Revolutionizing AI with Stochastic Physics-based Computing

The Challenge of Scaling AI

The semiconductor industry and the AI industry are facing a significant challenge - the rapid scaling of AI demands. As GPUs are constantly sold out and AI models continue to grow in size, the need for more data and energy also increases. Some propose the idea of using nuclear power plants to power massive clouds for AGI, but there is another way forward.

The End of Moore's Law

For years, Moore's Law, which states that the number of transistors on a chip doubles approximately every two years, has been the driving force behind innovation in Silicon Valley. However, we are reaching the limits of transistor scaling. The main issue lies in the thermal fluctuations of matter at a very small scale, which causes electrons to sometimes get stuck or not hop across a transistor. This means that transistors are becoming stochastic or probabilistic as we try to scale them down.

The Solution: Physics-based Computing

Instead of trying to make transistors smaller and smaller, EX Tropic is taking a different approach. They are harnessing the stochastic physics of electrons to instantiate probabilistic machine learning, which is the parent concept of AGI (Artificial General Intelligence). By implementing AI algorithms using the stochastic physics of the world, they aim to solve the scaling problem in the semiconductor and AI industries.

Introducing EX Tropic's Neurons

EX Tropic has developed a full-stack physics-based computing paradigm focused on AI. Their first chips, made out of superconductors, are called neurons. These neurons, although loosely referred to as such, are actually analog stochastic circuits. This non-digital approach allows them to leverage the efficiency of superconductors and the stochastic behavior of electrons.

The Power of Sampling

One of the key functionalities of EX Tropic's devices is their ability to accelerate sampling, an essential process in probabilistic models. Digital computers are inefficient at sampling due to the complexity of running intricate circuits of high-power transistors. In contrast, EX Tropic's chips embed the sampling problem directly into the continuous time physics of the chip. By operating in a low-power regime where the movement of single electrons is significant and the current and voltages in the circuit are random, the chips become programmable sources of randomness.

The Speed Advantage

The use of analog stochastic circuits and the power of nature grants EX Tropic a significant speed advantage. While traditional Monte Carlo algorithms on digital computers may achieve around 100,000 steps per second, electrons on EX Tropic's chip fluctuate nearly 100 billion times a second. This massive speedup is made possible by leveraging the inherent stochasticity of the physical world.

The Magic of Probabilistic Machine Learning

Probabilistic machine learning, enabled by EX Tropic's hardware, generates a multitude of hallucinations that help guide the learning process. These hallucinations are used to correct the learning landscape and ensure that the sampling aligns with the desired data set. EX Tropic's chips, running energy-based models, bring machine learning algorithms closer to the physics of the world, making them more efficient and effective.

The Future of AI

EX Tropic's launch marks the beginning of a new era in AI. They aim to disrupt the current state of machine learning algorithms and hardware. By demonstrating that LLMs (Large Language Models) and digital computers are not the final evolution of AI, they invite talented individuals to join their mission. Whether you are a machine learning researcher or a hardware expert, EX Tropic is looking for the most capable people to help scale their AI Manhattan project.

Scaling Intelligence for Global Prosperity

The ultimate goal of EX Tropic is to save the world by embedding AI more efficiently into the physics of the world. They believe that without solving this challenge, scaling intelligence to the entire planet and creating widespread prosperity will not be possible. As the capabilities of current-day GPUs and approaches reach their limits, EX Tropic's unique approach to hardware engineering opens up new possibilities and opportunities.

Join the Revolution

If you are a semiconductor device physics expert, an experienced analog engineer, or a physicist looking to get back to the world of physics, EX Tropic wants to hear from you. They are seeking individuals who are passionate about pushing boundaries and working on cutting-edge technology. By joining EX Tropic, you can contribute to the next curve of human capability and make a significant impact on the future of AI. In conclusion, EX Tropic's launch brings forth a revolutionary concept: stochastic physics-based computing for AI. By leveraging the stochastic behavior of electrons and implementing probabilistic machine learning, they aim to overcome the current challenges in scaling AI. Their full-stack physics-based computing paradigm, combined with their efficient superconductor-based neurons, opens up new possibilities for faster and more efficient AI. If you are interested in being part of this groundbreaking movement, get in touch with EX Tropic and be part of the future of AI.