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Google has launched Med-Gemini, an advanced AI fine-tuned for medical Tasks. It significantly outperforms earlier models, including GPT-4, on most medical benchmarks. Achieves top scores, particularly on the MedQA-USMLE benchmark with a groundbreaking 91.1% accuracy.🚀 Demonstrates superior performance over GPT-4 by 44.5% on average across seven multimodal benchmarks. Excels...

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Pascal Bornet

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14,393 次观看 • 1 年前 •via X (Twitter)

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India takes a decisive leap towards AI-enabled healthcare with the launch of a national training programme for doctors Union Minister of State for Health and Family Welfare, Smt. Anupriya Patel, today launched an online training programme on Artificial Intelligence (AI) in Medical Education, developed by the National Board of Examinations in Medical Sciences (NBEMS). Designed to train 50,000 doctors across the country, the programme will equip medical professionals with foundational knowledge and practical skills to apply AI in clinical practice, diagnostics, decision-making, research, and medical education.
4:55

India takes a decisive leap towards AI-enabled healthcare with the launch of a national training programme for doctors Union Minister of State for Health and Family Welfare, Smt. Anupriya Patel, today launched an online training programme on Artificial Intelligence (AI) in Medical Education, developed by the National Board of Examinations in Medical Sciences (NBEMS). Designed to train 50,000 doctors across the country, the programme will equip medical professionals with foundational knowledge and practical skills to apply AI in clinical practice, diagnostics, decision-making, research, and medical education.

Ministry of Health

17,896 次观看 • 8 个月前

🚀 VitaNova is now LIVE on Robinhood. This is more than a token launch. It marks the beginning of a new chapter where AI meets healthcare, life science, and onchain innovation. VitaNova is building an AI-powered healthcare ecosystem designed to connect medical data, biomedical research, intelligent agents and next-generation healthcare applications. And the vision is already moving beyond the blockchain. VitaNova has partnered with Stanford’s TRAM Center to support medical research and advance next-generation AI-powered medical data analysis. To accelerate this mission, VitaNova has launched the VitaNova Medical Charitable Fund, creating a dedicated pathway to support medical research and future AI healthcare initiatives. Now, with its launch on Robinhood, VitaNova is bringing this Stanford-connected healthcare vision into one of the most visible next-generation digital asset ecosystems. Stanford × AI × Healthcare × Robinhood From scientific innovation, to intelligent healthcare, to global access. The future of healthcare is being built now. 🌱 VitaNova — AI for Life.
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🚀 VitaNova is now LIVE on Robinhood. This is more than a token launch. It marks the beginning of a new chapter where AI meets healthcare, life science, and onchain innovation. VitaNova is building an AI-powered healthcare ecosystem designed to connect medical data, biomedical research, intelligent agents and next-generation healthcare applications. And the vision is already moving beyond the blockchain. VitaNova has partnered with Stanford’s TRAM Center to support medical research and advance next-generation AI-powered medical data analysis. To accelerate this mission, VitaNova has launched the VitaNova Medical Charitable Fund, creating a dedicated pathway to support medical research and future AI healthcare initiatives. Now, with its launch on Robinhood, VitaNova is bringing this Stanford-connected healthcare vision into one of the most visible next-generation digital asset ecosystems. Stanford × AI × Healthcare × Robinhood From scientific innovation, to intelligent healthcare, to global access. The future of healthcare is being built now. 🌱 VitaNova — AI for Life.

