Samuel Neural Checkers

Training compute

4.3×10⁸ FLOP

Parameters

16

Published

Jul 1, 1959

Samuel Neural Checkers is an AI model developed by IBM (United States), first published in July 1959. It works in the games domain, on tasks such as checkers. It counts among the frontier models: the systems trained with the most compute of their moment.

Training it took an estimated 4.3×10⁸ FLOP of compute. The model has 16 parameters.

The reference paper has 5,063 citations. Epoch AI rates the confidence of this record as likely.

Full record

Organization: IBM
Country of organization: United States
Domain: Games
Task: Checkers
Training compute: 4.3×10⁸ FLOP
Parameters: 16
Citations: 5,063
Epoch confidence: Likely