My old phone went dead last March right before the first lockdowns in my country, and between the difficulty of getting it repaired and not having to actually leave the house, I never got around to repairing it. My family did eventually buy me a new phone themselves last October because of the trouble of reaching me when they went outside, but for those nine months, my main takeaway was that I read a lot more than I had in years. I can't speak much to relative productivity because while it was definitely a more productive time than before March, I was also more productive after that, so it could be independent.
I'm done with the frontend, but the guy working on it with me got caught up with some stuff, so the backend still needs some tweaks. He's free this week, so it's possible it'll be done in time. I'll post about it if it is.
Not an article, but I have a link to an interview where Ian tells that story (timestamp around 3:40 if you only want that part, 2:44 if you want it as part of the complete story).
I agree with your points on practical programming in the course, but I also think that's not even Andrew Ng's core intent with his courses. As Teja Prabhu mentioned in his comment, learning through taking on projects of your own is a method that I can't think of many good alternatives to, as far as practical usage goes. But getting there requires that you cast a wide net breadth-wise to at least know what's possible and what you can use, in machine learning. You can, and probably will, learn the math depth-wise as you try working on your own projects, but to get there? I think he throws just the right amount of technical math at you. Trying to fit all the math involved in all the different ML methods he covers, from the ground up, is probably infeasible as anything but a year-long degree, and you don't need that to start learning it yourself depth-wise.
That, and a working understanding of ML theory are what I think his primary intent is, with his courses. I did his Deep Learning specialization a couple months ago, and while the programming is slightly more hands-on there, it's still massively aided by hints and the like. But he even says in one of those videos that the point of doing the programming exercises is only to further your understanding of theory, not as practice for building your own projects - writing code from scratch for a predefined goal in a course wouldn't be a great way of motivating people to learn that stuff. Incidentally, this is why I think MOOCs for learning programming actually are pointless.
Are Google, Facebook, and Deepmind currently working on GPT-like transformers? I would've thought that GPT-2 would show enough potential that they'd be working on better models of that class, but it's been two and a half years and isn't GPT-3 the only improvement there? (Not a rhetorical question, I wasn't reading about new advances back then.) If yes, that makes me think several other multimodal transformers similar in size to GPT-3 would be further away than 2022, probably.
I also think it's more audience identification. Actually optimizing for that would give clearer game systems, though they'd probably be much more controversial. Like having sport categories consisting of "people that would inspire marginalized classes to see performing at the highest level". But you know, phrased in a way that won't probably ruin the whole thing.
I think it could also be more about dominance specifically. I can't tell if this is actually true, but it feels like chess would be less popular/exciting if there weren't one player clearly dominant over the others. There I don't think it comes down to identifying, because having more equal players at the top would mean broader classes of people identifying with them.
You're right. I remember being surprised by what I saw on the wiki back when I wrote this, but looking at the edit history of that page, I can't find anything that would have made me write "conflicting". Thank you for bringing that up, I've edited the post. I apologize for not noticing that earlier.
It could be that the Tool Use in the graph is the "Tool Use Gap" task instead of the "Tool Use Climb" task. But they don't specify anywhere I could find easily.
From the description of that figure in the paper, it says "three points in training" of a generation 5 agent, so probably the performance of that agent on the task at different learning steps?
Edit: To clarify, I think it's 0-shot learning on the six hand-authored tasks in the figure, but is being trained on other tasks to improve on normalized score percentiles. That figure is meant to show the correlation of this metric with improvement on the hand-authored tasks.
I agree that this would be helpful if Redwood shares their curriculum. If someone is willing to take up lead organizing, I'd be happy to help out as much as I can (and I suspect this would be true for a non-insignificant number of people who applied to the thing). I'd do it myself, but I expect not to have the free time to commit to that and do it right in the next few months.