VitaNova

113,055 次观看 • 19 天前

🚀 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
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🚀 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

178,666 次观看 • 2 年前

🎉 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
0:59

🎉 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

140,336 次观看 • 2 年前

Meta presents Sapiens Foundation for Human Vision Models discuss: We present Sapiens, a family of models for four fundamental human-centric vision tasks - 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are extremely easy to adapt for individual tasks by simply fine-tuning models pretrained on over 300 million in-the-wild human images. We observe that, given the same computational budget, self-supervised pretraining on a curated dataset of human images significantly boosts the performance for a diverse set of human-centric tasks. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. Our simple model design also brings scalability - model performance across tasks improves as we scale the number of parameters from 0.3 to 2 billion. Sapiens consistently surpasses existing baselines across various human-centric benchmarks. We achieve significant improvements over the prior state-of-the-art on Humans-5K (pose) by 7.6 mAP, Humans-2K (part-seg) by 17.1 mIoU, Hi4D (depth) by 22.4% relative RMSE, and THuman2 (normal) by 53.5% relative angular error.
2:00

Meta presents Sapiens Foundation for Human Vision Models discuss: We present Sapiens, a family of models for four fundamental human-centric vision tasks - 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are extremely easy to adapt for individual tasks by simply fine-tuning models pretrained on over 300 million in-the-wild human images. We observe that, given the same computational budget, self-supervised pretraining on a curated dataset of human images significantly boosts the performance for a diverse set of human-centric tasks. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. Our simple model design also brings scalability - model performance across tasks improves as we scale the number of parameters from 0.3 to 2 billion. Sapiens consistently surpasses existing baselines across various human-centric benchmarks. We achieve significant improvements over the prior state-of-the-art on Humans-5K (pose) by 7.6 mAP, Humans-2K (part-seg) by 17.1 mIoU, Hi4D (depth) by 22.4% relative RMSE, and THuman2 (normal) by 53.5% relative angular error.

AK

151,614 次观看 • 2 年前

One of our signature projects in the healthcare sector is the Enugu International Hospital, a 300-bed ultramodern facility being developed to strengthen access to quality and specialised healthcare services in Enugu State. The hospital is being equipped with advanced medical infrastructure, including cancer diagnosis, treatment, and other critical care services, to position it as a centre for excellence and a destination for advanced medical treatment within and beyond Nigeria. A productive state is built on a healthy population, and we remain committed to strengthening our healthcare system to ensure accessible, modern, and quality healthcare for all.
1:30

One of our signature projects in the healthcare sector is the Enugu International Hospital, a 300-bed ultramodern facility being developed to strengthen access to quality and specialised healthcare services in Enugu State. The hospital is being equipped with advanced medical infrastructure, including cancer diagnosis, treatment, and other critical care services, to position it as a centre for excellence and a destination for advanced medical treatment within and beyond Nigeria. A productive state is built on a healthy population, and we remain committed to strengthening our healthcare system to ensure accessible, modern, and quality healthcare for all.

Peter Ndubuisi Mbah

55,791 次观看 • 5 个月前

Databricks is excited to partner with OpenAI on GPT-5.5, their latest frontier model. GPT-5.5 will be available in Unity AI Gateway on launch. You can use it with coding tools such as Codex, or to power your enterprise agents. GPT-5.5 is state-of-the-art on many benchmarks including OfficeQA Pro, our benchmark for evaluating grounded reasoning on enterprise tasks. We are partnering with OpenAI to co-launch on Databricks. Hear more from our co-founder Patrick Wendell and OpenAI CRO Denise Holland Dresser on GPT-5.5 in Databricks.
1:53

Databricks is excited to partner with OpenAI on GPT-5.5, their latest frontier model. GPT-5.5 will be available in Unity AI Gateway on launch. You can use it with coding tools such as Codex, or to power your enterprise agents. GPT-5.5 is state-of-the-art on many benchmarks including OfficeQA Pro, our benchmark for evaluating grounded reasoning on enterprise tasks. We are partnering with OpenAI to co-launch on Databricks. Hear more from our co-founder Patrick Wendell and OpenAI CRO Denise Holland Dresser on GPT-5.5 in Databricks.

