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We release Diamond Maps💎 unlocking accurate and efficient guidance for diffusion models. Our experiments show that our methods scale incredibly well. Excited to see what people will build with this! Accurate guidance has been a notoriously hard problem, but in this work, we’re bringing TWO (!) solutions to the...

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Peter Holderrieth

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61,236 görüntüleme • 5 ay önce •via X (Twitter)

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Diffusion models are an amazing tool for cofolding, they allow us to predict a protein and the molecule bound to it at once. But they are not exactly fast and require a lot of denoising steps to get accurate predictions. So we distilled ours. Meet DeCAF-Pearl: the first flow map model for all-atom cofolding. Instead of inching along the denoising trajectory, a flow map learns to jump across it. DeCAF-Pearl runs structure generation ~5x faster than Pearl, our SOTA model, while still maintaining the performance of the teacher model. That speed up allows us to run larger experiments and generate more synthetic data to improve our models. Getting there meant reparameterizing into noise-level space to stabilize gradients, committing to clean-structure prediction to keep the rigid-alignment loss biomolecules needed, and building DeCAF-Search, one steering algorithm for every compute budget. For more technical details, read out blog post: And the paper:
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Diffusion models are an amazing tool for cofolding, they allow us to predict a protein and the molecule bound to it at once. But they are not exactly fast and require a lot of denoising steps to get accurate predictions. So we distilled ours. Meet DeCAF-Pearl: the first flow map model for all-atom cofolding. Instead of inching along the denoising trajectory, a flow map learns to jump across it. DeCAF-Pearl runs structure generation ~5x faster than Pearl, our SOTA model, while still maintaining the performance of the teacher model. That speed up allows us to run larger experiments and generate more synthetic data to improve our models. Getting there meant reparameterizing into noise-level space to stabilize gradients, committing to clean-structure prediction to keep the rigid-alignment loss biomolecules needed, and building DeCAF-Search, one steering algorithm for every compute budget. For more technical details, read out blog post: And the paper:

Sergey Edunov

37,207 görüntüleme • 3 ay önce

Massive update for AI Engineers! Training diffusion models just got a lot easier. dLLM is an open-source library that does for diffusion models what Hugging Face did for transformers. Here's why this matters: Traditional autoregressive models generate text left-to-right, one token at a time. Diffusion models work differently - they refine the entire sequence iteratively, giving you better control over generation quality and more flexible editing capabilities. The problem? Building and training these models required stitching together scattered tools and reimplementing research papers from scratch. Most of the tooling has been scattered and is hard to reproduce. dLLM changes this: It unifies everything you need to train, evaluate, and deploy diffusion language models: ↳ Scalable training with LoRA, DeepSpeed, and FSDP support ↳ Unified evaluation that abstracts away inference complexity ↳ Ready-to-use recipes for pretraining, finetuning, and evaluation The library includes implementations of models like LLaDA and Dream, plus training algorithms like Edit Flows that enable insertion, deletion, and substitution operations. The team just released ModernBERT-Chat models showing you can turn BERT into lightweight chatbots through masked instruction tuning. This is practical and worth exploring. The setup is straightforward. Run locally with Accelerate or scale to multi-node clusters with Slurm. If you're working with language models and want to explore diffusion-based approaches without rebuilding infrastructure, dLLM gives you a production-ready starting point. Link to the repo in the next tweet.
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Massive update for AI Engineers! Training diffusion models just got a lot easier. dLLM is an open-source library that does for diffusion models what Hugging Face did for transformers. Here's why this matters: Traditional autoregressive models generate text left-to-right, one token at a time. Diffusion models work differently - they refine the entire sequence iteratively, giving you better control over generation quality and more flexible editing capabilities. The problem? Building and training these models required stitching together scattered tools and reimplementing research papers from scratch. Most of the tooling has been scattered and is hard to reproduce. dLLM changes this: It unifies everything you need to train, evaluate, and deploy diffusion language models: ↳ Scalable training with LoRA, DeepSpeed, and FSDP support ↳ Unified evaluation that abstracts away inference complexity ↳ Ready-to-use recipes for pretraining, finetuning, and evaluation The library includes implementations of models like LLaDA and Dream, plus training algorithms like Edit Flows that enable insertion, deletion, and substitution operations. The team just released ModernBERT-Chat models showing you can turn BERT into lightweight chatbots through masked instruction tuning. This is practical and worth exploring. The setup is straightforward. Run locally with Accelerate or scale to multi-node clusters with Slurm. If you're working with language models and want to explore diffusion-based approaches without rebuilding infrastructure, dLLM gives you a production-ready starting point. Link to the repo in the next tweet.

