General Natural Intelligence

Build it with us. Then try to break it.

UNI is a working hypothesis on an attainable path toward General Natural Intelligence: a natural, active-inference approach whose evidence is growing, evidence-classed, and tested in the open. Don't take the claim on faith — test the build, inspect the gates, and help us find where it fails.

The wager
Don't believe the claim. Test the build. This is not an announcement that we have arrived — it is a falsifiable build program you can run, inspect, and attack.

What you build

Twelve weeks. You ship, not just watch.

No prior coding background is required to start — but it is not effortless. The work is technically scaffolded, built gently into the math, hands-on with the workbench from week one.

By week 5

Your first running generative model

Not a slide. A model that runs on your own machine, that you built and can inspect.

By week 12

A non-LLM agent on a CPU

A working, constrained-domain active-inference conversational agent with inspectable state, preferences, and uncertainty — running on an ordinary CPU.

Throughout

The workbench, in your hands

You clone and follow the open Active-Inference Learning Workbench, or watch the lab run live. Every step reproduces.

Said plainly
“No LLM” means no large language model at the agent's runtime. Any use of other tools for teaching, documentation, or debugging is disclosed separately and is not part of the deployed agent. “Working” means a constrained-domain agent with inspectable state and predictable behavior — not open-domain, general-purpose chat.

The Gates

How to prove us wrong

A real challenge needs public rules. These are the gates the build must pass — what is tested, what would count as failure, and what would disconfirm the claim. Where claims are public, we publish the gate definitions: the test, the tolerance, the reproduced example, the expected failure under ablation.

Build gate

Nine structural checks

Probabilities conserve, the free-energy bound holds, actions follow the posterior, the Markov blanket boundary is respected. If a check fails, the build fails — publicly.

Trust gate

Deterministic agreement

Specified reference calculations reproduce to roughly 1e-9. This tolerance applies only to those named tests — never as a global claim about intelligence or real-world validity.

Capstone gate

Ablations must break it

Remove the right piece and the agent must fail in the predicted way. A model that cannot be broken on cue was never doing the work it claimed.

The methods are aligned with the worked examples in Parr, Pezzulo & Friston (2022). Where a mapping is public, we show exactly which examples, which equations, and which tolerances. UNI's internal method stays private; the gates that any correct build must satisfy do not.

Format & commitment

What it is, and what it costs

Length: 12 weeks, hands-on with the workbench, building gently into the math.
Who it is for: Designed for non-programmers to start — motivated, not effortless. Technically scaffolded, not technically empty.
By week 5: Your first running generative model.
By week 12: A working non-LLM active-inference agent on a CPU (constrained-domain, inspectable).
Tuition: $75,000 USD for the full 12 weeks, plus a 4-hour examination to sit for certification.
Outcome: You can build and ship, and you can read the gates to judge the claim for yourself.

The relational strand

Learning with another person, in the open

Part of the same workshop is trauma-informed learning design and relational reflection practice: consent, repair, co-regulation, and making uncertainty explicit when a learning interaction misattunes.

To be clear
This is educational, not therapy. UNI does not diagnose, treat, cure, or claim to reduce trauma. The relational strand teaches trauma-informed ways to learn with another person — slowing down, checking predictions, lowering blame, practicing repair.

The evidence so far

Growing, evidence-classed, in the open

We are a work in progress. We do not claim arrival. We show receipts and let you check them.

Reproducible

The Stratified Palimpsest benchmark

A falsifiable active-inference benchmark world. Same seed, identical trace; integers exact, floats within 1e-6. A Markov-blanket monitor verifies no world state leaked.

Unrefereed preprint

The collaborative review paper

A preprint on active-inference free-energy minimization. Peer review pending — read it and disagree.

Clone & run

The live workbench

The Active-Inference Learning Workbench portal — the same environment the workshop builds in.

Open the workbench portal The benchmark Read the preprint What we do not claim

Join a cohort

Preregister

Capture your spot. No payment is taken here. We follow up with dates, the full syllabus, the published gates, and enrollment — so you decide with the whole picture in front of you.