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📢 ScanNet++ v2 Benchmark Release! 🏆 Test your state-of-the-art models on: 🔹 Novel View Synthesis 📸➡️🖼️ 🔹 3D Semantic & Instance Segmentation 🤖🔍🕶️ Shoutout to Chandan Yeshwanth and Yueh-Cheng Liu for their incredible work👏 🚀Check it out:

Angela Dai

9,774 subscribers

13,438 görüntüleme • 1 yıl önce •via X (Twitter)

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@chandan__yes @liuyuehcheng amazing work, that it's going to help us with our research as well.

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RT @InstaMAT_io: 👀 Are you looking for an alternative to Adobe Substance tools? Do not miss out on InstaMAT from killer node-based material…

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@chandan__yes @liuyuehcheng Incredible!

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Excited to announce ScanNet++ v2!🎉 Chandan Yeshwanth and Yueh-Cheng Liu have been working tirelessly to bring: 🔹1006 high-fidelity 3D scans 🔹+ DSLR & iPhone captures 🔹+ rich semantics Elevating 3D scene understanding to the next level!🚀 w/ Matthias Niessner

Excited to announce ScanNet++ v2!🎉 Chandan Yeshwanth and Yueh-Cheng Liu have been working tirelessly to bring: 🔹1006 high-fidelity 3D scans 🔹+ DSLR & iPhone captures 🔹+ rich semantics Elevating 3D scene understanding to the next level!🚀 w/ Matthias Niessner

Angela Dai

47,588 görüntüleme • 1 yıl önce

Looking for a challenging dataset for novel view synthesis and 3D semantics? Check out ScanNet++ at #ICCV23 (Oral)! 460+ scenes w/ 1mm laser scans, semantics, DSLR images, iPhone RGBD video Chandan Yeshwanth Yueh-Cheng Liu Matthias Niessner

Looking for a challenging dataset for novel view synthesis and 3D semantics? Check out ScanNet++ at #ICCV23 (Oral)! 460+ scenes w/ 1mm laser scans, semantics, DSLR images, iPhone RGBD video Chandan Yeshwanth Yueh-Cheng Liu Matthias Niessner

Angela Dai

10,288 görüntüleme • 3 yıl önce

SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians Contributions: • We propose SuperGSeg: a 3D segmentation method with neural Gaussians, designed to learn hierarchical instance segmentation features from 2D foundation models. • We introduce the concept of Super-Gaussian, a novel representation that integrates hierarchical instance segmentation features, enabling the embedding of high-dimensional language features. This approach addresses previously unfeasible challenges in representing complex scenes with rich semantic details. • Extensive experiments on the LERF-OVS and ScanNet datasets demonstrate the effectiveness of the proposed method, achieving significant improvements in open-vocabulary 3D object-level and scene-level semantic segmentation. It shows particular strength in capturing fine-grained scene details and dense pixel semantic segmentation tasks for the first time.

SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians Contributions: • We propose SuperGSeg: a 3D segmentation method with neural Gaussians, designed to learn hierarchical instance segmentation features from 2D foundation models. • We introduce the concept of Super-Gaussian, a novel representation that integrates hierarchical instance segmentation features, enabling the embedding of high-dimensional language features. This approach addresses previously unfeasible challenges in representing complex scenes with rich semantic details. • Extensive experiments on the LERF-OVS and ScanNet datasets demonstrate the effectiveness of the proposed method, achieving significant improvements in open-vocabulary 3D object-level and scene-level semantic segmentation. It shows particular strength in capturing fine-grained scene details and dense pixel semantic segmentation tasks for the first time.

MrNeRF

13,594 görüntüleme • 1 yıl önce

🚀Announcing NeRSemble 3D Head Avatar Benchmark v2 Version 2 of the NeRSemble 3D Head Avatar Benchmark systematically evaluates several aspects of 3D head avatar creation. Our goal is to drive progress toward more realistic, robust, and generalizable avatar methods. 🔬Benchmark Tasks The NeRSemble Benchmark v2 features three core challenges: - Dynamic Novel View Synthesis - Monocular FLAME-driven Avatar Creation (updated) - Single-view 3D Face Reconstruction (new) 👉Explore the online leaderboard and submission system: 🆕What's new? 1. New Task: Single-view 3D Face Reconstruction Given a single portrait image, reconstruct an accurate 3D mesh either showing the input expression or a fully neutral one. Unlike prior benchmarks, the NeRSemble benchmark emphasizes diverse and challenging facial expressions, better reflecting real scenarios. For technical details, see the Pixel3DMM paper. 2. Updated task: Monocular FLAME-driven Avatar Creation We have improved the FLAME tracking that is used for both avatar creation from the monocular videos and avatar driving on the hidden test sequences. The updated benchmark task has: - more stable torso tracking - more expressive lip closures during speech - Improved mouth tracking for challenging facial expressions We hope that these improvements to the benchmark help drive the field forward. 🏆 CVPR 2026 Workshop & Prizes The NeRSemble benchmark will be featured at the CVPR 2026 Workshop on Photo-realistic 3D Head Avatars. Participants in the new and updated tasks have the opportunity to win: - 🎁RTX 5080 GPUs (sponsored by NVIDIA) - 🎤15-minute oral presentation at the workshop ⏰ Submission Deadline - May 26, 2026 Reach out to the amazing Tobias Kirschstein and Simon Giebenhain for more details :)

