Category Archives: Phil

Phil 6.22.20

Cornell University was having a sale, so I got a book:

Mental Territories

  • Rarely recognized outside its boundaries today, the Pacific Northwest region known at the turn of the century as the Inland Empire included portions of the states of Washington and Idaho, as well as British Columbia. Katherine G. Morrissey traces the history of this self-proclaimed region from its origins through its heyday. In doing so, she challenges the characterization of regions as fixed places defined by their geography, economy, and demographics. Regions, she argues, are best understood as mental constructs, internally defined through conflicts and debates among different groups of people seeking to control a particular area’s identity and direction. She tells the story of the Inland Empire as a complex narrative of competing perceptions and interests.

DtZ:

  • Change the code so that there is a 30 day prediction based on the current rates regardless of trend. I think it tells the story of second waves better:

30_days

GPT-2 Agents

  • The ACSOS paper was rejected, so this is now the only path going forward for mapmaking research.
  • Used the known_nearest to produce a graph:
  • The graph on the left is the full graph, and the right is culled. First, note that node c is not in the second graph. There is no confirming link, so we don’t know if it’s an accident. Node e is also not on the chart, because it has no confirming link back through any 2-edge path.
  • Ok, I tried it for the first time on the chess data. There is a bug where [a-h] and [1-8] are showing up as nodes that I have to figure out. But they show up in the right way! Orthogonal and in order!

chess_nearest_bug

  • The bug seems to be in the way that List.extend() works. It seems to be splitting the string (which is a List, duh), and adding those elements as well? Nope, just doing one nesting too many
  • Ok, here are the first results. The first image is of all neighbors. The second is of only verified nearest neighbors (at least one edge chain of 2 that lead back to the original node)

chess_all_neighbors

chess_nearest_neighbors

  • In both cases, the large-scale features of the chessboard are visible. There is a progression from 1 to 8, and a to h. It seems clearer to me in the lower image, and the grid-like nature is more visible. I think I need to get the interactive manipulation working, because some of this could be drawing artifacts
  • Trying out the networkx_viewer. A little worried about this though:

networkxviewer

  • And rightly so:

kablooee

  • Going to try cloning and fixing. Nope. It is waaaaaaayyyyyy broken, and depends on earlier version of networkx
  • Networkx suggests Gephi, and there is a way to export graphs from networkx. Trying that
  • Seems usable?

Gephi

GOES

  • Kind of stuck. Waiting on Vadim
  • Probably will be working on a couple of SBIRs for the next few weeks

Phil 6.19.20

stampede

12:00 – Sy’s defense at noon!

GPT-2 Agents

  • Fixed the regex in ChessMovesToDb
  • More work on finding closest neighbors.
    • Maybe keep a record of the number and type of pieces that are used?
    • Looks like the basics are working. Here’s the test graph:

known_nearest

    • And here are the results. I made the code so that it only shows each neighbor once, but it may be useful to keep track of the number of times a neighbor shows up in a list. This might not be important in chess, but in less structured text environments (RPGs to Reddit threads), it may be valuable:
      find_closest_neighbors(): nodes = ['a', 'b', 'c', 'd', 'e', 'f', 'g']
      {'node': 'a', 'known_nearest': ['f', 'd']}
      {'node': 'b', 'known_nearest': ['f', 'd']}
      {'node': 'c', 'known_nearest': []}
      {'node': 'd', 'known_nearest': ['f', 'a', 'b', 'g']}
      {'node': 'e', 'known_nearest': []}
      {'node': 'f', 'known_nearest': ['a', 'g', 'd', 'b']}
      {'node': 'g', 'known_nearest': ['f', 'd']}

       

    • At this point it’s not recursive, but it could be. I’m worried about combinatorial explosion though

GOES

  • Submit GVSETS paper – done!
  • Meeting with Vadim and Issac at 11:00
    • Goal is to move all the RW code out of the sim class and into its own and call methods from the sim class

Phil 6.18.20

Hotel reservations!

Sent a ping to Don about a paper to review

GPT-2 Agents

  • Started on common neighbor algorithm. Definitely a good place for recursion
  • Generating larger file

adjacency

moves

  • If you look at the center of the plot and squint a bit, you can see a bit of the grid:

networkx

  • There is an error: The string ‘, White moves pawn from h3 to g4. White takes black pawn. LCZero v0.24-sv-t60-3010 moves black knight from h5 to g7. White moves pawn from g4 to h5. LCZero v0.24‘ is parsing incorrectly due to the truly bizarre name (The little known Grand Master LCZero v0.24-sv-t60-3010). Need to fix the regex. I think I just need to make it so that there has to be a space in front and a space/period after.

