Category Archives: Writing

Phil 7.3.20

Today is a federal holiday, so no rocket science

Huggingface has a pipeline interface now that is pretty abstract. This works:

from transformers import pipeline

translator = pipeline("translation_en_to_fr")
print(translator("Hugging Face is a technology company based in New York and Paris", max_length=40))
  • [{‘translation_text’: ‘Hugging Face est une entreprise technologique basée à New York et à Paris.’}]

Wow: GPT-3 writes code!

DtZ is back up! Too many countries have the disease and the histories had to be cropped to stay under the data cap for the free service

GPT-2 Agents

  • Work on more granular path finding
    • Going to try the hypotenuse of distance to source and line first – nope
    • Trying looking for the distances of each and doing a nested sort
    • I had a problem where I was checking to see whether a point was between the current node and the target node using the original line between the source and target nodes. Except that I was checking on a lone from the current node to the target, and failing the test. Oops! Fixed
    • I went back to the hypotenuse version now that the in_between test isn’t broken and look at that!

granular

    • Added the option for coarse or granular paths
  • Start thinking about topic extraction for a given corpus

#COVID

  • Evaluate Arabic to English translation. Got it working!
    from transformers import MarianTokenizer, MarianMTModel
    from typing import List
    src = 'ar'  # source language
    trg = 'en'  # target language
    sample_text = "لم يسافر أبي إلى الخارج من قبل"
    sample_text2 = "الصحة_السعودية تعلن إصابة أربعيني بفيروس كورونا بالمدينة المنورة حيث صنفت عدواه بحالة أولية مخالطة الإبل مشيرة إلى أن حماية الفرد من(كورونا)تكون باتباع الإرشادات الوقائية والمحافظة على النظافة والتعامل مع #الإبل والمواشي بحرص شديد من خلال ارتداء الكمامة "
    mname = f'Helsinki-NLP/opus-mt-{src}-{trg}'
    
    model = MarianMTModel.from_pretrained(mname)
    tok = MarianTokenizer.from_pretrained(mname)
    batch = tok.prepare_translation_batch(src_texts=[sample_text2])  # don't need tgt_text for inference
    gen = model.generate(**batch)  # for forward pass: model(**batch)
    words: List[str] = tok.batch_decode(gen, skip_special_tokens=True) 
    print(words)
  • It took a few tries to find the right model. The naming here is very haphazard.
  • Asked for a sanity check from the group
    • This:
      الصحة_السعودية تعلن إصابة أربعيني بفيروس كورونا بالمدينة المنورة حيث صنفت عدواه بحالة أولية مخالطة الإبل مشيرة إلى أن حماية الفرد من(كورونا)تكون باتباع الإرشادات الوقائية والمحافظة على النظافة والتعامل مع #الإبل والمواشي بحرص شديد من خلال ارتداء الكمامة
    • Translates to this:
      Saudi health announces a 40-year-old corona virus in the city of Manora, where his enemy was classified as a primary camel conglomerate, indicating that the protection of the individual from Corona would be through preventive guidance, hygiene, and careful handling of the Apple and the cattle by wearing the gag.

       

  • Write script that takes a batch of rows and adds translations until all the rows in the table are complete

Book chat

Phil 6.29.20

ACM IUI 2021 is the 26th annual premier international forum for reporting
outstanding research and development on intelligent user interfaces.

  • ACM IUI is where the Human-Computer Interaction (HCI) and Artificial 
    Intelligence (AI) communities meet, with contributions from related fields 
    such as psychology, behavioral science, cognitive science, computer 
    graphics, design, the arts, and more. Our focus is on improving the
    interaction between humans and digital technology, by leveraging both HCI
    approaches and state-of-the art AI techniques from machine learning,
    natural language processing, data mining, knowledge representation and
    reasoning.

GOES:

  • Ping Erik about collaborative VR coding environments. Done
  • 2:00 Meeting with Vadim
    • Walked through the deep hierarchy example
    • He’s now running 4 wheels and starting to get close, though the RW speed plots are not close to the actuals. It makes me think that there is more feedback control in the satellite implementation than there is implied in the documentation.

