noanabeshima

8 ms·

1. 1.▲
   A Neural Scaling Law from the Dimension of the Data Manifold(arxiv.org)

   2 points by noanabeshima 6y ago|0 comments
2. 2.▲
   Lifetimes of Cryptographic Hash Functions(valerieaurora.org)

   7 points by noanabeshima 6y ago|1 comments
3. 3.▲
   Dissolving the Fermi Paradox(slatestarcodex.com)

   3 points by noanabeshima 6y ago|0 comments
4. 4.▲
   by noanabeshima 6y ago

   Looking for SE or finance internship or job after graduating with a Mathematics BS. Location: Bay Area Remote: Either Willing to relocate: Yes Technologies: Python, Bash, PyTorch, Reinforcement Learning, C++ Résumé&#x2
5. 5.▲
   by noanabeshima 6y ago

   The amount of compute used in the largest AI training runs has been exponentially growing: https://openai.com/blog/ai-and-compute/ The amount of compute required for Imagenet classification has been exponentially
6. 6.▲
   by noanabeshima 7y ago

   The author uses these ([1][2]) diagrams to argue that more compute has diminishing returns. But the 'diminishing returns' are on the accuracy of correctly picking the single right category for a photo out of one thousand. Photos m
7. 7.▲
   by noanabeshima 8y ago

   Not being able to know things with certainty is one thing, but I don't think your latter argument about truth being literally different in different times is what the parent is getting at. We can get really, really certain about some t
8. 8.▲
   by noanabeshima 8y ago

   https://www.gwern.net/Causality