Richard Feynman Messenger Lectures at Cornell The Character of Physical Law Part 1 The Law of Gravitation

Ignoring the moral question in the story, many people are satisfied by saying "haha funny, the cat is both dead and alive" and they just move on. Reading Pratchett for almost 30 years, it has fueled quite an interested in quantum mechanics in me, he subtly introduces QM teasers in almost every book I read. Particularly Mort. As I dropped out of highschool, I didnt have the luck to properly study physics, but in my 30s, I my existential crisis hit, as they say, a man dies twice, and the stupid half dead cat wouldn't leave me alone. And one night the omnipotent youtube algorithm recommended Susskind's lectures to me, then Feynman, Shankar, .. it just kept going. Now I watch and re-watch them but take screenshots and send them to claude when I am confused (which is still most of the time). If you are a layman like me and just want to know what is up with the cat, I encourage you to watch Sean Carroll's lectures (dont miss the Q&A videos) and it will for sure allow you to ask at least better questions. I love that cat so much. --- Sean Carroll: link Richard Feynman: ▶ this video and link Leonard Susskind: link Ramamurti Shankar: link Barton Zwiebach: link Carl Bender: link If you struggle with calculus, I found Herb Gross to be partucularly good Herb Gross: link Of course watch 3blue1brown, alphaphoenix, styropyro etc as well

It's ironic that the audio quality of most AI online courses is terrible. I'm expectirng the moment when AI can be used to improve the audio of these lectures especially some old like Feynman lectures[1] [1] ▶ this video

> Take your example about StyleGAN vs BigGan, I assume once it became clear that the latter was superior to the former that likely resulted in changes to existing architectures that then found additional improvements. This change in consensus is what enables that and is a good thing. Well, no. :) But I think the "no" is because of the uniqueness of ML rather than a "no" to your point in general. You might be right about other fields; I don't have experience there. In ML, there an enormous number of techniques. Style mixing was presented as a core feature of StyleGAN ( link ) and was enabled by default in the codebase ( link ). So there's a lot of "inertia" -- for example, when StyleGAN 2 came out, style mixing was still the default ( link ). I haven't had time to dig into StyleGAN 3, but I suspect that style mixing might still be enabled by default. It wasn't until we did a detailed, methodical analysis side-by-side with BigGAN, specifically to answer the question " Why is BigGAN so much better for diverse datasets?" that, on a whim, I turned off style mixing and was astonished to see BigGAN type quality pop out of a StyleGAN type arch. Discoveries like that usually go unnoticed, frankly because it's a lot of effort to write a paper specifically to say "Hey, if you're training StyleGAN, definitely turn off style mixing. It only seems to work well on faces." However, if such a paper were to be written, and accepted into a peer-reviewed journal, then your original point w