Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

New research from the team officially drops tomorrow: scaling decentralized training from 72B to frontier models. SparseLoCo solved gradient sync. Heterogenous SparseLoCo solves activation transmission between pipeline stages. What's it mean? The internet can be your datacenter. Inter-datacenter training that connects datacenters with consumer GPUs, university clusters, and distributed... show more

templar

12,286 subscribers

10,047 Aufrufe • vor 7 Monaten •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Prime Intellect engineers explained how they train reasoning models over the open internet in 30 minutes - better than $3000 distributed training courses. split policy and rollouts across nodes -> run agents in parallel envs -> verify with LLM judges -> gradient-sync over the internet -> train Llama, Qwen, Gemma at cluster scale on rented GPUs. That loop is why open reasoning models are catching closed labs without owning a data center. Prime-RL + verifiers + distributed rollouts + LLM judges + multi-cloud GPUs - that's the stack. Watch and save it, then launch your first distributed RL run this week.

Prime Intellect engineers explained how they train reasoning models over the open internet in 30 minutes - better than $3000 distributed training courses. split policy and rollouts across nodes -> run agents in parallel envs -> verify with LLM judges -> gradient-sync over the internet -> train Llama, Qwen, Gemma at cluster scale on rented GPUs. That loop is why open reasoning models are catching closed labs without owning a data center. Prime-RL + verifiers + distributed rollouts + LLM judges + multi-cloud GPUs - that's the stack. Watch and save it, then launch your first distributed RL run this week.

h100envy

39,620 Aufrufe • vor 1 Monat

There's no point in doing decentralized training without efficient communication. >> DiLoCo (H=15) ships ~480mb/merge with 163 syncs. >> SparseLoCo (H=15) ships ~5.5–17mb/merge at 0.78–3.12% density with 163 syncs Top-K Compression + 2 bit comms ~28–89× smaller per sync than DiLoCo. Subnet 3 :: Luis el grande If you have the algorithm, you can train large language models across disparate compute, collectively. "In the space of eight months or nine months, we've been able to scale our model from 1.2B to 70B, which represents 58x improvement" Distributed State Research paper :: Full Episode059 + const :: The holy grail of distributed AI training SN3 :: Templar :: Luis el grande_ai SN39 :: Basilica :: basilica SN81 :: Grail :: grail #SN3 #SN39 #SN81 #Bittensor

There's no point in doing decentralized training without efficient communication. >> DiLoCo (H=15) ships ~480mb/merge with 163 syncs. >> SparseLoCo (H=15) ships ~5.5–17mb/merge at 0.78–3.12% density with 163 syncs Top-K Compression + 2 bit comms ~28–89× smaller per sync than DiLoCo. Subnet 3 :: Luis el grande If you have the algorithm, you can train large language models across disparate compute, collectively. "In the space of eight months or nine months, we've been able to scale our model from 1.2B to 70B, which represents 58x improvement" Distributed State Research paper :: Full Episode059 + const :: The holy grail of distributed AI training SN3 :: Templar :: Luis el grande_ai SN39 :: Basilica :: basilica SN81 :: Grail :: grail #SN3 #SN39 #SN81 #Bittensor

Openτensor Foundaτion

17,767 Aufrufe • vor 11 Monaten

$Successfully deployed Deepseek R1 Distilled 70B (AWQ) across 8x NVIDIA RTX 3080 10G GPUs, achieving 60 tokens/s with full tensor parallelism via PCIe. Total hardware cost: $6,400 This demonstrates that consumer GPUs can deliver substantial ML inference capabilities at a fraction of the cost of datacenter hardware. For perspective, a single A100 80G costs $17,550, while a H100 80G runs $25,000. Our testing validates an often-overlooked opportunity: millions of idle crypto mining rigs could be repurposed into a powerful, distributed AI infrastructure. The performance-to-cost ratio of consumer GPUs, especially when properly optimized for tensor operations, presents a compelling case for decentralized AI compute. We're excited to share more insights as we continue pushing the boundaries of consumer hardware in AI workloads.$