Databricks

12,917 次观看 • 5 个月前

As you know, I am passionate about medical and mental health reform. I am so impressed by the medical school Alice L. Walton is founding to drive disruption and reform in the field. It was an honor to be on a panel with Arianna Huffington, Henry Timms, and Maya Yiadom to discuss mental health and the role of AI in wellness. A heartfelt thank you to Alice L. Walton School of Medicine and Stanford Medicine for hosting this incredible event!
0:39

As you know, I am passionate about medical and mental health reform. I am so impressed by the medical school Alice L. Walton is founding to drive disruption and reform in the field. It was an honor to be on a panel with Arianna Huffington, Henry Timms, and Maya Yiadom to discuss mental health and the role of AI in wellness. A heartfelt thank you to Alice L. Walton School of Medicine and Stanford Medicine for hosting this incredible event!

Jewel

24,090 次观看 • 1 年前

We are proud of the Gateway Medical Centre of Excellence (GMC), Abeokuta, a 250-bed multi-specialty hospital equipped to provide world-class healthcare services across several fields, including obstetrics and gynaecology, radiodiagnostics, orthopaedics, neurology, neurosciences, surgical and other specialised medical services. The facility features a fully equipped 10-bed Intensive Care Unit and four state-of-the-art operating theatres. Since commencing radiodiagnostic services, the Centre has already attended to over 600 patients, with capacity being expanded to serve between 3,000 and 4,000 patients in the coming months. This investment reflects our administration’s commitment to prioritising healthcare and providing our people with access to quality medical services in a purpose-built facility that meets international standards. At the Gateway Medical Centre of Excellence, our focus is on delivering excellent patient care, ensuring the best possible healthcare experience and building a stronger, more responsive healthcare system for Ogun State.
2:03

We are proud of the Gateway Medical Centre of Excellence (GMC), Abeokuta, a 250-bed multi-specialty hospital equipped to provide world-class healthcare services across several fields, including obstetrics and gynaecology, radiodiagnostics, orthopaedics, neurology, neurosciences, surgical and other specialised medical services. The facility features a fully equipped 10-bed Intensive Care Unit and four state-of-the-art operating theatres. Since commencing radiodiagnostic services, the Centre has already attended to over 600 patients, with capacity being expanded to serve between 3,000 and 4,000 patients in the coming months. This investment reflects our administration’s commitment to prioritising healthcare and providing our people with access to quality medical services in a purpose-built facility that meets international standards. At the Gateway Medical Centre of Excellence, our focus is on delivering excellent patient care, ensuring the best possible healthcare experience and building a stronger, more responsive healthcare system for Ogun State.

Prince Dr. Dapo Abiodun, CON

92,983 次观看 • 21 天前

Revolutionizing Bladder Cancer Diagnosis with ZayaAI We heard you Zayans, From our last videos we understood you all want a much easier to understand explanation of our AI models. So let's dive into the latest breakthrough in AI-powered healthcare, our Bladder Cancer Detection Model. This isn't just another AI tool, it's a game-changer for pathologists. Precision, Speed and Seamless Integration: Traditional diagnosis of urothelial carcinoma (bladder cancer) is often slow and prone to errors. Our AI model automatically detects low and high-grade tumors, along with lymphovascular invasion, with unmatched accuracy and speed. That means faster results, fewer mistakes and a more efficient workflow for medical professionals. Why This Matters: 1.Faster Diagnoses → AI speeds up the process, allowing pathologists to focus on critical cases. 2.Higher Accuracy → Reduces human error and reliance on costly, time-consuming tests. 3.Seamless Integration → No need for complex system overhauls. ZayaAI fits right into existing workflows. The Future of AI in Healthcare: Embracing our AI models isn’t just about using new technology, it’s an investment in a future where healthcare is more precise, efficient and patient-focused. Pathologists get to make faster, more informed decisions and ultimately, patient outcomes improve. This is how AI is redefining medical diagnostics and can help save lives. DeSci + AI = $ZAI ! 🔬⚕️
1:39