Proposal

  • After digging into the existing text, we realized that a lot of the technical sections were flat wrong, and depended on a kind of “magical ML thinking” that should have been in our phase III. So, lots of writing.

GPT-2 Agents

  • Working on trajectory plotting
  • Fix the listbox select. I was using the wrong event. It should be like this.

ListBoxSelect

  • Aaaand then there were a bunch of weird errors. For some reason, the call to a new ListBox also calls the previous ListBox with no args(?) so I get an error. Chased down and fixed.
  • Plot main line. Done!

NodeLine

  • Plot legal connections of closest lines
    • I think this can be done by looking at the nodes that are connected to the start (current) node, then looking at the coordinates of all the children. The one that is closest to the line and between the current and the target gets added to the list
    • Plotting all the node connections so there can be a sanity check:

NodeLine

  • Use the weight of the lines to choose the lines
  • Build a narrative rutter that describes the route (Here be there stampedes!)

Phil 6.26.20

Let’s not forget that things are not going well here:

DtZ

python_data_libs

Many useful links in the replies (like Stumpy for time series)

GPT2-Agents

  • Working on plotting nodes correctly, being able to select them, then plotting closest legal moves that reach a destination
  • Got node selection working!

2020-06-26

With loaded nodes as well. I have an issue where the callback from the mouse ius happening before the selection in a list, so I need to fix that:

2020-06-26 (1)

GOES

  • 10:00 Meeting with Vadim
  • Realized that I had been too fancy to remember how to deal with commands to individual controllers. Figuring that out now – done!

Book

  • Meeting with Michelle to discuss editing – went very well. Sent her a copy of the “book” version of the dissertation

Phil 6.25.20

Latent Embeddings of Point Process Excitations

  • When specific events seem to spur others in their wake, marked Hawkes processes enable us to reckon with their statistics. The underdetermined empirical nature of these event-triggering mechanisms hinders estimation in the multivariate setting. Spatiotemporal applications alleviate this obstacle by allowing relationships to depend only on relative distances in real Euclidean space; we employ the framework as a vessel for embedding arbitrary event types in a new latent space. By performing synthetic experiments on short records as well as an investigation into options markets and pathogens, we demonstrate that learning the embedding alongside a point process model uncovers the coherent, rather than spurious, interactions.

Misinformation, Crisis, and Public Health—Reviewing the Literature

  • The Covid-19 pandemic has been accompanied by a parallel “infodemic” (Rothkopf 2003; WHO 2020a), a term used by the World Health Organization (WHO) to describe the widespread sharing of false and misleading information about the novel coronavirus. Misleading information about the disease has been a problem in diverse societies around the globe. It has been blamed for fatal poisonings in Iran (Forrest 2020), racial hatred and violence against people of Asian descent (Kozlowska 2020), and the use of unproven and potentially dangerous drugs (Rogers et al. 2020). A video promoting a range of false claims and conspiracy theories about the disease, including an antivaccine message, spread widely (Alba 2020) across social media platforms and around the world. Those spreading misinformation include friends and relatives with the best intentions, opportunists with books and nutritional supplements to sell, and world leaders trying to consolidate political power.

GPT-2 Agents

  • Well, networkx can write a gefx file that Gephi can read, but not the other way around.
  • Networkx CAN read and write gml files, though. Switching to that.
  • That seems to be working well:

gml_read_write

  • Now let’s see if we can draw it in the app
  • Things are starting to get very specific. creating a subclass
  • Pulling attributes is not obvious. Here’s how you do it for the nodes read in from gml:
    attrs = nx.get_node_attributes(self.gml_model, 'graphics')
    for key, val in attrs.items():
        print("{} = {}".format(key, val))

 

  • Loading and displaying the nodes! Next, I need to get piece data from the database. Also, since the graphics attribute can be a dictionary, it may be possible to add attributes like that to the edge data? Then I won’t need to re-access the db. Conversely, another way to do this might be to update the table in the db with positions, etc. Hmmmm

GraphNavigator

GOES

  • 10:00 Meeting with Vadim. Nope, he broke his code. Rescheduled for tomorrow

Proposal

  • Work on technical section with Aaron?

