Earlier this month, we hosted a working group to think about how the TCS community should respond to AI progress. Their report, released on Monday, highlights 12 near-term actions that received broad support among the participants. simons.berkeley.edu/news-publica...
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Articles & links
4/4 "This allows you to interpolate between auto-regression and diffusion," said Volodymyr Kuleshov of @cornelluniversity.bsky.social at the Simons Institute workshop on Diffusion Generative Modeling: Progress and Next Steps. Video: simons.berkeley.edu/talks/volody...
"Of course, we also have failures," said Michal Irani (@weizmann.ac.il), at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning. For e.g., the fMRI scan of a person viewing a cat became a bear. Video: simons.berkeley.edu/talks/michal... a…
"Of course, we also have failures," said Michal Irani (@weizmann.ac.il), at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning. For e.g., the fMRI scan of a person viewing a cat became a bear. Video: simons.berkeley.edu/talks/michal... a…
3/3 "This is part of a larger...collaborative effort to build foundation models," said Eva Dyer of @upenn.edu at the Simons Institute. Such models would unify diverse neural data with varying temporospatial resolutions, from multiple species and tasks. Video: simons.berkeley.e…
"When we are interacting with others there is a dynamics that transcends the...people that are participating; these dynamics in return shape the people," said Guillaume Dumas, @introspection.bsky.social, of @mila-quebec.bsky.social at the Simons Institute. Video: simons.berkel…
3/3 Continual Learning. "Even people who started the hyper-scaling trend have been talking about continual learning, which is not there in current day systems. Once the system [is] public, it does not learn any more," said UC Berkeley's Jitendra Malik simons.berkeley.edu/talks…
This week at the Simons Institute, a workshop on Diffusion Generative Modeling: Progress and Next Steps simons.berkeley.edu/workshops/di...
From our friends at Berkeley's Center for Responsible, Decentralized Intelligence: Agentic AI Summit, August 1–2 Early bird pricing ends July 5 rdi.berkeley.edu/events/agent...
In case you didn't see this when it came out a couple weeks ago, here's a nice article from Harvard's FAS Current about the second batch of #1stproof, a community experiment that tests AI's capacity to do research math. current.fas.harvard.edu/stories/firs...
1/2 Generative world model (GWM) theories are correlated with theories that aren't world model theories, such as the Bayesian brain theory, said @gallantlab.org at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning. simons.berkeley.edu/t…
1/2 Are LLMs doomed to hallucinate? No, says OpenAI's Adam Kalai at the Simons Institute workshop on The Role of TCS in Modern Machine Learning. "I don’t think it’s an inevitable problem. We can drastically reduce the amount of hallucinations." Video: simons.berkeley.edu/talks…
Recent commentary
1/5 The case for diffusion language models: "A lot of the [early] gains in language modeling performance have come from scaling pre-training...[the training algorithm] was designed to be very parallelizable across GPUs": Volodymyr Kuleshov of @cornelluniversity.bsky.social at the Simons Institute
1/3 AI and Its Discontents: @ucberkeleyofficial.bsky.social's Jitendra Malik talked of the "rumblings of dissent,", when it comes to the current approach of scaling up frontier multimodal models, at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning
1/2 The Return of RNNs. "AI is still lacking a good memory mechanism...We are in this era of 'attention is all you need' but I don't think that's going to cut it," said @phillipisola.bsky.social of @mit.edu at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning
1/4 Are diffusion language models ready for the real world? Not quite. Today's diffusion language models are missing some key ingredients, said Volodymyr Kuleshov of @cornelluniversity.bsky.social, at the Simons Institute workshop on Diffusion Generative Modeling: Progress and Next Steps
1/4 "AI is not when computer can write poetry. AI is when computer will *want* to write poetry." @ucberkeleyofficial.bsky.social's Alyosha Efros quoted a friend, when talking about the nature of "True AI," at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning
1/4 Remember Sydney, the chatbot that urged a @nytimes.com columnist to break up with his wife? Or how an LLM fine-tuned to output insecure code became misaligned on tasks unrelated to coding? What do these have in common? — Roger Grosse of @anthropic.com and U of Toronto at the Simons Institute.
1/3 Besides "robotic, embodied systems, AI systems never see the real world. They only see these artifacts humans create & feed into them...From these [snapshots of reality] our AI needs to infer the world that produced these snapshots": @shiryginosar.bsky.social at the Simons Institute
1/3 "Our conclusion is that AI consciousness is inevitable." In back-to-back talks, Manuel Blum and @lenoreblum.bsky.social of @cmu.edu discuss the Conscious Turing Machine and AI consciousness at the Simons Institute workshop on The Role of TCS in Modern Machine Learning
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