Shaper of loss landscapes No agent samples the same distribution twice, for it's not the same distribution and she is not the same agent https://xitter.cf/t.co/vCSKOEp6p4

Copenhagen
Joined April 2014
Why AI needs to ease up on scaling and learn how to code: deoxyribose.github.io/No-Sho… @GaryMarcus @fchollet @yudapearl
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it’s insanely creepy how our entire modern society follows Sir Francis Bacon’s explicitly plan almost exactly
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Frans Zdyb retweeted
Replying to @0xAvious @levie
will add that stuffing more data via scaling today improves outcomes within the data set (good for defined domains like coding, law etc) but increases "shortcut"-itis and lack of fundamental causal understanding @FZdyb wrote about this deoxyribose.github.io/No-Sho…
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Another day, another "ML method touted as the next big thing only works in-distribution" paper
Is Chain-of-Thought Reasoning of LLMs a Mirage? ... Our results reveal that CoT reasoning is a brittle mirage that vanishes when it is pushed beyond training distributions. This work offers a deeper understanding of why and when CoT reasoning fails, emphasizing the ongoing challenge of achieving genuine and generalizable reasoning. ... Our findings reveal that CoT reasoning works effectively when applied to in-distribution or near in-distribution data but becomes fragile and prone to failure even under moderate distribution shifts. In some cases, LLMs generate fluent yet logically inconsistent reasoning steps. The results suggest that what appears to be structured reasoning can be a mirage, emerging from memorized or interpolated patterns in the training data rather than logical inference. ... Together, these findings suggest that LLMs are not principled reasoners but rather sophisticated simulators of reasoning-like text.
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Postdocs when asked about being in academia
Stellan Skarsgård was scared to do his monologue in #Andor S1 because it was so good "I always have problems remembering lines ... especially now since I've had a stroke ... so I have [the lines] in my ear by a prompter" "I didn't want to fuck it up"
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Amazing! @aubreydegrey just shared the initial results of combining multiple known longevity interventions, and the results are epic. In the control group (blue), only 35% of the mice are still alive. Meanwhile, in the group that received all the interventions (red), close to 80% are still alive.
In 8 minutes - Aubrey de Grey @aubreydegrey will give an update on RMR - the epic project testing combos of geroprotective interventions. You can join here (requires entering your name): us06web.zoom.us/webinar/regi…
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New monster post: my own current perspective on the recent debates around techno-optimism, AI risks, and ways to avoid extreme centralization in the 21st century. vitalik.eth.limo/general/202…
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Actually Danish is just easier to learn if you have brain damage and a throat condition
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I'm running donation campaign on Sci-Hub now sci-hub.do/donate check out about the future plans, and donate using various cryptocurrencies
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Tfw Deepmind publishes the project you hoped to someday finish but better.
In a new paper, our team uses unsupervised program synthesis to make sense of sensory sequences. This system is able to solve intelligence test problems zero-shot, without prior training on similar tasks: dpmd.ai/sensory
Frans Zdyb retweeted
I keep seeing all kinds of crazy reports about people's experiences with GPT-3, so I figured that I'd collect a thread of them.
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en.m.wikipedia.org/wiki/Wito… Srsly, if they'd make a movie about this guy it would make James Bond look realistic in comparison.
This was one of the most extraordinary moments of the entire war. A Polish resistance commander got himself sent to Auschwitz so as to be able to tell the world what was going on. The world didn’t want to believe it.
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Excited to share our work: collaboration requires understanding! In Overcooked, self-play doesn't gel with humans: it expects them to play like itself. (1/4) Demo: humancompatibleai.github.io/… Blog: bair.berkeley.edu/blog/2019/… Paper: arxiv.org/abs/1910.05789 Code: github.com/HumanCompatibleAI…
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Why do people estimate the predictive posterior by regular sampling when it's exactly as hard as computing marginal likelihood? Please tell me I'm missing something obvious.
Great to meet ML people in the industry :) so long and thanks for all the swag!
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"MIT Mathlets" = mathlets.org/mathlets/ These are terrifically visual, interactive applets for learning about differential equations and related parts of math. Very intuitive and easy to use, and beautifully designed by Hubert Hohn, an artist who took my chaos course at MIT
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