GOES

  • Readthrough of GVSETS paper
  • 2:00 Meeting

Waikato

  • Alex had a really good insight in that groups that are working at coming to consensus use terms to discuss their level of agreement that are independent of the points being argued. That’s could really be important in text analysis.

Phil 6.17.20

Listened to a fantastic interview with Nell Irvin Painter (White Supremacy at Home and Abroad):

GPT-2 Agents

  • Working on finding the connections between nodes
  • Now that I know how to add weights to edges, I think I want to add the piece that made the move. It needs to be a list, since multiple types of pieces can connect two squares. Added a dict_array per edge:
    if target not in nlist:
        self.G.add_edge(source, target, weight=0)
        self.G[target]['dict_array'] = []
    self.G[target]['weight'] += 1
    for key, val in data_dict.items():
        a:List = self.G[target]['dict_array']
        a.append({key:val})
  • I also realize that moves that repeatedly connect squares are more likely to be close, simply because the available squares of more distant moves increase in a geometric fashion. I added a method that writes out moves to Excel where I can play with them. Here are some moves:

moves

  • In looking at these moves, it does seem to be that the majority of the moves seem to be short (e.g. b6-b7, b6-a7, b6-b5). The only exception is the knight (b6-d7). So I think there is a confidence value that I can calculate for the ‘physical’ adjacency of nodes in a network. This could also apply to belief spaces as well. Most consensus requires coordination and common orientation (pos, heading, speed), so commonly connected topics can be said to be ‘closer’
  • Good chat with Aaron about CVPR and algorithms

GOES

  • Finish revisions and send to T and Aaron for review. Last thing is to tie back to ground vehicles in the discussion. Done! I think… Need to read the whole thing and see if it still hangs together
  • 2:00 – Meeting

Phil 6.15.20

The nice thing about riding big distances is that even though nothing has changed, everything is different and better for a while

therapy

GPT-2 Agents

  • Try to make a adjacency matrix from the DB. That may work, but it sure doesn’t generate anything human-readable. Need to roll my own
  • After accidentally blowing away my database (Yay, backups!), I’m reading in the network. This actually looks really good. I’d not an 8×8 grid, but the system found 63 nodes, and you can see that many adjacent nodes are connected:

networkx

  • You can also see that common moves, such as e2 and d2 are in the center and well connected.
  • Hand-rolled an adjacency matrix using pandas.DataFrame and exported to Excel. I have to think about what this means now. I think that it’s clear that moves tend to be nearby. I’m clearly not setting something right, because I don’t have the weight of the edge between nodes (I’m currently using the number of times a node was visited). Now I need to figure out how to use this:

adjacency

GOES

  • Continue with revisions
  • After trying the pipeline image, (very small text and pix!), I’m going to try a new, more vertical layout. I think this is a little better. It’s a lot more legible:
pipeline_vert2

Enter a caption

  • Ok, back to writing actual text

Good chat with Aaron about the Conspiracy as mode collapse experiments

Fika

  • Sy’s Presentation

Phil 6.12.20

Hey! My dissertation is online now!

Optimizing Multiple Loss Functions with Loss-Conditional Training

  • The idea behind our approach is to train a single model that covers all choices of coefficients of the loss terms, instead of training a model for each set of coefficients. We achieve this by (i) training the model on a distribution of losses instead of a single loss function, and (ii) conditioning the model outputs on the vector of coefficients of the loss terms. This way, at inference time the conditioning vector can be varied, allowing us to traverse the space of models corresponding to loss functions with different coefficients

GPT-2 Agents

  • Applied to get on the OpenAI API waitlist
  • Started figuring out igraph. Welp, it doesn’t plot because cannot load library ‘libcairo-2.dll’: error 0x7e Diesn’t seem to be a good fix. It’s a shame, because igraph seems to be great for analyzing graphs mathematically. Removing everything
  • Looks like I can use networkx combined with networkx_viewer (pypi)(github). Look into that next. Upgraded from 2.1 to 2.4
  • Pulled my NetworkxGraphing.py class over from Antibubbles and verified that it still works!