Successfully deployed Deepseek R1 Distilled 70B (AWQ) across 8x NVIDIA RTX 3080 10G GPUs, achieving 60 tokens/s with full tensor parallelism via PCIe. Total hardware cost: $6,400 This demonstrates that consumer GPUs can deliver substantial ML inference capabilities at a fraction of the cost of datacenter hardware. For perspective, a single A100 80G costs $17,550, while a H100 80G runs $25,000. Our testing validates an often-overlooked opportunity: millions of idle crypto mining rigs could be repurposed into a powerful, distributed AI infrastructure. The performance-to-cost ratio of consumer GPUs, especially when properly optimized for tensor operations, presents a compelling case for decentralized AI compute. We're excited to share more insights as we continue pushing the boundaries of consumer hardware in AI workloads.

TensorBlock

16,219 Aufrufe • vor 1 Jahr

Professor Alex Tonks and his team from Cardiff University have found a protein that is altered in people with AML. We want to thank you, the Blood Cancer UK community for helping us to conduct this life-saving research.

Professor Alex Tonks and his team from Cardiff University have found a protein that is altered in people with AML. We want to thank you, the Blood Cancer UK community for helping us to conduct this life-saving research.

Blood Cancer UK

12,797 Aufrufe • vor 2 Jahren

Claude Fable + Notch = your entire growth department. Research, angle, creative, launch. All handled without a single human hand. Your growth team just became a math equation. Here's how the equation solves. → Fable took the brief. Studied the market, competitors, and reviews. Produced a research doc. → Notch took the doc. Picked the angle, wrote the ad, launched it. → From brief to 8x ROAS in 9 hours. No meetings between. Fable is the intelligence. Notch is the growth marketer. Marketers used to be people. Researchers used to be people. Your next two hires won't be. Fable + Notch. Your AI growth team. Comment TEAM to get both set up for your brand.

Vinay Jain

468,241 Aufrufe • vor 1 Monat

“Nobody had hope in crypto any more… But we posted about our 72B parameter run and it caught fire.” — Templar creator Sam Dare Hear about how this subnet incentivized miners to train a massive new AI model, what’s holding it back from competing with the likes of GPT-5 and Opus 4.6, and the advantages of decentralizing and democratizing the training process on a brand new TWiST. PLUS Sam’s reaction to hearing his project discussed on the The All in podcast with special guest Jensen Huang. cc: @jason, Lon Harris, templar, Distributed State

This Week in Startups

30,422 Aufrufe • vor 4 Monaten

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

Latent.Space

62,796 Aufrufe • vor 1 Monat

Someone created the Hyper Research skill for Claude Code It turns the model into a strong team of researchers that goes through 16 stages of search Here is what the AI agents do: > Break down the task into a topic coverage matrix and conduct deep research > Find and save hundreds of sources while identifying contradictions between them > Actively search for evidence against their own conclusions to avoid mistakes > Create a report draft, then review it for weak points, improve readability, and remove filler content > Deliver a complete report with a source base and save all materials to a local knowledge store Advanced Deep Research tool here:

Someone created the Hyper Research skill for Claude Code It turns the model into a strong team of researchers that goes through 16 stages of search Here is what the AI agents do: > Break down the task into a topic coverage matrix and conduct deep research > Find and save hundreds of sources while identifying contradictions between them > Actively search for evidence against their own conclusions to avoid mistakes > Create a report draft, then review it for weak points, improve readability, and remove filler content > Deliver a complete report with a source base and save all materials to a local knowledge store Advanced Deep Research tool here:

beamnxw ./

62,986 Aufrufe • vor 25 Tagen

“While most models are trained in these huge warehouses stacked from floor to ceiling with GPUs, we don’t rely on that. With IOTA, you can train an equivalent model using compute from around the world. When you don’t do everything in a single data centre, the capex is massively alleviated, with much less impact on local communities, and less detriment to the environment”. CTO and Co-Founder crux was on the Eye On A.I. podcast, discussing IOTA ・ SN9, distributed training, and how we’re building a meaningful alternative to the centralised status quo. See the full podcast below

