I didn't read the article because it's paywalled and I am on a phone with no easy way to get around it.
That said, those three examples (LLMs, driverless cars, and humanoid robots) are extremely different problems.
LLMs are mostly language calculators, which are extremely useful within the scope of their capabilities and when applied to a relevant problem. They are basically "solved".
Driverless cars are also close to being technically solved when used in contexts they work well in (not snow). On pre-mapped roads, with established rules and road markings that are visible.
Humanoid robots on the other hand are far from solved and I doubt will be anytime soon. This is partially because of limited training data and partially because we underestimate how much distributed intelligence there is in our bodies. My guess is humanoid robotics that come anywhere close to human level dexterity are 10-20 years away.
That said, those three examples (LLMs, driverless cars, and humanoid robots) are extremely different problems.
LLMs are mostly language calculators, which are extremely useful within the scope of their capabilities and when applied to a relevant problem. They are basically "solved".
Driverless cars are also close to being technically solved when used in contexts they work well in (not snow). On pre-mapped roads, with established rules and road markings that are visible.
Humanoid robots on the other hand are far from solved and I doubt will be anytime soon. This is partially because of limited training data and partially because we underestimate how much distributed intelligence there is in our bodies. My guess is humanoid robotics that come anywhere close to human level dexterity are 10-20 years away.