They are not completely independent. It's a good assumption though. If a model encounters something out of distribution then all five of the generations will fail. If the model knows and went in a wrong direction (due to lack of reliability), within five generations, it can be corrected. You need evals, runtime verifiers as basic harness for AI systems.
If that is the same as the macbook air's of that year, than good luck to ya. The chipsets of that year are a horrible mess. Even in macos is it was finicky with a 1+ year bug that wifi would not come up automatically if your macbook went to sleep with bluetooth on.
Any suggestions for how I can get to anywhere close to Lina's skills? It's just mad skills..
I don't believe simply putting in huge amount of time in front of the machine is adequate. Neither is simply being smart.
Is it just a combination of being smart, sinking in a lot of time, interests etc?
Any suggestions? I'll start with something that might be a good beginner friendly way to get familiar with some of the concepts: 'The Soul of a New Machine' by Tracy Kidder.
I listened to the episode with Yann. Compared to other talks (e.g. the previous one with Brian Keating) it was a bit dull and uninteresting. The answers were not that insightful.
> One should give credit to Joel for correctly identifying the issue: great thinkers excel in abstract thought. Many of the comments here, however, seem to misunderstand his point about the propensity of creative minds to continue the refinement process and "not knowing when to stop". One (codeulike) apparently thinks simply being "bored" is what drives abstract thinkers. Let me assure you that is not the case, nor is it an inability to code. Many architecture astronauts started out as wiz coders.
I think you are just reading something that's not there.
> My advice to serious young software engineers is to not accept ''m and imprecise thinking as acceptable standards for their chosen vocation.
Hmm? What straw man are you attacking?