“While most models are trained in these huge warehouses stacked from floor to ceiling with GPUs, we don’t rely on that. With IOTA, you can train an equivalent model using compute from around the world. When you don’t do everything in a single data centre, the capex is massively alleviated, with much less impact on local communities, and less detriment to the environment”. CTO and Co-Founder crux was on the Eye On A.I. podcast, discussing IOTA ・ SN9, distributed training, and how we’re building a meaningful alternative to the centralised status quo. See the full podcast below

Macrocosmos

227,896 Aufrufe • vor 3 Monaten

Applications for our 2025 summer internship are open! Interns on our research track experience what it's like to be a member of a CNA research team. Upon hire, each intern is assigned to a mentor and a project that matches his or her area of study. With the mentor’s guidance, the intern assists the team in structuring and solving a complex problem, developing sound analytical methods to derive findings and conclusions. Check out the video below to hear more about it from past research interns. Click the link below to apply for the program. Applications close November 30.

Applications for our 2025 summer internship are open! Interns on our research track experience what it's like to be a member of a CNA research team. Upon hire, each intern is assigned to a mentor and a project that matches his or her area of study. With the mentor’s guidance, the intern assists the team in structuring and solving a complex problem, developing sound analytical methods to derive findings and conclusions. Check out the video below to hear more about it from past research interns. Click the link below to apply for the program. Applications close November 30.

CNA

13,357 Aufrufe • vor 9 Monaten

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

Satya Nadella

2,022,693 Aufrufe • vor 10 Monaten

#SERASpace Highlights from The Inspired 24 /CHaSE Spaceflight Training Class #1 with the University of Arizona in Tucson. - Pressure suit familiarization from July 2023. 👨‍🚀👩‍🚀🧑‍🚀 --------------------- I am ready to be part of the 6 person team flying aboard New Shepard for SERA: Space Exploration and Research Agency. Please support my dream of spaceflight by signing up on Telegram for the SERA Mission Control app…Link in my bio. 🚀🌎🌍🌏🧑‍🚀 #space #astronaut

#SERASpace Highlights from The Inspired 24 /CHaSE Spaceflight Training Class #1 with the University of Arizona in Tucson. - Pressure suit familiarization from July 2023. 👨‍🚀👩‍🚀🧑‍🚀 --------------------- I am ready to be part of the 6 person team flying aboard New Shepard for SERA: Space Exploration and Research Agency. Please support my dream of spaceflight by signing up on Telegram for the SERA Mission Control app…Link in my bio. 🚀🌎🌍🌏🧑‍🚀 #space #astronaut

Andrew ‘Titan’ Parris 🚀🌏🌎🌍 The Inspired 24

11,274 Aufrufe • vor 10 Monaten

Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!

Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!

Nathan Lambert

26,872 Aufrufe • vor 6 Monaten

The verdict is in! Frontier models can pass the bar, yet they struggle on comprehensive legal research Today we're releasing Legal Research Bench, a benchmark that measures models’ ability to solve realistic legal research tasks across eight areas of U.S. law Instead of awarding partial credit, Legal Research Bench measures whether a model can conduct exhaustive legal analysis. We grade against a strict, all-pass rubric written by practicing lawyers. A model only receives full credit if every required legal element is correct Claude Opus 4.8 leads with 43.8% all-pass accuracy, followed by GPT 5.5 (40.4%) and Claude Sonnet 4.6 (38.5%). While top models score around 80% with partial credit, none exceed 44% when every required legal element must be correct The gap between partial and all-pass accuracy shows how difficult it remains for AI to produce complete, reliable legal research. We hope that Legal Research Bench helps better measure, and ultimately close that gap Lots of exciting work happening in Legal AI from Harvey and Crosby. Excited for the legal research benchmarks ahead!

The verdict is in! Frontier models can pass the bar, yet they struggle on comprehensive legal research Today we're releasing Legal Research Bench, a benchmark that measures models’ ability to solve realistic legal research tasks across eight areas of U.S. law Instead of awarding partial credit, Legal Research Bench measures whether a model can conduct exhaustive legal analysis. We grade against a strict, all-pass rubric written by practicing lawyers. A model only receives full credit if every required legal element is correct Claude Opus 4.8 leads with 43.8% all-pass accuracy, followed by GPT 5.5 (40.4%) and Claude Sonnet 4.6 (38.5%). While top models score around 80% with partial credit, none exceed 44% when every required legal element must be correct The gap between partial and all-pass accuracy shows how difficult it remains for AI to produce complete, reliable legal research. We hope that Legal Research Bench helps better measure, and ultimately close that gap Lots of exciting work happening in Legal AI from Harvey and Crosby. Excited for the legal research benchmarks ahead!

