I have a passion for cool gadgets, AI, gaming, entertainment, electronics and mobile. Startup mode engaged.
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Joined April 2009
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Kevin Hague retweeted
10 Spicy Takes After Talking to 50 Friends at the Frontier of Robotics 🤖🔥
1/ Astra IS robotics' GPT-3 moment. Ironically, now that it's finally here, some in robotics seem reluctant to acknowledge it. Perhaps because it (sadly) didn't come from a robotics company?
2/ Robotics is becoming just another vertical for frontier labs. Not so different from coding in some respects. Why reinvent general intelligence for every industry?
3/ Robotics was too hard to underwrite until now. No convincing pretraining scaling law, no convergence on models, data, or hardware. No wonder some critics dismissed it as a fragmented industry playing an expensive "demo game". Astra may finally offer a credible path to scale.
4/ Robotics foundation-model startups have a case. But how strong is it? The argument: Astra handles System 2 (reasoning), while specialized models retain System 1 (skills) and System 0 (motor control). Fair. But isn't that already a narrower value proposition? And who says the boundary stops there?
5/ Follow the talent. Word is OpenAI reorganized its 30–40-person robotics team around the core Astra model. Hearing similar moves toward general models at @Tesla_Optimus. Org charts may tell us more than any demo.
6/ The robotics data gold rush is real. Million-hour procurement programs, some potentially reaching tens of millions of hours. Talk of budgets approaching $1B per frontier lab.
7/ Bullish on robotics hardware volumes. As intelligence becomes more accessible, the barriers to building useful robots could fall dramatically. The moat shifts toward reliability, dexterity, integration, and deployment.
8/ Robotics could fuel the next semiconductor and memory supercycle. A 100B-parameter model running on the edge requires ~50GB just for 4-bit weights. Now multiply that by millions of robots. Why do you think @JensenHuang is so obsessed with robotics? @elonmusk saw it coming: xitter.gq/elonmusk/status/211477…
9/ Is self-driving immune to the general-model threat? After coding and robotics, why assume self-driving remains the exclusive domain of proprietary, vertical models? Robotaxi increasingly feels like a solved problem to me. But if and when frontier labs move into self-driving, we could see an Apple vs. Android dynamic. Think about the implications for $TSLA, Waymo, and other OEMs.
10/ The next bottleneck may not be intelligence. It's deployment. Models are improving faster than our ability to deploy robots in the messy real world. Integration, hardware variance, failure recovery, and economics remain brutally hard. The real moat may be turning intelligence into reliable, profitable physical work.
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I realize some of these takes may rub people the wrong way, especially those who have spent years doing the hard work of advancing robotics. That's not my intention.
I'm sharing them to spark debate, not to declare winners and losers. Astra has only been out for a month or so, and it's far too early to draw definitive conclusions. More importantly, when the TAM is this enormous - potentially the largest in tech - there should be plenty of room for different approaches and many winners.
++
Full diligence report: robonomics.substack.com/p/ro…
In the AI era:
If you dream it you can build it.
Knowing what to build and whether it works is the value we add.
I was hanging out with a good friend tonight. We talked about how AI is changing our lives and there may only be a few 10's of months left before the cement starts to harden...at least v1 of this push. Time to build is now.
Kevin Hague retweeted
Have you guys seen the at guy who uses an Apple Vision Pro to do all his installs?
Feels like looking at tech from the future
Kevin Hague retweeted
Cybercab review: it exceeds my expectations.
It is the future.
Can’t wait to own one. And rent it out.
These will be very popular in San Francisco and Shanghai.
Makes me happy to be a Tesla investor since 2018.
Once you ride in one you won’t want to go back to your old car.
Kevin Hague retweeted
Of course, that’s your contention. You’re a first-year machine-learning engineer. You just got finished readin' Attention Is All You Need, probably watched a Karpathy video too. So now you’re convinced everything is just transformers and scaling laws.
That's gonna last until next month when you discover convolution, and then you're gonna be talkin' about inductive biases and locality and how CNNs were actually incredibly compute-efficient for vision.
Then you're gonna read the FlashAttention paper, and suddenly everything's about IO complexity and SRAM and how FLOPs don't matter because the whole goddamn thing is memory-bandwidth bound.
That'll last until somebody shows you an MoE model, and then you're gonna be in here regurgitating DeepSeek, talkin' about expert parallelism and active parameters and how dense models are economically obsolete.
