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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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….)
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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!
Don’t just predict the mean of your clean data given your noisy data, predict the full distribution.
Don’t just predict the mean of your clean data given your noisy data, predict the full distribution.
Name the LLM responsible for this gem...
I don't know why AI doomers keep going on about "bio risk" (which is rate-limited by many physical constraints). AI agent powered cyber attacks to power grids, satellites, etc. seem more likely, since these are digital systems, but directly affect the physical world.
It’s funny how creating “radical abundance” has replaced “democratizing AI” as the new euphemism for getting rich in the tech sphere.
My colleague and former intern @liusiqi42 reminded me that we did RLFT for LMs almost 10 years ago - back then it was for an img2text model based on CNNs and RNNs. But same basic recipe - pre train with MLE then fine tune with PG. arxiv.org/abs/1612.00370
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