Vals AI

17,351 Aufrufe • vor 1 Monat

Vlad Feinberg (Vlad Feinberg) is Google DeepMind’s pre-training area lead and I asked him all about how to land a job at a frontier lab like Google DeepMind, Anthropic or OpenAI. In this episode: • Skills frontier labs need • Differences between software engineering and AI research • Domains that matter for frontier research • Concrete steps engineers can take to get closer to research • Jeff Dean spot bonus story Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:33 - Skills frontier labs need 08:45 - The difference between AI research and engineering 21:41 - Domains that matter for the frontier 30:50 - Marketing yourself to frontier labs 35:13 - Concrete steps engineers can take 38:29 - Overview of pre-training areas 47:23 - Jeff Dean spot bonus story 50:14 - Favorite Gemini war story 58:59 - Advice for his younger self 01:03:07 - Outro

Vlad Feinberg (Vlad Feinberg) is Google DeepMind’s pre-training area lead and I asked him all about how to land a job at a frontier lab like Google DeepMind, Anthropic or OpenAI. In this episode: • Skills frontier labs need • Differences between software engineering and AI research • Domains that matter for frontier research • Concrete steps engineers can take to get closer to research • Jeff Dean spot bonus story Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:33 - Skills frontier labs need 08:45 - The difference between AI research and engineering 21:41 - Domains that matter for the frontier 30:50 - Marketing yourself to frontier labs 35:13 - Concrete steps engineers can take 38:29 - Overview of pre-training areas 47:23 - Jeff Dean spot bonus story 50:14 - Favorite Gemini war story 58:59 - Advice for his younger self 01:03:07 - Outro

Ryan Peterman

189,558 Aufrufe • vor 1 Monat

Ilya Sutskever says the AI scaling era is running out of road because pre-training is about to hit a finite-data wall. "At some point though, pre-training will run out of data. The data is very clearly finite." "Then, okay, what do you do next? Either you do some kind of a souped-up pre-training... or you're doing RL, or maybe something else." "In some sense, we are back to the age of research." His timeline is the part most AI investors and founders should hear: "From 2012 to 2020, it was the age of research. Now from 2020 to 2025, it was the age of scaling... But now the scale is so big." "Is the belief really that... if you had 100x more everything would be so different?" "I don't think that's true. So it's back to the age of research again, just with big computers."

Ilya Sutskever says the AI scaling era is running out of road because pre-training is about to hit a finite-data wall. "At some point though, pre-training will run out of data. The data is very clearly finite." "Then, okay, what do you do next? Either you do some kind of a souped-up pre-training... or you're doing RL, or maybe something else." "In some sense, we are back to the age of research." His timeline is the part most AI investors and founders should hear: "From 2012 to 2020, it was the age of research. Now from 2020 to 2025, it was the age of scaling... But now the scale is so big." "Is the belief really that... if you had 100x more everything would be so different?" "I don't think that's true. So it's back to the age of research again, just with big computers."

Karl Mehta

200,003 Aufrufe • vor 1 Monat

Luma’s mission is to make 3D creation accessible to all. We believe that neural rendering will be a significant piece of this puzzle. Sparked by NeRF and follow-up papers – including works from the now Luma research team members – neural rendering has become an incredibly productive field of research! To further accelerate the rate of advancement and help the research community grow and build, today we are excited to sponsor nerfstudio along with the Berkeley AI Research Lab (Berkeley AI Research), led by Professor Angjoo Kanazawa (Angjoo Kanazawa). Nerfstudio is a collaboration-friendly project that has greatly benefited NeRF research by providing a well-built codebase to experiment with and try new ideas in, and we are proud to be a part of this effort. 3D creation is about to change, deeply. It’s time for new ideas, weird ideas, cool ideas. Open source and product-driven research are both crucial to realize this huge vision. Join us at Luma or contribute to Nerfstudio to help us build this future!