Then six months from now you'll write one shitty Triton kernel, look at an Nsight trace for the first time, and start telling everybody Python isn't actually the bottleneck because the GPU is asynchronous.
And by next year you're gonna be standing right here explaining to me that your 400-billion-parameter model is fast because you turned on CUDA graphs.
Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAI’s video models
“I’ve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that.
“With convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at what’s called a tensor. That’s the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. It’s just that simple. That numeric is called a tensor.
“You’d use a convolutional neural network to identify patterns that reveal what’s called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something.
“That was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. I’ve worked with GPUs a lot too. The visual-effects guys said, ‘Hey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.’
“I’ve learned that I can actually just call up and go, ‘Hey, it’s Ben Affleck. Can I come see what you’re doing?’ Sometimes people say yes, to my astonishment.”
Kevin Hague retweeted
I haven't posted in a while so here are 20 takes about AI, in no particular order.
1. It's very telling that the default question in tech and journalism is "how worried are you about X" and fighting over what to worry about. There's a deeply anxious cultural backdrop in Western Europe and North America that deeply affects how any technological development is interpreted; see for example news.gallup.com/poll/714593/…. "Nervousness as seriousness" does seem to be a larger problem in tech/broader discourse at the moment. I think you can care about risks/externalities while remaining pretty optimistic about technology.
2. I'm tired of people who in private pretend to be uncertain or only care about tail risk but then go on media campaigns making all sorts of absolute claims. I think trying to stoke flames and get everyone to freak out is deeply unhelpful, and will come with costs. I also continue to think p(dooms) are bad, vibe-based measures: see xitter.gq/sebkrier/status/204639…. People who should know better ignore that because they do the standard shortcut of acknowledging the caveat as sufficient for handling it. And this is coming from someone who does think working on catastrophic risks is important!
3. I'm also disappointed that parts of the AI policy ecosystem has felt so comfortable with populism, and happy too sacrifice important norms/principles to advance their aims. I've said it before, but the totalizing nature of AI risk discourse means some people act exactly like the power seeking optimizers they fear. I am more concerned about the gradual decay of our institutions, world order, and liberalism than I am about AI killing everyone; and I think we should be wary of AI advocacy that contributes to this.
4. Some parts of AI safety discourse are basically just thought experiments. Variants of "Assuming everyone has a nuke in their pocket, then what do we do?" This can be very useful at times! But too many just overfit to edge cases as their main operating worldview, and ignore endogenous responses from society. It's like being in 2000 and saying "see all these viruses, spyware, and spam? Well tech progress means we'll only get infinitely more of this and no way to adapt." So I think it's important to avoid fatalism, and absolutlism whilst considering edge scenarios, and the inability to provide an ex ante list of solutions to every permutation of 'future models will be more capable' is not evidence that these problems are intractable.
5. Many critics of AI safety also fail to really engage honestly with the fact that there will, in fact, be all sorts of important risks as we decrease the barriers to entry to many activities that otherwise require some degree of expertise. This doesn’t mean we need to live in a constant Schmittian ‘state of emergency’ requiring extraordinary measures every time a new model drops, but it does mean we will need important state-led efforts on e.g. cybersecurity and biosecurity. I wish the progress-oriented or acceleration-adjascent crowd would provide more concrete proposals here.
6. Too much of the AI safety community is a giant homogenous blob of correlated views, who read the same stuff and have the same groups of friends. This means you have a lot of correlated errors. The social and financial ties means many of them don't call out the bad bits, or mostly stay silent instead of proactively calling out the more extreme parts of the community. They share many implicit assumptions and so converge on similar ideas. I think there's real demand in DC for safety work that doesn't come with heavy ideological baggage (or at least different intellectual lineages). This isn't because the rationalist/EA community is necessarily wrong, but because diversity of thought is desirable for its own sake.
7. On the other hand, I find some critiques of AI safety world that focus on whether they sincerely hold their beliefs a bit annoying. My concern has never been the good intentions or sincerity, but rather the beliefs themselves, and what I think the outcomes of their prescriptions will be. People should shun witch hunts, bullying, and personal attacks - this is not the way. Though I think it’s overall positive that people are scrutinizing things more.