Revolutionizing Bladder Cancer Diagnosis with ZayaAI We heard you Zayans, From our last videos we understood you all want a much easier to understand explanation of our AI models. So let's dive into the latest breakthrough in AI-powered healthcare, our Bladder Cancer Detection Model. This isn't just another AI tool, it's a game-changer for pathologists. Precision, Speed and Seamless Integration: Traditional diagnosis of urothelial carcinoma (bladder cancer) is often slow and prone to errors. Our AI model automatically detects low and high-grade tumors, along with lymphovascular invasion, with unmatched accuracy and speed. That means faster results, fewer mistakes and a more efficient workflow for medical professionals. Why This Matters: 1.Faster Diagnoses → AI speeds up the process, allowing pathologists to focus on critical cases. 2.Higher Accuracy → Reduces human error and reliance on costly, time-consuming tests. 3.Seamless Integration → No need for complex system overhauls. ZayaAI fits right into existing workflows. The Future of AI in Healthcare: Embracing our AI models isn’t just about using new technology, it’s an investment in a future where healthcare is more precise, efficient and patient-focused. Pathologists get to make faster, more informed decisions and ultimately, patient outcomes improve. This is how AI is redefining medical diagnostics and can help save lives. DeSci + AI = $ZAI ! 🔬⚕️

ZayaAI

18,015 次观看 • 1 年前

MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.
0:48

MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

AK

125,353 次观看 • 3 年前

Superior methodology beats raw parameter count. 🧠 Introducing QVAC MedPsy: Local-first medical AI that redefines the possible. 1/ Unprecedented Power: MedPsy 1.7B model outperforms Google’s MedGemma 4B by 11 points and our 4B model beats MedGemma 27B on real-world health benchmarks. 2/ Extreme Efficiency: 3.2x fewer tokens means near-instant inference on your phone or wearable. 3/ Absolute Privacy: Expert-level reasoning running 100% locally. No data leaves your device. We aren’t simply shrinking models; we’re anchoring intelligence where it matters most. High-level medical logic is now a sovereign right. The future of healthcare is local. Learn more:
2:16

Superior methodology beats raw parameter count. 🧠 Introducing QVAC MedPsy: Local-first medical AI that redefines the possible. 1/ Unprecedented Power: MedPsy 1.7B model outperforms Google’s MedGemma 4B by 11 points and our 4B model beats MedGemma 27B on real-world health benchmarks. 2/ Extreme Efficiency: 3.2x fewer tokens means near-instant inference on your phone or wearable. 3/ Absolute Privacy: Expert-level reasoning running 100% locally. No data leaves your device. We aren’t simply shrinking models; we’re anchoring intelligence where it matters most. High-level medical logic is now a sovereign right. The future of healthcare is local. Learn more:

QVAC

2,416,342 次观看 • 4 个月前

As AI systems grow more capable, it is “not hypothetical” that AI could potentially abet biological weapons development, says Johns Hopkins professor and adviser to Anthropic, Ben Buchanan, calling it a “legitimate concern.” “They can do expert tasks in a wide variety of areas, not just in cyber, also in bio and the like,” Buchanan says. “There’s upside. AI can do a lot for medical discovery, but there’s a legitimate concern that AI is outperforming PhD-level virologists on virology questions.”
0:58

As AI systems grow more capable, it is “not hypothetical” that AI could potentially abet biological weapons development, says Johns Hopkins professor and adviser to Anthropic, Ben Buchanan, calling it a “legitimate concern.” “They can do expert tasks in a wide variety of areas, not just in cyber, also in bio and the like,” Buchanan says. “There’s upside. AI can do a lot for medical discovery, but there’s a legitimate concern that AI is outperforming PhD-level virologists on virology questions.”

