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From a scrappy side project built to solve their own LLM optimization problems to becoming the industry’s de-facto independent scoreboard, Micah Hill-Smith and George Cameron went through the arc of launching Artificial Analysis for free, paying benchmarking costs out of pocket, and growing it into what many now call...

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Latent.Space

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33,143 次观看 • 5 个月前 •via X (Twitter)

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We’re unveiling a new look for Artificial Analysis! We’ve come a long way since launching Artificial Analysis over 2 years ago. Today, we benchmark 400+ models, 50+ inference providers, and benchmark not only language models but also image, video, speech, music, hardware, and agents. Our mission to support the AI ecosystem with independent benchmarking remains the same, but our brand and website refresh is designed to better reflect how much we’ve grown and how much further we plan to go. A huge thank you to everyone who has been part of the Artificial Analysis community along the way: from developers choosing models and building agents, to labs, inference and hardware providers, and fellow independent researchers.
0:25

We’re unveiling a new look for Artificial Analysis! We’ve come a long way since launching Artificial Analysis over 2 years ago. Today, we benchmark 400+ models, 50+ inference providers, and benchmark not only language models but also image, video, speech, music, hardware, and agents. Our mission to support the AI ecosystem with independent benchmarking remains the same, but our brand and website refresh is designed to better reflect how much we’ve grown and how much further we plan to go. A huge thank you to everyone who has been part of the Artificial Analysis community along the way: from developers choosing models and building agents, to labs, inference and hardware providers, and fellow independent researchers.

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Discussed the future of AI with Professor Zhang Weiwei: “We need to think ahead. We need to think about what we should do right now with artificial intelligence. When young children interact with their AI assistant, and it becomes closer to them than their parents, their friends, their nannies, preschool, or daycare. And this artificial intelligence keeps learning. And as its subject of study, as the textbooks for its own training, it uses Western platforms, which means it uses Western narratives. And very soon, we will have a completely different history of the world in their heads.”
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Discussed the future of AI with Professor Zhang Weiwei: “We need to think ahead. We need to think about what we should do right now with artificial intelligence. When young children interact with their AI assistant, and it becomes closer to them than their parents, their friends, their nannies, preschool, or daycare. And this artificial intelligence keeps learning. And as its subject of study, as the textbooks for its own training, it uses Western platforms, which means it uses Western narratives. And very soon, we will have a completely different history of the world in their heads.”

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Theoretical physicist Michio Kaku on why aging is simply an accumulation of errors that AI can now identify: He opens with a bold claim about what artificial intelligence might finally make possible: "Artificial intelligence may give us something that the kings and queens of old could never conquer and that is the aging process." For Dr. Michio Kaku, the science behind why we age is no longer a mystery. It comes down to one thing: errors building up inside our cells over time. "We now know why things age. Things get old and die because they accumulate error. Error in the form of DNA mistakes. Accumulation of errors inside cells." The challenge, until now, has been the sheer scale of the problem. Identifying exactly where and how these errors occur across the human genome has been beyond what conventional research could handle. That's where artificial intelligence changes everything: "But now we have artificial intelligence. We can analyze millions of genomes of old people, compare them to millions of genomes of young people, and identify where aging takes place in the body."

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As AI reshapes our world, we must ensure it benefits everyone – and that it is accessible and safe. The first global scientific body on artificial intelligence brings together independent experts to assess how AI is transforming our lives and how it can serve the common good.
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"I think one of the truths is that the demand for AI intelligence and smarter AI intelligence is going to be insatiable. Some people disagree that once we reach certain thresholds then you don't need more intelligence. To that I ask people, 'Have you ever worked with or managed someone in a work environment and wouldn't press a button to make them smarter?'" George elaborates on his thesis that the demand for smarter AI intelligence will only increase over time even as models get more powerful. George Cameron
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"I think one of the truths is that the demand for AI intelligence and smarter AI intelligence is going to be insatiable. Some people disagree that once we reach certain thresholds then you don't need more intelligence. To that I ask people, 'Have you ever worked with or managed someone in a work environment and wouldn't press a button to make them smarter?'" George elaborates on his thesis that the demand for smarter AI intelligence will only increase over time even as models get more powerful. George Cameron

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How will artificial intelligence transform the way we live, work, and shape our lifestyles over the next decade? And why isn’t this revolution attracting the scrutiny it deserves? Join Rory Stewart and Matt Clifford for How AI Will Change The World, a brand-new TRIP+ exclusive mini-series exploring the opportunities, risks, and realities of the AI revolution. Episode one available now.
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57:23

François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI

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We built Lit Protocol 🔑 to usher in a new paradigm of digital identity that empowers individuals to maintain sovereignty in the AI era. This infrastructure is crucial as we navigate the complex intersection of human agency and artificial intelligence.

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