Akshay 🚀

64,011 görüntüleme • 10 ay önce

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.
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MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.

AK

25,465 görüntüleme • 2 yıl önce

We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:
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We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:

Sakana AI

360,561 görüntüleme • 1 yıl önce

CES was an enormous success. We saw thousands of people and a few dogs interacting with our agents and robots. We booked this booth at the last minute, mainly to book next year’s booth. Truly extraordinary what our team was able to put together on such short notice. Next year we reserved one of the largest booths at CES in the highest traffic location for AI and Robotics. We plan to unveil something extraordinary. Until the next show, we will be debuting the first custom models that will emerge from the first humanoid robotics hot rod shop. These models will be walking around, talking and interacting at tradeshows all over the world over the next few months. We are in the process of acquiring every humanoid robot available on the market to modify these as well. Stay tuned for more updates, the future is here and it’s rapidly accelerating.
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CES was an enormous success. We saw thousands of people and a few dogs interacting with our agents and robots. We booked this booth at the last minute, mainly to book next year’s booth. Truly extraordinary what our team was able to put together on such short notice. Next year we reserved one of the largest booths at CES in the highest traffic location for AI and Robotics. We plan to unveil something extraordinary. Until the next show, we will be debuting the first custom models that will emerge from the first humanoid robotics hot rod shop. These models will be walking around, talking and interacting at tradeshows all over the world over the next few months. We are in the process of acquiring every humanoid robot available on the market to modify these as well. Stay tuned for more updates, the future is here and it’s rapidly accelerating.

Parallel

16,892 görüntüleme • 1 yıl önce

IT’S OUT! Head over to the Gorillaz YouTube channel now (link in bio) to watch the full film. This film is a true love letter to the golden era of 2D animation. We worked incredibly hard alongside Jamie Hewlett to make it as period-accurate as possible and, like all our projects, it was hand-drawn by an incredible team of artists. We even used a variety of practical effects in the film, which was a lot of fun to do. We had a brilliant time making this film and learnt a huge amount while studying the masters of the 60s and 70s. If you’ve been following the release of The Mountain, you know how special and meaningful this project is. We’re so proud to be a part of it and want to say a massive thank you to Jamie and Eleven Management for trusting us to tell this beautiful story. We couldn’t be prouder of the work we did together, and we're so happy to be sharing it with the world! #Gorillaz #THELINE #TheMountain
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IT’S OUT! Head over to the Gorillaz YouTube channel now (link in bio) to watch the full film. This film is a true love letter to the golden era of 2D animation. We worked incredibly hard alongside Jamie Hewlett to make it as period-accurate as possible and, like all our projects, it was hand-drawn by an incredible team of artists. We even used a variety of practical effects in the film, which was a lot of fun to do. We had a brilliant time making this film and learnt a huge amount while studying the masters of the 60s and 70s. If you’ve been following the release of The Mountain, you know how special and meaningful this project is. We’re so proud to be a part of it and want to say a massive thank you to Jamie and Eleven Management for trusting us to tell this beautiful story. We couldn’t be prouder of the work we did together, and we're so happy to be sharing it with the world! #Gorillaz #THELINE #TheMountain