Phil 6.24.20

GPT-2 Agents

  • Starting work on the navigator app
  • Today’s progress:

TkCanvasBase

  • I think that this can be the core of the initial navigation capability for any corpus. You should be able to identify a topic on the map or in the list, and the system will figure out the most direct route (linear distance).
  • I think there also needs to be an ability to see the directly connected neighbors as well, since they might be farther away due to mapping constraints. For example, we can see that d2 is linked directly to d7, which is almost completely across the board. This is the result of the white queen making a pretty aggressive move. It’s not common, but it does happen. It might be interesting for someone working their way from arithmetic to calculus to see, for example, how Johann Carl Friedrich Gauss did it:

nearest

GOES

  • 10:00 Meeting with Vadim
    • We’re going to try to get a single RW to move the vehicle through two successive 90-degree maneuvers, then verify that everything is working correctly on the other RWs, then go to RW sets
  • 2:00 Status meeting

Proposal

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 6.10.20

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 6.1.20

century

It’s all been a bit much recently, so yesterday I took advantage of the wonderful weather and went on a long ride with a few friends.

D20 – Nagged Zach with this image. He responses generally were “It is generally pretty optimistic around here”, and “According to google is is getting better. I wonder where their data comes from”.

Colorado

GPT-2 Agents

  • Still some debugging. added output of the raw move files to find games better
  • Dates aren’t right either – fixed
  • Added some better triggering of the print_board method
  • WOW! I mean it shouldn’t be that surprising, but the pgn is wrong. Going to add a flag for games with problem moves. Then I think I should be able to generate text.

GOES

  • Put paper in the right format (word?)
  • Create the slides. Verify the speaking duration – done. It’s 20 minuts, I think probably 15 for talk and 5 for questions
  • Found the technical paper repo. Looks like I didn’t have to worry about length! http://gvsets.ndia-mich.org/publications.php#MSTV
  • Uploaded! Just use the info in the email from GVSETS Tech Session Admin

Google is profiting from dozens of websites that peddle hoaxes and conspiracy theories about Covid-19, according to a Tech Transparency Project (TTP) investigation, revealing a major hole in the company’s claims that it’s fighting misinformation about the pandemic.

Google

Phil 5.29.20

WaPo

Race/police riots. We’re not even halfway through the year

GPT-2 Agents

  • Wrote my first pgn code! It moves the rooks out, down to the other side of the board and then back
    1. h4 h5 2. Rh3 Rh6 3. Ra3 Ra6 4. Rh3 Rh6 5. 1/2-1/2
  • And it’s working! I did have to adjust the subtraction order to get pieces to move in the right direction. It even generates game text:
    Game.parse_moves(): Move1 = ' h4 h5 '
    Evaluating move [h4 h5]
    piece string = '' (blank is pawn)
    piece string = '' (blank is pawn)
    Game.parse_moves(): Move2 = ' Rh3 Rh6 '
    Evaluating move [Rh3 Rh6]
    piece string = 'R' (blank is pawn)
    piece string = 'R' (blank is pawn)
    Game.parse_moves(): Move3 = ' Ra3 Ra6 '
    Evaluating move [Ra3 Ra6]
    piece string = 'R' (blank is pawn)
    Chessboard.check_if_clear() white rook is blocked by white pawn
    piece string = 'R' (blank is pawn)
    Chessboard.check_if_clear() black rook is blocked by black pawn
    Game.parse_moves(): Move4 = ' Rh3 Rh6 '
    Evaluating move [Rh3 Rh6]
    piece string = 'R' (blank is pawn)
    piece string = 'R' (blank is pawn)
    Game.parse_moves(): Move5 = ' 1/2-1/2'
    Evaluating move [1/2-1/2]
    
