“When the students poured into Tiananmen Square, the Chinese government almost blew it. Then they were vicious, they were horrible, but they put it down with strength. That shows you the power of strength” – Donald Trump, 1990
- Finished finetuning the model yesterday, and tried running it with the following seeds:
text_list = ['The game begins as ', 'White moves ', 'Black moves ', 'In move 1, ', 'In move 40, ']
- The results are pretty incredible:
- Opening moves (“The game begins as”, “In move 1”) make sense. White always moves first. Pieces move in reasonable, permissible ways (e.g d3 to d4)
- The model knows how to take pieces correctly. The red lines connect moves by White and the counter-move by black, then the taking of the piece.
- Moves that occur later in the game are also sensible. (“In move 40”, “White moves”. “Black moves”)
- Names occur very infrequently. And Loek is probably the most frequent name, since his entire career is in a pgn file
- I think the next steps are:
- Create marker text for SBoW analysis, like “In move 1, White moves pawn from d2 to d4. Black moves knight from g8 to f6.“, and “In move 40, White moves rook from d1 to d7. Black moves pawn from e5 to e4.” so that I can use the antibubble analytics
- Create a parser that looks for the movements of particular pieces (white pawns, black knights, etc) and see if I can build a map from that using the agent tools. Pawns and kings may provide the projection, while other pieces move over that
- Good results today!!!
- Create Github repo for brownbag – done
- Status report – done
- Ping Biruh about accessing the on-site InfluxDB – done
- Ping Boris and Bruce for mnemonics and yaw flip times
- 2:00 meeting
- Build a set of stress tests for influx.
- samples 300 x 7,000 floating point
- tags: benchmark tags 2, 4, 8, 16, 32, etc
- Vadim for Cassie DB questions
- Meeting cancelled