Matthias Niessner

30,006 görüntüleme • 4 ay önce

🚀 The team at Google DeepMind just released Gemini Embedding 2, a frontier embeddings model with 3072 dimensions and state-of-the-art semantic quality. 👩‍💻 We built a demo showing how to integrate it across the LlamaIndex ecosystem, from LlamaParse to LlamaAgents: 𝗮𝘂𝗱𝗶𝗼-𝗸𝗯, a knowledge base for your audio notes. With audio-kb, you can: 🔹 Upload an MP3 or record directly from your terminal 🔹 LlamaParse extracts the transcript from the audio 🔹 Gemini Embedding 2 generates embeddings 🔹 Metadata + vectors are stored in SurrealDB and indexed with HNSW 🔍 Once ingested, you can search all your audio notes directly from the terminal. 🎙️ Perfect for turning voice memos, meetings, or lectures into a searchable knowledge base. 📖 Full blog: 💻 GitHub: ⚡ Try LlamaParse:

🚀 The team at Google DeepMind just released Gemini Embedding 2, a frontier embeddings model with 3072 dimensions and state-of-the-art semantic quality. 👩‍💻 We built a demo showing how to integrate it across the LlamaIndex ecosystem, from LlamaParse to LlamaAgents: 𝗮𝘂𝗱𝗶𝗼-𝗸𝗯, a knowledge base for your audio notes. With audio-kb, you can: 🔹 Upload an MP3 or record directly from your terminal 🔹 LlamaParse extracts the transcript from the audio 🔹 Gemini Embedding 2 generates embeddings 🔹 Metadata + vectors are stored in SurrealDB and indexed with HNSW 🔍 Once ingested, you can search all your audio notes directly from the terminal. 🎙️ Perfect for turning voice memos, meetings, or lectures into a searchable knowledge base. 📖 Full blog: 💻 GitHub: ⚡ Try LlamaParse:

LlamaIndex 🦙

34,945 görüntüleme • 5 ay önce

🚀 Advance Your Coaching Career with The Curriculum! 🚀 We’re excited to announce the launch of “The Curriculum”—the ultimate training program developed by The Farm System! Featuring insights from industry-leading experts, including a former MLB strength coach, leaders in mental health, and other expert trainers, this program is designed to elevate your coaching career and transform player development. 🔹 Learn from Joey Cunha, renowned coach who has worked with MLB All Stars and potential Hall of Fame candidates. 🔹 Access over 14.5 hours of expert-led content across 16 comprehensive chapters. 🔹 Benefit from industry-leading training products, curriculums, protocols, and evaluations. 🔹 Special discount: Save $450 for the first 40 customers! Join The Farm System’s state-of-the-art facility in Las Vegas, where players and organizations from around the globe come to benefit from their wisdom and set a new standard in the sport. This expertise is sought after worldwide, with pro organizations from Japan and top-tier Power 5 schools investing in this wisdom to elevate their programs to the next level. 💪 Don’t miss out on this opportunity to transform your coaching approach and advance your career. Invest in your future and the future of your athletes today! 🔗 Enroll Now & Save👉🏼

