The schedule of talks for our Aug. 24–28 ICM Satellite Conference on Spectral Theory, High-Dimensional Expansion, and Pseudorandomness is now online: simons.berkeley.edu/workshops/ic...

4/4 "The second key ingredient to [True] AI is not having an objective, [but] to have an emergent objective. To have [an] intrinsic motivation, where...you just want to learn about the world and to discover better data," said Alyosha Efros at the Simons Institute simons.berkeley.edu/talks/alyosh...

3/4 "Will this scraped data give us next Borges or the next Bach. I don't think so. At least, I hope not," said @ucberkeleyofficial.bsky.social's Alyosha Efros at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning.

2/4 The first ingredient for True AI is data. But is it enough to scrape data and distill 2000 years of human written knowledge into a model? No, said @ucberkeleyofficial.bsky.social's Alyosha Efros, "There is something inferior about stealing knowledge instead of generating it yourself."

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

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/jitend...

2/3 The Era of Experience: UC Berkeley's Jitendra Malik spoke of Rich Sutton's argument that scaling up AI models "is not autonomous learning, [it's] just memorized regurgitation of some form." Also, there's work on World Models / Dynamics Models as an alternative to scaling up current AIs

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

"Robotics is behind [NLP] and computer vision," because the main methods for collecting training data — using teleoperations, human videos and simulations — are all "unsatisfactory," said @ucberkeleyofficial.bsky.social's Jitendra Malik at the Simons Institute. simons.berkeley.edu/talks/jitend...

In robotics, "there are problems to be solved which are to do with shortage of data and only when we solve them will we solve robotics," said @ucberkeleyofficial.bsky.social's Jitendra Malik at the Simons Institute's workshop on Topics in Intelligence: World Models and Social Reasoning.

Thursday and Friday this week: a workshop on Quantum Circuits and Algorithms for Cryptography simons.berkeley.edu/workshops/qu...

Join us! simons.berkeley.edu/events/encry...

2/2 "But if I make a small number of measurements...I flatten my data. There is no way to recover the curved structure," said Tatyanna Sharpee, @salkinstitute.bsky.social, at the Simons Institute w/shop on Topics in Intelligence: World Models and Social Reasoning simons.berkeley.edu/talks/tatyan...

1/2 If data about the natural world lives in a low-dimensional hyperbolic space, then a large number of measurements embedded in a high-dimensional Euclidean space can preserve info. about the “curved inner world," said Tatyanna Sharpee, @salkinstitute.bsky.social, at the Simons Institute

3/3 "What surface does this distance matrix imply?" In the case of strawberries, hyperbolic geometry in 3D best fits the data, said Tatyanna Sharpee, @salkinstitute.bsky.social, at the Simons Institute. Video: simons.berkeley.edu/talks/tatyan...

2/3 For e.g., collect data about volatiles emitted by different varieties of strawberries and evaluate the statistical distance between these molecules. Smaller distances imply stronger correlation between molecules, said Tatyanna Sharpee, @salkinstitute.bsky.social, at the Simons Institute.

1/3 "All data in the natural world and in neural systems have hyperbolic geometry in them," said Tatyanna Sharpee of @salkinstitute.bsky.social, at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning.

3/3 "Dopamine neurons can change their tuning from self evaluation during practice to social feedback during performance," said @vikramgadagkar.bsky.social (Columbia) at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning. simons.berkeley.edu/talks/vikram...

2/3 "We found that the dopamine system was robustly activated by female calls, especially when the calls overlapped with male song. These results are the first demonstration of the social modulation of dopaminergic evaluation signals," said @vikramgadagkar.bsky.social at the Simons Institute

1/3 Musicians, human or avian, don't seem to care about their own mistakes when performing. But do they care how the audience — for the male zebra finch, the audience is the female of the species — responds to their singing? asked @vikramgadagkar.bsky.social at the Simons Institute.

5/5 "The brain's self-evaluation system is active during practice but turned down during performance," said @vikramgadagkar.bsky.social, @columbiauniversity.bsky.social, at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning. simons.berkeley.edu/talks/vikram...

4/5 Using a novel dopamine photometry system in singing birds, @vikramgadagkar.bsky.social's team showed that the dopamine evaluation signal was substantially more active during practice than during female-directed performance. Gadagkar spoke at the Simons Institute.

3/5 "[So,] can the brain handle motor errors differently during practice and performance? Zebra finches are a great system to ask this question, because male zebra finches practice their songs alone with the goal of performing to a female," said @vikramgadagkar.bsky.social at the Simons Institute

Congratulations to Simons Institute Director Venkat Guruswami, whose 2006 paper with Atri Rudra, “Explicit capacity-achieving list-decodable codes,” received a STOC 2026 Test of Time Award this week. Kudos to the other awardees as well! Full list here: sigact.org/prizes/stoc_...

2/2 @lenoreblum.bsky.social invoked Kenneth Craik's view (1943) of the nervous system as a "calculating machine" to argue that 1st person exp/consciousness is computational, at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning Video: tinyurl.com/p7rhvdzs

1/2 "Many view the hard problem as needing a new kind of science to explain subjective experience. We view the hard problem as a challenge to show that subjective consciousness surely is computational," said @lenoreblum.bsky.social at the Simons Institute.

3/3 This raises interesting questions. "How can I reach into someone's head, human or AI, and read out this mental model?" asked @shiryginosar.bsky.social at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning. Video: simons.berkeley.edu/talks/shiry-...

2/3 "This is like looking at the outputs of a bunch of art students and trying to reconstruct what the subject matter was," said @shiryginosar.bsky.social of @tticconnect.bsky.social at the Simons Institute workshop on Topics in Intelligence: World Models and Social Reasoning

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

5/5 "As a physicist, weaver ants defy my intuition. How can large-scale will and cognition emerge from the action of tiny ignorant individuals?" asked Ofer Feinerman, @weizmanninstitute.bsky.social, at the Simons Institute. Video: simons.berkeley.edu/talks/ofer-f...