networkx

GOES

  • Send Jason my download code
  • Work on GVSETS paper
    • Added formatting changes and moved footnotes to citations
    • Adding a figure for the pipeline. Hmmm. It’s um… big

pipeline

Phil 6.11.20

Call Simon

GPT-2 Agents

  • Embeddings and plots
  • Got the sequences generated. They look pretty cool too, like codes:
    e2 e4 c7 c5 g1 f3 b8 c6 d2 d4 c5 d4 f3 d4 g7 g6 b1 c3 f8 g7 f1 e2 d7 d6 c1 g5 a7 a6 d1 e2 f6 e8 f2 f3 e8 c7 g5 f4 f7 f5 e4 f5 g6 f5 e2 f3 c7 d5 f3 g4 c8 d7 e2 d2 d8 c7 b2 b3 f5 f4 d4 b3 d7 f5 f1 e1 e7 f5 e1 e6 f5 g4 b3 d4 a8 c8 d4 f5 c6 f5 e6 f6 f5 d4 a1 e1 g4 h5 f3 f4 f8 f6 d2 f6 c8 f8 f6 h4 d4 e6 c2 c3 e6 d4 c3 d4 h5 f3 e1 e7 f8 f7 e7 f7 g7 f7 g1 f2 b7 b5 c4 b5 a6 b5 g2 g4 f7 g6 h2 h3 g8
    e2 e4 c7 c6 d2 d4 d7 d5 b1 d2 g8 f6 f1 d3 d5 e4 d2 e4 b8 d7 g1 f3 e7 e6 d1 e2 f6 e4 d3 e4 d8 c7 e4 b1 d7 f6 c1 g5 f6 g4 h7 h6 g5 h4 c7 d7 e2 e3 g4 e5 h1 g1 e5 c6 f2 f3 f8 e7 e3 e2 g2 g4 f8 d8 f3 e5 d7 d3 e2 d3 d8 d3 g1 d1 a7 a6 c1 b1 d3 d6 b1 a1 e7 f6 a2 a3 c8 e6 f3 e4 b7 b5 b2 b4 f6 g7 b4 a5 b5 a4 e5 c6 a8 b8 d1 f1 a4 a3 c6 e5 a3 a2 h4 e1 a2 a1 f1 a1 b8 a1 d4 d5 e6 c8 d1 b1 g8 f8 a1 b1 f8 e7 b1 c2 e7 d6 e5 d7 g7 d4
    e2 e4 e7 e6 d2 d4 d7 d5 b1 c3 f8 b4 e4 e5 b4 c3 b2 c3 g8 e7 d1 b3 c7 c5 a2 a3 b8 c6 f2 f4 b7 b5 a3 a4 b5 b4 b3 b2 a4 a5 c5 d4 c3 d4 e7 g6 g1 f3 c6 e7 c8 a6 c1 g5 e7 g8 a1 b1 a8 c8 e5 d6 g8 f6 g5 f6 d8 f6 f1 f2 h7 h6 b2 b5 f6 d6 f3 h4 d6 e7 h4 g6 a6 g2 g6 e7 f8 e8 e7 f5 g2 f3 g1 g2 c8 c2 b1 c1 c2 c8 a5 a6 c8 a8 h2 h3 f3 e4 b5 b3 f7 f6 b3 b2 f6 f5 f5 d6 e6 e5 b2 a1 e8 a8 f2 f5 f5 e4 f5 f7 a8 b8 c1 f1 b8 b5 a1 a2 a8 a7
    e3 d2 d4 g8 f6 c2 c4 e7 e6 b1 c3 f8 b4 e2 e3 c1 d2 d7 d5 c4 d5 f6 d5 f2 f3 b8 c6 g1 f3 f7 f5 g2 g4 f5 g4 d1 e2 d5 f4 d2 f4 e6 f5 e2 e5 b4 c3 e5 c3 d8 d3 e1 e2 d3 e2 e2 e2 f5 f4 e2 e1 f4 f3 e1 f2 a8 d8 f2 f1 c8 e6 f4 e3 f8 f7 f3 h4 e6 d5 f1 g2 f7 f3 g2 f1 f3 h3 f1 e1 d5 e4 e1 f2 h3 h4 g4 h5 h4 h5 f2 f3 h5 f5 f3 g2 f5 h5 e3 g5 h5 h3 g5 e3 h3 h4 e3 g5 e4 g2 g2 g3 g2 d5 g3 h3 d5 e6 g5 f6 g8 h7
    e2 e4 e7 e5 g1 f3 b8 c6 b1 c3 g8 f6 f1 b5 d7 d6 d2 d3 a7 a6 f8 e7 b5 a4 b7 b5 a4 b3 h2 h3 c8 b7 a2 a4 b5 b4 a4 b5 c6 b8 c1 g5 f6 e8 f3 e5 e8 d6 g5 f4 d8 e7 f4 d6 e7 d6 e5 f3 d6 e7 c3 a4 c7 c6 a4 c5 c6 c5 d3 d4 e7 e5 d4 c5 b7 c8 a1 c1 b8 d7 f3 e5 f7 f6 e4 f5 d7 f6 c1 c6 e5 e4 g1 h1 f6 h5 f1 e1 h5 g3 e5 d7 e4 h4 c6 c4 h4 g3 d1 g4 g3 h3 h1 g1 h3 h1 g1 h2 h1 h5 g4 f5 c8 b7 c4 c5 h5 e2 e1 d1 e7 f6 b2 b3 f6 e5 c5 c8 a8 c8

