Gourav Sharma

Avanmag • 22K followers

Jensen Huang explains his decision to start NVIDIA as a parent with young children Jensen was 30 years old when he quit his job at LSI Logic to co-found NVIDIA in 1993. Asked how he made this decision as a young parent at the time, he responds: “I believed in [my co-founders], and I believed in myself… Even though we had a family and our kids were young — they were just one and two — and that could cause us to be quite risk averse, I was never concerned about being able to do something else if it didn’t work out. And so I felt like I wasn’t risking anything. Maybe that’s too careless by some other standards, but I really believed it. I believed that we weren’t putting our family in harm’s way. And if things didn’t work out, there’ll be an even better job for me somewhere, someday… Lori and I were young and it wasn’t a decision that was difficult per se. It was probably even less than a dinner conversation. Maybe even less than that.” Jensen offers the following advice to the Berkeley students in the audience: “All of you are young and bright, and there’s so much opportunity out there. I genuinely don’t believe that when you make a decision to start a company or join a startup that it’s a horribly difficult life decision. The only thing that really matters, in my estimation, is are you going to love the people that you work with? Are you going to love the work that you’re going to do? Are you going to love it so much that all the pain and suffering that’s going to come your way — which I promise you will be lots: setbacks, disappointments, the list of bad days — you’ll be able to keep carrying on. So long as you love the work that you do, you’ll be able to keep carrying on. That’s really it. That’s 100% of the wisdom.” Video source: University of California, Berkeley, Haas School of Business (2023)

Ripudaman Singh

Harvey Nash • 29K followers

$1B+ bet on the next frontier of AI: Physical & Spatial Intelligence Today, Yann LeCun’s new startup, AMI - Advanced Machine Intelligence, announced a massive $1.03 billion funding round to challenge the dominance of Large Language Models (LLMs). Current AI is "trapped" in a digital box of text. To reach human-level autonomy, AI needs to understand the physical world. It needs to know that if you push an object, it falls; it needs to navigate a room without a map; it needs to plan complex tasks in real-time. From Digital to Physical: AI is moving into robotics, smart glasses (like Ray-Ban Meta), and industrial automation. From Prediction to Planning: Moving away from probabilistic guessing toward goal-oriented reasoning. The "Ami" (Friend) Approach: Building AI that is controllable, safe, and grounded in reality. The next frontier isn't just "generative"—it’s spatial and physical. We are moving from AI that talks to us, to AI that works alongside us in the physical world. Congrats to the AMI team on this milestone. The era of "World Models" is here. https://lnkd.in/gE99YfGJ #AI #Robotics #MachineLearning #YannLeCun #AMI #Innovation #SpatialIntelligence #TechNews

Bruce Burke

Neural News Networks • 17K followers

Nvidia CEO Jensen Huang is set to detail the company's hardware and software ‌plans to a large crowd in San Jose, California, at the company's annual developer conference on Monday. During a keynote address at a hockey arena with a capacity of more than 18,000, Huang is expected to lay out how the top AI chipmaker plans to adapt to a rapidly changing AI landscape. Nvidia, the world's most valuable listed company, with a market capitalization of more ⁠than $4.3 trillion, is likely to detail a next-generation AI chip called Feynman, named after American physicist Richard Feynman, at the four-day conference. Huang is also likely to talk about data centers, Nvidia's chip programming software CUDA, digital assistants known as AI agents and physical AI such as robots. Another focus is likely to be Groq, a chip startup from which Nvidia licensed technology for $17 billion in December. Groq specializes in fast and cheap "inference" computing work, in which an AI model takes what it has already learned and uses it to answer a question or make a prediction in real time. After spending hundreds of ‌billions ⁠of dollars in recent years on chips for training their AI models, companies such as OpenAI, Anthropic and Facebook owner Meta Platforms (META.O) are shifting toward serving …