Imagine sitting at your desk with three objects in front of you:
1. Yesterday's newspaper: you've already read it, and you remember every word. 2. A screen of random static: the noise flickering on a TV with no signal. 3. A Rubik's cube you've never solved: you haven't cracked it yet, but you sense there's an elegant pattern hidden inside.
Which one do you find 'interesting'?
Almost everyone picks the cube. The newspaper is too easy (already known), the static is too chaotic (pure randomness)—only the cube sits at that 'edge of being understood.' The pull toward investigating it is what we call curiosity, or interestingness.
We've long treated 'interesting' as a subjective, emotional experience. But in May 2026, AI pioneer Jürgen Schmidhuber and his team published a landmark paper: 'Interestingness as an Inductive Heuristic for Future Compression Progress.'
They used rigorous mathematics to prove a striking conclusion: 'interestingness' is a computable quantity, and it is the best predictor of future progress.
The Mathematical Definition of Interestingness
Feynman once said that the essence of understanding is simplification. In computer science, this is called data compression.
The paper's core logic: if you can compress a piece of data, you've understood its regularities.
- The newspaper: already compressed to its limit—nothing left to gain, so it's 'boring.'
- The static: no patterns at all, incompressible—also 'boring.'
- Interesting things: things where you sense that with a bit more study, you could dramatically simplify them.
- It samples massive data streams.
- When it detects that the regularities of some domain (say, quantum mechanics) are being rapidly simplified by its own learning, its interestingness metric spikes.
- It then spontaneously invests more compute in that domain.
Interestingness = the first derivative of learning progress. In other words, finding something interesting is essentially the pleasure of experiencing a 'surge in compression rate.'
Key Finding: Interestingness Predicts the Future
The most powerful part of the paper is that this feeling isn't just about the present—it's forward-looking. Using Kolmogorov complexity, the researchers show:
1. The 'strike while the iron is hot' effect: the math shows that if you've just discovered a new pattern in a domain (compressed a bit of data), you're highly likely to discover more patterns in that same direction soon. 2. The 'plateau penalty': if you've spent a long time on something without achieving any new compression, exponential decay suggests it probably isn't for you (either too hard, or pure noise). You should decisively move on to the next 'interesting' target.
Why This Matters for AGI
For AI, the paper provides a navigation system for autonomous evolution.
Previously, we had to tell AI what to learn: 'study math,' 'write code.' AI was a passive executor. With this 'interestingness formula,' AI can decide what to pursue on its own:
Summary
'Interestingness' isn't emotional noise from the brain—it's nature's most elegant shortcut.
When a book, a hard problem, or a new model feels extremely interesting, that's your intuition using mathematical law to tell you: 'Hey, look here! Truth is lined up waiting to be compressed—don't walk away!'
As Feynman preferred exploring uncertainty to gaining false certainty, this paper tells us that interestingness is the beam of light guiding us through uncertainty toward the deepest understanding.
The essence of intelligence is finding complexity that can be simplified. That is the ultimate insight of the 'mathematics of curiosity' in 2026.