🚀 Advance Your Coaching Career with The Curriculum! 🚀 We’re excited to announce the launch of “The Curriculum”—the ultimate training program developed by The Farm System! Featuring insights from industry-leading experts, including a former MLB strength coach, leaders in mental health, and other expert trainers, this program is designed to elevate your coaching career and transform player development. 🔹 Learn from Joey Cunha, renowned coach who has worked with MLB All Stars and potential Hall of Fame candidates. 🔹 Access over 14.5 hours of expert-led content across 16 comprehensive chapters. 🔹 Benefit from industry-leading training products, curriculums, protocols, and evaluations. 🔹 Special discount: Save $450 for the first 40 customers! Join The Farm System’s state-of-the-art facility in Las Vegas, where players and organizations from around the globe come to benefit from their wisdom and set a new standard in the sport. This expertise is sought after worldwide, with pro organizations from Japan and top-tier Power 5 schools investing in this wisdom to elevate their programs to the next level. 💪 Don’t miss out on this opportunity to transform your coaching approach and advance your career. Invest in your future and the future of your athletes today! 🔗 Enroll Now & Save👉🏼

Joey Cunha

49,343 görüntüleme • 2 yıl önce

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: Highlights: 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

Bo Wang

178,579 görüntüleme • 2 yıl önce

🚀 Today, we are introducing SmolTools! 🚀 Last week, at Hugging Face we made a significant leap forward with the release of SmolLM2, a compact 1.7B language model that sets a new benchmark for performance among models of its size. But beyond the impressive stats, SmolLM2 truly shines in practical, real-world applications, unlocking new possibilities for on-device inference. To demonstrate its potential, we're thrilled to introduce Smol-Tools, a suite of simple yet powerful applications that showcase the capabilities of SmolLM2. 🌟 Today, we present you two key tools: Summarize and Rewrite. 🔹 Summarize: Feed SmolLM2 a text up to 20 pages long, and it will provide you with a concise summary. Need to dig deeper? Just ask follow-up questions to clarify any details – it's that simple. 🔹 Rewrite: Draft a response to a message with your main points, and SmolLM2 will transform it into a clear, polished version that's easy for your recipient to read and understand. These tools are designed to make your workflow smoother and more efficient, leveraging the power of SmolLM2 in practical ways. 💡We hope you give these tools a try and see their potential for yourself! Feel free to build your own SmolTools and contribute them to the project. We'd love to see what you build! Check out the code here:

🚀 Today, we are introducing SmolTools! 🚀 Last week, at Hugging Face we made a significant leap forward with the release of SmolLM2, a compact 1.7B language model that sets a new benchmark for performance among models of its size. But beyond the impressive stats, SmolLM2 truly shines in practical, real-world applications, unlocking new possibilities for on-device inference. To demonstrate its potential, we're thrilled to introduce Smol-Tools, a suite of simple yet powerful applications that showcase the capabilities of SmolLM2. 🌟 Today, we present you two key tools: Summarize and Rewrite. 🔹 Summarize: Feed SmolLM2 a text up to 20 pages long, and it will provide you with a concise summary. Need to dig deeper? Just ask follow-up questions to clarify any details – it's that simple. 🔹 Rewrite: Draft a response to a message with your main points, and SmolLM2 will transform it into a clear, polished version that's easy for your recipient to read and understand. These tools are designed to make your workflow smoother and more efficient, leveraging the power of SmolLM2 in practical ways. 💡We hope you give these tools a try and see their potential for yourself! Feel free to build your own SmolTools and contribute them to the project. We'd love to see what you build! Check out the code here:

Andi Marafioti

100,813 görüntüleme • 1 yıl önce

Today we're excited to introduce Vy, our AI that sees and acts on your computer. At Vercept, our mission is to reinvent how humans use computers–enabling you to accomplish orders of magnitude more than what you can do today. Vy is a first glimpse at AI that sees and uses your computer just like you do. It runs natively on your computer, with access to all your applications and accounts that you’re already signed into. It is available for download starting today! As part of the release, we’ve built VyUI, an AI model bridging the gap between language and your screen. It sets a new state-of-the-art on UI grounding benchmarks like ScreenSpot v1, ScreenSpot v2, and GroundUI Web–beating leading models from OpenAI, Google, and Anthropic. Check out what Vy can do in the thread below: 🧵👇

Today we're excited to introduce Vy, our AI that sees and acts on your computer. At Vercept, our mission is to reinvent how humans use computers–enabling you to accomplish orders of magnitude more than what you can do today. Vy is a first glimpse at AI that sees and uses your computer just like you do. It runs natively on your computer, with access to all your applications and accounts that you’re already signed into. It is available for download starting today! As part of the release, we’ve built VyUI, an AI model bridging the gap between language and your screen. It sets a new state-of-the-art on UI grounding benchmarks like ScreenSpot v1, ScreenSpot v2, and GroundUI Web–beating leading models from OpenAI, Google, and Anthropic. Check out what Vy can do in the thread below: 🧵👇