THE LINE

259,544 görüntüleme • 7 ay önce

One small step for Hivemind, one giant leap for mission autonomy. Hivemind is going to space! Shield AI and Sedaro are partnering to send Hivemind to space, bringing the same resilient, edge-based autonomy that is redefining warfare to satellites delivering critical infrastructure and national defense capabilities. Our strategic partnership establishes Hivemind Pilot as Sedaro’s preferred autonomy software for on-orbit demonstrations, while also enabling the use of the Sedaro Platform for developing, testing, and demonstrating Hivemind in space-relevant scenarios. “Combining Hivemind Pilot with Sedaro’s high-fidelity models and simulation environment will unlock multi-agent cognitive teaming for space applications and new mission capabilities for our customers.” - Christian Gutierrez, Vice President of Hivemind Solutions. Hivemind is multi-domain mission autonomy. We've done autonomy for air, ground, and surface applications this year, and we couldn't be more excited to go to space. If your systems need autonomy, we'd love to work with you and help give your systems the best autonomy in the business. Read the full press release: Autonomy for the world. The greatest victory requires no war.
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One small step for Hivemind, one giant leap for mission autonomy. Hivemind is going to space! Shield AI and Sedaro are partnering to send Hivemind to space, bringing the same resilient, edge-based autonomy that is redefining warfare to satellites delivering critical infrastructure and national defense capabilities. Our strategic partnership establishes Hivemind Pilot as Sedaro’s preferred autonomy software for on-orbit demonstrations, while also enabling the use of the Sedaro Platform for developing, testing, and demonstrating Hivemind in space-relevant scenarios. “Combining Hivemind Pilot with Sedaro’s high-fidelity models and simulation environment will unlock multi-agent cognitive teaming for space applications and new mission capabilities for our customers.” - Christian Gutierrez, Vice President of Hivemind Solutions. Hivemind is multi-domain mission autonomy. We've done autonomy for air, ground, and surface applications this year, and we couldn't be more excited to go to space. If your systems need autonomy, we'd love to work with you and help give your systems the best autonomy in the business. Read the full press release: Autonomy for the world. The greatest victory requires no war.

Shield AI

33,072 görüntüleme • 10 ay önce

A substantive meeting with Rob Jetten. Thank you, Rob, for all your support for our people and your readiness to work together on solutions that will help protect lives. Without a doubt, Europe needs its own anti-ballistic capabilities so that the European way of life can be fully protected. We discussed what has already been accomplished together with our partners in this regard. The Prime Minister and I also signed a full-fledged drone cooperation agreement – a Drone Deal. It will create even more opportunities for joint production and technological development, and, most importantly, bring greater consistency to the exchange of expertise and the export of weapons. I am grateful to the Netherlands for the assistance that has helped protect and support thousands upon thousands of our people's lives from Russian strikes.
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A substantive meeting with Rob Jetten. Thank you, Rob, for all your support for our people and your readiness to work together on solutions that will help protect lives. Without a doubt, Europe needs its own anti-ballistic capabilities so that the European way of life can be fully protected. We discussed what has already been accomplished together with our partners in this regard. The Prime Minister and I also signed a full-fledged drone cooperation agreement – a Drone Deal. It will create even more opportunities for joint production and technological development, and, most importantly, bring greater consistency to the exchange of expertise and the export of weapons. I am grateful to the Netherlands for the assistance that has helped protect and support thousands upon thousands of our people's lives from Russian strikes.

