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Probably around ~100B has been sent on self-driving research since 2005 -- the supermajority of that after the adoption of DL in that space (which started in 2014 and took off in 2015). The real thing is still a few years away.
I think most of it is money well spent, because we will eventually reap the rewards, and the rewards are significant. But it goes to show just how hard it is to build AI systems that interface with a wide range of situations in the real world with little margin for error.
It’s about 40Bn over the last 5/6 years (Crunchbase AV hub) but you could add a bunch of peripheral projects to this.
Money that would have been better spent on public transit and high speed rail.
Even if it's $100bn that only buys one California high speed rail. If that project ever even gets off the ground.
Hard to know what China is spending, Thiel is right AI is military tech, but wrong to think only that, huge upside for humanity if done right, big if
I believe it's much less than $100bn.
You cant have good object / scenario identification until you have good AGI. There is no shortcut. Only the data equivalent of a life of experience warns you that a cyclist might fall in your path because of a banana peel ahead on the road.
P(cyclist falls) almost exactly equals P(cyclist falls | banana peel in road that human driving car notices). Both humans and AI are best served by assigning a high probability of "will do random shit" to cyclists and leaving wide safety margins, regardless of the reasons.
Any new technology is nontransparent fractal. Finish the first visible sequence of tasks, uncover the next sequence. Would be great to know the way to assess fractal dimension of any new technology at the planning stage. Alas, its a gamble against rigged roulette
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