Face The Nation

32,223 次观看 • 3 个月前

We benchmarked leading multimodal foundation models (GPT-4o, Claude 3.5 Sonnet, Gemini, Llama, etc.) on standard computer vision tasks—from segmentation to surface normal estimation—using standard datasets like COCO and ImageNet. These models have made remarkable progress; however, it is unclear exactly where they stand in terms of understanding vision in detail. Especially when it comes to tasks beyond question-answering. How well do they understand an object's segments or geometry? Our analyses yield an assessment that is quantitatively and qualitatively detailed and is compatible with evaluations developed in the field of computer vision over the past decades. Observed trends: 🔹 The foundation models consistently underperform task-specific SOTA models across all tasks. However, they are respectable generalists, which is remarkable as they are presumably trained primarily on image-text-based tasks. 🔹 They perform semantic tasks notably better than geometric ones. 🔹 GPT-4o performs the best among non-reasoning models, getting the top position in 4 out of 6 tasks. 🔹 Reasoning models, e.g., o3, show improvements in geometric tasks. 🔹 The 'image generation' models, e.g., GPT-40 Image Generation, which have been natively trained multimodally, exhibit quirks. E.g., hallucinated objects, misalignment between the input and output, etc. 🔹 While the prompting techniques affect performance, better models exhibit less sensitivity to variations in prompts. We control for the variance introduced by the prompting methods in our experiments. 🌐 Detailed analyses, visualizations: ⌨️ code: 🧵 1/n
2:36

We benchmarked leading multimodal foundation models (GPT-4o, Claude 3.5 Sonnet, Gemini, Llama, etc.) on standard computer vision tasks—from segmentation to surface normal estimation—using standard datasets like COCO and ImageNet. These models have made remarkable progress; however, it is unclear exactly where they stand in terms of understanding vision in detail. Especially when it comes to tasks beyond question-answering. How well do they understand an object's segments or geometry? Our analyses yield an assessment that is quantitatively and qualitatively detailed and is compatible with evaluations developed in the field of computer vision over the past decades. Observed trends: 🔹 The foundation models consistently underperform task-specific SOTA models across all tasks. However, they are respectable generalists, which is remarkable as they are presumably trained primarily on image-text-based tasks. 🔹 They perform semantic tasks notably better than geometric ones. 🔹 GPT-4o performs the best among non-reasoning models, getting the top position in 4 out of 6 tasks. 🔹 Reasoning models, e.g., o3, show improvements in geometric tasks. 🔹 The 'image generation' models, e.g., GPT-40 Image Generation, which have been natively trained multimodally, exhibit quirks. E.g., hallucinated objects, misalignment between the input and output, etc. 🔹 While the prompting techniques affect performance, better models exhibit less sensitivity to variations in prompts. We control for the variance introduced by the prompting methods in our experiments. 🌐 Detailed analyses, visualizations: ⌨️ code: 🧵 1/n

Amir Zamir

73,398 次观看 • 1 年前

VIDEO | Delhi: On the reduction of NEET PG cut-off scores, Dr Rohan Krishnan, Chief Patron of Federation of All India Medical Association (FAIMA), says, “The cut off now has been reduced to 7 percentile, 5 percentile, and 0 percentile. This means that a doctor who has even scored minus 40 marks in MBBS will face no objection or restriction in pursuing a specialist degree in postgraduate studies. This is a very controversial decision taken by the Indian government, and the medical community strongly condemns and opposes it. It is being said that this has been done to fill the seats. But on behalf of the entire FAIMA Doctors Association, I demand that it be clarified whether this is an exercise to fill seats in government medical colleges or to increase the supply of students for private medical colleges, where seats are sold for two to three crore rupees each, and thousands of such seats are lying vacant.” (Full video available on PTI Videos-
3:45

