I'm not entirely sure "general" intelligence is a meaningful concept but I take judgement to mean something like "the ability to make decisions under uncertainty without direct feedback." People seem to be able to do this using a lot of context and tacit knowledge. It's not really clear how you hill-climb to that capability through RL.
> the history of artificial intelligence research is littered with examples of humans confidently declaring that task X requires general intelligence, then getting humiliated by a neural network doing task X better than humans a few years later
True, but the history of AI research is also littered with AI researchers confidently predicting X job will be replaced by AI and being completely wrong because they don't actually understand what those jobs actually are. See Geoff Hinton predicting that Radiologists would be obsolete by ~2020, or predictions that truck drivers would all be replaced by self-driving tech.
I think all this debate about whether an LLM can write (or download) a chess engine is sort of missing the point. For basically any economically valuable work there is no equivalent of a chess engine for it. If there were we wouldn't need humans or AI to begin with.
It is pretty common to be prompted to tip by the PoS system in any place you can buy made-to-order food. This includes stores that are normal retail stores but also have some sort of made-to-order food. Like a fancy wine shop or gourmet food store that also sells sandwiches. But this isn't really a new phenomenon either. It's just that there used to be a tip jar sitting by the register, but now the PoS system prompts you because nobody carries cash anymore.
> You get better at modeling the trajectory because the neurons physically move closer together as you learn
I think "modeling the trajectory" is not necessarily what we are doing either. It's more likely we are using much simpler heuristics. If you are trying to catch a ball flying through the air, you can just look at the ball and modulate your running speed to keep your eyes at a fixed angle until you catch the ball. It's much more analogous to a PID controller than a model of the trajectory.
The brain doesn't process vision anywhere near fast enough for some of the stuff you see in fast sporting actions if it were purely reactive - batting in baseball/cricket, returning a table-tennis shot, etc. In some cases the time available for movement is extremely restricted and yet people can respond accurately. We may not know exactly how the brain implements it, but it clearly uses prediction to estimate where the object is going to be - i.e. it is, in some sense, modelling its trajectory.
> LLM-generated code has a tendency to “over-justify”
This is the thing that drives my nuts about LLM-generated code. I will see PRs that fix a bug and the entire bug fix is re-explained in 10 different places in a code comment.
> 55% chance for a $10 payout; 45% chance to lose $10. You would keep on rolling that die, right?
The answer is that it depends? If I have $1B then yeah I would roll the dice and keep rolling as long as they let me. If I have $100? Maybe not because you can go very easily go broke even if each bet is positive EV.
I'm in my early forties and both my parents worked (as did all my neighborhood friend's parents) and we still spent a lot of time wandering around. Honestly I think people are really overthinking this. We spent a lot of time wandering around outside because we were bored. Now kids have an endless well of entertainment to choose from so staying at home is a much more appealing option. It's always tempting to romanticize your childhood but if I'm being honest, most of that time wandering around outside I was bored out of my skull. I was just marginally less bored than I would have been sitting at home.
> The best endurance runners run in such a way that their feet land on the front of their foot during running
This is not really true and the whole fore foot vs heal striker thing is a bit of a red herring. There are elite distance runners that are forefoot, mid-foot (probably the majority) and heal strikers. The main thing is that wherever on their foot hits first, the foot itself is under their center of gravity and not out in front of them.
> the history of artificial intelligence research is littered with examples of humans confidently declaring that task X requires general intelligence, then getting humiliated by a neural network doing task X better than humans a few years later
True, but the history of AI research is also littered with AI researchers confidently predicting X job will be replaced by AI and being completely wrong because they don't actually understand what those jobs actually are. See Geoff Hinton predicting that Radiologists would be obsolete by ~2020, or predictions that truck drivers would all be replaced by self-driving tech.
reply