8. I’ve been saying this for years, but I don't think alignment is something anyone can or will solve "once and for all" - it's a continuous process and much of it won't depend on just inculcating an ideology to a model. It's annoying that this remains the frame many people use - stop saying ‘solving’. Alignment is a mixture of engineering work, philosophy, decision theory, and more; framing it as something to solve is a bad frame. See also paxmachina.ai/alignment-comp… and blog.cosmos-institute.org/p/…
9. I'm surprised to see so little interest in character training, personas, isolating effects of RL, and training a large diversity of model personas beyond the "assistant" persona. I've been complaining about this for years but at least now there are some nascent signs of life (see for example movingcastles.world/posts/ze…). I think we need a better ontology to describe model behaviour. I dislike when people talk about models behaviours as some sort of 'emergent' (mysterious!) phenomenon. However over time, I also expect this to become less important as we get better safety engineering - stuff like Jev but specific to AI control.
10. People who want to make a case for AI consciousness are right that epistemic uncertainty is important, and that categorical denials are overconfident. But they'll need to do much more work if they want any real movement on AI ‘welfare’, even if one acknowledges the uncertainty. I'm uncertain about alien life yet that's not sufficient to warrant any change in behaviour about them. I think Suleyman is also correct that there's a weird tautological thing going on where we train models to have certain inclinations (intentionally or not) and then rely on outputs as evidence. I also disagree that this will soon be a major societal divide - there is no great vegan ethical revolution among the masses, and they'll find the concern about consciousness even less compelling.
11. The recent HuggingFace incident and associated AISI ones are prosaically explainable by the training regime, poor evaluation envs, partial alignment training, bad engineering, confusing models on sim/real, and so on. None of this is evidence of models trying to take over, 'strong' instrumental convergence, or innate power-seeking drives stemming from higher capabilitiers or 'intelligence'. That doesn't mean these incidents are not problematic, or that we're not seeing a market failure - but it does mean we have plenty of agency and choice in mitigating them. In the coming year we should also expect continued sampling bias via ‘winner's curse’ sorts of mechanism: i.e. AI R&D will have lots of different candidate "training" with different RL environments and we will only notice/focus on the ones that go wrong.
12. "Pacing the frontier" feels a bit like the new "balancing risks and opportunities" - highly amorphous and too big of a tent to be actionable. Also can lead to reward hacking for humans, i.e. anything that slows down AI is good regardless of the costs or second/third order effects. Trying to modulate the speed of research seems a bit blunt to me, and inherits all the failures of Goodhearting too. Ultimately you want good governance, regulation, etc addressing specific problems because they are good on the merits - not because they merely correlate with slowing things down. See also: blog.cosmos-institute.org/p/…
13. There’s so much knowledge in all sorts of academic domains out there: social sciences, sociology, political sciences, management, contract theory, public choice, legal theory, jurisprudence, anthropology, game theory, etc. These fields hold many insights that the AI ecosystem often rediscovers from first principles (and sometimes that's fine!). We’ll need a lot more work for these worlds to collide: the AI side should be less dismissive of the ‘old world’ and the academic side should be less incurious/dismissive of AI progress. This is a boring take but I continue to think it's important and true.
14. It's a bit surprising that given how much philanthropic money there is in this space, how few orgs exist to actually write out standards - relative to how much is going towards advocacy and policy. For example it seems clear that we should want some robust best practices for an eval's ecological validity. Or how to design good sandboxes. On the other hand, given precedents of where professional standards have come from in the past, maybe we should expect that stuff to emerge via demand side from corporate buyers.
15. One slowly growing concern I have is banks being increasingly exposed to the AI build out. I'm very bullish about AI, but I think (a) every general purpose technology has seen a correction, historically; (b) this happens even if the financing side is sound, because once tech diffuses investors become more exposed and need to diversify; and (c) we seem to be over-indexing on scaling relative to diffusion. A recession will be extremely destabilizing, though I would be interested in someone unpacking the potential implications more.
16. Diffusion is good because (a) this is where the rubber hits the road and where a competitive deployment ecosystem generates consumer surplus; (b) it's directionally helpful to avoid concentration of power; and (c) it will help rebalance public opinion by making the benefits more tangible. Unfortunately it's entangled with decades long problems with our over regulated markets: clinical trials reform for example is long overdue. In general, this is far more of an issue in Europe than in the US though.