     

  • Drawing the embeddings. Fun, but not really useful. And this is kind of my point about embeddings like W2V that don’t take into account the trajectory of the sentence the word is part of. We know that the structure of the board is represented in the text. We need a more sophisticated embedding to to extract it

square_embeddings

  • Something that might make sense is to see how these points cluster as well
  • I think I might try plotting individual columns later, but first I’m going to try building some from/to networks by piece

GOES

  • Try downloading yaw flip
  • Was able to connect to the server, though now I don’t need a port number?
  • Specifying the queries. Fixed a few mnemonics.
  • Had to try a few times, but I got it!

influx_copy

  • Not sure what to do next. Update GVSETS paper?
  • 2:00 CASSIE meeting – learned a lot of things
  • I got promoted!
  • Implemented a perplexity measure. Looking at this as a way of understanding mode collapse, and maybe conspiracy theories?
  • Done for the day

Phil 6.10.20

Finish ACSOS review

GPT-2 Agents

  • Generate embeddings
    • Trying running much longer sequences (max_length = 1000). The lets games run long enough  that they often conclude (the term “resigns”, “wins”, or “draw occurs in the text)
    • Put together a simple regex ‘[a-h][1-8]’ that pulls out all the squares in sequence from a game
    • Extracting game square sequences to create files that will feed into Word2Vec. The class is started and most of the issues are worked out. I added a check for game endings so beginning and endings are not place together oddly.
    • Here’s the trimmed input text
      The game begins as white uses the Sicilian opening. and black countering with Najdorf, Adams attack. Loek Van Wely moves white pawn from e2 to e4. Black moves pawn from c7 to c5. In move 2, White moves knight from g1 to f3. Black moves pawn from d7 to d6. White moves pawn from d2 to d4. Black moves pawn from c5 to d4. Black takes white pawn. White moves knight from f3 to d4. White takes black pawn. Black moves knight from g8 to f6. In move 5, White moves knight from b1 to c3. Arseniy Nesterov moves black pawn from a7 to a6. Loek Van Wely moves white bishop from c1 to e3. Black moves pawn from e7 to e6. In move 7, White moves pawn from f2 to f4. Black moves knight from b8 to d7. White moves queen from d1 to d2. Black moves pawn from b7 to b5. Loek Van Wely queenside castles. Black moves bishop from f8 to e7. White moves bishop from f1 to d3. Arseniy Nesterov kingside castles. White moves king from c1 to b1. Black moves rook from a8 to b8. White moves pawn from g2 to g3. Black moves queen from d8 to a5. Loek Van Wely moves white king from b1 to a1. Black moves bishop from e7 to d6. Black takes white knight. Loek Van Wely moves white bishop from d3 to e4. White takes black pawn. Black moves rook from b8 to b2. Black takes white pawn. In move 17, White moves bishop from e4 to h7. White takes black pawn. Check. Arseniy Nesterov moves black king from g8 to h8. White moves bishop from h7 to d3. Black moves bishop from d6 to f4. Black takes white pawn. Check. In move 19, White moves bishop from e3 to f4. White takes black bishop. Black moves rook from b2 to f2. White moves rook from h1 to f1. Black moves knight from d7 to e5. White moves queen from d2 to e2. Black moves queen from a5 to d2. In move 22, White moves knight from c3 to e2. White takes black queen. Black moves rook from f2 to e2. Black takes white knight. Loek Van Wely moves white bishop from f4 to e3. Black moves rook from e2 to e3. Black takes white bishop. White moves pawn from f4 to f5. Black moves rook from f8 to d8. White moves pawn from a2 to a4. Arseniy Nesterov moves black bishop from c8 to b7. White moves pawn from a4 to a5. Arseniy Nesterov moves black bishop from b7 to c8. White moves pawn from a5 to b6. White takes. Arseniy Nesterov moves black pawn from a6 to b5. Black takes white pawn. White moves queen from e2 to b5. White takes black pawn. Black moves knight from e5 to c4. White moves pawn from h2 to h3. Black moves knight from c4 to a5. In move 30, Loek Van Wely moves white queen from b5 to a4. Arseniy Nesterov moves black pawn from h7 to h6. White moves bishop from d3 to b1. Black moves rook from d8 to d1. Check. Loek Van Wely
    • And here’s the sequence
      e2 e4 c7 c5 g1 f3 d7 d6 d2 d4 c5 d4 f3 d4 g8 f6 b1 c3 a7 a6 c1 e3 e7 e6 f2 f4 b8 d7 d1 d2 b7 b5 f8 e7 f1 d3 c1 b1 a8 b8 g2 g3 d8 a5 b1 a1 e7 d6 d3 e4 b8 b2 e4 h7 g8 h8 h7 d3 d6 f4 e3 f4 b2 f2 h1 f1 d7 e5 d2 e2 a5 d2 c3 e2 f2 e2 f4 e3 e2 e3 f4 f5 f8 d8 a2 a4 c8 b7 a4 a5 b7 c8 a5 b6 a6 b5 e2 b5 e5 c4 h2 h3 c4 a5 b5 a4 h7 h6 d3 b1 d8 d1

