to start pushing units first, and then use human operators to start building up some of the "real world" datasets they need to actually train those robots to be more capable of autonomous operation.

Having to deal with Capital H Hardware is the big non-AI issue. You can push ChatGPT to 100 million devices, as long as you have a product people want to use for the price of "free", and the GPUs to deal with inference demand. You can't materialize 100 million actual physical robot bodies out of nowhere for free, GPUs or no GPUs. Scaling up is hard and expensive.

Re: The Bitter Lesson Is Misunderstood

#76
post #49

Hey 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.

We did.

Like, to the point that the AI that radically impacted blue collar work isn't even part of what is considered “AI” any more.

Re: The Bitter Lesson Is Misunderstood

#77

Earlier 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…

> 10+ years ago I expected we would get AI that would impact blue collar work long before AI that impacted white collar work. We did. Like, to the point that the AI that radically impacted blue collar work isn't even part of what is considered “AI” any more.

I think it's Benedict Evans who frequently posts about 'blue collar' AI work not looking like humanoid robots but instead Amazon fulfillment centers keeping track of millions of individual items or tomato picking robots with MV cameras only keeping the ripe ones as it picks at absurd rates.

There are endless corners of the physical world right now where it's not worth automating a task if you need to assign an engineer and develop a software competency as a manufacturing or retail company, but would absolutely be worth it if you had a generalizable model that you could point-and-shoot at them.

Re: The Bitter Lesson Is Misunderstood

#78

Earlier 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…

> So it makes me wonder, is embodiment (advanced robotics) 1000x harder than LLMs from an information processing perspective? Essentially, yes, but I would go further in saying that embodiment is harder than intelligence in and of itself. I would argue that intelligence is a very simple and primitive mechanism compared to the evolved animal body, and the effectiveness of our own intelligence is circumstantial. We man…

It took about the same amount of time to evolve human-level intelligence as human-level mobility. Pretty much no other animal walks on two legs...

Re: The Bitter Lesson Is Misunderstood

#79
post #49

Hey 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…

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 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. Even if LLMs end up being strictly more capable than every human on every subject, I'm sure we'll find some new excuse why they don't have minds or aren't really intelligent.

Re: The Bitter Lesson Is Misunderstood

#80
It's a boot-strapping problem. LLMs have shown that we can reproduce data that's already in the form we want, and use that data to solve novel problems. There is no shortage of data, it's just data that's in a form you want is hard to come by. You want to create a model that generates steps for a robot with a particular shape? First you have to create a robot with that shape that can walk, then create a million of them and record them walking all over the place. Now you have something that's probably going to be too slow to run. Not fesible in the real world, the closest we have today is something like driverless car, (which is already a solved problem they are called trains)

This is why I think China will ultimately win the AI race, they will be able to put tens of millions of people to a specific task until there is enough data generated to replace humans on that task in 99.99% of cases, and they have the manufacturing capability to make the millions of IO devices needed for this.

Yes, humanoid robots are a good idea, but only if you can train them with walking data from real people, I think it will probably translate well enough to most humanoid robots, but ideally you are designing the physical robot from the ground up to model human movement as close as possible. You have to accept that if we go the LM route for AI that the optimal hardware behaves like human wetware. The neuromorphic computing people get it, robotics people should too.

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