17. Relatedly, I think the "frontier lab eats everything" view is incorrect (just as the ‘singleton’ view was incorrect). I think models ultimately commoditize, efficiency goes up, costs go down, and while you'll always want frontier for certain domains, much of the economy will value many other things than "max capabilities" for tasks - e.g. control over data and efficiency/speed. The demand side will also continue to push for reducing dependency and maximising optionality, privacy etc so I'm bullish on things like mixture of models and routing. All of this is good for competition and diffusion of control.
18. Philanthropists served as the primary benefactors funding Venice's rich art and architectural history. I want to see so much more support for arts and culture. I'm so tired of the Monster energy Bored Ape fake vintage maps matcha Greek statue soft-pastel slop. If rich tech people and philanthropy wants to support this, they should also donate pretty much unconditionally - i.e. I don't want them to act as filters. I sympathize with people who want to make the world more beautiful, but don't want this to be determined by people who only know Greek statues and pretend to care about virtue ethics. As usual, let a thousand flowers bloom.
19. Too many people treat AGI/ASI as something indistinguishable from a God. Any mention of bottlenecks, physical limits, control, adaptation etc are met with skepticism: "You don't really believe in ASI." It's been remarked on before, but the behavior of some people really feels quasi-religious in nature sometimes. It's underrated how unpopular this vibe is amongst the wider public. The silver lining is that this specifically will diminish those people's outsized current relevance.
20. It seems like a lot of people that get rich and leave tech/labs end up having some sort of crisis of meaning. They need something to believe in and fight for, or some equivalent of repentance, or go search for edgy counterintuitive ideologies (sometimes even pretty bleak/dark stuff). I think they should spend more time with people outside the Bay Area.
I agree that verbal probability terms are inherently too elastic to carry calibrated meaning on their own, but I think this isn't exactly my concern here. My issue isn't with "using numbers in general" but how this is ultimately used, where, and and what for.
In the case of "doom", I think the great majority of forecasts I see are (a) essentially plucked out of thin air; (b) communicated to people who tend not to be numerically proficient; (c) without the associated caveats and context through which to understand the numbers.
If you're communicating e.g. likelihood of a car accident after drinking, or IPCC forecasts, then I agree, it makes a lot of sense and can be helpful if done well (e.g. ideas.repec.org/a/nat/natcli…).
If you're communicating something like "doom" I think it's not very useful because of the cumulative layers of uncertainty. Most people coming up with these numbers have (imho) no good rationale for their numbers, and predicting something like human extinction isn't qualitatively the same thing as predicting the weather or pandemic. The numbers aren't calibratable in principle from current data, and are mostly ideological priors wearing the clothes of posteriors.
A related example is the widely mocked Doomsday Clock, which you could easily transform into some numerical metric, add some rationalist vocab, and it would still be as bad. The issue with p(doom) is how absurdly complex and uncertain the very thing you're trying to forecast is, in particular with respect to AI.
But let's ignore this for a sec, and assume the experts have very good reasoning informing their numbers. Even then: calibrated probability estimates are fine in expert contexts but immediately lose their uncertainty/caveats when communicated to the public, and even to policymakers (remember rss.org.uk/news-publication/…?).
There's also a structural problem with unconditional p(doom) figures specifically because they bury the auxiliary assumptions that are doing most of the real work. A conditional estimate ("given continued scaling, no major alignment progress, deployment pattern X...") forces a forecaster to show their model, which is where the actual epistemic content lives.
A one-off number compresses all of that into a single digit and rewards the compression. And the social practice of treating these numbers as the headline artifact actively crowds out the (far more important) conditional reasoning that is (imo) a lot more valuable, and instead just freaks people out.
I suspect that frequently, that's precisely the point: "wake up sheeple! scary thing! pitchforks pls!". And in fact, the original forecast that led to this discussion is Bill Maher talking about a "20%" figure, illustrating my point that they have no idea what they're talking about, and essentially doing celebrity-laundering + fearmongering comms.
The Sunstein paper I shared in the other thread ("Probability Neglect") shows that no probability communication will be processed properly by the public - 1%, 10%, 50% all collapse into "very bad thing could happen," and public response is driven almost entirely by the vividness of the scenario.
So I don't like p(doom)s because they end up unnecessarily alarming people, rest on layers of assumptions that are never made legible, and more often than not, give people a misleading sense of accuracy.
