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AI vision models perform well on standard benchmarks like ImageNet, often matching or exceeding human accuracy. But here's what they don't tell you: Move an image by ONE PIXEL and these same models have a meltdown 40% of the time. This brittleness is breaking real systems.

Jack 🤖

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122,506 次观看 • 11 个月前 •via X (Twitter)

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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

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

73,244 次观看 • 1 年前

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

Daniel

151,698 次观看 • 7 个月前

How are world models changing enterprise systems? Here the founders of Skyfall AI tell me how. Don't have time to watch this video? Click the Grok button and ask it "what will I learn by watching this video?" (I'll post a link below).

How are world models changing enterprise systems? Here the founders of Skyfall AI tell me how. Don't have time to watch this video? Click the Grok button and ask it "what will I learn by watching this video?" (I'll post a link below).

Robert Scoble

10,490 次观看 • 7 个月前

Image Edit is heating up in the Arena - 3 new models have been added! ✨ Flux-Kontext-Max by BFL ✨ Bagel by ByteDance ✨ Step1X-edit by StepFun This brings the Image Edit Arena to a total of 7, with more coming! Upload an image and test them out, let's see what you think of these new models🖼️

Image Edit is heating up in the Arena - 3 new models have been added! ✨ Flux-Kontext-Max by BFL ✨ Bagel by ByteDance ✨ Step1X-edit by StepFun This brings the Image Edit Arena to a total of 7, with more coming! Upload an image and test them out, let's see what you think of these new models🖼️

Arena.ai

18,500 次观看 • 1 年前

Right now, almost everything you do online feeds centralized AI models. Reddit posts helped train large language models. Instagram photos power image generation. Now your daily online activity is being used to automate work itself. Action Model is pushing back with a community-owned AI model, where users stay in control and own what they help train. - Train passively or guide it step-by-step - Private data stays masked and in your control - Review, edit, or delete training anytime This isn’t a vision, it’s the standard the future of AI should be built on.

Right now, almost everything you do online feeds centralized AI models. Reddit posts helped train large language models. Instagram photos power image generation. Now your daily online activity is being used to automate work itself. Action Model is pushing back with a community-owned AI model, where users stay in control and own what they help train. - Train passively or guide it step-by-step - Private data stays masked and in your control - Review, edit, or delete training anytime This isn’t a vision, it’s the standard the future of AI should be built on.

Action Model

80,918 次观看 • 5 个月前

What you type into ChatGPT is analyzed, flagged, used to train models, and shared with governments. But you can have both privacy AND AI. Here's how to use AI privately, by self-hosting, or using platforms that don't log your data.

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Naomi Brockwell priv/acc

14,065 次观看 • 5 个月前

When people talk about AI, they often focus on the models. But behind every AI system are millions of human contributions. The data comes from human writing, speech, images, and behavior. The labels that help models learn patterns are created by people. The feedback that improves responses comes from people. And the moderation that keeps systems safe is handled by people. AI may look automated on the surface, but human intelligence powers every layer underneath it. Really enjoyed hearing Ishank Gupta and Maria Vovchok discuss this reality and KGeN's role in the evolving AI ecosystem during the recent X Space hosted by Gate. A great reminder that the future of AI is still deeply connected to human participation.

When people talk about AI, they often focus on the models. But behind every AI system are millions of human contributions. The data comes from human writing, speech, images, and behavior. The labels that help models learn patterns are created by people. The feedback that improves responses comes from people. And the moderation that keeps systems safe is handled by people. AI may look automated on the surface, but human intelligence powers every layer underneath it. Really enjoyed hearing Ishank Gupta and Maria Vovchok discuss this reality and KGeN's role in the evolving AI ecosystem during the recent X Space hosted by Gate. A great reminder that the future of AI is still deeply connected to human participation.

Bahdfella™ ❤️‍🔥

36,089 次观看 • 1 个月前

Most AI tools give everyone access to the same generic models. The result? Everything looks the same. We have a different vision for AI creation. Introducing TITLES, a new creative studio built around AI models trained and owned by artists. In Studio, you can create with distinct visual perspectives developed by artists, across image and video — all in one place. This is the future we're building toward: not one model for everyone, but a growing network of unique styles you can build with — where artists get credited and paid as the work spreads. Enter Your Creative Studio:

Most AI tools give everyone access to the same generic models. The result? Everything looks the same. We have a different vision for AI creation. Introducing TITLES, a new creative studio built around AI models trained and owned by artists. In Studio, you can create with distinct visual perspectives developed by artists, across image and video — all in one place. This is the future we're building toward: not one model for everyone, but a growing network of unique styles you can build with — where artists get credited and paid as the work spreads. Enter Your Creative Studio:

TITLES

2,746,536 次观看 • 3 个月前

NEURALINK CO FOUNDER: AI ISN’T COPYING US, IT’S FINDING THE SAME TRUTHS WE DID Max Hodak, a Neuralink co-founder, says something remarkable is happening between AI and neuroscience. The deeper researchers look, the more they see the same mathematical patterns emerging in machines that exist in human thought. Different systems, trained in totally different ways, are somehow ending up at the same destination. “There’s been this really interesting unification happening between what’s going on in artificial intelligence and what’s happening in neuroscience. A year or two ago, people thought these models were just glorified autocompletes or stochastic parrots, but that doesn’t seem to be the case. When you look inside these AI models, you see mathematical objects that look a lot like what you see in the brain. Different models trained on different data sets are converging toward the same underlying representations. That means these systems are learning something deep about the universe, the same things the brain has already figured out. It gives us reason to believe that AI is on the right track.” Source: Jon Hernandez Neuralink

NEURALINK CO FOUNDER: AI ISN’T COPYING US, IT’S FINDING THE SAME TRUTHS WE DID Max Hodak, a Neuralink co-founder, says something remarkable is happening between AI and neuroscience. The deeper researchers look, the more they see the same mathematical patterns emerging in machines that exist in human thought. Different systems, trained in totally different ways, are somehow ending up at the same destination. “There’s been this really interesting unification happening between what’s going on in artificial intelligence and what’s happening in neuroscience. A year or two ago, people thought these models were just glorified autocompletes or stochastic parrots, but that doesn’t seem to be the case. When you look inside these AI models, you see mathematical objects that look a lot like what you see in the brain. Different models trained on different data sets are converging toward the same underlying representations. That means these systems are learning something deep about the universe, the same things the brain has already figured out. It gives us reason to believe that AI is on the right track.” Source: Jon Hernandez Neuralink

Mario Nawfal

88,154 次观看 • 7 个月前

This video is a reminder that data is what truly makes a model perform at its best. Bytedance’s latest whitepaper, Omnihuman, showcases human-like video generation like never before. Here’s how other models Luma , Kling AI , Runway , Hailuo AI (MiniMax) react to the same image/prompt. 👇 Comment which model did it best!

This video is a reminder that data is what truly makes a model perform at its best. Bytedance’s latest whitepaper, Omnihuman, showcases human-like video generation like never before. Here’s how other models Luma , Kling AI , Runway , Hailuo AI (MiniMax) react to the same image/prompt. 👇 Comment which model did it best!

OpenLedger

17,398 次观看 • 1 年前

India’s Sovereign Move - Sarvam Vision Cracks the Code across 22 Indian languages Sarvam Vision is an AI model from Sarvam AI that excels in optical character recognition (OCR) for 22 Indian languages, outperforming global models like Gemini and GPT-4o on Indic benchmarks. This achievement supports India's push for sovereign AI by digitizing real-world documents and archives in local scripts. Sarvam Vision, a 3-billion-parameter model, handles "messy" scanned paperwork across languages like Hindi (95.91% accuracy), Bengali (92.61%), Tamil (93.42%), Marathi (93.13%), and even low-resource ones like Santali and Dogri (over 80%). It structures data natively without relying on English translation layers, enabling applications in cultural recovery and document intelligence. The model powers India's sovereign AI infrastructure under the IndiaAI Mission, where Sarvam was selected to build a national LLM. This launch fits Sarvam's broader stack, including prior models like Sarvam-Translate (also for 22 languages) and audio models for code-mixed speech. By focusing on Indic challenges ignored by Western AI, it unlocks centuries of knowledge in non-English archives. Recent demos, like digitizing historical texts, highlight its real-world impact beyond benchmarks. Credit : AIM Network.

India’s Sovereign Move - Sarvam Vision Cracks the Code across 22 Indian languages Sarvam Vision is an AI model from Sarvam AI that excels in optical character recognition (OCR) for 22 Indian languages, outperforming global models like Gemini and GPT-4o on Indic benchmarks. This achievement supports India's push for sovereign AI by digitizing real-world documents and archives in local scripts. Sarvam Vision, a 3-billion-parameter model, handles "messy" scanned paperwork across languages like Hindi (95.91% accuracy), Bengali (92.61%), Tamil (93.42%), Marathi (93.13%), and even low-resource ones like Santali and Dogri (over 80%). It structures data natively without relying on English translation layers, enabling applications in cultural recovery and document intelligence. The model powers India's sovereign AI infrastructure under the IndiaAI Mission, where Sarvam was selected to build a national LLM. This launch fits Sarvam's broader stack, including prior models like Sarvam-Translate (also for 22 languages) and audio models for code-mixed speech. By focusing on Indic challenges ignored by Western AI, it unlocks centuries of knowledge in non-English archives. Recent demos, like digitizing historical texts, highlight its real-world impact beyond benchmarks. Credit : AIM Network.

