Doesn't this then turn into a problem of sample quantity? You would need to shift into a quality mindset because with a robot you can't perform a billion iterations, you're locked into much more complex world with unavoidably real time interactions. Failure is suddenly very costly.

With a million robots you can perform a billion iterations. We won't need a billion iterations on every task; we will start to see generalization and task transfer just as we did for LLMs once we have LLM-scale data.

You are right that failure is costly with today's robots. We need to reduce the cost of failure. That means cheaper and more robust robots. Robots that, like a toddler, can jump off a couch and fall over and still be OK.

Tying back to the article, this is the real evolutionary advantage that humans have over AIs. Not innate language skills or anything about the brain. It's our highly optimized, perceptive, robust, reliable, self-repairing, fail-safe, and efficient bodies, allowing us to experiment and learn in the real physical world.

> robust, reliable, self-repairing, fail-safe, and efficient bodies

you must be young and healthy because I cannot imagine using any of these words to describe this continuously decaying mortal coil in which we are all trapped and doomed

I wish! Hopefully AI can help with that too, but (contra Kurzweil) I fear medicine moves too slowly and it is already too late to save our generation from aging. Hopefully our kids can reap the benefits.

AI's advantage would be that their learning can be shared

For example if Robot 0002 learns that trying to move a pan without using the handle is a bad idea, Robot 0001 would get that update (even if it came before)

But that ends up with weirdly dogmatic rules because it's not always a bad idea to move a pan without using the handle, it's just in some situations. It still takes a ton of potentially destructive iterations to be sure of something.

Yea its tricky and costly. I believe we should bet on specificity to make this more optimal.

I know the trend with AI is to keep the scope generic so it can tackle different domains and look more like us, but I believe that even if we reach that, we'll always come back to make it better for a specific skill set, because we also do that as humans. No reason for an AI driver to know how to cook.

If we narrow the domain as much as possible it will cut the number of experiments it needs to do significantly

Edit: I wonder if its even going to be useful to devote so much resources into making a machine as similar as us as possible. We don't want a plane to fly like a bird, even if we could build it.

Then we will continue to have a Temperature variable in the Action Models.

My roomba can't do the whole room without screwing up or getting stuck, it feels like we are eons away from a robot being able to do what you describe autonomously.

A few short years ago we were eons away from passing the Turing test.

What?

There has been some odd people fooled by computers since the 70s, and 10 years ago we had competitions where the best hint people claimed they had was if their interlocutors were good at math or not.

It was clear 10 years ago that the Turing test would be beat soon.

My point is really that a lot of people thought we were eons away. "It was clear" to you, but your opinion was not shared by the majority probably until at least GPT-2 at the very earliest (2019). (Note that the sibling comment to yours still doesn't believe we are close...)

Similarly, today a lot of people think we are eons away from useful general purpose robots, but it is clear to me that they are coming soon.

That we'd beat the Turing test soon was a majoritarian opinion around here.

Honestly, I wasn't even that well informed to make my own. I kept postponing looking at the chatbots until after it was beat.

I disagree. 10 years ago there were people who thought that but they were far from the majority.

Are we not still? How can you tell?

We're still eons away from passing the Turing test, we just keep lowering the bar for what the test is because some people spend too much time on the internet and want Her to be real too badly. This is a conversation I try every time a new version of some LLM comes out: https://chatgpt.com/share/eee38567-99d2-4dd2-a781-08b297e86d...

Why does that conversation fail the Turing test?

Granted there are some people on earth who can speak those 3 languages fluently, but it greatly diminishes the pool of available people. If you wanted to take the example to the absurd, you could just start listing more languages, and ChatGPT would happily spit out the answer translated to 100 languages in a few seconds. No person could do that.

ChatGPT is not trained to pass the Turing test. It is trained to be as superhuman as possible. I have no doubt that OpenAI could train a system to pass the Turing test within a year if that was their objective. In fact it seems like an anti-objective for them.

Now that's not to say that such a system would be undetectable by any possible adversarial technique. The Turing test is not unambiguously defined, but my definition would have it conducted with average well-educated people not specializing in AI and not having special knowledge of adversarial techniques for the specific machine being tested.

I’d say it goes very much against the spirit and idea behind the Turing test to fail an AI for having superhuman abilities it fails to hide.

As much as I admire Turing, I don’t think this particular idea of his was a sound one- an intelligent computer is not human, and lacks a human brain. It should be expected to be better at some things, worse at others, and overall noticeably different… even long after it exceeds human intelligence levels.