    The game begins as white uses the Polish (Sokolsky) opening opening. 
    Aye Bee moves white pawn from h2 to h4. Black moves pawn from h7 to h5.
    White moves rook from h1 to h3. Cee Dee moves black rook from h8 to h6.
    White moves rook from h3 to a3. Black moves rook from h6 to a6.
    Aye Bee moves white rook from a3 to h3. Black moves rook from a6 to h6.
    In move 5, Aye Bee declares a draw. Cee Dee declares a draw
    
  • Ok, it’s not quite working. I can’t take pieces any more. Fixed. Here’s the longer sequence where the white rook takes the black rook and retreats back to its start, moving past the other white rook:
    1. h4 h5 2. Rh3 Rh6 3. Ra3 Ra6 4. Rxa6 c6 5. Ra3 d6 6. Rh3 e6 7. Rh1 f6 8. 1/2-1/2
  • And here’s the expanded game:
    The game begins as white uses the Polish (Sokolsky) opening opening. 
    White moves pawn from h2 to h4. Black moves pawn from h7 to h5.
    Aye Bee moves white rook from h1 to h3. Black moves rook from h8 to h6.
    In move 3, White moves rook from h3 to a3. Black moves rook from h6 to a6.
    White moves rook from a3 to a6. White takes black rook. Black moves pawn from c7 to c6.
    In move 5, White moves rook from a6 to a3. Black moves pawn from d7 to d6.
    White moves rook from a3 to h3. Black moves pawn from e7 to e6.
    In move 7, White moves rook from h3 to h1. Black moves pawn from f7 to f6.
    In move 8, Aye Bee declares a draw. Cee Dee declares a draw
  • It’s a little too Friday to try a full run and find new bugs though. Going to work on the paper instead

D20 – Ping Zach. Made contact. Asked for a date to have the maps up or cut bait.

GOES

  • Add accuracy/loss diagram and paragraph – done
  • Finish first pass
  • Nope, All hands plus 90 minutes of required cybersecurity training. To be fair, the videos were nicely done, with a light touch and good acting.

Phil 5.28.20

GPT-2 Agents

  • Back to bug hunting today’s job is to figure out why this:
    1. Nf3 Nf6 2. g3 c5 3. Bg2 Nc6 4. O-O e5 5. e4 Nxe4 6. Re1 Nf6 7. Nxe5 Be7 8. c4
    O-O 9. Nc3 Nxe5 10. Rxe5 d6 11. Re1 Be6 12. Bxb7 Rb8 13. Bg2 Bxc4 14. d4 Be6 15.
    b3 Rb4 16. dxc5 dxc5 17. Qxd8 Rxd8 18. Ba3 Rbb8 19. Na4 Rdc8 20. Rac1 Nd7 21.
    Bd5 Bxd5 22. Rxe7 Bc6 23. Nxc5 Nxc5 24. Rxc5 a6 25. f4 h6 26. Kf2 Bb5 27. Ke3
    Rd8 28. Rcc7 Rd3+ 29. Ke4 Rd2 30. Rxf7 Re8+ 31. Kf5 Bd3+ 32. Kg4 Rxh2 33. Rxg7+
    Kh8 34. Bd6 Rf2 35. Bc5 Rd2 36. Bb4 Rc2 37. Rxc2 Kxg7 38. Rc7+ Kg6 39. Rc6+ Kf7
    40. Rxh6 Re2 41. Rd6 Re3 42. Kh4 Be2 43. g4 Rf3 44. Rd4 Rf2 45. Kg5 1-0
  • breaks the system.
  • So I never added logic to see if the path was clear for a move. The game has a move where white rook moves from e1 to e5 and then back. For the move back, the system looks for the closest rook, which is actually at a1, as the search algorithm works. But that way is actually blocked by the white bishop and white queen. It should take the clear path and discard blocked paths. I think this fix is pretty straightforward

chess

  • Wrote the test, but I’m not sure if it’s right. We’ll test tomorrow:
        def check_if_clear(self, loc:Tuple, candidate:Tuple, piece:PIECES) -> bool:
            if piece == PIECES.WHITE_PAWN or piece == PIECES.BLACK_PAWN:
                return True
            if piece == PIECES.WHITE_KNIGHT or piece == PIECES.BLACK_KNIGHT:
                return True
            if piece == PIECES.WHITE_KING or piece == PIECES.BLACK_KING:
                return True
            
