The new model, known as "Un-0," was created by Unconventional AI, a recently launched technology company founded by a group of prominent AI researchers.
Un-0 represents the first proof of concept for the company’s underlying technology, which combines Achour’s work in nonlinear physical substrates — a physical material or hardware device that performs mathematical computations by letting its own natural, continuous laws of physics run — with Carbin’s research into machine learning and physical dynamics. The model itself is a "physical dynamical system," which uses physical motion over time to perform computations.
Neural networks, like the kind that power established "stable diffusion" AI image generation tools such as Midjourney or Dall-E, work by layering millions of these calculations on top of each other. Essentially, the system starts with an image made of pure static. The network then examines the static and tries to mathematically predict what visual information (or noise) it needs to subtract from the image to get closer to the target picture. This process is repeated between 20 and 50 times (or occasionally up to 100, although the returns beyond 50 are marginal), with each pass getting closer to a recognizable image.
According to the scientific principles at work, two oscillators that share a physical connection — even if they’re moving at completely different rates — will eventually settle into the same rhythm by mutually influencing each other's movement. By scaling up this principle to thousands of physically linked oscillators — known as a "Kuramoto model" — the startup AI posited that the concept could be used to perform computational tasks such as image generation.
These oscillators are then physically connected to the wider group, using a preset configuration of different connection strengths. When the oscillators are set into motion, this "control group" naturally pulls the rest of the oscillators toward the desired pattern over time.
Current AI models are known for consuming large amounts of energy. (Image credit: Getty Images)
Challenging AI's energy consumption
One of Unconventional AI's most eye-catching claims has been its stated goal of having its model use 1,000 times less power than current systems do. In traditional AI image generation models, the calculations needed to perform operations involve flipping billions of tiny transistor switches on and off trillions of times per second, to force the electrical current to move in specific patterns through the circuit.
With the Un-0 model, however, the idea is that rather than forcing transistors to rapidly flip between open and closed, the system consists of a series of closed loops, where the natural path of the current forms the individual oscillators. Because the current is allowed to flow unobstructed, the researchers said in the study, the system is theoretically much more energy efficient than traditional computing architecture.
Testing the model
To test the model's performance, Unconventional AI put it through two common AI industry image generation benchmarks: CIFAR-10, a dataset of low-resolution color images split across 10 categories, and ImageNet 64×64, a much larger collection of over 1.2 million pictures at a higher resolution.
In the CIFAR-10 test, scores ranged from an FID of 11.01 with 1,024 oscillators to 8.76 with 4,096 oscillators. In the more demanding ImageNet 64x64 test, a pool of 6,656 oscillators achieved a score of 8.41 FID, while 16,384 oscillators clocked in at 6.74.
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The scientists released the model weights — the internal mathematical parameters that the model alters as it learns — as well as training and ablation scripts — specialised code files used to build and test the system — allowing other researchers to test the models and run their own simulations. They hope to close the gap with new algorithms and models.
"Taken together, Un-0's system of coupled Kuramoto oscillators offers the promise of learning with physical dynamics at a scale that's beyond what has been done before," they said in the technical blog post. "Un-0 points in the direction of the opportunity for a new computer that exploits physics to achieve our top-line goal of energy efficiency."
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