       

    • I can do other things like split into white and black, but that’s pretty tricky and I don’t think it’s worth it
  • Start building networks. Here are some api possibilities

GOES

  • If the devlab is still up, work on pulling down data. Nope, the VPN is working so badly today that I can’t even load my webmail
  • Going to work on the download and transfer using my local Influx – done!

influx_copy

Complete copy of remote data on local server

  • 2:00 Meeting

Phil 6.9.20

I’ve been thinking about writing a paper about how the development of conspiracy theories resembles the mode collapse condition in GAN creation. There is some recent research in the GAN community on developing tools for detecting mode collapse (Jai & Zhao, 2019) that I think could be extended to identifying conspiracy theories and the processes that create them. Maybe for ICTAI 2020?

Studying Programming in the Neuroage: Just a Crazy Idea?

  • What we were proposing to do was simple yet ambitious. Using functional magnetic resonance imaging, we might better understand what goes on in the minds of programmers as they read and understand code.
  • The results indicated that a specific network of brain areas in the left hemisphere was used by participants to understand code, including areas related to working memory, divided attention, and reading comprehension. Surprisingly, we did not observe cognitive processes related to mathematical and logical reasoning, which would be consistent with the perspective that programming is a formal, logical, and mathematical process.

GPT-2

  • Start storing data!

db

  • Try new probes – done!
    probe_list = ['The game begins as ', 'In move 10', 'In move 20', 'In move 30', 'In move 40', 'White takes black ', 'Black takes white ', 'Check. ']
  • Added the raw text to the table_moves instead of comments. Here’s the new and improved:

db

  • Start thinking about metrics, like adjacency matrices by piece?
  • Build gensim embeddings of games and pieces
  • Build agent network graphs by piece type and from/to

GOES

  • Nothing back from Vadim, so I think I’ll continue on my Wasserstein Loss algorithm
  • Hey! The DevLab Influx is reachable again!

influx

  • Going to take advantage of this and find more mnemonics, Done!
  • Now I need to figure out a good way to pull down the data and load it on my system
  • Working on DevLabInfluxQuery class that extends InfluxQuery. Done, but fails with one of these exceptions:
    • Unable to parse CSV response. FluxTable definition was not found.
    • Connection aborted.’, TimeoutError(10060, ‘A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond’, None, 10060, None)
    • Tried upgrading my influxdb-client (pip install –upgrade influx-client) from 1.5 to 1.9. Hopefully i didn’t break too much. Didn’t fix it with that, but I got the port number for Boris, which fixed things

devlabInflux

ML seminar

  • Discussed options for visualizing relationships between nodes. Starting with just straightforward plotting of connected nodes. Also, plotting by piece. Another option is to use the gensim library to do a word2vec embedding and visualize it. I think I’ll start there because I’m curious.
  • To improve the embedding, it might be useful to generate entire games and parse them.