VIDEO | Delhi: On the reduction of NEET PG cut-off scores, Dr Rohan Krishnan, Chief Patron of Federation of All India Medical Association (FAIMA), says, “The cut off now has been reduced to 7 percentile, 5 percentile, and 0 percentile. This means that a doctor who has even scored minus 40 marks in MBBS will face no objection or restriction in pursuing a specialist degree in postgraduate studies. This is a very controversial decision taken by the Indian government, and the medical community strongly condemns and opposes it. It is being said that this has been done to fill the seats. But on behalf of the entire FAIMA Doctors Association, I demand that it be clarified whether this is an exercise to fill seats in government medical colleges or to increase the supply of students for private medical colleges, where seats are sold for two to three crore rupees each, and thousands of such seats are lying vacant.” (Full video available on PTI Videos-

Press Trust of India

17,064 次观看 • 8 个月前

For the first time in human history, we are teaching a Foundation Model to master the diverse tasks of medicinal chemists, biologists, and computational scientists all in one place. In our latest collaboration with Liquid AI, we are moving away from fragmented, specialized tools toward a single, super-intelligent model. What surprised me most? This model isn't just performing at reasonable levels—it has started outperforming specialist models across physics-based tasks, imaging, and longitudinal data. Why this changes everything: -Synergy over Specialization: Fine-tuning on specific tasks has unlocked unexpected capabilities in synergetic areas, opening a new frontier in multimodal AI research. -Zero-Shot Potential: We are building a model that can perform out-of-scope tasks, moving us closer to an "AI deity" for drug discovery. -Quality First: The goal isn't just to bypass regulations to save time; it’s about using these synergies to develop better, more effective drugs. We are no longer just looking at linear regression or simple text; we are looking at the future of how humanity fights disease. #LiquidAI #InsilicoMedicine #GenerativeAI #DrugDiscovery #DeepTech #BiotechInnovation
1:53

For the first time in human history, we are teaching a Foundation Model to master the diverse tasks of medicinal chemists, biologists, and computational scientists all in one place. In our latest collaboration with Liquid AI, we are moving away from fragmented, specialized tools toward a single, super-intelligent model. What surprised me most? This model isn't just performing at reasonable levels—it has started outperforming specialist models across physics-based tasks, imaging, and longitudinal data. Why this changes everything: -Synergy over Specialization: Fine-tuning on specific tasks has unlocked unexpected capabilities in synergetic areas, opening a new frontier in multimodal AI research. -Zero-Shot Potential: We are building a model that can perform out-of-scope tasks, moving us closer to an "AI deity" for drug discovery. -Quality First: The goal isn't just to bypass regulations to save time; it’s about using these synergies to develop better, more effective drugs. We are no longer just looking at linear regression or simple text; we are looking at the future of how humanity fights disease. #LiquidAI #InsilicoMedicine #GenerativeAI #DrugDiscovery #DeepTech #BiotechInnovation

Alex Zhavoronkov, PhD (aka Aleksandrs Zavoronkovs)

10,544 次观看 • 5 个月前

It is on record that Dr. Nasir Mohammed the Medical Officer “Fulani”, Department of State Services (DSS) main assignment was to kill Mazi Nnamdi Kanu. Dr Nasir prescribed “overdose” medication for MNK with his cohorts “sacked lawyers” for over 4 years, until his physician Emeritus Professor M.A.C Aghaji fas, facc took over. Nasir hurriedly went to court and lied on oath by deposing an affidavit against the Medical Report because, a quota Medical Doctor like him in Nigeria, it is unbeknown to him the implications of a patient having a low potassium. Nigerian Medical Association, Nigerian Medical Association should withdraw the licence of this fellow and investigate his experimental activities on MNK. Department of State Foreign, Commonwealth & Development Office UN Political and Peacebuilding Affairs United Nations European Commission Daily Post Nigeria Sahara Reporters The Guardian Nigeria Vanguard Newspapers Daily Times Nigeria Leadership Review UK in Nigeria🇬🇧🇳🇬 U.S. Mission Nigeria
3:28

