10× better by what metric? Progress on LLMs has been amazing but already appears to be slowing down.
Human brains seem like an existence proof for what’s possible, but it would be surprising if humans also represent the farthest physical limits of what’s technologically possible without the constraints of biology (hip size, energy budget etc).
We’ve been building actuators for 100s of years and we still haven’t got anything comparable to a muscle. And even if you build a better hydraulic ram or brushless motor driven linear actuator you will still never achieve the same kind of behaviour, because the technologies are fundamentally different.
I don’t know where the ceiling of LLM performance will be, but as the building blocks are fundamentally different to those of biological computers, it seems unlikely that the limits will be in any way linked to those of the human brain. In much the same way the best hydraulic ram has completely different qualities to a human arm. In some dimensions it’s many orders of magnitudes better, but in others it’s much much worse.
It’s not just that ‘we don’t know how to build them’, it’s that the actuators aren’t a standalone part - and we don’t know how to build (or maintain/run in industrial enviroments!) the ‘other stuff’ economically either.
For text generation, it seems like the fast progress was mainly due to feeding the models exponentially more data and exponentially more compute power. But we know that the growth in data is over. The growth in compute has a shifted from a steep curve (just buy more chips) to a slow curve (have to make exponentially more factories if we want exponentially more chips)
Im sure we will have big improvements in efficiency. Im sure nearly everyone will use good LLMs to support them in their work, and they may even be able to do all they need to do on-device. But that doesn’t make the models significantly smarter.
The thing about the latter 1/3rd of a sigmoid curve is, you're still making good progress, it's just not easy any more. The returns have begun to diminish, and I do think you could argue that's already happening for LLMs.
There is a lag in how humans are reacting to AI which is probably a reflexive aspect of human nature. There are so many strategies being employed to minimize progress in a technology which 3 years ago did not exist and now represents a frontier of countless individual disciplines.
If you took a Tesla or a Waymo and dropped into into a tier 2 city in India, it will stop moving.
Driving data is cultural data, not data about pure physics.
You will never get to full self driving, even with more processing power, because the underlying assumptions are incorrect. Doing more of the same thing, will not achieve the stated goal of full self driving.
You would need to have something like networked driving, or government supported networks of driving information, to deal with the cultural factor.
Same with GenAI - the tooling factor will not magically solve the people, process, power and economic factors.
Absolutely driving is cultural (all things people do are cultural) but given 10’s of millions of miles driven by Waymo, clearly it has managed the cultural factor in the places they have been deployed. Modern autonomous driving is about how people drive far more than the rules of the road, even on the highly regulated streets of western countries. Absolutely the constraints of driving in Chennai are different, but what is fundamentally different? What leads to an impossible leap in processing power to operate there?
I definitely recall reading some thinkpieces along the lines of "In the year 203X, there will be no more human drivers in America!" which was and still is clearly absurd. Just about any stupidly high goalpost you can think of has been uttered by someone in the world early on.
Anyway, I'd be interested in a breakdown on reliability figures in urban vs. suburban vs. rural environments, if there is such a thing, and not just the shallow take of "everything outside cities is trivial!" I sometimes see. Waymo is very heavily skewed toward (a short list of) cities, so I'd question whether that's just a matter of policy, or whether there are distinct challenges outside of them. Self-driving cars that only work in cities would be useful to people living there, but they wouldn't displace the majority of human driving-miles like some want them to.
As others will attest, when adherence to driving rules is spotty, behavior is highly variable and unpredictable. You need to have a degree of straight up agression, if you want to be able to handle an auto driver who is cheating the laws of physics.
Another example of something thats obvious based on crimes in India; people can and will come up to your car during a traffic jam, tap your chassis to make it sound like there was an impact, and then snatch your phone from the dashboard when you roll your window down to find out what happened.
This is simply to illustrate and contrast how pared down technical intuitions of "driving" are, when it comes to self driving discussions.
This is why I think level 5 is simply not happening, unless we redefine what self driving is, or the approach to achieving it. I feel theres more to be had from a centralized traffic orchestration network that supplements autonomous traffic, rather than trying to solve it onboard the vehicle.
Do you really think Waymos in SF operate solely on physics? There are volumes of data on driver behavior, when to pass, change lanes, react to aggressive drivers, etc.
And the point that I am making, is that this view was never baked into the original vision of self driving, resulting in predictions of a velocity that was simply impossible.
Physical reality does not have vibes, and is more amenable to prediction, than human behavior. Or Cow behavior, or wildlife if I were to include some other places.
This is a semantic discussion, because it is about what people mean when they talk about self driving.
Just ditching the meaning is unfair, because goddamit, the self driving dream was awesome. I am hoping to be proved wrong, but not because we moved our definition.
Carve a separate category out, which articulates the updated assumptions. Redefining it is a cop out and dare I say it, unbecoming of the original ambition.
Networked Autonomous vehicles?
Or actual intelligence. That observes its surroundings and learns what's going on. That can solve generic problems. Which is the definition of intelligence. One of the obvious proofs that what everybody is calling "AI" is fundamentally not intelligent, so it's a blatant misnomer.
Lol. If you dropped the average westerner into Chennai, they would either: a) stop moving b) kill someone
Decades of machine learning research would like to have a word.