Luma’s mission is to make 3D creation accessible to all. We believe that neural rendering will be a significant piece of this puzzle. Sparked by NeRF and follow-up papers – including works from the now Luma research team members – neural rendering has become an incredibly productive field of research! To further accelerate the rate of advancement and help the research community grow and build, today we are excited to sponsor nerfstudio along with the Berkeley AI Research Lab (Berkeley AI Research), led by Professor Angjoo Kanazawa (Angjoo Kanazawa). Nerfstudio is a collaboration-friendly project that has greatly benefited NeRF research by providing a well-built codebase to experiment with and try new ideas in, and we are proud to be a part of this effort. 3D creation is about to change, deeply. It’s time for new ideas, weird ideas, cool ideas. Open source and product-driven research are both crucial to realize this huge vision. Join us at Luma or contribute to Nerfstudio to help us build this future!

Luma AI

55,591 Aufrufe • vor 3 Jahren

For a hundred years, the Hebrew University of Jerusalem has been a home for extraordinary individuals—those who question, challenge conventions, and push boundaries. The innovators, the changemakers, the pioneers who carve new paths and ensure they remain open. It all began with a remarkable group of intellectual giants, including Albert Einstein, Chaim Weizmann, Chaim Nachman Bialik, Sigmund Freud, and Martin Buber, who united to realize a shared vision: the establishment of the Hebrew University. They, along with many others, founded a pioneering academic institution dedicated to training leaders in research, science, public service, and society—for the benefit of Israel and all of humanity. From the moment this vision became reality, the University has placed excellence in research and teaching at its core. Over the years, it has become a hub of knowledge and innovation, fostering groundbreaking research across diverse fields and cultivating generations of leaders, scholars, and thinkers. The Hebrew University of Jerusalem — Dreaming. Creating. Innovating. Since 1925

For a hundred years, the Hebrew University of Jerusalem has been a home for extraordinary individuals—those who question, challenge conventions, and push boundaries. The innovators, the changemakers, the pioneers who carve new paths and ensure they remain open. It all began with a remarkable group of intellectual giants, including Albert Einstein, Chaim Weizmann, Chaim Nachman Bialik, Sigmund Freud, and Martin Buber, who united to realize a shared vision: the establishment of the Hebrew University. They, along with many others, founded a pioneering academic institution dedicated to training leaders in research, science, public service, and society—for the benefit of Israel and all of humanity. From the moment this vision became reality, the University has placed excellence in research and teaching at its core. Over the years, it has become a hub of knowledge and innovation, fostering groundbreaking research across diverse fields and cultivating generations of leaders, scholars, and thinkers. The Hebrew University of Jerusalem — Dreaming. Creating. Innovating. Since 1925

Hebrew University

18,082 Aufrufe • vor 1 Jahr

What if you could train AI agents on a laptop as easily as on a GPU cluster? Researchers from UIUC's U Lab, led by Prof. Jiaxuan You, just open-sourced OpenTinker. It's a new "Reinforcement-Learning-as-a-Service" (RLaaS) system that decouples the complex training pipeline into simple, distributed services with friendly APIs. The result? It breaks down the major engineering barriers to RL, outperforming traditional frameworks in accessibility and ease of deployment, finally making agent training viable for more developers and teams. Project: Code: U Lab: Our report: 📬 #PapersAccepted by Jiqizhixin

What if you could train AI agents on a laptop as easily as on a GPU cluster? Researchers from UIUC's U Lab, led by Prof. Jiaxuan You, just open-sourced OpenTinker. It's a new "Reinforcement-Learning-as-a-Service" (RLaaS) system that decouples the complex training pipeline into simple, distributed services with friendly APIs. The result? It breaks down the major engineering barriers to RL, outperforming traditional frameworks in accessibility and ease of deployment, finally making agent training viable for more developers and teams. Project: Code: U Lab: Our report: 📬 #PapersAccepted by Jiqizhixin

机器之心 JIQIZHIXIN

15,893 Aufrufe • vor 7 Monaten