Vercept

123,748 görüntüleme • 1 yıl önce

So many people my age and younger don’t even know what the state pension triple lock is, yet £224.6 BILLION of OUR taxes are spent on it. Don’t you want to know where your money goes? The reason political parties are so afraid to challenge the triple lock for pensioners is that older generations actually get out and vote. Politicians cater to them because they show up. They are the loudest in the room and politicians rely on their vote. If we want to be heard, younger generations need to vote on the issues that directly affect our future, such as: 🔹Student debt reform - tackling high interest rates and unaffordable repayments. 🔹First-time buyer support - easier access to mortgages and schemes for lower deposits. 🔹Childcare for young families - affordable, accessible options so parents can work. 🔹Flexible working rights - ensuring jobs adapt to modern parenting and lifestyle needs. 🔹Housing supply and infrastructure – building more affordable homes in areas with good transport and schools. 🔹Parental leave and pay – improving support for both parents in the early stages of raising a family. 🔹Lower taxes - for those under 25, scrap IR35, lower corporation tax, overhaul the tax rates/codes. Don’t complain that politics only cares about the older generation when they are the ones who vote and actively participate. If we want change, we need to turn up, speak out, and vote. It’s the laziest thing to say “there’s no point, they’re all the same” force them to implement policy by being involved. Start taking an interest in policy and politics, or they won’t take an interest in you.

So many people my age and younger don’t even know what the state pension triple lock is, yet £224.6 BILLION of OUR taxes are spent on it. Don’t you want to know where your money goes? The reason political parties are so afraid to challenge the triple lock for pensioners is that older generations actually get out and vote. Politicians cater to them because they show up. They are the loudest in the room and politicians rely on their vote. If we want to be heard, younger generations need to vote on the issues that directly affect our future, such as: 🔹Student debt reform - tackling high interest rates and unaffordable repayments. 🔹First-time buyer support - easier access to mortgages and schemes for lower deposits. 🔹Childcare for young families - affordable, accessible options so parents can work. 🔹Flexible working rights - ensuring jobs adapt to modern parenting and lifestyle needs. 🔹Housing supply and infrastructure – building more affordable homes in areas with good transport and schools. 🔹Parental leave and pay – improving support for both parents in the early stages of raising a family. 🔹Lower taxes - for those under 25, scrap IR35, lower corporation tax, overhaul the tax rates/codes. Don’t complain that politics only cares about the older generation when they are the ones who vote and actively participate. If we want change, we need to turn up, speak out, and vote. It’s the laziest thing to say “there’s no point, they’re all the same” force them to implement policy by being involved. Start taking an interest in policy and politics, or they won’t take an interest in you.

Lin Mei

26,750 görüntüleme • 6 ay önce

Every working parent’s nightmare: getting laid off while out on leave. Now imagine this happens to you, only you’re out on leave with your second set of twins. That’s exactly what happened to Aaron Francis and he joined me on Startup Dad to talk about it. He’s the co-founder of Try Hard Studios and makes incredible, developer-education videos. Mind-blowingly incredible. He’s also been an analyst, a developer and started his career as a Big 4 Accountant. Not exactly a traditional path to making videos for developers. “At the time my wife and I had two 2-year olds and we had just had a second set of twins. We had four kids under the age of three. I was set to go back to work on Friday and I got the call on a Wednesday. It was one of those moments where professionally it was the best time to go out on my own, but personally it was absolutely the worst time. Hear our entire conversation about: 🔹 Navigating a loss of one of their triplets during the first pregnancy 🔹 Life with two sets of twins 🔹 How he and his wife managed after he was laid off on paternity leave 🔹 The challenges of starting a business with newborns 🔹 How to stay present with all the pings from an extremely online life 🔹 Parenting each of his four kids differently 🔹 Finding ways to focus on only what matters at this stage of life 🔹 Hilarious and fascinating stories: falling asleep in a chair at the gym, building a non-traditional office space, and celebrating a birthday with a solo-staycation. If you’ve ever gone to the gym to take a nap (or wanted to) or celebrated a birthday with a solo, stay-cation at a local hotel... you’ll love this conversation. One of my favorite takeaways: I see two opposing sides online often, which is like becoming a parent is wonderful and therefore it's easy and everyone should do it. And then the other side is, becoming a parent is very hard, and you should only do it if you're a thousand percent sure... the reality is, there are days, there are minutes in a day, where you will go from, this is a wonderful, amazing, and this is incredibly hard, and I don't want to do it. And I think you just need to be prepared to experience all of that in a day. Watch our conversation on Youtube or listen in your favorite podcast player.