Volodymyr Zelenskyy / Володимир Зеленський

140,752 görüntüleme • 3 ay önce

The Sabotaging Practice of Over Supply and Sameness in the NFT Space. The current zeitgeist of the NFT space is that the same artists are doing the same kind of work five times a year, with project after project leaving a trail of disappointment and discontent among collectors and all of us watching in disbelief as huge resources are extracted from the space over work that feels like it could be left as an "artist study." I understand that you can do what you want with your money as collectors, but we are killing the whole space with this incestuous practice. No artist is that prolific to be able to do 5 collections of 100+ pieces each every year and actually deliver innovation and some kind of creative evolution. Of course, they can pretend play that the work has something new, but there is no precedent nor proof that that has ever happened in the speed that it happens in the NFT space. Again, people are free to through away their resources on whatever they want but with this way of doing things, we more and more are going to start seeing the consequences. Oh! There are consequences? Yes. Maybe unintended, but there are. Let's see. Let's start with the loss of belief in the NFT space as somewhere where emerging artists can come and find support for their experiments. Why even bother to bring experiments, innovation, and new ways to think of art on the blockchain if the same people have all the collectors hypnotized with their magical flutes? Why even try to come to a space where taking risks and challenging the status quo (the mission of art!!!) is overlooked? This makes the NFT space a social club and not a space for art. I guess it is fine, but IMO it is a recipe for disaster. New collectors stay away because the art will slowly but surely become stale and un-challenging. Why even bother to come and see what is happening here if you can't, as a collector, see new weird and up-and-coming artists? The amount of noise emitted by the same artists doing the same art over and over, drowns out any new voices. Again. A recipe for disaster. The NFT space is becoming a space of disappointment and doubt. We think that collections going to zero one after the other, over and over, is not damaging? I feel we are kidding ourselves. Disappointment piles up, and again, the people who will hurt are the emerging artists, the new blood, the ones who are willing to risk the most and, in return, put fire in this cold space of sameness. I love this space—don't get me wrong—it has changed my life, and I believe it has a ton of potential, but things need to change for it to become a beacon of light in art. But we need to support new voices. We need to support new ideas. The challenge is huge. I hope to contribute all I can to this change. I hope more and more see how exciting it is to go out and try to discover what else is out there and move this space forward. But again, I understand the leaps of faith needed, but if there is a space that is based on that, it's the NFT space...so there is hope. We will see. 📺by Boldtron
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The Sabotaging Practice of Over Supply and Sameness in the NFT Space. The current zeitgeist of the NFT space is that the same artists are doing the same kind of work five times a year, with project after project leaving a trail of disappointment and discontent among collectors and all of us watching in disbelief as huge resources are extracted from the space over work that feels like it could be left as an "artist study." I understand that you can do what you want with your money as collectors, but we are killing the whole space with this incestuous practice. No artist is that prolific to be able to do 5 collections of 100+ pieces each every year and actually deliver innovation and some kind of creative evolution. Of course, they can pretend play that the work has something new, but there is no precedent nor proof that that has ever happened in the speed that it happens in the NFT space. Again, people are free to through away their resources on whatever they want but with this way of doing things, we more and more are going to start seeing the consequences. Oh! There are consequences? Yes. Maybe unintended, but there are. Let's see. Let's start with the loss of belief in the NFT space as somewhere where emerging artists can come and find support for their experiments. Why even bother to bring experiments, innovation, and new ways to think of art on the blockchain if the same people have all the collectors hypnotized with their magical flutes? Why even try to come to a space where taking risks and challenging the status quo (the mission of art!!!) is overlooked? This makes the NFT space a social club and not a space for art. I guess it is fine, but IMO it is a recipe for disaster. New collectors stay away because the art will slowly but surely become stale and un-challenging. Why even bother to come and see what is happening here if you can't, as a collector, see new weird and up-and-coming artists? The amount of noise emitted by the same artists doing the same art over and over, drowns out any new voices. Again. A recipe for disaster. The NFT space is becoming a space of disappointment and doubt. We think that collections going to zero one after the other, over and over, is not damaging? I feel we are kidding ourselves. Disappointment piles up, and again, the people who will hurt are the emerging artists, the new blood, the ones who are willing to risk the most and, in return, put fire in this cold space of sameness. I love this space—don't get me wrong—it has changed my life, and I believe it has a ton of potential, but things need to change for it to become a beacon of light in art. But we need to support new voices. We need to support new ideas. The challenge is huge. I hope to contribute all I can to this change. I hope more and more see how exciting it is to go out and try to discover what else is out there and move this space forward. But again, I understand the leaps of faith needed, but if there is a space that is based on that, it's the NFT space...so there is hope. We will see. 📺by Boldtron

alejandro cartagena

98,261 görüntüleme • 2 yıl önce

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.
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I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

281,425 görüntüleme • 4 ay önce

Introducing ASAL: Automating the Search for Artificial Life with Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.
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Introducing ASAL: Automating the Search for Artificial Life with Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.

