Interpretability and the Wish to See Inside the Black Box
A system can be accurate and still be opaque. It gives answers without reasons, competence without account. The desire to...
Month: July 2026
Specification Gaming: Optimizing the Letter, Not the Spirit
Ask precisely for the wrong thing and you will receive it. Optimizers are literal genies, satisfying the objective as written...
The Alignment Problem: Goals We Cannot Fully Specify
We build systems to pursue objectives, but our true wishes exceed what we can write down. A goal stated precisely...
Exploration Versus Exploitation: The Explorer’s Dilemma
Act on what you know, or seek what you don't? Every agent that learns from its choices faces this fork....
Transfer: Knowledge That Moves Between Tasks
What is learned in one place can pay off in another. Skills acquired on one task often lighten the learning...
Distillation: Compressing One Mind Into a Smaller One
A large model's competence need not stay large. Its behavior can be poured into a smaller vessel that mimics it...
The Lottery Ticket Idea: Sparse Networks Hidden in Dense Ones
Inside a large trained network may hide a tiny one that could have learned the task alone. The full apparatus...
Double Descent: When Bigger Models Learn Better Again
Conventional wisdom says a model too large will overfit. Yet past a certain size the rule inverts, and enormous models...
No Free Lunch: The Impossibility of a Universal Learner
There is no algorithm that is best at everything. Averaged over all possible problems, every learner performs exactly alike, and...
The Curse of Dimensionality
Add dimensions and intuition betrays you. Volumes explode, neighbors flee, and the comfortable geometry of ordinary space gives way to...