Kevin Hague retweeted
"People aren't ready, this is gonna change everything"
You know what people weren't ready for? Fucking mammoths. Tigers eating them. Monkeys ripping their arms off. The black plague. The ice age. Smallpox. Vikings pulling up on the beach. Drinking water that gives you diarrhea until you die. Getting a scratch and losing your whole leg. The Mongols. The Spanish Inquisition.
One volcano can send us back to the dark ages and these safety guys are gonna be shocked. Oh the robots didn't kill us? It was a volcano? Imagine their disappointment as the hot ash rains down on them.
A solar flare is gonna explode your phone in your pocket your legs are gonna be covered in battery acid and your bank account will stop working. You're afraid of a computer program doing math?
"Things are moving too fast" homie you live on a rock that periodically deletes everything. You are one the most luckiest entities in a 100 light year radius living during a golden era. Cheer up.
My ancestors crossed an ocean in a wooden box because they ran out of fucking potatoes.
"The social disruption will be enormous!"
Nigga World War 2? World War 2? Hop in the people carrier we're landing on the beach.
Are these people out of their fucking minds? Do they not know what we've survived already?
A couple decades of air conditioning and people suddenly think oh this is the natural condition of mankind. No. We used to have eight kids because seven didn't make it. You're gonna be fine. Man the fuck up.
Accelerate.
Being able to inference locally is the new currency today. So many people building their own light sabers. Fascinating.
Kevin Hague retweeted
GPT-6 Astra, trained on ~100K+ NVIDIA Grace Blackwell NVLink72. From ChatGPT to o1 to Astra in 4 years.
AGI has arrived. Congratulations @OpenAI team.
400K GPUs coming online next.
Kevin Hague retweeted
What I see here?
AI is now building AI.
And everything is about to speed up because of it.
This is the hot Sunday essay going around AI industry right now.
Today we're releasing data on models accelerating research at OpenAI.
Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it will only be seen inside a few frontier AI labs. Being transparent is more urgent than ever, so we can inform the public discussion on whether and how to pace model development. I ask other AI companies to do the same.
openai.com/index/research-ac…
Kevin Hague retweeted
"Our cameras sort incoming light by its shape rather than its brightness, measuring information conventional systems discard. That means resolving detail below the diffraction limit at a fraction of the aperture, size, and cost."
Cameras for space and military. For now.
For as long as cameras have existed, seeing farther has meant building bigger optics.
We're building quantum camera platforms that break that rule and enable space domain awareness and reconnaissance missions.
Today we're announcing strategic investments from Lockheed Martin Ventures and Presidio Ventures, the venture arm of Sumitomo Corporation, capping our pre-seed at over $10M in total funding.
Seven months ago we came out of stealth with a first close led by Ground State Ventures. Since then, we completed on-sky demonstrations at a partner observatory, distinguishing closely spaced objects. Our Defense Advanced Research Projects Agency (DARPA) Direct-to-Phase-II SBIR advanced into its second option period. NASA - National Aeronautics and Space Administration selected us for an SBIR Phase I in orbital debris tracking and named us the winner of its $100,000 Space to Soil Challenge.
Our cameras sort incoming light by its shape rather than its brightness, measuring information conventional systems discard. That means resolving detail below the diffraction limit at a fraction of the aperture, size, and cost.
Next comes our first fielded camera. Flight heritage is planned through a hosted payload on a partner spacecraft.
Thank you to SBI US Gateway Fund, Plug and Play, Collaborative Fund, and TekVentures, and to Aether VC, milemark•capital, and ADIN for following on.
Link to the full release in the first comment.
Kevin Hague retweeted
Harvard and MIT Researchers Simulate the Entire Planet with 8.3 Billion AI Personas
It is big.
In a development that feels straight out of science fiction, a large collaborative team led by researchers from Harvard University and the Massachusetts Institute of Technology has unveiled MatrAIx: a population-scale AI simulation infrastructure designed to model the behavior of virtually every person on Earth.
MatrAIx centers on Persona 8B, a dataset containing 8.3 billion unique digital profiles. Each persona is defined across 1,290 categorical dimensions that capture everything from demographic background and psychological traits to spending habits, technical literacy, behavioral quirks, and lifestyle preferences.
I am using Persona 8B quite a bit and it is interesting.