            c_col_i = self.char_index.index(candidate[0])
            c_row_i = self.num_index.index(candidate[1])
            l_col_i = self.char_index.index(loc[0])
            l_row_i = self.num_index.index(loc[1])
            col_dist = l_col_i - c_col_i
            row_dist = l_row_i - c_row_i
            dist = max(abs(col_dist), abs(row_dist))
            col_vec = 0
            row_vec = 0
            if col_dist != 0:
                col_vec = col_dist/dist
            if row_dist != 0:
                row_vec = row_dist/dist
    
            col_i = l_col_i
            row_i = l_row_i
            for i in range(dist):
                num = self.num_index[row_i]
                char = self.char_index[col_i]
                pos = (char, num)
                p = self.get_piece_at(pos)
                if p != PIECES.NONE:
                    return False
                col_i += col_vec
                row_i += row_vec
    
            return True

     

GOES

  • More paper writing
    • Finished the first pass of section 2, which describes the whole model.

Phil 5.27.20

Drop off the truck today!

Agents and expensive information

  • Antonio sent a note asking if I’d be interested in contributing to a chapter. Sent him this response:
    • There is something that I’d like to explore that might fit. It’s the idea that in most environments, agents (animal, human, machine, etc.) are incentivized to cheat. I think this is because information is expensive to produce, but essentially free to copy. The problem is that if all the agents cheat, then the system will collapse because the agents become decoupled from reality (what I call a stampede). So the system as a whole is incentivized to somehow restrict cheating.
    • I think this could be very interesting to work through, but I don’t have a model (or even an approach really) developed that would describe it. I think that this might be related to game theory, though I haven’t found much in the literature.

GPT-2 Agents

  • Working on building a text corpora. Going to add a search for “Opening” and “Variation” which I’ll try before using the DB version – done
  • Having some problem that starts after a few games. Found the culprit game. Will work on tomorrow. It might be tied to a linefeed?

GOES

  • Working on the GVSETS paper and slide deck

Phil 5.11.20

Cut my hair for the second time. It looks ok from the front…

I’m also having dreams with crowds in them. Saturday night I dreamed I was at some job with a lot of people in a large building. Last night I dreamed I was sharing a dorm at the Naval Academy?

A foolproof way to shrink deep learning models

  • Train the model, prune its weakest connections, retrain the model at its fast, early training rate, and repeat, until the model is as tiny as you want. 

Graph Neural Networks (GNN)

  • Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. Unlike standard neural networks, graph neural networks retain a state that can represent information from its neighborhood with arbitrary depth.

D20

  • Zach’s having issues getting the map to work on mobile
  • Need to start pulling off controlled entities like China and Diamond Princess
  • Made a duplicate of the trending code to play with

GPT-2 Agents

  • More PGNtoEnglish
  • I have pawns and knights moving!

chessboard

  • With expanded text!
    • ‘Fred Van der Vliet moves white pawn from d2 to d4’
    • ‘Loek Van Wely moves black knight from g8 to f6’

GOES

  • Continue with NoiseGAN
  • Isolating noise. Done!

noise

  • Now I need to subsample to produce the training and test sets. Seems to be working
  • Fitting the timeseries sampling into the GAN

Noise_untrained

  • Try training the GAN?

Fika

  • Community Spaces for Interdisciplinary Science and Engagement
    • Dr. Lisa Scheifele is an Associate Professor at Loyola University Maryland and head of the Build-a-Genome research network, where her research focuses on designing and programming cells for new and complex functions. She is also Executive Director at the Baltimore Underground Science Space (BUGSS) community lab. BUGSS provides unique and creative projects to members of the public who have few other opportunities to engage with modern science. As an informal and nontraditional science space, BUGSS’ activities blend biotechnology research, computational tools, artistic expression, and design principles to accomplish interdisciplinary projects driven by community interest and need.