Student of Games

Training compute

3.7×10²² FLOP

Published

Dec 6, 2021

Student of Games is an AI model developed by DeepMind (United Kingdom), first published in December 2021. It works in the games domain, on tasks such as chess, go and poker.

Training it took an estimated 3.7×10²² FLOP of compute. It was trained on roughly 245.8B datapoints.

Access: Unreleased. Its weights are not openly released. The reference paper has 32 citations. Epoch AI rates the confidence of this record as speculative.

Full record

Organization: DeepMind
Country of organization: United Kingdom
Domain: Games
Task: Chess, Go, Poker
Training compute: 3.7×10²² FLOP
Dataset size: 245.8B
Model accessibility: Unreleased
Open weights: No
Citations: 32
Epoch confidence: Speculative