Every working parent’s nightmare: getting laid off while out on leave. Now imagine this happens to you, only you’re out on leave with your second set of twins. That’s exactly what happened to Aaron Francis and he joined me on Startup Dad to talk about it. He’s the co-founder of Try Hard Studios and makes incredible, developer-education videos. Mind-blowingly incredible. He’s also been an analyst, a developer and started his career as a Big 4 Accountant. Not exactly a traditional path to making videos for developers. “At the time my wife and I had two 2-year olds and we had just had a second set of twins. We had four kids under the age of three. I was set to go back to work on Friday and I got the call on a Wednesday. It was one of those moments where professionally it was the best time to go out on my own, but personally it was absolutely the worst time. Hear our entire conversation about: 🔹 Navigating a loss of one of their triplets during the first pregnancy 🔹 Life with two sets of twins 🔹 How he and his wife managed after he was laid off on paternity leave 🔹 The challenges of starting a business with newborns 🔹 How to stay present with all the pings from an extremely online life 🔹 Parenting each of his four kids differently 🔹 Finding ways to focus on only what matters at this stage of life 🔹 Hilarious and fascinating stories: falling asleep in a chair at the gym, building a non-traditional office space, and celebrating a birthday with a solo-staycation. If you’ve ever gone to the gym to take a nap (or wanted to) or celebrated a birthday with a solo, stay-cation at a local hotel... you’ll love this conversation. One of my favorite takeaways: I see two opposing sides online often, which is like becoming a parent is wonderful and therefore it's easy and everyone should do it. And then the other side is, becoming a parent is very hard, and you should only do it if you're a thousand percent sure... the reality is, there are days, there are minutes in a day, where you will go from, this is a wonderful, amazing, and this is incredibly hard, and I don't want to do it. And I think you just need to be prepared to experience all of that in a day. Watch our conversation on Youtube or listen in your favorite podcast player.

Adam Fishman

53,793 görüntüleme • 1 yıl önce

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,082 görüntüleme • 1 yıl önce

🚀BitMask v1.0 Beta is LIVE on the Chrome Store!🚀 BitMask was the first wallet extension to support RGB20 and RGB21 on #Bitcoin — and this new release is accumulation of work, accelerating #RGB adoption — a release on bitcoin mainnet. 🔥 A complete wallet security and infrastructure overhaul, coupled with lightning-fast performance ⚡️ like you’ve never seen before. OGs, go check it out for yourself. 👀 Main Features: 🔸RGB 0.11 Mainnet support (RGB20 + RGB21) 🔸Transfer, import & create RGB20 tokens 🔸Deterministic, state management 🔸AES-encrypted wallet storage 🔸Smart UTXO Manager for RGB Vaults 🔸Brand-new, scalable infrastructure 🔸Full Rootstock (RSK) integration + BTC RBTC bridge 🔸Better and clearer invoice management for RGB operations --> For users that want a test environment to play with RGB20, please use signet.

BitMask ⚡

10,303 görüntüleme • 1 yıl önce

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...

Ricardo

173,716 görüntüleme • 1 ay önce

S10 Preseason Leeks 💖 The most exciting preseason for Origins is right around the corner. If everything goes well, Origins will go into maintenance for the preseason update on August 7 at 11:30 AM PHT / August 6 at 11:30 PM EST. Lets dive into the latest developments as we get ready for the S10 Preseason Launch 👇 🪜 Collectible Leaderboard As we bring much needed gameplay utility to Collectible Axies with Meta Morph, Collectible Leaderboard will serve as the true battleground for the most precious axies in Lunacia. 🔹 Prizepool of Collectible Leaderboard has been increased from 4,000 AXS to 6,000 AXS. 🔹 Collectible Leaderboard will be live for 3 weeks from Aug 7, 11:30 AM PHT to Aug 28, 11:00 AM PHT. 🔹 Era based testing will be carried out with 1 week dedicated to each era. 🔹 Preseason Cup will not be hosted during this preseason. The focus will be on Collectible Leaderboard for balance patch testing. ✨ Meta Morph After getting important feedback regarding Meta Morph from the community and further internal review, we've decided to implement the following changes: 🔹 Body parts of the morphed cards will be visually changed once battle begins. This is necessary to make it easier for players to be able to check quickly what team the opponent is playing. An example of Meta Morph visuals can be seen in the attached video below. 🔹 The bonus HP of Collectible Axies will be removed. As Collectible Axies will now be much more versatile and able to play all types of teams, the bonus HP is no longer deemed necessary. Therefore, it will be removed when the S10 Preseason launches. 🔹 Clarification around morphing: To morph into a card, player needs to have at least one NFT axie with that card in their inventory. For example: If I want to morph the back card of my collectible axie into a Cupid then I need to have at least one NFT axie with Cupid in my inventory. 🔹 Adjusted various values for the Morph process for different collectibles. Check out the full details here: 🐻 Roguelike Mode v1.0 The groundwork for Origins PvE will be laid with v1.0 of Roguelike Mode. This will be the first version as we work with the community to test how a PvE game mode will function in Origins. 🔹 Roguelike Mode v1.0 will be live during the S10 preseason and last for 3 weeks, from August 7 to August 28. After this, we will gather feedback from the community and work towards the next update for Roguelike Mode to improve it even further. 🔹 Roguelike Mode will launch with 3 exciting contests for the community to participate in. Details for the contests will be shared on August 7th when preseason launches. 🔹 Check out the previously shared details for Roguelike Mode v1.0 here: 🗒️ S10 Balance Patch The S10 balance patch info will be shared on Sunday, Aug 4, 9 PM PHT / 9 AM EST As always, keep that valuable feedback coming as we head towards the most exciting period for Axie Origins. See you in the arena! 🔥

