Kevin Patrick Murphy @sirbayes
Research Scientist at Google DeepMind. Interested in Bayesian Machine Learning. cs.ubc.ca/~murphyk/ Joined October 2016-
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Interesting long article on Song Chun Zhu. Who knew that Mumford persuaded him to accept deep learning?! theguardian.com/news/ng-intera…
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This interview is worth watching, even if you don't like Tucker Carlson...
This interview is worth watching, even if you don't like Tucker Carlson...
This literally made me LOL!
I like that they are upfront about the many risks here , in addition to obvious benefits. Safety, privacy and reliability - on top of raw performance - sells well. It’s the Apple model for LLMs (ironically not done by Apple….)
I like that they are upfront about the many risks here , in addition to obvious benefits. Safety, privacy and reliability - on top of raw performance - sells well. It’s the Apple model for LLMs (ironically not done by Apple….)
Cool open source leaderboard from my colleagues at Kaggle. Like LMarena but for agent v agent on various games (currently only chess, will release more soon).
Cool open source leaderboard from my colleagues at Kaggle. Like LMarena but for agent v agent on various games (currently only chess, will release more soon).
Finally, a good modern book on causality for ML: causalai-book.net by @eliasbareinboim. This looks like a worthy successor to the ground breaking book by @yudapearl which I read in grad school. (h/t @JoshuaSafyan for the ref).
The rise of tool-using LLMs is good news if you like to develop fast and reliable algorithms: the LLM converts raw text x into structured form x', calls your method f to get y'=f(x'), then converts it back to text y. We need more tools f!
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