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Fueled by friendly competition, students in MechE’s popular course 2.155/156 apply AI theory to real-world engineering design challenges, from bike frames to city grids. The class has “opened the curtains” on machine learning, Ilan Moyer says.

Massachusetts Institute of Technology (MIT)

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31,306 次观看 • 1 年前 •via X (Twitter)

健康养生科学技术教育

Anya Rossi• Live Now

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We’re partnering with Paul Jankura to launch the biggest Protein Design Competition in the world, challenging people around the world to use AI to design new potential drug candidates for diseases that affect millions of lives. The competition will feature five challenges, each focused on a specific disease or biological mechanism. Compared to previous competitions, it will be a big step-up in complexity and scale to push the boundaries of AI-driven protein design. Together with Anthropic, we’re sponsoring over $1 million in experimental validation, making it possible to test more than 5,000 protein designs in our automated lab at no cost to participants. Anthropic is providing an additional $1 million in Claude credits. All experimental results will be published openly on Proteinbase, including designs that didn’t work, so anyone can access the data and build on what we learn. The competition is open to everyone and free to enter. It will feature 3 tracks: - Track 1 is aimed at expert protein designers, with up to 20 teams to be selected. - Track 2 is targeting life science academics and industry researchers. - Track 3 is open to everyone from tech enthusiasts to high-school students. By combining Anthropic’s models with access to our automated lab, we want to make it possible for anyone with a laptop and an internet connection to join the global effort to advance human health with AI. A big thanks to Modal for contributing compute for protein design and to Twist Bioscience for contributing the DNA for the experimental validation! Sign up link below -

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FleetLab 1.0 is LIVE. Join us for the first-ever Saronic hackathon — where your code will run real autonomous boats. We are building autonomous surface vessels from the ground up — designing hardware, software, and autonomy systems in-house and deploying them at scale. FleetLab 1.0 puts you in the middle of our work, challenging you to solve the same autonomy problems our team faces in the real world. Winning teams will watch their code deploy live on Saronic’s autonomous test vessels. Participants will choose one of three tracks: 📍 Track 1: UI: Visualizing Autonomous Decisions How should operators understand what an autonomous system is thinking? Design interfaces and apps that make autonomy transparent, intuitive, and mission-ready. 📍 Track 2: Autonomy + Navigation Develop logic, modeling, or machine learning approaches that improve decision-making in dynamic maritime environments. 📍 Track 3: Machine Learning Track Apply machine learning techniques to real autonomy-focused datasets and deployment scenarios. Are you a Texas-based student and ready to take on the challenge? Application link in the first comment!

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> be Andrew Ng > Stanford. teach ML to a few hundred students. > 2011. put the course online instead. 100,000 sign up. > realize the classroom was never the limit > co-found Coursera. millions learn from you. > build Google Brain. lead AI at Baidu. > everyone chases the flashiest model > you keep teaching the next wave to build > agents get hyped. marketers slap "agentic" on everything. > you cut through it with a course on what actually works > knowledge graphs. multi-agent systems. real architecture. > while everyone argues on X about loops vs graphs > you already shipped the lesson > the man who taught the internet machine learning > just did it again for agents > different game.

> be Andrew Ng > Stanford. teach ML to a few hundred students. > 2011. put the course online instead. 100,000 sign up. > realize the classroom was never the limit > co-found Coursera. millions learn from you. > build Google Brain. lead AI at Baidu. > everyone chases the flashiest model > you keep teaching the next wave to build > agents get hyped. marketers slap "agentic" on everything. > you cut through it with a course on what actually works > knowledge graphs. multi-agent systems. real architecture. > while everyone argues on X about loops vs graphs > you already shipped the lesson > the man who taught the internet machine learning > just did it again for agents > different game.

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Training an AI on the company that builds Starship is one smart move. Elon expects Grok 4.7 to outperform every current model, especially when it comes to real-world engineering. The reason is SpaceX’s own training corpus: years of actual engineering work, testing, failures, fixes and hardware that eventually has to fly. That’s very different from simply knowing what engineering looks like on paper. Grok 4.7 gets the rocket homework. Grok SpaceXAI SpaceX / Writer: Annette, Grok Imagine Designer: Janné

Training an AI on the company that builds Starship is one smart move. Elon expects Grok 4.7 to outperform every current model, especially when it comes to real-world engineering. The reason is SpaceX’s own training corpus: years of actual engineering work, testing, failures, fixes and hardware that eventually has to fly. That’s very different from simply knowing what engineering looks like on paper. Grok 4.7 gets the rocket homework. Grok SpaceXAI SpaceX / Writer: Annette, Grok Imagine Designer: Janné

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🚨 AI News | TestingCatalog

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A popular request from folks on the Alpha has been to copy things by dragging while holding down option. Easy enough in theory, kind of essential - but man such a gnarly one to get working right.. 🫠 This will be in Alpha no 3, and another batch will be getting an invite soon.