S10 Preseason Leeks 💖 The most exciting preseason for Origins is right around the corner. If everything goes well, Origins will go into maintenance for the preseason update on August 7 at 11:30 AM PHT / August 6 at 11:30 PM EST. Lets dive into the latest developments as we get ready for the S10 Preseason Launch 👇 🪜 Collectible Leaderboard As we bring much needed gameplay utility to Collectible Axies with Meta Morph, Collectible Leaderboard will serve as the true battleground for the most precious axies in Lunacia. 🔹 Prizepool of Collectible Leaderboard has been increased from 4,000 AXS to 6,000 AXS. 🔹 Collectible Leaderboard will be live for 3 weeks from Aug 7, 11:30 AM PHT to Aug 28, 11:00 AM PHT. 🔹 Era based testing will be carried out with 1 week dedicated to each era. 🔹 Preseason Cup will not be hosted during this preseason. The focus will be on Collectible Leaderboard for balance patch testing. ✨ Meta Morph After getting important feedback regarding Meta Morph from the community and further internal review, we've decided to implement the following changes: 🔹 Body parts of the morphed cards will be visually changed once battle begins. This is necessary to make it easier for players to be able to check quickly what team the opponent is playing. An example of Meta Morph visuals can be seen in the attached video below. 🔹 The bonus HP of Collectible Axies will be removed. As Collectible Axies will now be much more versatile and able to play all types of teams, the bonus HP is no longer deemed necessary. Therefore, it will be removed when the S10 Preseason launches. 🔹 Clarification around morphing: To morph into a card, player needs to have at least one NFT axie with that card in their inventory. For example: If I want to morph the back card of my collectible axie into a Cupid then I need to have at least one NFT axie with Cupid in my inventory. 🔹 Adjusted various values for the Morph process for different collectibles. Check out the full details here: 🐻 Roguelike Mode v1.0 The groundwork for Origins PvE will be laid with v1.0 of Roguelike Mode. This will be the first version as we work with the community to test how a PvE game mode will function in Origins. 🔹 Roguelike Mode v1.0 will be live during the S10 preseason and last for 3 weeks, from August 7 to August 28. After this, we will gather feedback from the community and work towards the next update for Roguelike Mode to improve it even further. 🔹 Roguelike Mode will launch with 3 exciting contests for the community to participate in. Details for the contests will be shared on August 7th when preseason launches. 🔹 Check out the previously shared details for Roguelike Mode v1.0 here: 🗒️ S10 Balance Patch The S10 balance patch info will be shared on Sunday, Aug 4, 9 PM PHT / 9 AM EST As always, keep that valuable feedback coming as we head towards the most exciting period for Axie Origins. See you in the arena! 🔥

