
Head of AI @AsteraInstitute Prev: AGI @DeepMind, cofounder @vicariousai (acqd by Alphabet), cofounder @Numenta. IIT-Bombay, MS&PhD Stanford. https://xitter.cf/t.co/IlsczdBtZo
San Francisco, CA
Joined June 2017
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The fact that Geoff didn't name it Schmidhuber Machine is a clue that he secretly admires @SchmidhuberAI 😇
If Ludwig Boltzmann was given a choice he’d have preferred the transformer architecture to be named after him rather than the Boltzmann machine.
Hmm. I did read the peer reviews and I’d call it peer review extra super lite. I’d be happy to do the job elife reviewers didn’t — I feel it shouldn’t be this joyous, stay tuned. :)
I have one too! My name is on the same line as THE Nitesh's Tiwari's name in this honor roll! Sometimes life just lines you up with superstars, giving you eternal bragging rights. @niteshtiwari22
Dileep George retweeted
FYI, for those wondering: we are working on organizing everything, and will be releasing data and code for our paper on Monday 10/26/26.
Dileep George retweeted
Check out our new model for creating realistic motion in a virtual rat during RL!
I think of this as providing the "brainstem and spinal cord" for NeuroAI researchers who want to simulate realistic neuroscience experiments.
Congrats to @aidan_sirbu for getting this out!
1/14) Virtual animals are a long-standing goal in neuro-AI, but they should actually behave like real animals.
We present SCAMPER: a virtual rat for RL studies that moves naturally during both exploration, and goal-directed behavior.
Project page: aidansirbu.com/SCAMPER-site/
Dileep George retweeted
We added Gemini 4 Argon to our AutoResearchExam Pareto curve. It is 2-3 times cheaper compared to the other frontier models for almost the same ML engineering performance. Congratulations to the GDM team.
congratulations on the launch @tarinziyaee . Super interesting approach. Good luck!
Dileep George retweeted
The ideal amount of healthcare is zero.
Progress ep09 is live with @annewoj23, co-founder of 23andMe.
We explore why "it's much cheaper if people die of a heart attack," running your own health with AI, and winning 23andMe back out of bankruptcy.
excited about what this team is up to!
Replying to @EkaRobotics
@EkaRobotics has raised a $150M Series A.
Generality v/s Performance is the fundamental tension at the frontier of robotics. Generality without performance is just a prototype. Performance without generality is just a single-purpose tool.
Customers shouldn’t have to choose. Eka breaks the tradeoff. Powered by our Vision-Force-Action (VFA) model, we deliver the performance and versatility customers need to put robots to work every day.
Eka is built on three convictions:
⚡️Force is the language of manipulation.
🚀 Reinforcement learning is how dexterous manipulation scales.
🎂 System 1—fast, reliable physical execution—is the bottleneck to solve.
With this funding, we will scale our model and accelerate customer deployments.
Grateful to be building alongside a dream team and investors who share our ambition.
Dileep George retweeted
Replying to @EkaRobotics
@EkaRobotics has raised a $150M Series A.
Generality v/s Performance is the fundamental tension at the frontier of robotics. Generality without performance is just a prototype. Performance without generality is just a single-purpose tool.
Customers shouldn’t have to choose. Eka breaks the tradeoff. Powered by our Vision-Force-Action (VFA) model, we deliver the performance and versatility customers need to put robots to work every day.
Eka is built on three convictions:
⚡️Force is the language of manipulation.
🚀 Reinforcement learning is how dexterous manipulation scales.
🎂 System 1—fast, reliable physical execution—is the bottleneck to solve.
With this funding, we will scale our model and accelerate customer deployments.
Grateful to be building alongside a dream team and investors who share our ambition.
Recursively Saturating Intelligence?
This is similar to what we see. On some tasks, there are still major returns on intelligence. However, for the large majority of tasks, we are either close to saturation or are already at saturation. Once intelligence saturates, tacit knowledge, data, supply chains, and operational ability become the bottlenecks.
xitter.cf/theinformation/status/…
Dileep George retweeted
The outpouring of interest in our paper has been … a bit insane.
May I make four points?
1. We have not shown that “consciousness has a frame rate.”
I can see why people draw this conclusion: we report that inferotemporal and prefrontal cortex alternate between conscious and suppressed states roughly every 60 ms during binocular rivalry.
Consciousness depends on many brain areas, and our paper reports results from only a few select nodes.
We still don’t know how conscious access works--the actual recipe for generating conscious experience. What we have shown is that deep in the brain there is an extremely strong signal, rich enough to reconstruct a high-dimensional face in detail, that is inaccessible to consciousness. As I argued in the original tweet, this makes the problem of conscious access a lot more experimentally tractable. But to claim “consciousness has a frame rate,” one would need to have actually solved the mechanism for conscious access and shown that this mechanism operates at a frame rate.
While we currently don’t know conclusively one way or the other (see VanRullen & Koch, 2003 for a review of positive evidence), I personally am skeptical that consciousness has a frame rate. I believe the brain, within posterior parietal cortex, has continuous tracking mechanisms that actively represent the persistence of matter, including ourselves as physical bodies. The coupling between these persistent tracking processes and feature representations across the brain (like those in face patches) is, I think, the essential mechanism of conscious access.
