[candiodari]

But ... all this goes for humans too! Is the argument that we should just outlaw driving alltogether, all possible forms?

One famous example of that is how to react correctly when the car starts to slip due to speed, braking, or driving on water, mud, sand, snow or ice. I think everyone knows people's reflexes are to floor the brakes, and start wildly turning the steering wheel, which only results in total loss of control over the vehicle. Is anyone demanding drivers learn to correctly handle cars or other vehicles under these circumnstances? There are only very minimal efforts, because it is completely impractical to teach many humans better driving practices. So we just accept the flaws ... and the constant stream of victims this generates.

Reality: Yes, a driving AI is not ready for all possible situations. It just isn't. It will never be. Is that a problem?

Also reality: Humans drive drunk. Humans drive while under the influence of drugs. Humans drive trucks near kids when they're so tired they can't keep their head lifted up. And roads are full of dead cats, squirrels, mice, ...

Also also reality: AI driving software can, after an accident, be taught to handle the situation that caused the accident, and the result of this learning process can then be uploaded to all instances of this software. Humans will keep making the same mistakes, with the same consequences, over and over and over again. Perhaps there is very slow improvement (mostly by modifying roads), but it takes decades.

Practical view: I have driven around in Mountain View next to self-driving cars. One thing's for sure: self-driving cars behave much better than human drivers. Including me. It's very irritating how good they behave on the road. If the roads have many self-driving cars, I'm pretty damn sure it'll result in much fewer accidents and lower transit times. Never mind that self-driving cars of course solve the parking problem. I don't get why people hate them.

And I hate this goalpost moving where AIs are compared to multiple top-performing humans, that you see everywhere. Of course, there are now cases where AIs have actually beaten groups of top-performing humans (translation, chess, Go, robot control, ...)

[moe_sc]

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.

[fnordpiglet]

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.