The most important clue to solving a difficult problem is knowing that somebody else has already solved it.

> The [goal] of machine learning research is to [do better than humans at] theorem proving, algorithmic problem solving, and drug discovery.

Naively, one of those things is not like the others.

When I run into things like this, I just stop reading. My assumption is that a keyword is being thrown in for grant purposes. Who knows what other aspects of reality have been subordinated to politics by the writer.

How does this compare to just reducing the likelihood of negative samples?

Failure doesn’t teach by default; it teaches only when you design for it. Three dials matter: cost, frequency, and observability.

Make failures cheap and reversible. Shrink the scope until a rollback is boring. If a failure requires a committee or a quarter to undo, you’ll avoid the very experiments you need.

Raise frequency deliberately. Schedule “bad ideas hour” or small probes so you don’t wait for organic disasters to learn.

Max out observability. Before you try, write the few assumptions the test could falsify. Log what would have changed your mind earlier (counterfactual triggers), not just what happened.

Two practices that compound:

1. Pre-mortem → post-mortem symmetry. In the pre-mortem, list concrete failure modes and “tripwires”; in the post-mortem, only record items that map back to one of those or add a new class with a guardrail/checklist—not “be more careful.”

2. Separate noise from surprise. Tag outcomes as variance vs. model error. Punishing variance breeds risk aversion; fixing model error improves judgment.

Hard problems rarely yield to heroics; they yield to lots of small, instrumented failures.