Cluster: gates and falsifiers

Reading a UNI Claim: A User's Guide

Every public sentence about UNI is meant to be inspected. This guide gives you the parts to look for and the shape they take, so you can decide for yourself what to believe.

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. Do not take the claim on faith. Test the build, inspect the gates, and help us find where it fails.

What a UNI claim actually contains

A well-formed UNI claim carries four pieces. If any of them is missing, that is a signal to slow down before you accept it.

The evidence classes, briefly

These tags do a lot of work. They are the honest label on the tin.

Two examples make the difference concrete. "The Precision Lab's sensory precision dial produces the behavioral regimes described in Parr, Pezzulo and Friston (2022)" is Class E: it rests on a citation. "The Cell Lab's active-inference controller lost to the neural baseline on memory_leak and cpu_noisy_neighbor" is Class A: it is a recorded outcome from a pre-registered run. Both are useful. They are not the same thing.

How to read a claim in five steps

What a UNI claim will never do

The public copy is fenced. UNI does not claim general intelligence, does not claim to be the artificial kind, does not claim to heal or treat anything, and does not claim endorsement from people who have not signed off in writing. Where the labs win, the ledger says so. Where they lose, the ledger says that too. The three losses in the Cell Lab benchmark are on the site because hiding them would be the tell.

Where to go next

Once you know the parts, use them. Open a claim from a post or a lab page and try to walk it. If a piece is missing, that is worth telling us about.

How we log honesty in the UNI ledger ›
The mechanics of the append-only ledger, and why write-once matters more than write-clever.
Evidence classes A to E, and what they mean ›
The full taxonomy in one place, with examples of each class from the labs.
Gates and falsifiers: how we know when we are wrong ›
The pre-registered conditions that would refute UNI, and the losses already on the board.
The workshop ›
Where these methods get taught, applied to a real deliverable, with the ledger open.