Mitch Wagner’s Post

Most LLMs are masters of text, but plop them inside a 3D space and they struggle to understand how things fit together. It's like they've spent their entire existence immersed in books and websites, without ever experiencing the physical world outside. If you could design a model that thinks more like an architect — seeing the world not just as a collection of words but as real-life objects — it could unlock new capabilities, such as designing buildings, optimizing supply chains, or even assisting in medical diagnoses. That’s the idea behind World Labs, a new startup from “Godmother of AI” @Fe-Fei Li that just brought in $230M in a new funding round. The legendary Stanford computer scientist’s interest in “spatial intelligence” goes back to 2009, when she developed ImageNet — an image database that was used to train some of the first visual-based neural networks. https://lnkd.in/gm-hYyzZ

🚀 Excited to dive into the realm of Artificial Intelligence today! 🤖💡 🌟 What do you get when you cross a computer with human intelligence? 🧠 A.I. to the rescue! Let's explore this fascinating world together. 🌐 AI: the technology of the future, today! From self-driving cars to personalized recommendations, the possibilities are endless. 🔬 Delving into the intricate world of Machine Learning and Neural Networks, where algorithms learn from data and make decisions without explicit programming. 🔮 Imagine a world where AI can predict outcomes, find patterns in complex data, and assist in solving some of society's biggest challenges. The future is bright with AI leading the way! 🤔 Let's discuss the ethical implications of AI: should machines be given autonomy to make decisions? How do we ensure bias doesn't seep into AI algorithms? 🔍 The quest for Artificial General Intelligence

Stanford University's #computerscientist Fei-Fei Li is co-founding a company called WorldLabs. The startup aims to teach #AISystems #deepknowledge of #physicalreality. Unlike current #GenerativeAI, WorldLabs plans to create systems that can #construct #complete #worlds with the #physics, #logic, and #rich #detail of our #physicalreality. Despite skepticism about AI progress, World Labs has secured $230 million in funding and is valued at $1 billion. The article contrasts the optimism of Li and her investors with the broader market sentiment that suggests an #AISlowdown. #PhygitalAI #DeepAI #RealWorldAI

The Godmother of AI Wants Everyone to Be a World Builder According to market-fixated tech pundits and professional skeptics, the artificial intelligence bubble has popped, and winter’s back. Fei-Fei Li isn’t buying that. In fact, Li—who earned the sobriquet the “godmother of AI”—is betting on the contrary. She’s on a part-time leave from Stanford University to cofound a company called World Labs. While current generative AI is language-based, she sees a frontier where systems construct complete worlds with the physics, logic, and rich detail of our ... Read more here: https://lnkd.in/d9JtnT4R . . Like 💝 Comment below ⏬ Share ✅ For More Such Updates Follow Us @qnewshub @qnewscrunch . . #qnewshub #qnewscrunch #Business

Unleashing Creativity: The Revolutionary Power of Generative Adversarial Networks In the realm of artificial intelligence, one groundbreaking technology stands out for its ability to push the boundaries of creativity and innovation: Generative Adversarial Networks (GANs). These remarkable algorithms have revolutionized the field of machine learning, enabling computers to generate incredibly realistic and diverse content, ranging from images and music to text and even entire virtual environments. At the heart of a GAN lies a fascinating concept: two neural networks, known as the generator and the discriminator, engage in a high-stakes game of cat and mouse. The generator aims to produce synthetic data that is indistinguishable from real data, while the discriminator strives to accurately differentiate between real and fake samples. Through this adversarial process of competition and collaboration, GANs learn to generate increasingly convincing outputs, achieving levels of realism that were once thought to be beyond the reach of machines. The applications of GANs are as diverse as they are impactful. In the realm of visual arts, GANs have empowered artists and designers to create stunningly lifelike images, animations, and even entire virtual worlds. From generating photorealistic landscapes to designing novel fashion trends, GANs are reshaping the way we think about creativity and artistic expression. Beyond the realm of aesthetics, GANs hold immense potential for practical applications in fields such as healthcare, finance, and manufacturing. In medicine, GANs are being used to generate synthetic medical images for training diagnostic algorithms and simulating the effects of treatments. In finance, GANs are employed to generate synthetic financial data for risk assessment and portfolio optimization. In manufacturing, GANs are revolutionizing product design and prototyping by enabling rapid iteration and exploration of design spaces. However, like any powerful technology, GANs also pose ethical and societal challenges. The ability to create highly realistic fake images and videos raises concerns about the spread of misinformation and the potential for malicious use. Furthermore, issues of bias and fairness must be carefully addressed to ensure that GAN-generated content reflects diverse perspectives and respects human values. As we continue to unlock the full potential of Generative Adversarial Networks, it is essential that we approach their development and deployment with caution and responsibility. By harnessing the creative power of GANs for the greater good, we can usher in a new era of innovation and discovery, where the boundaries between imagination and reality blur like never before. #AIinnovation #creativeAI #GANs #artificialintelligence #machinelearning #futureTech #digitalart #ethicalAI #innovationleadership #techtrends