Jaatster.ron | जाटस्टर 🇮🇳

44,740 görüntüleme • 2 yıl önce

A thunderous round of applause to the winners who conquered the #RollupWithCartesi hackathon! 🎉 & huge thanks to every single participant on this journey. Your passion & ingenuity have set the stage on fire, pushing the boundaries of what's possible in Web3 with Cartesi tech! 🔥 Check out all the winners: 👇 Pool Prize Winners: 🔹 DNN (Digital Native NFTs) | Create NFTs derived from only geometric objects. It's math turned into art. Eric 🔹 BlogAnalyzr | An AI tool designed to analyze the quality of blog posts of technical writers. The more points an author's work gets, the higher they get in the ranks. ᴉqouᴉʇsnɾɔ 🔹 The Prism △ The Prism △ | Transforms AI art into hyper-personalized wearable items, starting with t-shirts. Easily create unique designs, mint as NFTs, and connect with producers— all secured by Cartesi Rollups. Gustavo Sanchez Pedro Peres Ryan Viana 🔹 Cartesi Labs | A tutorial creation platform that allows developers to create Cartesi project tutorials and make the learning process more interactive. 🔹 BlockDemy | A Decentralized Education platform to provide users with access to educational courses on purchase with tokens, and NFT certificates upon course completion. Purity Elizbeth Glory Sunday #WID💫 Yinka Babalola Nofisat Abiodun Ayanlola The Main Podium: 🥉 Oware-Cartesi | Implementation of the classic game, played by millions with a Strategic Gameplay approach leveraging AI Agents. Kagwe.stark 🥉 CartesiKit | An all-in-one CLI tool designed to streamline the process of setting up new projects for Cartesi Rollups. gconnect.base.eth | Glory Agatevure🌿 🥈 Ultimate Football | A build your football dream team onchain game, let 'em beat your opponent's and win prizes! The Elder of Jos @Blessed_mayowa konies jay 🥇 CarteZcash | What if you could shield your ETH assets directly from Ethereum by depositing them into a Zcash L2? This is it! Willem Olding

A thunderous round of applause to the winners who conquered the #RollupWithCartesi hackathon! 🎉 & huge thanks to every single participant on this journey. Your passion & ingenuity have set the stage on fire, pushing the boundaries of what's possible in Web3 with Cartesi tech! 🔥 Check out all the winners: 👇 Pool Prize Winners: 🔹 DNN (Digital Native NFTs) | Create NFTs derived from only geometric objects. It's math turned into art. Eric 🔹 BlogAnalyzr | An AI tool designed to analyze the quality of blog posts of technical writers. The more points an author's work gets, the higher they get in the ranks. ᴉqouᴉʇsnɾɔ 🔹 The Prism △ The Prism △ | Transforms AI art into hyper-personalized wearable items, starting with t-shirts. Easily create unique designs, mint as NFTs, and connect with producers— all secured by Cartesi Rollups. Gustavo Sanchez Pedro Peres Ryan Viana 🔹 Cartesi Labs | A tutorial creation platform that allows developers to create Cartesi project tutorials and make the learning process more interactive. 🔹 BlockDemy | A Decentralized Education platform to provide users with access to educational courses on purchase with tokens, and NFT certificates upon course completion. Purity Elizbeth Glory Sunday #WID💫 Yinka Babalola Nofisat Abiodun Ayanlola The Main Podium: 🥉 Oware-Cartesi | Implementation of the classic game, played by millions with a Strategic Gameplay approach leveraging AI Agents. Kagwe.stark 🥉 CartesiKit | An all-in-one CLI tool designed to streamline the process of setting up new projects for Cartesi Rollups. gconnect.base.eth | Glory Agatevure🌿 🥈 Ultimate Football | A build your football dream team onchain game, let 'em beat your opponent's and win prizes! The Elder of Jos @Blessed_mayowa konies jay 🥇 CarteZcash | What if you could shield your ETH assets directly from Ethereum by depositing them into a Zcash L2? This is it! Willem Olding

Cartesi

15,242 görüntüleme • 2 yıl önce

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

AI at Meta

2,265,635 görüntüleme • 1 yıl önce

🎉 Exciting news! 🎮🚀 Eimolad is thrilled to announce the release of our latest game version, "MVP 2.0"! 🎉 Join us in this epic adventure and become part of our testing squad! 🎮🔥 🔍 We're looking for passionate gamers to try out the new features and provide valuable feedback. 🎮📝 Your input will help us shape the game and make it even better! 💪 🏆 As a token of our gratitude, we have an amazing reward pool waiting for our testers: 💰 1,000,000 eGold 🔱 1 Adit 🧙‍♂️ 3 Dwarves 🗡️ 10 Weapons 🎭 OG Roles 📝 Fill out this form to be part of the action: ⏰ Don't miss out on this incredible opportunity to be among the first to explore "MVP 2.0" and earn epic rewards! ⚔️ Join us now and let the gaming adventure begin! 🎮💥 #Eimolad #DFINITY #MVP2 #ICPeople #BetaTesting #GetReadyToPlay

