Andreas Tolias Lab @ Stanford University

I had a great time participating in the panel on the future of AI at Davos 2026.

Nice work V-JEPA 2.1 from Meta. Our team has also been exploring for a long time on dense and hierarchical video SSL (e.g. FlowE, PooDLe, and Midway). Glad to see it works on a larger scale.

@SussilloDavid Congratulations @SussilloDavid I will buy it from SFO next week

Me and Michelle holdin' it down in the airport bookstore!! 🤩🤩

Excited that to we have contributed to this work !

Excited to be speaking at the @thought_channel Conference on the Mathematics of Neuroscience and AI in Rome! neuromonster.org With @RMBattleday, @jcrwhittington, and others!

Our new paper: "Solving adversarial examples requires solving exponential misalignment", expertly lead by @AleSalvatore00 w/ @stanislavfort arxiv.org/abs/2603.03507 Key idea: We all want to align AI systems to human values and intentions. We connect adversarial examples to AI alignment by showing they are a prototypical but exponentially severe form of misalignment at the level of perception. The fact that adversarial examples remain unsolved for over a decade thus serves as a cautionary tale for AI alignment, and provides new impetus for revisiting them. We shed light on why adversarial examples exist and why they are so hard to remove by asking a basic question: what is the dimensionality of neural network concepts in image space? For ResNets, and CLIP models, we show that neural network concepts (the space of images the network confidently labels as a concept) fill up almost the ENTIRE space of images (~135,000 dimensions out of ~150,000 for ImageNet & ~3000 out of 3072 for CIFAR10). In contrast natural image concepts are only ~20 dimensional. This indicates exponential misalignment between brain and machine perception (neural networks perceive exponentially many images as belonging to a concept that humans never would). This also explains why adversarial examples exist: if a concept fills up almost all of image space, ANY image will be close to that concept manifold. We further do experiments across > 20 networks showing that adversarial robustness inversely relates to concept dimensionality, though the most robust networks do not completely align machine and human perception. Overall the curse of dimensionality raises its ugly head as an impediment to both adversarial examples and alignment: if can be difficult to get AI systems to behave in accordance with human intentions, values, or perceptions over an exponentially large space of inputs. See @AleSalvatore00's excellent thread for more details: x.com/AleSalvatore00…

So, some people are asking me why this EON fly video doesn’t show real ‘uploading’ since it does simulate a real connectome. The most important reason is that the functional parameters that define the dynamic behavior of individual neuron and synapse types in the connectome are unknown. Instead, they used an existing model (nature.com/articles/s4158…) which substitutes these with guessed parameters and grossly simplified dynamics. As made clear in that older paper, these are not sufficient to recreate the activity patterns that would be seen in the real fly. The simplified dynamics would not, for example, be able to choreograph the timing of leg muscles during walking or grooming, or the dynamics of the compass neurons encoding the fly’s heading direction, or the myriad other neuronal dynamics that make up the fly ‘mind’. So not an ‘upload’ by any reasonable definition. In fact, the simplified dynamics they used have only been demonstrated to approximate gross correlations along major sensory-motor pathways for a handful of neurons. For example: activating a sugar sensing neuron causes gross downstream activation that elevates the activity of feeding neurons. It is this handful of very, very crude and basic correlations in the simulated connectome that are being used to drive the EON simulated fly. If they had said that from the start, then I would have had no issue. But instead, they made the bold claim that they had “uploaded a fly” and presented a video of said fly walking over a landscape with highly articulate legs, visually navigating through the terrain to a food source, grooming its antenna with eerily fly-like leg motions, etc. Any reasonable layperson would assume that these visually exciting articulations are the ones being controlled by the simulated brain’s dynamics instead of being faked by computational add-on routines. There are now many secondary reports of this on YouTube and all of them seem to make this reasonable assumption (e.g. youtube.com/shorts/Z7NNP1Z…). And who could blame them? Many neuroscientists also made that assumption before EON started to spell out what was really behind the video millions of views and over a day later. To make clearer just how misleading EON Systems’ video is and how outlandishly laughable their ‘uploading’ claim is, below is an imagined back-and-forth discussion between a [Reasonable Layperson] and a [Neuroscientist] trying to explain to them what is really behind the video: [Reasonable Layperson] “Look at the complicated leg motions as the fly walks… the timing of all those dozens of individual muscles being controlled by the dynamics of the simulated neurons… and they say that they used no reinforcement learning to tune parameters, just the connectome… that is really impressive!” [Neuroscientist] “Well actually no… those leg movements are actually coming from a program unrelated to the connectome. The connectome used didn’t even include the central pattern generator circuits in the ventral nerve cord responsible for controlling leg muscles.” [Reasonable Layperson] “Oh… so in what sense is the simulated connectome controlling walking?” [Neuroscientist] “It looks like they just found a few neurons in the brain connectome that are correlated with right/left/forward motion and used these to ‘steer’ the pretend walking routine.” [Reasonable Layperson] “Oh… But the activations of those ‘steering’ neurons are reflecting the complicated dynamics of tens of thousands of simulated neurons in the fly visual system as it moves through the virtual world, avoiding objects and heading toward its visual goal, right?” [Neuroscientist] “Well actually no … The visual system and virtual world are essentially ‘decoration’… the flashing dynamic neural responses as the fly moves through the virtual environment are designed to give the viewer the impression that the simulated fly is actually seeing the world and making walking decisions based on those visual responses. But, in fact, they could turn off the lights and the fly would behave identically.” [Reasonable Layperson] “Oh… so how does the fly walk toward the food then?” [Neuroscientist] “Well… it looks like they simply imposed an odor gradient in the virtual environment that is centered on the virtual food. The fly has two sets of odor receptors (right and left) that sense this gradient and the activation of these in the connectome is correlated with the activation of the ‘steering’ neurons. So if the left odor neuron activates more than the right then the fly steers left.” [Reasonable Layperson] “Oh… so it is like one of those toy cars that moves toward a light because it has right and left light sensors cross-connected to right and left motors… Gee, I thought a fly was more complicated than that.” [Neuroscientist] “Well actually a real fly is. Real flies have dozens of behavioral states that allow intelligent behavior in a complicated visual and sensory environment. In fact, a real fly contains a set of neurons which act as an internal compass updated by the visual environment and the fly’s walking.” [Reasonable Layperson] “Oh… and their connectome has those internal compass neurons?” [Neuroscientist] “Yes. They used the full brain connectome that contains those compass neurons.” [Reasonable Layperson] “...And their compass neuron activations are tracking the visual environment just like in the real fly?” [Neuroscientist] “Oh sweet summer child… those compass neurons exist in their connectome simulation, but no one knows enough about their functional parameters (synaptic weights, time constants, etc.) to simulate them accurately. They light up in pretty patterns totally unrelated to how they would in a real fly walking through that visual world.” [Reasonable Layperson] “Oh… and the complicated leg movements it shows during antenna grooming… is that also just a faked recording?” [Neuroscientist] “Yes. All the complicated leg motions shown during grooming are faked by a hard-coded program. But they turn that fake routine on or off by looking at some neurons in the connectome that are correlated with actual grooming behavior triggered by dust accumulation on the antenna… well really they fake the dust too by just activating a set of neurons after a delay.” [Reasonable Layperson] “And what did EON Systems do? Did they acquire the connectome? Did they determine the neurotransmitter types? Did they do the calcium imaging experiments to determine the steering and grooming neurons? Did they make the mechanical fly model?” [Neuroscientist] “No. Those were all done by real labs who were kind enough to carefully write up their results in open journals and to post their results and code openly online…. It looks like Eon Systems just took their code and put it together with a virtual environment designed specifically to trick viewers by triggering behaviors in misleading ways.”