AI Revolution: Transforming the Future of Technology Artificial Intelligence (AI) is no longer a futuristic concept confined to the realms of science fiction. It has evolved into a groundbreaking force that is reshaping industries, businesses, and everyday life. The  AI revolution  is now a pivotal component of technological advancement, with applications ranging from healthcare and finance to entertainment and transportation. In this article, we will explore how  AI is transforming the future of technology , creating new opportunities, solving complex problems, and driving unprecedented innovation. The Rise of AI: A Historical Overview The development of AI traces its roots back to the mid-20th century, when pioneers like  Alan Turing  and  John McCarthy  laid the groundwork for what would become one of the most disruptive forces in modern technology. Turing’s work on the Turing Test and McCarthy’s coining of the term “Artificial Intelligence” in 1956 marked the beginning of a journey that has seen AI evolve from basic algorithms to the complex neural networks and machine learning systems we have today. As computational power has increased, so too has the  potential of AI . The integration of AI into everyday technologies has been accelerated by the growth of  big data ,  cloud computing , and the development of sophisticated machine learning models. Today, AI systems can process vast amounts of information at lightning speed, learning from data to improve performance and make autonomous decisions. https://lnkd.in/ekNmAtM4 #AI #ArtificialIntelligence #MachineLearning #Automation #TechInnovation #FutureOfTechnology #BusinessTransformation #AIinHealthcare #AutonomousVehicles #AIRevolution #AIethics #DataScience #DigitalTransformation #TechTrends #AIapplications #SmartTechnology #AIinBusiness #AIresearch #DeepLearning #TechLeadership

The Evolution of AI: From Sci-Fi Dreams to Reality 💎 Artificial Intelligence (AI) has come a long way since its conceptualization in the mid-20th century. What was once a topic of science fiction novels has now become an integral part of our daily lives. The journey of AI began with Alan Turing's groundbreaking work on theoretical computer science and the Turing machine, laying the foundation for modern AI research. Early AI research in the 1950s and 60s focused on symbolic reasoning and problem-solving, but progress was slow due to limited computational power. The 1980s saw a resurgence of interest in AI with the rise of expert systems, which used rule-based approaches to mimic human decision-making in specific domains. However, these systems were brittle and struggled to handle uncertainty. The real breakthrough came in recent years with the advent of machine learning and deep learning techniques, fueled by advancements in computing power and the availability of massive datasets. AI systems can now learn from data, identify patterns, and make predictions with unprecedented accuracy. Today, AI is transforming industries across the board, from healthcare and finance to transportation and entertainment. It's revolutionizing how we work, live, and interact with technology. As we look to the future, the potential of AI is limitless. From self-driving cars to personalized medicine, AI has the power to solve complex problems and create a better world for all. #AI #ArtificialIntelligence #MachineLearning #DeepLearning #Technology #Innovation #FutureofWork #LJNCORP #ROBOWORLD

Get some popcorn for this one. But seriously, with the diversity of channels to share research results and accelerating tech advances (like AI) mean we need to think differently about how we define 'science'. “...The problem is that a machine ingesting scientific literature and then creating statistical inferences does not confer understanding to the machine. It’s not an objective and rational way of creating scientific theories...” #science #scientificresearch #researchanddevelopment #artificialintelligence

Researchers at Massachusetts Institute of Technology and the University of Washington have made a game-changing discovery in the quest to build AI systems that can collaborate effectively with humans. The secret? Modeling humans' often irrational and suboptimal decision-making processes.  Traditional AI models have long struggled to account for the computational constraints that lead to human irrationality. However, this new approach, automatically inferring an agent's 'inference budget' through the analysis of just a few traces of their previous actions, has the potential to revolutionize the field.  🔍By understanding and predicting human behavior, AI systems can better respond to their human collaborators. Imagine an AI assistant that can anticipate mistakes, offer better solutions, or adapt to the weaknesses of its human counterparts.  The research opens up exciting possibilities for Human-AI collaboration across various domains, from navigation to reinforcement learning. By modeling humans' planning processes and decision-making, we can create AI systems that truly understand and work alongside us.  🌍🤝The future of Human-AI collaboration is bright, and it all starts with understanding human behavior's beautifully irrational nature. #artificalintelligence