Gaia-1 a 9B parameter generative world model for autonomous driving

...Yeah, that is more like it.

Oh but it can’t, but with a sufficiently complex vector space it can seem like it can. What seems like extrapolation is an interpolation in the semantic vector space, particularly in the transformer / attention model. This is a key difference between human intelligence and current AI, it’s not able to “create” and see beyond what it’s been trained on. Any approximation of that is simply indicative of a very complete training set, and it is sufficiently powerful enough to fool people with its expectation based inference - but when you dig into the details of cutting edge stuff you’re an expert in and ask it conceptual questions that extend beyond the semantic corpus embedded in its vector space, it will hallucinate, or if well fine tuned admit lack of knowledge, because the best it can do is interpolate within its own semantic vector space.

But listen, I’m a big buyer of generative AI, what it does is incredible. But it’s useful to not ascribe more power to a tool than the math allows.

And there are very few machine learning algorithms that do extrapolation at all with any precision. Generally they project an expectation,often of some complex highly dimensional non linear system, which is amazing, but when they are confronted with a novel input pattern they are thrown off. The issue is they’re at their core probabilistic systems, and if the data experiences a regime change that’s unexpected the model will misbehave and output garbage.

It can to a point, but it can’t to the extent that humans can with sufficiently complex problems.

Deep learning models like this can theoretically approximate pretty much any problem that can be expressed as a function.

It’s entirely possible that there just doesn’t exist a function from visual data (maybe even including LIDAR and RADAR etc) to correct driver decisions.

Humans can also intuit the behavior of other humans to an extent, even while driving (knowing that someone who is driving erratically is probably fucked up and will be dangerous to stay near). Kind of like a really shitty gossip protocol.

Your arguments don't really fit, what was previously said.

There was nothing said against driving AI in general, just that 4700h of videos seems low.

I also get that humans are pretty bad drivers, but isn't that exactly why we shouldn't use them as the baseline for AIs to compare to?

We are now at a point where we can set high standards for AI, so we get a best possible result, because while it isn't feasible to have everyone learn driving over a couple of years, a good AI has to be trained once and can be used by many, so we have the time.

And sure, it can be updated, but should we really trust companies to keep innovating once they are already allowed to have the AI in use? The incentive to do so is far bigger if they have to do so before they got any money out of it.

Interestingly there’s another thread I’m in about generative AI where someone asserts “this goes for humans too” in a sort of similar vein.

However it’s not the case. Humans have the ability to extrapolate from their training data and synthesize new thought and behavior from situations not seen before that’s fundamentally insightful and adaptive. Generative AI and all machine learning I’m aware of are fundamentally expectation driven probabilistic models that synthesize highly dimensional non linear functions from samples of those functions, which means they can’t adapt to new situations dynamically and extrapolate their experiences into new experiences and make decisions that are novel and intuitive given a new regime.

When these models encounter new situations they’ve never experienced or new regimes they interpolate in their learning space to a most likely behavior, but the learning space has nothing similar in it, so the behavior can become seemingly random and highly maladapted.

A classical example of this is obviously LLM hallucinations at the edge of their knowledge which an expert in a field can induce by asking questions beyond the horizon of the field. While humans might not have answers, they can pose interesting theories, while LLMs really can’t - if they appear to it’s simply because their training set is so massive they can interpolate into babbling that sounds good. You could assert humans do this to, and it’s true they do at times, but they also don’t at other times and have novel insights beyond their experience. The fact they can do this sometimes and AI mathematically due to its internal structure can’t at any time is the difference.

Another example is Go playing AI. They can do really well against expert players until someone plays a nonsense series of plays that are random and the AI begin to play worse than amateurs. You can do this with LLMs too, if you give them enough random nonsense or repeated strings, they just leap into some random spot in their vector space and rant about weirdness. Even GPT4 does this.

The answer isn’t to outlaw driving or to stop pursuing AI driving assistants. It’s to build models with an enormous well labeled corpus that covers almost every possible situation, but also build in fail safes that make it easy for a human to be called to attention and take control when things are confusing the AI.

> how many hours does a pilot need on a 'type' to fly unsupervised?

Excellent question. And the answer is between 45 hours and a few hundred million years. Hear me out.

45 hours is because that is the minimum amount of flying you need to get a private pilot licence: https://www.takeflightaviation.co.uk/ppl-guide.html

(Ignoring here ultra lights and paragliders where you sometimes don't need a licence in some jurisdictions.)

So that is the straightforward answer maybe you are looking for.

Then we might say that all pilots are required to be at least 17 years old to obtain a private pilot licence. So that is 148920 hours of pre-training in preceiving objects and movement, and coordinating one's actions with perception.

Then one might also say that one requires to be a human, and that comes with hundreds of millions of years of pre-training where all of our ancestors were evolutionary selected to be good at perceiving and moving. (At least good enough to survive until they could propagate their genes.)

Now this answer might come of as flippant, and maybe it is. What I'm trying to say is that it is hard to compare "training hours" directly between computers and humans. And it is hard because of these two things which humans have "pretraining by lived experience" and "pretraining by evolution".