Review papers – finished reading the first, write review today. First review done!
Realized that I really need to update my online resumes to include Python and Machine Learning. Can probably just replace the Flex and YUI entries with Python and Tensorflow
Read this today: Proposal: A Market for Truth to Address False Ads on Social Media. It’s by Marshall Van Alstyne, a Questrom Chair Professor at Boston University where he teaches information economics. From the Wikipedia entry
- Information has special characteristics: It is easy to create but hard to trust. It is easy to spread but hard to control. It influences many decisions. These special characteristics (as compared with other types of goods) complicate many standard economic theories.
- Information economics is formally related to game theory as two different types of games that may apply, including games with perfect information, complete information, and incomplete information. Experimental and game-theory methods have been developed to model and test theories of information economics,
- This looks as close to the description of decisions in the presence of expensive information that I’ve seen so far
- The run completed last night! I have 156,313 synthetic moves
- Reworking the queries from the actual moves to reflect the probes for the synthetic
- Created a view that combines the probe and the response into a description:
create or replace view gpt_view as select tm.move_number, tm.color, tm.piece, tm.`from`, tm.`to`, concat(tm.probe, tm.response) as description FROM table_moves as tm;
- Almost forgot to backup the db before doing something dumb
- Created a “constraint string” that should make the game space searched somewhat more similar:
and (move_number < 42 or description like "%White takes%" or description like "%Black takes%" or description like "%Check%")
- Made the changes to the code and am running the analysis
- My fancy queries are producing odd results. Pulling out the constraint string. That looks pretty good!
- As an aside, the chess queries and extraction is based on an understanding of movement tems like ‘from’ and ‘to’. Thinking about Alex’ finding of consensus metaterms, I think it would be useful to look for movement/consensus/compromise terms and then weighting the words that are nearby
- Vacation pix!
- Went over results shown above
- Arpita found some good embedding results using Tensorboard, but not sure where to go from there?