;
I was lane changing while a motorcyclist very aggressively lane changed and accelerated from behind the car behind me. Autopilot aborted the lane change and was right.
I’ve seen similar events play out in clip telemetry many times, but a few minutes ago is the first time Autopilot prevented an almost certain collision for me personally. Experiencing it in real life is something else.
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real-world data distribution is ~N(0,1)
good dataset is ~U(-2,2)
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Potentially unpopular opinion but I am so bored of fighting over definitions, chinese rooms and qualias. We can automate aspects of human intelligence and create real economic value. Let's measure, characterize and focus on that.
LLMs are going to 100% every text based IQ test you can devise and people will still be saying they aren't generally intelligent, but I don't know which concept that invalidates, IQ or general intelligence.
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This isn't to say that the "better models" will be "clever models", models that hardcode significant amounts of priors. I'd actually expect them to be simple models that scale. The key though, is the part that should scale is generalization ability as a function of experience.
"Task specific skill", when the task can be defined with arbitrary precision, can be achieved by scaling up memorization capability and training data, independently of the generalization ability of the model.
The goal should be better models, not merely bigger models.
For me, this points to the need to leverage better benchmarks of AI progress -- benchmarks that measure generalization efficiency (intelligence) rather than memorization capability. If you don't know where we are and where we're going, you can say anything.
I'm seeing people conflate "the bitter lesson" (which is a very good point: simple methods that scale deliver better results than clever methods that don't scale) and deep learning maximalism. Don't distort sensible ideas to launder hubristic nonsense!