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Simons Institute for the Theory of Computing @SimonsInstitute

The world's leading venue for collaborative research in theoretical computer science. Follow us at YouTube.com/SimonsInstitute.

Joined January 2018
  • Tweets 1,386
  • Following 340
  • Followers 9,579
647 Photos and videos
Simons Institute for the Theory of Computing retweeted
Simons Institute for the Theory of Computing@SimonsInstitute
Jun 11
2/2 But "Bayes theory is just a law of the universe. Any system that works optimally in the world has to obey Bayes theorem. So, it’s going to be really hard to find a situation where Bayes theorem isn’t a good model," said @gallantlab of @UCBerkeley at the Simons Institute.
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Simons Institute for the Theory of Computing retweeted
Simons Institute for the Theory of Computing@SimonsInstitute
Jun 11
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 at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning. simons.berkeley.edu/talks/ja…
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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 @phillip_isola at the Simons Institute worskhop on Topics in Intelligence: World Models and Social Reasoning
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2/2 "We already have the solution..Everybody knew that recurrent neural networks [would] eventually have to come back...They solve the memory problem from the ground up," said @phillip_isola of @MIT at the Simons Institute. Video: simons.berkeley.edu/talks/ph…
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Four AI systems were each given ten problems contributed by leading mathematicians. Their solutions were evaluated by thirty experts in a gathering last week. #1stproof 1stproof.org/assets/docs/rep…
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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 at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning. simons.berkeley.edu/talks/ja…
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2/2 But "Bayes theory is just a law of the universe. Any system that works optimally in the world has to obey Bayes theorem. So, it’s going to be really hard to find a situation where Bayes theorem isn’t a good model," said @gallantlab of @UCBerkeley at the Simons Institute.
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3/4 "Or is the brain actually instantiating some underlying model of the causes of the events that occur in the world that is really low-dimensional, highly compressed, highly efficient representation of the structure of the world," asked @gallantlab at the Simons Institute.
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4/4 "Nobody knows and the evidence sucks. Nobody has any idea in the brain which of these two groups of theories is true, and it's really a horror show of sad, vague results," @gallantlab at the Simons Institute w/shop on Topics in Intelligence: World Models and Social Reasoning
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1/4 World models are all the rage in both neuroscience and AI. But does the brain really build world models? Jack Gallant (@gallantlab @UCBerkeley) asked at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning. Video: simons.berkeley.edu/talks/ja…
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2/4 "Is the brain just picking up on correlations and predicting the next state of the world based on the correlation structure of the world?"—Jack Gallant (@gallantlab @UCBerkeley) at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning
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Interview with Nikhil Srivastava about AI's capabilities for research math. From @science_eye. #1stproof bit.ly/3PJfD1X
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1/3 "Our conclusion is that AI consciousness is inevitable." In back-to-back talks, Manuel Blum and @BlumLenore of @CarnegieMellon 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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2/3 Manuel Blum spoke of The Conscious Turing Machine (CTM), a formally defined Theoretical model of Consciousness, which offers an explanation for, among other things, the suffering of pain. Video: simons.berkeley.edu/talks/ma…
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3/3 @BlumLenore situated the Conscious Turing Machine in the landscape of consciousness science. Video: simons.berkeley.edu/talks/le…
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Some events coming up related to the #1stproof project (hosted by @Harvard this time): June 3 Introduction to First Proof: A Conversation cmsa.fas.harvard.edu/event/f… June 10 First Proof, Second Batch: Results  cmsa.fas.harvard.edu/event/f…

Introduction to First Proof: A conversation - CMSA

Introduction to First Proof: A conversation Date: June 3, 2026 Time: 1:00–2:00 pm Location: via Webinar Harvard CMSA Director Dan Freed will lead a dialogue with First Proof Editors Mohammed […]

cmsa.fas.harvard.edu
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Simons Institute for the Theory of Computing retweeted
Ryan Peterman
@ryanlpeterman
Jun 1
Avi Wigderson is the only person in history to have won both a Turing Award (computer science) and Abel Prize (math). I interviewed him all about his field. We discussed: • His intuition on a proof of P vs NP • Why we use SAT solvers for most NP problems • Zero knowledge proofs and their impact • Quantum computation and implications • Math and computer science's relationship Where to watch: • YouTube: youtu.be/5GUcvSAJcJw • Spotify: open.spotify.com/episode/4JZ… • Apple Podcasts: podcasts.apple.com/us/podcas… • Transcript: developing.dev/p/turing-awar… Thank you to this episode's sponsors for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at workos.com/ Timestamps: 00:00 - Intro 01:08 - P vs NP 14:51 - What if you relaxed correctness 25:38 - Why NP complete problems are equivalent 30:33 - Space vs time complexity 43:06 - Why people use SAT solvers 45:53 - Randomness is a resource 55:48 - Randomness depends on computational power 01:21:20 - Zero knowledge proofs and their significance 01:38:30 - Quantum computation and why it matters 01:56:24 - Math vs computer science 02:08:16 - Major breakthroughs and his experience 02:12:31 - Advice for his younger self 02:14:48 - Outro
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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/ad….
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2/2 "The trick is actually fixing the evaluations. It’s going to be a problem of the way we evaluate language models. We need to reward them for being humble, and saying occasionally, ‘I don’t know,’ or asking a question," said @OpenAI's Adam Kalai at the Simons Institute.
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1/2"Machine Learning is starting to look very different from what it looked like before": @tommmitchell of @CarnegieMellon at the Simons Institute workshop on The Role of TCS in Modern Machine Learning. Video: simons.berkeley.edu/talks/to…
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2/2 ML is changing mostly "because of language models and their ability to deal with natural language as [an informal] representation of information...and their ability to reason, imperfectly, about nearly anything": @tommmitchell of @CarnegieMellon the Simons Institute
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