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Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really need all this complexity? We design a small and simple one-step model, ROMS-IMLE, which attains an FID of 2.56 on ImageNet. Joint w/ Chirag Vashist 1/6

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Ke Li 🍁 @ ECCV 2026

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36,301 görüntüleme • 8 gün önce •via X (Twitter)

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Generative models (diffusion/flow) are taking over robotics 🤖. But do we really need to model the full action distribution to control a robot? We suspected the success of Generative Control Policies (GCPs) might be "Much Ado About Noising." We rigorously tested the myths. 🧵👇
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Generative models (diffusion/flow) are taking over robotics 🤖. But do we really need to model the full action distribution to control a robot? We suspected the success of Generative Control Policies (GCPs) might be "Much Ado About Noising." We rigorously tested the myths. 🧵👇

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“I need Chet, Chet needs me, we both need Hart and Dub, and K-Rich and so on. We all need each other. Jokic is a really talented guy but all we care about is winning the game and we got one tonight and we got to go get one more.” -SGA

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We just brought flow maps to language modeling for one-step sequence generation 💥 Discrete diffusion is not necessary -- continuous flows over one-hot encodings achieve SoTA performance and ≥8.3× faster generation 🔥 We believe this is a major step forward for discrete generative modeling and language modeling alike. 🚀 Full thread from first author Chanhyuk Lee:
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We just brought flow maps to language modeling for one-step sequence generation 💥 Discrete diffusion is not necessary -- continuous flows over one-hot encodings achieve SoTA performance and ≥8.3× faster generation 🔥 We believe this is a major step forward for discrete generative modeling and language modeling alike. 🚀 Full thread from first author Chanhyuk Lee:

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We are releasing 4M-21 with a permissive license, including its source code and trained models. It's a pretty effective multimodal model that solves 10s of tasks & modalities. See the demo code, sample results, and the tokenizers of diverse modalities on the website. IMO, the multitask learning aspect of multimodal models has really taken a step forward. We can train a single model on many diverse tasks with ~SOTA accuracy. But a long way to go in terms of transfer/emergence. 🌐 ⌨️ Joint work w/ EPFL Apple.

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The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."
2:40

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."

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GenCast is a diffusion model, similar to the machine learning models which also power generative AI. 🎨 We trained it on 40 years of historical data from ECMWF, which included variables such as temperature, wind speed, and pressure at various altitudes - enabling it to learn global weather patterns.

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[ENG TRANS] SUZY’s narration at 59th Baeksang Arts Awards “As always, all of us live a simple and an ordinary life. We go to school, we go to work, we meet friends and ask each other how they have been doing, but how are we really?” cont.
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[ENG TRANS] SUZY’s narration at 59th Baeksang Arts Awards “As always, all of us live a simple and an ordinary life. We go to school, we go to work, we meet friends and ask each other how they have been doing, but how are we really?” cont.

영원한배수지 (SUZY)

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I’ve really enjoyed working in Ukraine this year, in a small way helping design their veterans services. The scale and complexity is enormous; they will need lots of support in the year ahead. They are a great people and we must not waver in our commitment to them. 🇺🇦🤝🇬🇧

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All 11 sectors are firing on all cylinders, and the reason is people talk a lot about AI in a negative way, but the truth is it's found its way into all 11 sectors, added margin, and added productivity. Numbers are showing up in cash flow and earnings. It's that simple. No one saw this coming. It's just one of those serendipitous moments where technology adds a lot of value to an economy. It's doing it here. It's doing it all around the world.

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Trump: "We'll be putting an initially small tariffs on pharmaceuticals. But in one year, one in a half years maximum, it's gonna go to 150% and then it's gonna go to 250%, because we want pharmaceuticals made in our country."

