AI FOR SCIENCE · TIMELINE
The 5 entries the timeline holds for 2022, newest first. Each was read at source. The full timeline, with filters →
What happened: The system produces authoritative-looking false claims and citations, and the public demo disappears after three days.
Why it matters: Scientific fluency is shown to be different from scientific reliability.
What happened: Meta uses a protein language model to predict structures without the multiple-sequence alignments required by AlphaFold.
Why it matters: Protein language modeling offers a faster, complementary route to structure at unprecedented scale.
What happened: Reinforcement learning finds algorithms that improve on long-standing human-designed solutions for selected matrix sizes.
Why it matters: Scientific search becomes a game with an executable evaluator, a pattern later generalized by AlphaDev and AlphaEvolve.
What happened: The database grows from around one million structures to predictions covering nearly every catalogued protein.
Why it matters: Prediction at planetary scale changes protein structure from a scarce result into a queryable layer of biology.
What happened: A single neural controller commands the magnetic coils of Switzerland's TCV reactor and sustains difficult plasma shapes.
Why it matters: AI moves beyond offline prediction and directly operates a real scientific instrument.