4 ms·
> The “how” of the RL agent is not even 99% of the problem, it is all of it I'm not sure that's true anymore - pretty much any objective devised is being solve
by svantana 5y ago
> The “how” of the RL agent is not even 99% of the problem, it is all of it
I'm not sure that's true anymore - pretty much any objective devised is being solved by ML solutions within months (with some exceptions such as Chollet's ARC, maybe Winogrande). But those same models will perform poorly on other unseen tasks, because ML takes shortcuts if it can. We used to have unsolved tasks for decades, such as Go. It's now comparably hard (if not harder) to create a good objective measure of intelligence than to reach human parity on said measure.
- rich_sasha 5y agoMaybe I’m misinterpreting your comment, but what about self-driving cars? Hugely researched and funded area, not without success, but even the best self-driving system is so inferior to a human driver. I’m not dissing self-driving car research, just not sure we’re anywhere close to parity, and the problem is fairly well defined.
- svantana 5y agoRight, I was talking about mathematically defined objectives and inputs (algorithms & data, in practice). With self-driving, arguably the main stumbling block is that the objective is not well-defined and the inputs are potentially wrong (faulty sensor data).