Introducing our first model, Un-0!
We trained an image generator powered by a backbone of coupled oscillators in place of a more traditional conventional neural network.
We removed 93.25% of the connections in our Un-0 image model, fully expecting to pay for it in quality. But it got better.
FID 7.15 on ImageNet 64x64, roughly 1.9 ahead of the dense baseline at matched size. Same family of model, a fraction of the couplings, a better score.
Here
Building a new memory means a twenty-year grind to drive the error rate toward zero. AI just made that grind optional.
Because neural networks are remarkably tolerant of noise, we no longer have to chase punishing reliability targets. Training can settle into a good solution
What if your AI chip didn't calculate the answer, but physically settled into it?
That's the premise behind dynamical system hardware, and there's never been a standard way to program it. In an upcoming International Symposium on Computer Architecture (@ISCAConfOrg) 2026 paper,
Most companies use AI tools, but we are not a 'conventional company.'
We're building Unconventional AI for this new age as an AI-native organization.
In this latest blog, our [un]CFO, Ali Esfahani, shares how he has used AI in his first five months across finance, research,