🎉 Exciting news! 🎮🚀 Eimolad is thrilled to announce the release of our latest game version, "MVP 2.0"! 🎉 Join us in this epic adventure and become part of our testing squad! 🎮🔥 🔍 We're looking for passionate gamers to try out the new features and provide valuable feedback. 🎮📝 Your input will help us shape the game and make it even better! 💪 🏆 As a token of our gratitude, we have an amazing reward pool waiting for our testers: 💰 1,000,000 eGold 🔱 1 Adit 🧙‍♂️ 3 Dwarves 🗡️ 10 Weapons 🎭 OG Roles 📝 Fill out this form to be part of the action: ⏰ Don't miss out on this incredible opportunity to be among the first to explore "MVP 2.0" and earn epic rewards! ⚔️ Join us now and let the gaming adventure begin! 🎮💥 #Eimolad #DFINITY #MVP2 #ICPeople #BetaTesting #GetReadyToPlay

Eimolad

52,139 görüntüleme • 3 yıl önce

🎉 The best way to start the week is to find out that our MedSAM is finally published today in Nature Communications! **Segment anything in medical images** Paper: arXiv: Data & Code: MedSAM is the first promotable foundation model for medical image segmentation. **Highlights**: ⭐ Before its formal publication, we have received 220 citations and 1400+ GitHub stars 🙏🙏❤️‍🔥❤️‍🔥❤️‍🔥 📊 We curated a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. 🚀 Built on top of SAM (AI at Meta ) with transfer learning, we have significantly enhanced its segmentation performance of medical images. 📈 Comprehensive evaluations of 86 internal validation tasks and 60 external validation tasks demonstrate its better accuracy and robustness than modality-wise specialist models. **What is Next? --- Clinical Translation!!** 🍕Our next goal is to make the model deployable on laptops (CPUs) or other edge devices without reliance on GPUs. We have distilled a lightweight model, LiteMedSAM, offering a speed boost of 10x while maintaining accuracy. Plus, we have integrated it into the 3D Slicer plugin, providing an efficient tool for medical image segmentation. 🌐 To further promote developments in this field, we organize a competition on #CVPR2026: Segment Anything in Medical Images on Laptop! An out-of-the-box baseline has been released to reduce the entry barriers. Welcome to join us to push the boundary further: 🙏 Massive thanks to MetaAI AI at Meta for their open-source project SAM and many reviewers/users for their invaluable feedback. A huge shoutout to my postdoc Jun Ma (JunMa) for his leadership on this project!! UHN AI Hub Vector Institute Peter Munk Cardiac Centre AI Department of Laboratory Medicine & Pathobiology U of T Department of Computer Science University of Toronto University Health Network Brad Wouters 🇨🇦 Barry Rubin MD, PhD, FRCSC Shaf Keshavjee

🎉 The best way to start the week is to find out that our MedSAM is finally published today in Nature Communications! Segment anything in medical images Paper: arXiv: Data & Code: MedSAM is the first promotable foundation model for medical image segmentation. Highlights: ⭐ Before its formal publication, we have received 220 citations and 1400+ GitHub stars 🙏🙏❤️‍🔥❤️‍🔥❤️‍🔥 📊 We curated a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. 🚀 Built on top of SAM (AI at Meta ) with transfer learning, we have significantly enhanced its segmentation performance of medical images. 📈 Comprehensive evaluations of 86 internal validation tasks and 60 external validation tasks demonstrate its better accuracy and robustness than modality-wise specialist models. What is Next? --- Clinical Translation!! 🍕Our next goal is to make the model deployable on laptops (CPUs) or other edge devices without reliance on GPUs. We have distilled a lightweight model, LiteMedSAM, offering a speed boost of 10x while maintaining accuracy. Plus, we have integrated it into the 3D Slicer plugin, providing an efficient tool for medical image segmentation. 🌐 To further promote developments in this field, we organize a competition on #CVPR2026: Segment Anything in Medical Images on Laptop! An out-of-the-box baseline has been released to reduce the entry barriers. Welcome to join us to push the boundary further: 🙏 Massive thanks to MetaAI AI at Meta for their open-source project SAM and many reviewers/users for their invaluable feedback. A huge shoutout to my postdoc Jun Ma (JunMa) for his leadership on this project!! UHN AI Hub Vector Institute Peter Munk Cardiac Centre AI Department of Laboratory Medicine & Pathobiology U of T Department of Computer Science University of Toronto University Health Network Brad Wouters 🇨🇦 Barry Rubin MD, PhD, FRCSC Shaf Keshavjee

Bo Wang

140,229 görüntüleme • 2 yıl önce