Ineffable Intelligence
Writings
Essays, research notes, and perspectives from our team — on the ideas behind our work, the problems that motivate it, and the questions we have not yet answered.
Beyond the Label
Five years into building AI that learns without human-labelled data, here is where we think the field stands — and where the real difficulties lie.
Scaling Without Supervision
Scale has powered much of recent AI progress, but almost all of it has been scale in supervised settings. What happens when you scale the other kind?
Continual Learning in Practice
Catastrophic forgetting is well-understood theoretically. In practice, making a model accumulate knowledge over time without regression is still an open engineering challenge.
On Open-Endedness in AI Systems
Open-ended evolution has generated biological complexity without an objective function. AI systems, trained against fixed objectives, produce convergence rather than open-endedness. We examine what the difference tells us.
Intrinsic Motivation, Revisited
Count-based novelty, prediction error, information gain — curiosity has been formalised in many ways, with different failure modes. We compare them and argue for a notion of intrinsic motivation grounded in prediction complexity rather than state visitation.
Designing Reward Signals from First Principles
Before you can have reward-free learning, you have to understand what makes a reward signal work — and why most of them are poorer guides than we assume.
Representation Without Supervision
What does it mean for a model to understand something, as opposed to pattern-matching against a labelled training distribution? We examine what unsupervised representations capture that supervised ones do not, and why that difference matters.
Self-Play, Generalised
Game-playing agents learned to exceed human performance without any human labels, by competing against themselves. Can this principle be taken beyond games?
The Hidden Cost of Annotation
Human labelling is treated as a data pipeline problem. We think it is an architectural constraint — and one that fundamentally limits what AI can learn.
What 'Ineffable' Means to Us
A word that means 'too great or extreme to be expressed in words' turns out to be a precise description of the problems we care about most in AI.