I think Turing's idea was profound and prescient honestly. The reason he put emphasis on deception and imitation in the test is because he wanted to know whether we had build a thinking machine, or just a tool. As you say, superhuman intelligent computers are not like humans and it's indeed not right to require that from them if we just want to build a knowledge repository. But if you want to know whether we've build a mind, his conception matters. To deceive, you have to have the imagination of what it's like to be you imagining what it's like to be me. You need a model of someone's cognition and your own, in real time while you interact with the world. And just architecturally that's not the case with LLM's. They don't have cognitive processes going on as they're interacting with you, they can't even in theory beat this.

It's actually scary to me just how smart Turing was because he immediately recognized, at a time when computers barely existed, that the real test isn't if a machine can play smart but if it can play dumb. Because that eliminates all the parlor tricks and requires actual reflection. Just mind blowing to anticipate that in 1950.

You are making the mistake of confusing a mind with a human mind… a vastly different type of mind should not be expected to be good at everything we are good at, even if generally far more intelligent. I think people generally make the same mistake with discounting the more intelligent animals by focusing on their inability to act human, and ignoring or not noticing that they can do things we are incapable of- like a dog navigating a home in total darkness from scent alone- turning scents drifting in the air into visual/spacial type data is a complex task.

A human mind, for example is not sufficiently generally intelligent to convincing mimic even a fish mind- we cannot generally emulate minds structured differently either. Even an actual human mind with vastly different experiences- simulated perfectly in a computer or even just a regular person from a very different culture will fail your concept of the turing test, because our mind is shaped by our experiences.

Also, I think your conception of LLM limitations is mistaken. We don’t fully understand them in theory- but I think the way to think of LLMs is that they are a world model able to simulate almost any scenario if setup properly. They are not themselves a mind, but if sufficiently good, they will reliably simulate minds. Noam Chomsky published an article arguing how LLMs were incapable even in theory of doing things GPT4, which was released shortly after his article can do- showing that either his model of what they are, or of what is required for those tasks is mistaken .

Imagine if NASA-JPL had an LLM connected to all their active spacecraft, and at the terminal you could just type, "Hey V'Ger, how are conditions on Phobos over the past Martian Year?"

But then you'd have to have a NASA department of machine psychology to ensure it isn't hallucinating or going crazy.

And then it replies: "Cold"

The human language is great, but it fails utterly on some tasks. Which is why we have all the jargon in specialized environment. I'd take any system with a reduced command interface that works well than one that takes generic commands and tries to infer what I mean (meaning it will get it wrong most of the time),especially for vocal interface.

1966 called, and they're asking to speak to DWIM...

https://en.wikipedia.org/wiki/DWIM

I think it's reasonable to argue that data acquired via a sensorimotor loop in an embodied agent will go beyond what you can learn passively from a trove of internet data, but this argument goes beyond that - the "data" in evolution is "learned" (in a fashion) not just from a single agent, but from millions of agents, even those that didn't survive to replicate (the "selection", of course, being a key part of evolution).

A neat thing about the kind of artificial robots we build now is that the process can be massively sped up compared to the plodding trial and error of natural evolution.

Exactly. We have huge advantages over evolution in some regards. All of the experience from every robot can be combined into a single agent, so even if AI is not as sample efficient as human brains it could still far surpass us. And honestly the jury is still out on sample efficiency. We haven't yet attempted to train models on the same kind of data a human child gets, and once we do we may find that we are not as far away from the brain's sample efficiency as we thought.

> All of the experience from every robot can be combined into a single agent

I'm not so sure. It's not obvious that experience combines linearly, so you'll have to somehow figure out how to make the combination work in such a way that it doesn't mess up the receiver too badly--you still want some individuality among the robot fleet right?

That's interesting to think about. I'm not familiar with the literature on this but I'm 100% sure there's some interesting work on it (and related fields such as distributed and federated learning). I guess the simplest solution would be "centralized" - periodically aggregate all the raw data from all robots, train a model with all the data, redistribute the model. In that case there wouldn't be any "individuality", but (maybe again, by analogy with evolution) one could think it'd be advantageous to have some. But even if all the models all the same, the robots might be different types and operate in different environments, which raises issues of generalizability, transferability and specialization. Either way the centralized would have some scaling problems, naturally - some way to transfer/aggregate experience (possibly "peer to peer") without resorting to training from raw data then becomes attractive, and I'm sure something people are working on. It does turn out that at least in some recent LLMs, weights appear to be sort of linear and people have been using that to merge them with fairly naive methods with good results.

It's possible that different entities experience each other's experiences differently.. that is, if you were to magically teleport your experience of reading this post into my brain it might be overpoweringly disorienting and even painful. On the other hand it could just be "a little weird". Or would I instantly have everything that differentiates my mind from yours completely overwritten? This would probably catastrophically reduce my fitness because I'd have to--or more like you'd have to--learn how to operate my body.

Additional answer: we generate more text every day.