sun_harmonics — LessWrong

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Francois Chollet:

"In retrospect I think Keras would have been better off as an independent multi-backend framework -- but that would have required me quitting Google back then. Making Keras multi-backend again in 2023 has been one of my favorite projects to work on, both from the engineering & architecture side of things but also because the product is truly great."

"Fun to think that nearly all of the recommendations I consume come from Keras models: my YouTube homepage, Netflix, Spotify."

https://keras.io

https://blog.keras.io/author/francois-chollet.html

https://medium.com/@francois.chollet/notes-to-myself-on-software-engineering-c890f16f4e4d

Here's some general career advice that I think is good from a few well-known people. Do you have any writers or resources to share with others? Please leave a comment!

Harvy Mudd Commencement: Terence Tao https://www.youtube.com/watch?v=jE-vY5-3zhU

What "Follow Your Dreams" Misses Harvy Mudd Commencement: Grant Sanderson https://youtu.be/W3I3kAg2J7w?si=MUHjpTbQPcEStQNn

How to do what you love - Paul Graham https://paulgraham.com/love.html

"the most successful people will all like what they do" "The test of whether people love what they do is whether they'd do it even if they weren't paid for it — even if they had to work at another job to make a living"

When to do what you love - Paul Graham https://paulgraham.com/when.html

Terence Tao - Enjoy Your Work... (read more)

Thank you for sharing this with me! I agree that these criticisms are more about AI analyses of biological and neuroscientific data like some by MedARC, as antichain on reddit and Amodei in the quote above describe them. Edit: to be clear I didn't originally say what your comment said I did.

In Machines of Loving Grace, Dario Amodei writes, “...[biological] data is often lacking—not so much in quantity, but quality: there is always a dearth of clear, unambiguous data that isolates a biological effect of interest from the other 10,000 confounding things that are going on, or that intervenes causally in a given process, or that directly measures some effect (as opposed to inferring its consequences in some indirect or noisy way)... Given all this, many biologists have long been skeptical of the value of AI and “big data” more generally in biology. … there’s still a perception that AI is (and will continue to be) useful in only a limited set of... (read more)