It is on record that Dr. Nasir Mohammed the Medical Officer “Fulani”, Department of State Services (DSS) main assignment was to kill Mazi Nnamdi Kanu. Dr Nasir prescribed “overdose” medication for MNK with his cohorts “sacked lawyers” for over 4 years, until his physician Emeritus Professor M.A.C Aghaji fas, facc took over. Nasir hurriedly went to court and lied on oath by deposing an affidavit against the Medical Report because, a quota Medical Doctor like him in Nigeria, it is unbeknown to him the implications of a patient having a low potassium. Nigerian Medical Association, Nigerian Medical Association should withdraw the licence of this fellow and investigate his experimental activities on MNK. Department of State Foreign, Commonwealth & Development Office UN Political and Peacebuilding Affairs United Nations European Commission Daily Post Nigeria Sahara Reporters The Guardian Nigeria Vanguard Newspapers Daily Times Nigeria Leadership Review UK in Nigeria🇬🇧🇳🇬 U.S. Mission Nigeria

Kanunta kanu

27,222 次观看 • 1 年前

Great news! Through our amazing and incredibly talented in Gaza, we have set up a medical point in North Gaza that will serve 15,000 people! The Port Camp, west of Gaza City, now homes over 15,000 people who have been displaced after forced evacuation from other areas in the North. It is one of the poorest encampments since families fled there with nothing. It is also one of the most under supported areas due to the shear number of residents and the massive needs. The Translating Falasteen x The Sameer Project The Sameer Project North team chose the Port Camp for our new Medical Point for exactly these reasons. There are injured and sick, young and old, all with dire medical needs. The healthcare system in Gaza is non existant, particularly in the North where every hospital has been rendered either partially or completely out of service. Clinics have shut down, and doctors and nurses cannot keep up with the new injuries that come in daily and the ailments that grow worse with neglect. In this new medical point we have a general practitioner doctor, two nurses, a dentist, a physiotherapist, all offering services for free to those in need. With your help we will keep a fully stocked pharmacy with medication for fevers, chest infection, mild pain killers, skin rashes, respiratory illnesses, antibiotics, antiseptics, and more. We will clean and redress wounds, examine patients who are ill, and help people heal. However, a fully functioning medical point in an overcrowded area like the Port Camp is an immense undertaking nd we need your help. It cost $15,000-$20,000 each month to keep running and to source medication and supplies like gauze and iodine to keep in stock. Without us, this area has no other medical support. Be a part of caring for Gaza’s sick and wounded. Your initial contributions allowed us to open this Medical Point, and now we need to keep it running for all those who have benefited and the thousands more we will serve. To donate: Other options include: Other ways to donate include: (Paypal option, please make sure to add a message saying "Medical") (Venmo option, please make sure to add a message saying "Medical")
1:03

Great news! Through our amazing and incredibly talented in Gaza, we have set up a medical point in North Gaza that will serve 15,000 people! The Port Camp, west of Gaza City, now homes over 15,000 people who have been displaced after forced evacuation from other areas in the North. It is one of the poorest encampments since families fled there with nothing. It is also one of the most under supported areas due to the shear number of residents and the massive needs. The Translating Falasteen x The Sameer Project The Sameer Project North team chose the Port Camp for our new Medical Point for exactly these reasons. There are injured and sick, young and old, all with dire medical needs. The healthcare system in Gaza is non existant, particularly in the North where every hospital has been rendered either partially or completely out of service. Clinics have shut down, and doctors and nurses cannot keep up with the new injuries that come in daily and the ailments that grow worse with neglect. In this new medical point we have a general practitioner doctor, two nurses, a dentist, a physiotherapist, all offering services for free to those in need. With your help we will keep a fully stocked pharmacy with medication for fevers, chest infection, mild pain killers, skin rashes, respiratory illnesses, antibiotics, antiseptics, and more. We will clean and redress wounds, examine patients who are ill, and help people heal. However, a fully functioning medical point in an overcrowded area like the Port Camp is an immense undertaking nd we need your help. It cost $15,000-$20,000 each month to keep running and to source medication and supplies like gauze and iodine to keep in stock. Without us, this area has no other medical support. Be a part of caring for Gaza’s sick and wounded. Your initial contributions allowed us to open this Medical Point, and now we need to keep it running for all those who have benefited and the thousands more we will serve. To donate: Other options include: Other ways to donate include: (Paypal option, please make sure to add a message saying "Medical") (Venmo option, please make sure to add a message saying "Medical")