Proud with @UNSWRNA to have been involved & making the mRNA-LNP for Rosie. There are nuances here that the thread below misses but nevertheless, the intersection of RNA technology, genomic & AI poses an opportunity to change the way do medicine and make access more equitable 1/8

Important reminder: structure alone is not enough. Understanding neural computation requires measuring the dynamics of neural circuits during natural behavior.

@demishassabis @GoogleDeepMind Wow beautiful Move 37. Congratulations @demishassabis

London has incredible talent & entrepreneurial spirit. Thrilled to deepen @GoogleDeepMind’s roots here with our spectacular new building Platform 37 - a nod to AlphaGo’s legendary Move 37. It’s a tribute to Science & AI, and an inspirational space for our next big breakthroughs!

@doristsao Definitely - if you seek to understand function, study function.

@jamesfickel is an inspiring, visionary philanthropist and investor. We are honored to have the backing of James and the entire Amaranth team, and we share his enthusiasm for a future that benefits all of humanity. We’re ready for the adventure ahead, James! 🦁

@jamesfickel is an inspiring, visionary philanthropist and investor. We are honored to have the backing of James and the entire Amaranth team, and we share his enthusiasm for a future that benefits all of humanity. We’re ready for the adventure ahead, James! 🦁

In emerging fields like NeuroAI, thoughtful capital at the earliest stages really matters. Grateful to @jamesfickel and the Amaranth team for pushing things forward.

Subscribe to Amaranth Foundation Blog blog.amaranth.foundation/?r=4yybt9&utm_…

@jamesfickel is an inspiring, visionary philanthropist and investor. We are honored to have his backing and his enthusiasm for a future that benefits all of humanity. We’re ready for the adventure ahead, James! 🦁

Absolutely !

World Labs CEO Fei-Fei Li: Language alone is a lossy representation of the physical world. "Just a simple meal of making pasta... one could imagine using language to describe let's say about 15 minutes or 20 minutes of that process. But it’s still a lossy representation." "The nuance of how you cook the sauce, how you put the pasta in the water, what the pasta [does] in the water is impossible to use language alone to describe." "So much of the physical world’s process... is beyond the description of language." @drfeifei @theworldlabs

new blog! What methodologies do labs use to train frontier models? The blog distills 7 open-weight model reports from frontier labs, covering architecture, stability, optimizers, data curation, pre/mid/post-training + RL, and behaviors/safety djdumpling.github.io/2026/01/31/fro…

@demishassabis @GeminiApp @antigravity Congratulations @demishassabis and team. Improvement in ARC challenge is particularly impressive.

Excited to launch Gemini 3.1 Pro! Major improvements across the board including in core reasoning and problem solving. For example scoring 77.1% on the ARC-AGI-2 benchmark - more than 2x the performance of 3 Pro. Rolling out today in @GeminiApp, @antigravity and more - enjoy!