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The DEPRESSING reality of AI adoption curves Advanced AI just broke into it's third major paradigm since launching. Paradigm One was the simple autocomplete engine. GPT-2 and original GPT-3 were glorified autocomplete tools, just "next token predictors." Paradigm 1.5 was when we added "instruct-aligned" GPT-3, which set the stage for Paradigm Two. Paradigm Two was the chatbot era, with ChatGPT being the front-runner. Paradigm 2.5 was when we started adding reasoning, tool use, and RAG, which set the stage for agentic abilities (Paradigm 3). OpenClaw just blew the lid off Paradigm 3, and we're just at the beginning of this new ramp-up in capabilities. The thing is... each of these paradigm shifts creates fundamentally different UX and technical affordances. While most Fortune 500 companies are still struggling to figure out the cyber security, legal, and financial risks of chatbots, the industry is going whole hog into autonomous agents. And many people will try to graft their understanding of chatbots onto agents. But that's like trying to compare the electric lightbulb to the electric motor.... ...yes they both ran on electricity, but their uses, affordances, limitations, and risks were fundamentally different. Yes, autocomplete, chatbots, and agents all run on "next token prediction" but that's like saying "it all runs on electrons." My goal today is to help give you a better intuition as to why adoption is so slow and to give you a new reference frame to understand that agents are a phase change from chatbots. But also... the state and big companies are going to move depressingly slow on all of this...
30:02

The DEPRESSING reality of AI adoption curves Advanced AI just broke into it's third major paradigm since launching. Paradigm One was the simple autocomplete engine. GPT-2 and original GPT-3 were glorified autocomplete tools, just "next token predictors." Paradigm 1.5 was when we added "instruct-aligned" GPT-3, which set the stage for Paradigm Two. Paradigm Two was the chatbot era, with ChatGPT being the front-runner. Paradigm 2.5 was when we started adding reasoning, tool use, and RAG, which set the stage for agentic abilities (Paradigm 3). OpenClaw just blew the lid off Paradigm 3, and we're just at the beginning of this new ramp-up in capabilities. The thing is... each of these paradigm shifts creates fundamentally different UX and technical affordances. While most Fortune 500 companies are still struggling to figure out the cyber security, legal, and financial risks of chatbots, the industry is going whole hog into autonomous agents. And many people will try to graft their understanding of chatbots onto agents. But that's like trying to compare the electric lightbulb to the electric motor.... ...yes they both ran on electricity, but their uses, affordances, limitations, and risks were fundamentally different. Yes, autocomplete, chatbots, and agents all run on "next token prediction" but that's like saying "it all runs on electrons." My goal today is to help give you a better intuition as to why adoption is so slow and to give you a new reference frame to understand that agents are a phase change from chatbots. But also... the state and big companies are going to move depressingly slow on all of this...

David Shapiro (L/0)

17,266 görüntüleme • 7 ay önce

🪐 WORLD CREATORS Generative AI opened the door for everyone to tell their stories without the need for a crew or a big budget. We started slowly with images and saw an enormous interest in tools like Midjourney, DALL-E, or Leonardo. Now, a bit more than a year later, we find ourselves in front of new technologies that are transforming our way of life, how we create, and how we consume content. ChatGPT is rapidly replacing Googling, and all the generative AI tools are replacing all the stock photos and videos, with tools like Runway enabling storytelling for everyone. And we won't take over Hollywood. We will surpass it in ways we can't even imagine right now. We'll be able to create worlds and stories beyond our wildest imagination. Generative AI is enabling creative people to go 1000x on what was possible before. At the core of everything we'll still need human creativity, and we have to understand that AI tools without it will be nothing. Until then, enjoy a short video I've put together to illustrate a small fraction of the worlds we will create soon. What worlds and stories are you excited to create?
2:01

🪐 WORLD CREATORS Generative AI opened the door for everyone to tell their stories without the need for a crew or a big budget. We started slowly with images and saw an enormous interest in tools like Midjourney, DALL-E, or Leonardo. Now, a bit more than a year later, we find ourselves in front of new technologies that are transforming our way of life, how we create, and how we consume content. ChatGPT is rapidly replacing Googling, and all the generative AI tools are replacing all the stock photos and videos, with tools like Runway enabling storytelling for everyone. And we won't take over Hollywood. We will surpass it in ways we can't even imagine right now. We'll be able to create worlds and stories beyond our wildest imagination. Generative AI is enabling creative people to go 1000x on what was possible before. At the core of everything we'll still need human creativity, and we have to understand that AI tools without it will be nothing. Until then, enjoy a short video I've put together to illustrate a small fraction of the worlds we will create soon. What worlds and stories are you excited to create?