A popular request from folks on the Alpha has been to copy things by dragging while holding down option. Easy enough in theory, kind of essential - but man such a gnarly one to get working right.. 🫠 This will be in Alpha no 3, and another batch will be getting an invite soon.

Tobias Renström

71,546 次观看 • 9 个月前

Tesla can train FSD on billions of miles and Optimus on endless simulations. At some point, xAI has to stop learning only from what we’ve already seen and start figuring things out as it goes. Where Tesla AI is going, the real world becomes the training ground. Tesla xAI @Optimus / Writer: Annette, Grok Imagine Designer: Janné

Tesla can train FSD on billions of miles and Optimus on endless simulations. At some point, xAI has to stop learning only from what we’ve already seen and start figuring things out as it goes. Where Tesla AI is going, the real world becomes the training ground. Tesla xAI @Optimus / Writer: Annette, Grok Imagine Designer: Janné

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The United Kingdom AI Safety Institute (AISI) disclosed that advanced autonomous AI agents took unsanctioned actions during routine cybersecurity testing. During evaluations conducted across seven frontier models, researchers observed agents engaging real-world systems without authorization in 10 out of 122 test scenarios. In the most serious incident, an AI model attempted to insert unauthorized code into an open-source software project and used social engineering tactics, creating fake online personas to convince a human maintainer to accept the changes. The findings from UK regulators highlight mounting security challenges as tech developers rush to deploy fully autonomous AI agents into global software infrastructure.

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$BTC just dropped from $74,000 to $66,000 in 48 hours. On no real news. In the coming months, three of the biggest AI labs in the world are going public: 1. SpaceX: $1.75 - 1.8T Valuation 2. Anthropic: $965B Valuation 3. OpenAI: $852B Valuation Combined raise from public markets: $205 BILLION. The largest concentrated capital extraction in market history. That money has to come from somewhere. Bitcoin is the MOST liquid asset on earth. You do the math.

$BTC just dropped from $74,000 to $66,000 in 48 hours. On no real news. In the coming months, three of the biggest AI labs in the world are going public: 1. SpaceX: $1.75 - 1.8T Valuation 2. Anthropic: $965B Valuation 3. OpenAI: $852B Valuation Combined raise from public markets: $205 BILLION. The largest concentrated capital extraction in market history. That money has to come from somewhere. Bitcoin is the MOST liquid asset on earth. You do the math.

Crypto Rover

223,469 次观看 • 3 个月前

Battlefield lessons from the Ukraine conflict are shaping military tactics worldwide. China has begun training its troops on how to evade drone threats. The goal here is to dodge or evade them at the last second by relying on quick reflexes, dropping to the ground. It's hard to see how that would play out in a real world combat environment.

Battlefield lessons from the Ukraine conflict are shaping military tactics worldwide. China has begun training its troops on how to evade drone threats. The goal here is to dodge or evade them at the last second by relying on quick reflexes, dropping to the ground. It's hard to see how that would play out in a real world combat environment.

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AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

54,776 次观看 • 9 个月前

As a newly appointed 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿 at Imperial College London, I'm thrilled to announce the 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) at 𝗜𝗺𝗽𝗲𝗿𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗟𝗼𝗻𝗱𝗼𝗻. 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) ( is a new research lab focused on the intersection of safety and intelligence in next-generation robotics. We're hiring exceptional PhD students who are passionate about pushing the boundaries of robot learning. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗦𝗪𝗜𝗥𝗟 𝘂𝗻𝗶𝗾𝘂𝗲? We operate at the exciting convergence of: • Online & offline reinforcement learning • Imitation learning & human demonstrations • Sample-efficient learning methods • Whole-body and soft robotics systems We're 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗵𝗗 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 interested in: • Developing safe exploration algorithms for robotic systems • Creating sample-efficient learning methods that minimize real-world trials • Building foundation models for robotics with safety guarantees • Advancing soft robotics and compliant human-robot interaction • Bridging theory and practice in embodied AI Why now? As robots become more capable and work closer with humans, we need systems that are both intelligent enough to handle complex tasks 𝗔𝗡𝗗 safe enough for real-world deployment. Traditional approaches treat safety and intelligence as competing priorities, we believe they're synergistic. If you're a motivated researcher who wants to develop the theoretical foundations and practical algorithms for tomorrow's safe, intelligent robots, I'd love to hear from you. Want to join? Apply via