 

Phil 6.8.20

Not at all happy with this COVID weight gain. My preferred stress management tool is exercise, but I’m at a minimum of 20 miles/day. Usually about 100 miles+ on weekends.

Starting to think about writing something on the ethics of mode collapse

D20

Florida

GPT-2 Agents

  • Back to pulling move and piece information out of generated text – done
  • Added heuristic for move number
  • Created dicts for db data. Add writes tomorrow!

GOES

  • Adding read tests – done! Had to screw around with utc conversions for a while
    • Writes are roughly 1/2 sec per 1,000
    • Reads are about 2/100 sec per 1,000
  • Tried to log in and get on the devlab influx system – nope:

bad gateway

  • Trying to figure out what makes sense to do next. Ping Vadim? Done

Phil 6.5.20

GPT-2 Agents

  • Started a google doc for the GPT-2 Chess agents that will be grist for the paper(s)
  • Create probes for each piece, like:
    • white moves pawn from e2 to
    • black moves pawn from e7 to
    • A slightly more sophisticated parser will need to work with “The game begins
  • I can take the results of multiple probes and store them in the table_moves, then run statistics by color, piece, etc
  • Then see if it’s possible to connect one piece to another piece using a “from/to chain” across multiple pieces. There will probably be some sort of distribution where the median(?) value should be a set of adjacent squares.
  • The connections can be tested by building adjacency matrices by piece and by move number range
  • Started ChessMovesToDb. Might as well work on the tricky parse of  “The game begins “. Making progress. My initial thought on how to parse moves doesn’t handle weird openings like “4.e3, Gligoric system with 7…dc” Need to strip to the the first occurance of “move”, I think:
    white uses the Nimzo-Indian opening. and black countering with 4.e3, Gligoric system with 7...dc. White moves pawn from d2 to d4. Black moves knight from g8 to f

GOES

  • More timing tests
    • Add explicit time to the write
    • This should also be the basis of the system that will pull data from the DevLab Influx
  • Continue search for important mnemonics during yaw flip – nope, still can’t log into DevLab influx
  • Working on setting up arbitrary times spans and a new bucket for tests. Writing data works. Now I need to extend the number of series, tags, etc influxAll the writes are working. I’ll do the reads Monday. Everything looks pretty consistent, though.

timing

Phil 6.4.20

GPT-2 Agents

  • Thinking about how to parse the data to build maps.
    • Clearly, there are black and white agents (the players). Option 1 would be to simply collect the from-to points at the player level
    • One step down would be the piece family, pawns, rooks, bishops. The king and queen would be single instances
    • The most granular would be to track the individual pieces.
    • The issue I’m struggling with is when pieces pass over squares rather than through them. There is no explicit d3 when white moves the pawn from d2 to d4. Trying to think of the best way to uncover the latent information.
    • I think a good way to procrastinate about this problem is to parse the games into a database of moves. The format is always “<player/color> moves <(color)piece> from <start> to <end>. There is additional information as well (game, players, move number), but that could be added later.
    • Done
    • table_moves
    • Sent a note to Thomas Wolf at Huggingface
    • I know what I’m going to do!
      • Create probes for each piece, like:
        • white moves pawn from e2 to
        • black moves pawn from e7 to
        • A slightly more sophisticated parser will need to work with “The game begins
      • I can take the results of multiple probes and store them in the table_moves, then run statistics by color, piece, etc
      • Then see if it’s possible to connect one piece to another piece using a “from/to chain” across multiple pieces. There will probably be some sort of distribution where the median(?) value should be a set of adjacent squares.
      • The connections can be tested by building adjacency matrices by piece and by move number range

GOES

  • Hey! The VPN is much more responsive today! Logged into Influx
  • Getting the right time for the query from here
    • start: 2020-04-06 15:30:00.000
    • end: 2020-04-06 18:00:00.000
    • Need to get the right mnemonics. Pinged Bruce
  • Starting some timing tests on my local influx copy
  • The VPN has stopped again
  • 2:00 NSOF Meeting – Nice demo by Jason
  • 3:30 AIMS IRAD
    • Status. John is going part time
    • Railed against the poor VPN access to the DevLab

ML Brownbag – Aaron did a nice job

Phil 6.3.20

MarthaRaddatz

“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

GPT-2 Agents

  • 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:

generated_chess

  • 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!!!

GOES

  • 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

#COVID

  • Meeting cancelled