Alex Patrascu

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Ideogram CEO Mohammad Norouzi says image models need taste: "We think taste is extremely important and we really want our models to have taste, and it's very hard to explain it." "One element of taste is going outside of the normal a little bit and not conforming to the average opinion, which is a little against being on top of the leaderboard." "Ultimately, we work with all the arenas... but we care about our own internal evaluation." "AI is not very good at doing the actual taste evaluation yet, so we work with designers... to really push on the taste." Mohammad Norouzi with Yoko and Justine Moore
1:06

Ideogram CEO Mohammad Norouzi says image models need taste: "We think taste is extremely important and we really want our models to have taste, and it's very hard to explain it." "One element of taste is going outside of the normal a little bit and not conforming to the average opinion, which is a little against being on top of the leaderboard." "Ultimately, we work with all the arenas... but we care about our own internal evaluation." "AI is not very good at doing the actual taste evaluation yet, so we work with designers... to really push on the taste." Mohammad Norouzi with Yoko and Justine Moore

a16z

19,520 görüntüleme • 2 ay önce

The consumer is already the agent, but it's a very inefficient AI. We set price alerts, wait, then buy. AI will complete the loop by monitoring and buying all on its own. Alex Rampell (Alex Rampell): “This is the easiest form of predicting the future ever.”
0:41

The consumer is already the agent, but it's a very inefficient AI. We set price alerts, wait, then buy. AI will complete the loop by monitoring and buying all on its own. Alex Rampell (Alex Rampell): “This is the easiest form of predicting the future ever.”

a16z

38,107 görüntüleme • 11 ay önce

🎙️ Jeff Peterson on the trade deadline's success and asset management: "Over the next 7 drafts, we have 11 first-round picks, 15 second-round picks." "Being able to continue to stockpile those draft picks, it's always something that myself and our front office—it's been a goal of ours. We'll continue to add 'em, and when we have moments like this—when we see a player that we think is worth it or for the price—we'll continue to try to use those picks to go get them." 🎙️ On knowing when to make a big move: "We'll know when the time is right." "A lot of nuances go into it—which players are available, contract lengths, different dynamics that go into all of that. I promise you we will know when the time is right. This recent stretch has been beautiful to watch. All the credit goes to the players, Charles Lee, and his coaching staff... it's been really fun to see. All of this has just continued to add to the sample size that we need. In the summer, we'll figure out exactly what this team is and what else we need to do to continue to take the next step."
1:54

🎙️ Jeff Peterson on the trade deadline's success and asset management: "Over the next 7 drafts, we have 11 first-round picks, 15 second-round picks." "Being able to continue to stockpile those draft picks, it's always something that myself and our front office—it's been a goal of ours. We'll continue to add 'em, and when we have moments like this—when we see a player that we think is worth it or for the price—we'll continue to try to use those picks to go get them." 🎙️ On knowing when to make a big move: "We'll know when the time is right." "A lot of nuances go into it—which players are available, contract lengths, different dynamics that go into all of that. I promise you we will know when the time is right. This recent stretch has been beautiful to watch. All the credit goes to the players, Charles Lee, and his coaching staff... it's been really fun to see. All of this has just continued to add to the sample size that we need. In the summer, we'll figure out exactly what this team is and what else we need to do to continue to take the next step."

r/CharlotteHornets

31,689 görüntüleme • 7 ay önce