Augadh

20,645 次观看 • 5 个月前

Sam Altman: "We now have AI systems smart enough that our best scientists are saying this changes the game for science." "These models are discovering new knowledge on their own. This is the first time a tool humans have built is autonomously contributing to the sum total of human knowledge."

Sam Altman: "We now have AI systems smart enough that our best scientists are saying this changes the game for science." "These models are discovering new knowledge on their own. This is the first time a tool humans have built is autonomously contributing to the sum total of human knowledge."

vitrupo

26,992 次观看 • 3 个月前

Mathematician Terence Tao offers a counterintuitive take: AI doesn't look intelligent because our definition of intelligence was wrong all along. He argues that the entire history of AI has followed a predictable pattern: "The history of AI has been here's a task that only humans can do, like maybe it is read natural language or win at chess or solve a math problem, and then one by one someone finds some AI algorithm that also does that." But every time a machine cracks one of these "uniquely human" tasks, we move the goalposts. The solution never feels like real thinking: "You look at how it's done and it doesn't feel like intelligence. It's, oh, it was some trick. You just cobbled together these neural networks and you ran some algorithm, and we were looking for some elusive intelligent way of thinking, and we don't see it in the tools that actually solve our goals." Tao then flips the problem on its head. What if the issue isn't with the machines, but with us? "But maybe it's actually because intelligence is not what we think it is." He points to large language models as the clearest case. What they do sounds almost embarrassingly simple: "Large language models in particular become very successful, and a lot of what they're doing is just predicting the next token, clicking the next word in a sentence. And that doesn't sound like something which is intelligent." To show why this feels wrong, Tao draws a comparison to how we'd judge a human doing the same thing: "If you ask someone to improvise a speech and they have no preparation, and at every moment they're just saying the next word that comes to their mind, you don't think that this could actually work." And yet it works for LLMs. Which forces an uncomfortable possibility: "Maybe that's actually a lot of what humans do as well."

Mathematician Terence Tao offers a counterintuitive take: AI doesn't look intelligent because our definition of intelligence was wrong all along. He argues that the entire history of AI has followed a predictable pattern: "The history of AI has been here's a task that only humans can do, like maybe it is read natural language or win at chess or solve a math problem, and then one by one someone finds some AI algorithm that also does that." But every time a machine cracks one of these "uniquely human" tasks, we move the goalposts. The solution never feels like real thinking: "You look at how it's done and it doesn't feel like intelligence. It's, oh, it was some trick. You just cobbled together these neural networks and you ran some algorithm, and we were looking for some elusive intelligent way of thinking, and we don't see it in the tools that actually solve our goals." Tao then flips the problem on its head. What if the issue isn't with the machines, but with us? "But maybe it's actually because intelligence is not what we think it is." He points to large language models as the clearest case. What they do sounds almost embarrassingly simple: "Large language models in particular become very successful, and a lot of what they're doing is just predicting the next token, clicking the next word in a sentence. And that doesn't sound like something which is intelligent." To show why this feels wrong, Tao draws a comparison to how we'd judge a human doing the same thing: "If you ask someone to improvise a speech and they have no preparation, and at every moment they're just saying the next word that comes to their mind, you don't think that this could actually work." And yet it works for LLMs. Which forces an uncomfortable possibility: "Maybe that's actually a lot of what humans do as well."

Big Brain AI

69,395 次观看 • 2 个月前

"Most enterprise workflows don't require frontier AI models. Outside of coding, tasks like summarization, document generation, and briefing can often be handled effectively by lower-cost open-source models. The real value is knowing when you need frontier performance and when cheaper models are more than good enough." Michael Mignano What percent of enterprise workflows requires frontier models today Nikesh Arora Matan Grinberg Aaron Katz Lin Qiao

"Most enterprise workflows don't require frontier AI models. Outside of coding, tasks like summarization, document generation, and briefing can often be handled effectively by lower-cost open-source models. The real value is knowing when you need frontier performance and when cheaper models are more than good enough." Michael Mignano What percent of enterprise workflows requires frontier models today Nikesh Arora Matan Grinberg Aaron Katz Lin Qiao

