The Future of AI Computing: Rethinking the Paradigm

The remarkable progress in artificial intelligence (AI) comes at a price. More specifically, the cost of further advancements in AI is becoming as astonishing as a hallucination by ChatGPT. OpenAI, a leading AI research lab, has revealed that the training process for the algorithm powering ChatGPT alone cost over $100 million. However, the expenses are not limited to monetary figures. The demand for graphics processing units (GPUs), essential components for large-scale AI training, has skyrocketed, thereby driving up their prices tremendously. Furthermore, the pursuit of AI dominance has led to a concerning surge in energy consumption by data centers. In the midst of this AI gold rush, several startups are emerging with daring plans to create innovative computational tools that could revolutionize the field.

One such startup challenging the traditional norms of AI computing is Normal Computing. Founded by experts from Google Brain and Alphabet’s moonshot lab X, Normal Computing aims to reboot computing from first principles. To achieve this goal, they have developed a simple prototype known as a stochastic processing unit (SPU). Unlike conventional silicon chips that rely on binary bits (0s and 1s) to process information, Normal Computing’s SPU leverages the thermodynamic properties of electrical oscillators. By utilizing random fluctuations occurring within the circuits, this unique hardware can generate random samples useful for various computations, including solving linear algebra problems that are prevalent in science, engineering, and machine learning.

Faris Sbahi, the CEO of Normal Computing, asserts that their hardware is not only highly efficient but also well-suited for statistical calculations. This implies that it holds significant potential for building AI algorithms capable of handling uncertainty. Such advancements could potentially address the issue of large language models “hallucinating” outputs when faced with ambiguous situations. Sbahi highlights the ongoing success of generative AI but believes that better software architectures and hardware solutions are yet to emerge. With a background in quantum computing and AI at Alphabet, Sbahi and his cofounders were driven to explore alternative methods of harnessing physics for AI computations when progress in quantum machine learning was limited.

Extropic: The Thermodynamic Computing Pioneers

Extropic, another startup formed by former quantum researchers at Alphabet, has an even more ambitious plan for revolutionizing AI through thermodynamic computing. Their vision involves the integration of neural computing into an analog thermodynamic chip. Guillaume Verdon, the founder and CEO of Extropic, emphasizes their intention to leverage their expertise in quantum computing software and hardware to fully exploit the thermodynamic paradigm. Verdon’s recent revelation as the person behind the popular meme account “Beff Jezos,” associated with the “technocapital singularity” concept, adds a touch of intrigue to Extropic’s endeavors.

As the AI industry grapples with the challenges of maintaining the pace predicted by Moore’s Law, there is a growing realization that a broader rethink of computing is necessary. Moore’s Law predicts that the density of components on chips will continue to shrink over time, enabling exponential growth in computational power. However, even if Moore’s Law were not slowing down, it would still be unable to keep up with the rapidly increasing model sizes released by organizations like OpenAI. This realization has led experts like Peter McMahon, a professor at Cornell University, to argue that new computational approaches must be explored to sustain the unstoppable AI hype train.

The future of AI computing lies in reimagining the very foundations upon which it is built. Startups like Normal Computing and Extropic are pioneering novel hardware solutions that harness the power of thermodynamics to perform advanced computations for AI applications. By stepping away from the traditional binary-based computing paradigm, these companies are opening new avenues for handling uncertainty and pushing the boundaries of AI technology. As the industry faces escalating costs and limitations in traditional hardware, embracing a new era of computing is not only a necessity but also an opportunity for breakthroughs that could shape the AI landscape for years to come.


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