Unconventional AI

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.

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 even when the hardware underneath is imperfect (which is exactly what the loss landscape below is showing). That single shift lets us stop optimizing for zero errors and start optimizing for what matters now: more bits per cell, higher density, lower energy. The universal memory never showed up, but AI may be the workload that tells each technology exactly which job it was born to do. Read the full breakdown by Giacomo Pedretti (MTS, AI Hardware Architectures) and Srenik Mehta (VP of Engineering): unconv.ai/blog/smoothing…

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, Unconventional AI MTS Prof. Ang Li and co-authors introduce DS-ISA: a minimalist 9-instruction architecture that bridges digital processors and continuous-time analog physics. It's a proof-of-concept, but it's the abstraction layer the field needs to build real compilers and software stacks for dynamics-based AI hardware. Full deep dive: unconv.ai/blog/speaking-…

We’re at the beginning of the unconventional AI journey, but we are releasing artifacts to the community to enable us all to build together. We’ve open-sourced the Un-0 weights, training scripts, and ablation code. If you build physics-based models, plug them into our scaffold and share your success! Read the blog: unconv.ai/blog/introduci… Visit our GitHub: github.com/unconv-ai/Un-0

Is the physics actually doing the work? Yes. Our ablations show that the trained dynamics provide value over a decoder only baseline and a random Kuramoto feature reservoir, and that increasing the number of integration steps increases model quality.

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.

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, recruiting, legal, and other functions to help build a new kind of company. The result isn't just greater efficiency. It's a fundamentally different way of operating. Read more: unconv.ai/blog/the-makin…

Lewis Hamilton recently pointed to a mismatch between simulation and reality as a major factor behind a challenging race weekend. It's a problem every systems engineer recognizes: how do you simulate a system operating at the edge of chaos? In our latest blog, we explore how Unconventional AI builds digital twins that faithfully model hardware operating in these highly sensitive regimes, capturing the physical and numerical effects that can significantly influence real-world performance. Read the full post: unconv.ai/blog/system-mo…

Most real-world systems are dynamic. So why do we still treat computation as static? Our latest blog explores computation through motion using gyroscopes, rods, springs, and ordinary differential equations to perform handwritten digit classification. A deep dive into: • dynamical systems as compute • differentiable ODE solvers • physics-inspired machine learning • emergent computation through interaction Read here: unconv.ai/blog/machine-l…

Tomorrow, May 15, is the final day to submit pre-proposals for the Unconventional Grant. Over the past several weeks, we’ve seen proposals spanning: • computation as dynamics • in-memory and in-physics compute • architectures that minimize data movement • new abstractions beyond linear algebra Many converge on the same intuition: meaningful efficiency gains in AI will not come from scaling existing approaches alone, but from fundamentally different ways of representing and computing. We are looking for technically grounded ideas that challenge assumptions across hardware, systems, and learning. We’re not looking for taller ladders to the moon. We’re looking for rockets. unconv.ai/blog/unconvent…

Getting to 1000x energy efficiency in AI isn’t about one breakthrough. It’s about solving two hard constraints: 1. Data movement dominates energy 2. Amdahl’s Law caps system-level gains Which means you have to rethink everything: models, hardware, and how they’re designed together. If this kind of problem excites you, you’ll enjoy our latest blog: unconv.ai/blog/how-to-im…

We are proud to announce that Unconventional AI has been named on the 2026 @CBinsights AI 100 list. Find us here: cbinsights.com/learn/ai-100-t…

Attending #ODSCAIEast today? Join our cofounder @mcarbin at 2:35 PM ET in the Keynote Room. “Nonlinear Dynamics as the Next Substrate for Intelligence” explores a fundamental idea: we may be building AI on the wrong substrate. The human brain runs on ~20 watts. Today’s AI systems require orders of magnitude more. That gap isn’t just engineering, it’s architectural. Michael will share why moving beyond digital computation toward nonlinear physical dynamics could unlock a radically more efficient path to intelligence.

Analog vs. digital. Which is actually better? As AI systems hit efficiency limits, it might be time to rethink the abstractions we’ve taken for granted and focus on using the right tool for the job. Explore the case for a mixed-signal future in our latest blog post “Analog is dead, long live analog.” unconv.ai/blog/analog-is…

We’re hiring people who are endlessly curious, comfortable with discomfort, and drawn to the edge of chaos. If this sounds like you, apply here: unconv.ai/careers

We’re introducing the Unconventional Grant. A new research grant program supporting bold, unconventional ideas in AI. We’re allocating $500,000 in total funding, awarding up to five $100,000 grants to researchers exploring new paradigms in efficient, scalable, and biologically inspired AI systems. We’re especially interested in ideas that challenge how AI systems are built today, from unconventional circuits and architectures to new approaches in neural networks and theory. Not incremental work. Not safe bets. Ideas that push the field forward. unconv.ai/grant/