But this is where the tweet ends - and the paper begins.

Great work by @joelbot3000.bsky.social, Elliot Meyerson, Tarek El-Gaaly, Ken Stanley, and yours truly.

Paper: arxiv.org/abs/2501.13075

Knightian Uncertainty - and how evolution and life dealt with it - have huge implications and insights on how robust Intelligence operates in the world, AS IS.

In other words:

Anticipating the predictable and training for it,

VS

Acknowledging unpredictability and dealing with it - as it unfolds.

In the paper, we contrast search solutions from nature that have worked:

Persist & Filter, in the face of KU, (Nature's predominant paradigm)

VS

Anticipate & Train, ignoring KU. (ML's predominant paradigm)

Knightian Uncertainty - the notion that the sample space cannot be exhaustively anticipated - is a fundamental property of unstructured open-ended environments.

Also known as, the real world.

In closed and controlled environments, ML generalization can take us a long way.

But what if we're dealing with unstructured open-ended environments AS IS?

Physical AI systems are (still!) plagued with so called "corner cases" and "long tails".

But why?

The impact is real. The stakes are high.

"Corner cases".

"Long tails".

You can play whack-a-mole with them.

Or, you can confront their root causes directly.

Our new paper "Evolution and The Knightian Blindspot of Machine Learning" argues we should do the latter.