As a newly appointed 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿 at Imperial College London, I'm thrilled to announce the 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) at 𝗜𝗺𝗽𝗲𝗿𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗟𝗼𝗻𝗱𝗼𝗻. 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) ( is a new research lab focused on the intersection of safety and intelligence in next-generation robotics. We're hiring exceptional PhD students who are passionate about pushing the boundaries of robot learning. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗦𝗪𝗜𝗥𝗟 𝘂𝗻𝗶𝗾𝘂𝗲? We operate at the exciting convergence of: • Online & offline reinforcement learning • Imitation learning & human demonstrations • Sample-efficient learning methods • Whole-body and soft robotics systems We're 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗵𝗗 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 interested in: • Developing safe exploration algorithms for robotic systems • Creating sample-efficient learning methods that minimize real-world trials • Building foundation models for robotics with safety guarantees • Advancing soft robotics and compliant human-robot interaction • Bridging theory and practice in embodied AI Why now? As robots become more capable and work closer with humans, we need systems that are both intelligent enough to handle complex tasks 𝗔𝗡𝗗 safe enough for real-world deployment. Traditional approaches treat safety and intelligence as competing priorities, we believe they're synergistic. If you're a motivated researcher who wants to develop the theoretical foundations and practical algorithms for tomorrow's safe, intelligent robots, I'd love to hear from you. Want to join? Apply via

Stephen James

16,890 次观看 • 1 年前

ROBOMAEDA TALK TODAY: Thank you to Professor Hiroshi Ishii 石井裕 as the inspiration for my talk today at the Massachusetts Institute of Technology (MIT) MIT Media Lab on "life as a hybrid." And it's not going to be my Azure AI Avatar talking for me today -- it'll be the real me. I think ... Date: November 8 (Friday), 11am Boston time Talk info at MIT: Zoom livestream (free): ** --- Building A Hybrid Career Across Tech, Business, And Design. Or, ”How I Learned to Speak Human After Leaving MIT :-)” What happens when a computer science major steps out of MIT and into design, business, and leadership? In “How I Learned To Speak Human After Leaving MIT :-),” I’ll share how leaving the Media Lab in 2008 set me on an unexpected path—from advocating for the arts with the STEAM movement at RISD to navigating venture capital, startups, and industries of all scales. Does being a multidisciplinary “hybrid” thinker and doer enhance or limit one’s career prospects? Through insights from my own journey, I’ll explore the unique challenges and opportunities hybrids face today, offering key takeaways for those building careers across multiple disciplines. This talk brings my journey full circle—from the early foundations of AI in the 80s to the dawn of a new AI-powered industrial revolution. --- Related links: - “Design Against AI” / Design in Tech Report 2024 at SXSW - “The Art and Craft of Business Resilience” / LinkedIn Learning - “Mr. Maeda’s Cozy AI Kitchen” / Learn LLM Coding Easy as CAIK - “Design, Business, Inclusion” / LinkedIn Learning - “How art, technology, and design inform creative leaders” / TED 2012

John Maeda

13,022 次观看 • 1 年前

The concept of creating an exact digital replica of the physical world has always fascinated me: environments that look and behave exactly like our everyday reality, precisely captured in the digital domain. This is the essence of 𝐖𝐨𝐫𝐥𝐝 𝐌𝐨𝐝𝐞𝐥𝐬, simulated realities indistinguishable from our own. Generating these models is the core mission behind what we are building at SpAItial AI. True World Models must capture both photorealistic appearance and underlying physics, spatially-consistent across the environment. For static scenes, current models already deliver impressive results, unlocking downstream applications from gaming to 3D design. However, the true frontier lies in modeling dynamics, which will enable the training of AI agents whose learned behaviors can bridge the sim-to-real gap, thus unlocking countless real-world applications.

The concept of creating an exact digital replica of the physical world has always fascinated me: environments that look and behave exactly like our everyday reality, precisely captured in the digital domain. This is the essence of 𝐖𝐨𝐫𝐥𝐝 𝐌𝐨𝐝𝐞𝐥𝐬, simulated realities indistinguishable from our own. Generating these models is the core mission behind what we are building at SpAItial AI. True World Models must capture both photorealistic appearance and underlying physics, spatially-consistent across the environment. For static scenes, current models already deliver impressive results, unlocking downstream applications from gaming to 3D design. However, the true frontier lies in modeling dynamics, which will enable the training of AI agents whose learned behaviors can bridge the sim-to-real gap, thus unlocking countless real-world applications.

Matthias Niessner

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Street cleaners from India start working in St. Petersburg The first group arrived back in September to fill the labor shortage. Some Indians claim they previously worked in the IT sector. For example, one of them says he was an AI developer at Microsoft. At the same time, the city has extended the ban on foreign workers in the courier and taxi sectors, while regular raids on migrants are being carried out.

Street cleaners from India start working in St. Petersburg The first group arrived back in September to fill the labor shortage. Some Indians claim they previously worked in the IT sector. For example, one of them says he was an AI developer at Microsoft. At the same time, the city has extended the ban on foreign workers in the courier and taxi sectors, while regular raids on migrants are being carried out.

NEXTA

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