
How to read this finding
Game performance is specific to the match conditions. The protein result refers to the original AlphaFold, not AlphaFold 2 or later systems. Language-model progress did not establish general reasoning ability.
Pretrained language models learned reusable representations from large text collections, improving a range of downstream tasks. The report compared this development with the earlier impact of ImageNet-based transfer learning in vision.

Game performance is specific to the match conditions. The protein result refers to the original AlphaFold, not AlphaFold 2 or later systems. Language-model progress did not establish general reasoning ability.
Evidence you can use
Historical snapshot: June 2019. Dates and populations are specified per row.
| Measure | Reported value | Definition and source |
|---|---|---|
| AlphaStar match result | 5-0 | Reported match against a world-class player under constrained conditions.2019 report, PDF page 11 |
| Language pretraining | Large unlabeled text corpora | Training approach described for reusable language representations.2019 report, PDF page 21 |
| AlphaFold limitation | Protein complexes not covered | Limitation of the original system discussed in 2019.2019 report, PDF page 20 |
Game performance is specific to the match conditions. The protein result refers to the original AlphaFold, not AlphaFold 2 or later systems. Language-model progress did not establish general reasoning ability.
Historical snapshot published June 28, 2019. This web edition was prepared on 2026-10-11 from the online deck and original launch posts. Findings and forecasts retain their original time frame.
Benaich, Nathan, and Ian Hogarth. “What was the language-model breakthrough in 2019?” State of AI Report 2019. Historical report snapshot; web edition prepared 2026-10-11.