Harry Stebbings

36,428 次观看 • 22 天前

✈️ Starting the day in Istanbul. Let's talk AI. The future of AI won't be shaped by size, but by precision. Lately I've been diving into what OpenLedger has been building, and I think we're witnessing one of the most important hard forks in AI: 👉 From giant generalist models 👉 To focused, hightrust AI agents Here's why that shift matters , and why OpenLedger's vision makes perfect sense: ✅ Specialized > Generalized General AI can do many things. But specialized AI? It does one thing extraordinarily well. Tailored models don't waste compute on irrelevant context, every parameter is purposedriven. ✅ Explainability isn't optional anymore In highstakes sectors like finance or healthcare, because the model said so won't cut it. We need transparent reasoning paths. Models must show how they reached conclusions , not just what they concluded. ✅ Trust comes from traceability With OpenLedger's Proof of Attribution, each AI decision is traceable, verifiable, and tamperproof. We're talking onchain records of who contributed what , accountability by design. ✅ Less hallucination, more signal Smaller, specialized models trained on clean, domainspecific data are far less prone to hallucinations. Clear boundaries = higher reliability. ✅ Efficiency is the real scalability Deploying one massive model for everything? Expensive, slow, and unsustainable. Specialized AI is leaner, faster, and far more costeffective. In short, OpenLedger isn't just following a trend. They're laying the rails for the infrastructure layer of verifiable, domainaware AI. And in a space flooded with blackbox models and hype, that clarity hits different.

✈️ Starting the day in Istanbul. Let's talk AI. The future of AI won't be shaped by size, but by precision. Lately I've been diving into what OpenLedger has been building, and I think we're witnessing one of the most important hard forks in AI: 👉 From giant generalist models 👉 To focused, hightrust AI agents Here's why that shift matters , and why OpenLedger's vision makes perfect sense: ✅ Specialized > Generalized General AI can do many things. But specialized AI? It does one thing extraordinarily well. Tailored models don't waste compute on irrelevant context, every parameter is purposedriven. ✅ Explainability isn't optional anymore In highstakes sectors like finance or healthcare, because the model said so won't cut it. We need transparent reasoning paths. Models must show how they reached conclusions , not just what they concluded. ✅ Trust comes from traceability With OpenLedger's Proof of Attribution, each AI decision is traceable, verifiable, and tamperproof. We're talking onchain records of who contributed what , accountability by design. ✅ Less hallucination, more signal Smaller, specialized models trained on clean, domainspecific data are far less prone to hallucinations. Clear boundaries = higher reliability. ✅ Efficiency is the real scalability Deploying one massive model for everything? Expensive, slow, and unsustainable. Specialized AI is leaner, faster, and far more costeffective. In short, OpenLedger isn't just following a trend. They're laying the rails for the infrastructure layer of verifiable, domainaware AI. And in a space flooded with blackbox models and hype, that clarity hits different.

Crypto Sinan

13,636 次观看 • 11 个月前

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 次观看 • 2 年前

Traditional coding benchmarks do not reflect how software is actually built and maintained. That's why we built a new benchmark, APEX-SWE, in partnership with Cognition. It measures whether AI models can perform complex, real-world software engineering work to ship systems that work and debug them when they don't. OpenAI GPT 5.3 Codex (High) tops the leaderboard at 41.5% on Pass@1.

Traditional coding benchmarks do not reflect how software is actually built and maintained. That's why we built a new benchmark, APEX-SWE, in partnership with Cognition. It measures whether AI models can perform complex, real-world software engineering work to ship systems that work and debug them when they don't. OpenAI GPT 5.3 Codex (High) tops the leaderboard at 41.5% on Pass@1.

adarsh

212,522 次观看 • 4 个月前

.Dan Shipper 📧: "The AI jobpocalypse is not a thing. The mass unemployment thing that AI lab CEOs are talking about—that's not going to happen. AI models make yesterday's human competence cheap. But what's interesting is that since everyone's using the same models, it all looks the same. So it becomes commoditized. It's not valuable anymore. And what humans do is we go in there, and we're like, yeah, we have all this frozen human competence from yesterday, how do I use this to make something new and interesting, today?"

.Dan Shipper 📧: "The AI jobpocalypse is not a thing. The mass unemployment thing that AI lab CEOs are talking about—that's not going to happen. AI models make yesterday's human competence cheap. But what's interesting is that since everyone's using the same models, it all looks the same. So it becomes commoditized. It's not valuable anymore. And what humans do is we go in there, and we're like, yeah, we have all this frozen human competence from yesterday, how do I use this to make something new and interesting, today?"

Lenny Rachitsky

276,114 次观看 • 2 个月前