meken
Joined 9 years ago
Joined 9 years ago
I mostly used some basic string matching to pull out documentaries and put them in a spreadsheet [3]. I sorted them by random and was going through one a day, but the results weren’t great so I stopped.
I should give it another go with a more hands-off fully vibe-coded approach.
- [1] https://youtu.be/NyIgV84fudM?is=PTwtQ9v0sxQ1eCYl
- [2] https://youtu.be/QKSPk_0N4Jc?is=YGgIRFCpFpMbddu7
- [3] https://docs.google.com/spreadsheets/d/1uoZiAe5qHGIyUTsqFPLG...
Why would AI undermine quizzes? I took an intro to Japanese class where we would have a quiz at the beginning of each class which I quite enjoyed. I can’t see how AI would change the utility of that.
Seems like having doctors emigrate from other countries would work as well.
You can accomplish the same things in vi editing mode (with ? and !!) but they’re not as interactive.
It seemed very magical/trippy/cool.
I disagree with this and I think the reason is well captured here:
> The idea of pausing or slowing AI has been floated as far back as 2023, and I think it made little sense back then... The AI models of those days were not powerful enough to act as agents in the world in any coherent way, and were not capable of significant deception, manipulation, cheating, or cyberattacks... Today, however, the picture is totally different.
Did you read the essay?
> [Embedded evaluators] is something Anthropic is unilaterally committing to (and calls on governments to require other frontier companies to match).
I find using indices and summations generally confusing and hard to read/follow/understand. To me, it's so much simpler to draw out the computational graph, derive the gradient with a single element (so I'm in agreement with you guys here?), see how that applies the same to every element the same, then scale up to the vectorized/matrix version.
> and you need to memorize a bunch of new rules to apply it
There's nothing to memorize though..?
Is there something called "matrix calculus" that's different than just drawing the computational graph and deriving the gradient in the way I described..?
Like, is this "matrix calculus"?
- https://cs231n.github.io/optimization-2/
If so, what is there to memorize here?