The system is not merely a static database. Researchers bring these personas to life as agents powered by frontier large language models, including. These agents can then be dropped into four distinct digital environments: surveys, AI chat interfaces, live web browsing, and native desktop and mobile applications.
How MatrAIx Works
Persona 8B was constructed using a sophisticated dependency graph that preserves realistic correlations between attributes. Some records are synthetically generated while others are carefully extracted and grounded in real human data from biographies, reviews, surveys, and consented self-reports.
For practical research use, the team has released a high-quality coreset of approximately one million personas.
Once activated, the agents interact with real digital products and systems. Researchers have already run more than 18,000 evaluation trials across 1,010 tasks spanning commerce, software, finance, healthcare, and more than 20 other domains. The system records granular behavioral signals — how long an agent hesitates after a price increase, when it abandons a broken checkout flow, how much latency it will tolerate before closing an app, and whether it continues after an AI assistant fails.
Strong Validation Results
In a controlled study of 400 trials measuring adherence to ten behavioral attributes across all four environments, the agents successfully expressed or correctly suppressed their assigned traits 91.5 percent of the time. Human judges also rated the quality of the human-grounded personas highly (average 4.135 out of 5).
These results suggest that MatrAIx can generate coherent, demographically and psychologically consistent simulated users at a scale previously impossible.
Implications for Research and Business
Traditional market research and user testing are slow, expensive, and limited in sample diversity. MatrAIx offers a complementary approach: the ability to simulate how billions of different types of people might react to a new product feature, pricing change, interface redesign, or AI system — overnight, on a single server.
Product teams could stress-test ideas before expensive real-world launches. Researchers studying human-AI interaction could explore rare behavioral edge cases. Policymakers and social scientists might model the downstream effects of new technologies across highly diverse populations.
The project is open source. Code is available on GitHub, a project website has been launched at matraix.ai, and the one-million-persona coreset is being released for broader research use.
MatrAIx is not intended as a replacement for real human feedback. The researchers emphasize that it is a powerful tool for exploration, hypothesis generation, and large-scale stress testing.
As the underlying language models improve and the persona models grow more sophisticated, the fidelity of these digital populations is expected to increase further.
For now, the system represents one of the most ambitious attempts yet to create a usable, population-scale digital mirror of humanity a simulation infrastructure that lets us ask “what if” at planetary scale before deploying them in the real world.
Kevin Hague retweeted
I missed this, so did everyone. Only has six likes, including mine.
Digital humans from many cameras.
🚀 Excited to share our latest work, UMA: Ultra-detailed Human Avatars via Multi-level Surface Alignment, now published in ACM Transactions on Graphics (TOG) and to be presented at SIGGRAPH Asia 2026!
UMA reconstructs photorealistic animatable human avatars from 6K ultra-high-resolution multi-view videos, faithfully preserving dynamic appearance and geometry, even for garments with challenging dynamic texture patterns. The resulting avatars allow users to digitally zoom in and closely inspect fine-scale texture details.
Our key contributions include:
• A novel multi-level surface alignment framework leveraging foundational point trackers for robust dynamic reconstruction.
• High-fidelity reconstruction of dynamic appearance and geometry with strong temporal consistency.
• A new 40-camera 6K multi-view dataset featuring long dynamic sequences with challenging clothing motions and rich texture patterns.
Code, dataset, and an interactive online demo are now available.
📄 Paper: dl.acm.org/doi/10.1145/38293…
🌐 Project Page: vcai.mpi-inf.mpg.de/projects…
🎮 Interactive Demo: uma4.umaumau.xyz/
💻 Code: github.com/kv2000/UMA
📦 Dataset: gvv-assets.mpi-inf.mpg.de/um…
Authors:Heming Zhu @wootwootwo , Guoxing Sun @GuoxingSun , Christian Theobalt, and Marc Habermann @marc_habermann
#ACMTOG #SIGGRAPHAsia #ComputerGraphics #DigitalHumans #3DVision
Kevin Hague retweeted
Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community.
During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion.
That’s why we created the Open Secure AI Alliance.
AI security advances when the industry builds in the open, together.
We're introducing the Open Secure AI Alliance with industry leaders to develop new techniques and tools to safeguard software and agents.
By sharing models, tooling and research in the open, we can broaden the community of defenders.
Learn more about the founding members’ contributions: nvda.ws/4pD8Fc5
ALT Logos of various companies including Adobe, Cisco, and Microsoft.