This coupling need not happen at the rate of inferotemporal alternations (and inferotemporal cortex itself may not even alternate outside of the artificial situation of binocular rivalry). As an analogy: imagine the area crucial for conscious access holding a steady drone note, while inferotemporal cortex alternates between two notes, one in harmony and one dissonant. Inferotemporal content might couple to the drone only at moments of harmony, yet experience would still be continuous, because the drone itself (the tracking process that I believe is at the core of conscious perception) never stops. And areas without conflicting content, like auditory cortex during visual rivalry, could stay in continuous harmony with the drone.
In sum, there is no reason to conclude from our study that consciousness is discrete, with a "frame rate."
2. Our results resemble Jezek et al. 2011 (as @jjsakon notes).
Jezek et al. trained rats in two boxes that were identical except for their lighting cues, so that hippocampal place cells formed two distinct maps, one per box. They then “teleported” the rat by abruptly switching the lights. For a few seconds afterwards, CA3 place-cell activity flickered between the two maps, synced to the theta cycle (~125 ms).
@StanDehaene first pointed this paper out to me, and reading it blew me away. To my mind it is the strongest evidence for a bistable attractor in the brain, because the competing maps are stored within the hippocampus itself.
I think the phenomenon we see may be very similar at the circuit level: a bistable attractor network within inferotemporal cortex that alternately converges to one of two patterns, as cells in face patches work together to build a coherent representation of the input. In our case, both images are present in the input, so the competition could arise within IT or be inherited from earlier areas such as V1--recording there is an important next step. (It also still puzzles me that we didn’t see strong coherence between face patches ML and AM.)
More broadly, what’s different about our paper is the brain area (inferotemporal cortex, often modeled as a feedforward DNN) and the ability to relate the two competing patterns to consciousness, with one granted conscious access and the other not.
3. Please comment on bioRxiv!
The Twitter attention has been cool, but I really hope colleagues will be generous enough to post detailed commentary on bioRxiv, where there are currently 0 comments. As @seemaychou points out, peer review doesn’t have to come from just three anonymous reviewers. We would be truly grateful for your critical feedback.
4. Stay tuned
This work was done at Caltech, Berkeley, in collaboration with @EngelTatiana at Princeton, funded by NIH and HHMI.
I hope to share our vision soon for how we can solve consciousness @AsteraInstitute
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
Dileep George retweeted
Thank you, Brendon! This is amazing 🥹. I just showed it to my son who is in 4th grade.
Dileep George retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
idk Arvind I don't think it takes anything to steer AI toward partial automation.
Partial automation is what you get for free when the research agenda for full automation is only partially successful.
There is widespread frustration about the AI industry’s “automate everything" agenda. But what would it take to steer AI toward augmenting human capabilities instead? In a new essay, Gabriel Unger (@_gabrielunger) and I lay out a research agenda and plan for action. normaltech.ai/p/steering-ai-…
Our thesis is that the reason why AI-for-automation currently dominates isn’t just economic incentives but also deeper cultural issues in computer science as a field. And that can be changed.
- We sketch four important research directions that are needed in order to build collaborative or augmentative AI. The automation paradigm largely ignores these technical challenges.
- We advocate for Human-Computer Interaction and Human-Centered AI to be elevated as a subfield in status, influence, and funding, and changing AI curricula to emphasize the role of the human and decrease the centrality of the pure-automation problem framing.
- We point out that there are a surprising number of efforts in economics, computer science, industry, and policy all trying to bring about a shift toward augmentation, but not enough collective awareness. Better connecting these movements would amplify their power.
- We think the automation agenda is struggling on its own terms. Compiling evidence of the limits of general-purpose agents for automation and loudly making the case will encourage more companies to take collaboration more seriously as a goal.
- We predict that once more workers experience this way of working and recognize that there are alternatives to the delegate-and-supervise pattern, it will generate more demand, creating a flywheel effect.
- Targeted policy levers can play a small but important role in amplifying both the supply and demand for pro-worker AI. We don’t need clumsy top-down mandates.
We're not against automation. But we think the goal of AI must be broader.
This is the start of a long-term project. I know there are far more like-minded efforts out there than we’re aware of. I’d love to hear about some of them.
Dileep George retweeted
Gemini 4 Argon (gemini-4-argon) is now live across Google AI Studio, the Gemini API, Google Antigravity, Gemini Enterprise, and the Gemini app!
Gemini 4 Argon is awesome:
1. Unified 1M Context & 1M Output (Long Decode): Input, thinking, and output now share a single 1M-token window—lifting the old 64K output cap by 16x. With background=True on the Interactions API, our servers handle multi-hop continuation and checkpoint recovery automatically so the model can think for tens of minutes and emit complete multi-file repo migrations or formal proofs in a single pass.
2. Tunable Reasoning Depth: Control compute per turn via thinking_level ("low" | "medium" default | "high") plus native agentic video processing (media_processing="agentic").
3. Real-World Engineering: 77.9% on DeepSWE v1.1, 91.9% on Vibe Code Bench, and 68.9% on the Vals Index. Internally, we used Argon to migrate 800K+ lines of Fuchsia Zircon C/C++ kernel code to memory-safe Rust and replace 32K lines of hand-written SIMD in libgav1 for a 2.7x speedup.
4. Token Economics: Launching at an introductory rate of $2 / 1M input and $10 / 1M output tokens through Nov 30, with 95% off cached inputs ($0.10 / 1M) via automatic implicit caching.
Dileep George retweeted
OpenAI’s faster Fourier transform is less a mathematical breakthrough and more a malicious exploit of mathematical traditions.
argmin.net/p/faster-fourier-…


























