Key insights

• 🔬 Exotropy is developing a physics-based Computing Paradigm focused on AI
• ⚛️ Harnessing the stochastic physics of electrons for probabilistic machine learning
• 🌟 Chip achieves a massive speedup using the power of nature and energy-based models
• 🌐 Recruitment for a project aiming to embed AI into the world's physics for global prosperity
• 🔌 Exploring cutting-edge semiconductor engineering at the analog and electron levels
• 📈 Challenges of using more data and energy, and proposals for large-scale power solutions
• 💡 The need for Fusion Energy and Nvidia's pivotal role in AI development
• 🚪 Revolutionizing hardware engineering with new semiconductor principles

Q&A

• What is the emphasis in the exploration of semiconductor engineering and the call for professionals to join the team?

  The emphasis is on exploring cutting-edge semiconductor engineering to maximize performance at the analog and electron levels. They seek experienced semiconductor physics and analog engineers to revolutionize hardware engineering with new semiconductor principles.

• What is the focus of the AI project and the call for talented individuals?

  The AI project aims at scaling intelligence and embedding AI into the world's physics for global prosperity. It emphasizes the need for Fusion Energy, Nvidia's pivotal role in AI, and the distribution of access to GPUs. There is a call for talented individuals to join this project.

• How does the chip achieve a massive speedup in machine learning?

  The chip achieves a massive speedup by harnessing the power of nature. Probabilistic machine learning generates noise to guide sampling away from hallucinations and penalizes those not close to the dataset. The future of machine learning lies in Energy based models and a software stack natively compiling to hardware physics.

• What is the goal of embedding the sampling problem in the continuous time physics of the chip?

  By embedding the sampling problem in the continuous time physics of the chip, researchers aim to create programmable sources of Randomness based on analog stochastic circuits, leading to faster and more energy-efficient sampling for probabilistic models.

• What is Exotropy working on?

  Exotropy is developing a physics-based Computing Paradigm focused on AI. They utilize the stochastic physics of electrons to instantiate probabilistic machine learning. Their approach involves using superconductors as efficient neurons for non-digital, analog stochastic circuits. They have released a light paper and images of their first chips made of superconductors.

• What is the problem with the increasing demand for AI and the limitations of the semiconductor industry's scaling?

  As Moore's Law nears its end, there are challenges in scaling computing power, consistent GPU shortages due to increased AI demand, difficulties in managing more data and energy, and proposals for large-scale power solutions. Transistor limitations arise from thermal fluctuations at a small scale.

• 00:00 🚀 The team is launching a solution to the problem of increasing demand for AI and semiconductor industry's limitations, as Moore's Law nears its end. They propose an alternative approach to scaling computing power without relying on making transistors smaller.
• 01:46 A company called Exotropy is developing a physics-based computing paradigm for AI, using stochastic physics of electrons and superconductors to instantiate probabilistic machine learning. They have released a light paper and images of their first chips made of superconductors, which are described as the most efficient neurons in the universe.
• 03:32 Researchers are working on embedding the sampling problem directly in the continuous time physics of the chip, creating programmable sources of Randomness based on analog stochastic circuits, which can lead to faster and more energy-efficient sampling for probabilistic models.
• 05:20 Using the power of nature, the chip achieves a massive speedup. Problemistic machine learning generates noise to guide sampling away from hallucinations, penalizing those not close to the data set. The future lies in Energy based models and a software stack natively compiling to hardware physics.
• 06:58 A call for talented individuals to join an AI project aimed at scaling intelligence and embedding AI into the world's physics for global prosperity. Discussion about the need for Fusion Energy, Nvidia's pivotal role, and the distribution of access to GPUs.
• 08:32 Exploring cutting-edge semiconductor engineering to maximize performance at the analog and electron levels. Seeking experienced professionals and physicists to join the team in revolutionizing hardware engineering.