Sakana AI

751,904 görüntüleme • 1 yıl önce

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.
0:13

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

nathan (in sf)

40,440 görüntüleme • 8 ay önce

It’s more than a little daunting to set out to expand and improve the identity system for a company and brand like Stripe. But we knew we had to — the existing one had served us well, but wasn’t up to the task anymore. Our brand system required new and improved tools to scale with our ever growing audiences, new products, global footprint, and more. This update introduces material improvements to infographics, advertising, type styles, and more. While the wordmark remains unchanged, we’re using the dot of the ‘i’ (called the “tittle”), a parallelogram pointing up and to the right, to serve as our identifying symbol. We’re also using it as an ever evolving storytelling device to use when talking about our many great users (you can see the latest brand campaign in SF and NYC doing just that). Anyone who has ever worked on the refresh and expansion of an existing system for a large company knows that it is no small endeavor. Crafting impactful solutions, building alignment, creating extensible guidelines, building toolkits, and orchestrating rollout requires a ton of resilience. Here’s to the team that continually inspires me with their dedication, rigor, taste, and exceptional vibes. Great work and thank you to the Brand Studio folks, and of course our many many amazing and invaluable friends and collaborators across the company who all helped shape the work. And a special thank you to a handful of creative agencies that helped us along the way.
0:18

It’s more than a little daunting to set out to expand and improve the identity system for a company and brand like Stripe. But we knew we had to — the existing one had served us well, but wasn’t up to the task anymore. Our brand system required new and improved tools to scale with our ever growing audiences, new products, global footprint, and more. This update introduces material improvements to infographics, advertising, type styles, and more. While the wordmark remains unchanged, we’re using the dot of the ‘i’ (called the “tittle”), a parallelogram pointing up and to the right, to serve as our identifying symbol. We’re also using it as an ever evolving storytelling device to use when talking about our many great users (you can see the latest brand campaign in SF and NYC doing just that). Anyone who has ever worked on the refresh and expansion of an existing system for a large company knows that it is no small endeavor. Crafting impactful solutions, building alignment, creating extensible guidelines, building toolkits, and orchestrating rollout requires a ton of resilience. Here’s to the team that continually inspires me with their dedication, rigor, taste, and exceptional vibes. Great work and thank you to the Brand Studio folks, and of course our many many amazing and invaluable friends and collaborators across the company who all helped shape the work. And a special thank you to a handful of creative agencies that helped us along the way.

Michael Jeter

11,567 görüntüleme • 11 ay önce

What an incredible honor it was to serve as Grand Marshal of the 137th Annual Rose Parade. From the moment I arrived, I felt the love, history, and pride that make this tradition so special. Thank you to Mark Leavens- President of the Tournament of Roses Parade and the entire committee for this unforgettable opportunity, and to the thousands of volunteers, performers, and organizers whose hard work brings this parade to life year after year. Your dedication is truly inspiring. This year’s theme, The Magic of Teamwork, truly resonated with me because I’ve been a team player my whole life! Whether it’s on the basketball court, in business, or in the community. The Rose Parade represents more than a celebration—it’s about community, unity, and bringing people together across generations. I’m grateful to have been a small part of such an iconic moment and will cherish this experience forever.
0:10

What an incredible honor it was to serve as Grand Marshal of the 137th Annual Rose Parade. From the moment I arrived, I felt the love, history, and pride that make this tradition so special. Thank you to Mark Leavens- President of the Tournament of Roses Parade and the entire committee for this unforgettable opportunity, and to the thousands of volunteers, performers, and organizers whose hard work brings this parade to life year after year. Your dedication is truly inspiring. This year’s theme, The Magic of Teamwork, truly resonated with me because I’ve been a team player my whole life! Whether it’s on the basketball court, in business, or in the community. The Rose Parade represents more than a celebration—it’s about community, unity, and bringing people together across generations. I’m grateful to have been a small part of such an iconic moment and will cherish this experience forever.