Translating Falasteen (Palestine)

60,365 次观看 • 1 年前

An important chapter in the history of the Defence Forces came to a close with the final ceremonial parade at St. Bricin’s Military Hospital. Since becoming St. Bricin’s Military Hospital in 1922, and with military medical services having operated on the site since 1790, it has played a central role in the care, training and development of generations of Medical Corps personnel. From supporting Defence Forces members at home to preparing medical personnel for service overseas, its contribution to military medicine has left an enduring legacy. While we bid farewell to this historic institution, the values of service, professionalism, compassion and excellence that defined St. Bricin’s will continue through the personnel of the Medical Corps as they transition to a modern, fit for purpose Military Medical Facility. The legacy of St. Bricin’s Military Hospital will remain an enduring part of the proud history of the Defence Forces. #ÓglaighnahÉireann
1:44

An important chapter in the history of the Defence Forces came to a close with the final ceremonial parade at St. Bricin’s Military Hospital. Since becoming St. Bricin’s Military Hospital in 1922, and with military medical services having operated on the site since 1790, it has played a central role in the care, training and development of generations of Medical Corps personnel. From supporting Defence Forces members at home to preparing medical personnel for service overseas, its contribution to military medicine has left an enduring legacy. While we bid farewell to this historic institution, the values of service, professionalism, compassion and excellence that defined St. Bricin’s will continue through the personnel of the Medical Corps as they transition to a modern, fit for purpose Military Medical Facility. The legacy of St. Bricin’s Military Hospital will remain an enduring part of the proud history of the Defence Forces. #ÓglaighnahÉireann

Óglaigh na hÉireann

12,746 次观看 • 1 个月前

How well do today’s frontier models handle long-horizon, multi-step web agent tasks, such as identifying the top 25 U.S. CS PhD programs with ML/AI faculty likely accepting students and compiling the results into a structured sheet? Check out our new work on Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks Paper: Leaderboard: We introduce Odysseys, a benchmark of 200 long-horizon tasks derived from real browsing sessions and evaluated on the live Internet. We show that binary pass/fail is inadequate in this setting and propose rubric-based evaluation, which better aligns with human judgment and provides more informative signals. Across leading models, the best achieves only 44.5% success, leaving substantial headroom. We further introduce a Trajectory Efficiency metric (rubric score per step) and find efficiency remains extremely low (1.15%), highlighting a key bottleneck. Odysseys provides a realistic benchmark for measuring progress toward web agents capable of sustained, efficient, real-world operation. See a more detailed thread by Jing Yu Koh.
1:12

How well do today’s frontier models handle long-horizon, multi-step web agent tasks, such as identifying the top 25 U.S. CS PhD programs with ML/AI faculty likely accepting students and compiling the results into a structured sheet? Check out our new work on Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks Paper: Leaderboard: We introduce Odysseys, a benchmark of 200 long-horizon tasks derived from real browsing sessions and evaluated on the live Internet. We show that binary pass/fail is inadequate in this setting and propose rubric-based evaluation, which better aligns with human judgment and provides more informative signals. Across leading models, the best achieves only 44.5% success, leaving substantial headroom. We further introduce a Trajectory Efficiency metric (rubric score per step) and find efficiency remains extremely low (1.15%), highlighting a key bottleneck. Odysseys provides a realistic benchmark for measuring progress toward web agents capable of sustained, efficient, real-world operation. See a more detailed thread by Jing Yu Koh.

Russ Salakhutdinov

22,518 次观看 • 5 个月前