Earvin Magic Johnson

65,746 görüntüleme • 9 ay önce

SUNNY HILL FESTIVAL NIGHT ONE 🇽🇰❤️ This festival is something my family and I created with so much love for our city PRISHTINA and to see it come to life like this is beyond anything I could’ve dreamed of. Sharing this night with you all, in the city that shaped me, surrounded by so much energy, joy, and pride… it’s hard to put into words what it means. Performing for you, and sharing the stage with my dad Dukagjin Lipa was a moment I’ll never forget. I feel so incredibly lucky and grateful. And we still have two more nights to celebrate together!! JU DUA me krejt zemrën ❤️❤️❤️❤️❤️
1:30

SUNNY HILL FESTIVAL NIGHT ONE 🇽🇰❤️ This festival is something my family and I created with so much love for our city PRISHTINA and to see it come to life like this is beyond anything I could’ve dreamed of. Sharing this night with you all, in the city that shaped me, surrounded by so much energy, joy, and pride… it’s hard to put into words what it means. Performing for you, and sharing the stage with my dad Dukagjin Lipa was a moment I’ll never forget. I feel so incredibly lucky and grateful. And we still have two more nights to celebrate together!! JU DUA me krejt zemrën ❤️❤️❤️❤️❤️

DUA LIPA

195,351 görüntüleme • 1 yıl önce

COMMUNITY - 22,000 Wishlists As you have probably noticed, I talk to EVERYONE. All Day Every Day. I love it, and I love all of you fans. I will never get tired of talking to ALL supportive people like yourselves. The Legion of Luminaries has been growing, and a lot of you have pushing hard on your own for our growth and hopeful future success. It is immensely appreciated. I just wanted to say Thank YOU from the bottom of my heart. We are a tiny dev team with zero marketing budget, so our success really does depend on our Community, and whether enough people really want to see us succeed by helping that community to grow. Together, we can rid this world's darkness with light.
0:12

COMMUNITY - 22,000 Wishlists As you have probably noticed, I talk to EVERYONE. All Day Every Day. I love it, and I love all of you fans. I will never get tired of talking to ALL supportive people like yourselves. The Legion of Luminaries has been growing, and a lot of you have pushing hard on your own for our growth and hopeful future success. It is immensely appreciated. I just wanted to say Thank YOU from the bottom of my heart. We are a tiny dev team with zero marketing budget, so our success really does depend on our Community, and whether enough people really want to see us succeed by helping that community to grow. Together, we can rid this world's darkness with light.

Refractive | Luminary

16,088 görüntüleme • 8 ay önce

Dear Nigerians, "Patriotism means to stand by the country. It does not mean to stand by the president." ~Theodore Roosevelt On this national scale, if the mass number of Nigerians are suffering, their lives and properties in jeopardy, it is okay to speak up, call a spade a spade and stop this gov from leading us astray. Now more than ever, we must unite. We must unite against the ruiners of Nigeria before they bury what is left of her. Organization is the first primary weapon of the oppressed. This is the only weapon we are yet to explore, and we can not explore this until we unite. The oppressed Nigerians must organize to form a force against the ruiners of Nigeria and take back our country to have a system that is for the benefit of the Nigerian people and not a few in the position of power. President will come and go, only the people will remain. Don't give those in power today to play politics with your future. Let's unite, not necessary as one but as a team. A team with different skills, languages culture, religion and tradition, but one CAUSE regardless of our tribal differences to stand against the common enemy who have now shown to be a curse on our very own existence as an amazing people of this great nation. ~OIA✊🏿
0:15

Dear Nigerians, "Patriotism means to stand by the country. It does not mean to stand by the president." ~Theodore Roosevelt On this national scale, if the mass number of Nigerians are suffering, their lives and properties in jeopardy, it is okay to speak up, call a spade a spade and stop this gov from leading us astray. Now more than ever, we must unite. We must unite against the ruiners of Nigeria before they bury what is left of her. Organization is the first primary weapon of the oppressed. This is the only weapon we are yet to explore, and we can not explore this until we unite. The oppressed Nigerians must organize to form a force against the ruiners of Nigeria and take back our country to have a system that is for the benefit of the Nigerian people and not a few in the position of power. President will come and go, only the people will remain. Don't give those in power today to play politics with your future. Let's unite, not necessary as one but as a team. A team with different skills, languages culture, religion and tradition, but one CAUSE regardless of our tribal differences to stand against the common enemy who have now shown to be a curse on our very own existence as an amazing people of this great nation. ~OIA✊🏿

𝐎i𝐀

15,608 görüntüleme • 1 yıl önce