Category Archives: research

Phil 12.13.18

7:00 – 4:00 ASRC PhD/NASA

  • BBC Business Daily on making decisions under uncertainty. In particular, David Tuckett (Scholar), professor and director of the Centre for the Study of Decision-Making Uncertainty at University College London talks about how we reduce our sense of uncertainty by telling ourselves stories that we can then align with. This reminds me of how conspiracy theories develop, in particular the remarkable storyline of QAnon.
  • More Normal Accident review
  • NYTimes on frictionless design being a problem
  • Dungeon processing – broke out three workbooks for queries with all players, no dm, and just the dm. Also need to write up some code that generates the story on html.
  • Backprop debugging. I think it works? class_error
  • Here’s the core of the forward (train) and backpropagation (learn) code:
    def train(self):
        if self.source != None:
            src = self.source
            self.neuron_row_array = np.dot(src.neuron_row_array, src.weight_row_mat)
            if(self.target != None): # No activation function to output layer
                self.neuron_row_array = relu(self.neuron_row_array) # TODO: use passed-in activation function
            self.neuron_col_array = self.neuron_row_array.T
    
    def learn(self, alpha):
        if self.source != None:
            src = self.source
            delta_scalar = np.dot(self.delta, src.weight_col_mat)
            delta_threshold = relu2deriv(src.neuron_row_array) # TODO: use passed in derivative function
            src.delta = delta_scalar * delta_threshold
            mat = np.dot(src.neuron_col_array, self.delta)
            src.weight_row_mat += alpha * mat
            src.weight_col_mat = src.weight_row_mat.T
  • And here’s the evaluation:
  • --------------evaluation
    input: [[1. 0. 1.]] = pred: 0.983 vs. actual:[1]
    input: [[0. 1. 1.]] = pred: 0.967 vs. actual:[1]
    input: [[0. 0. 1.]] = pred: -0.020 vs. actual:[0]
    input: [[1. 1. 1.]] = pred: 0.000 vs. actual:[0]

Phil 12.12.18

7:00 – 4:30 ASRC NASA/PhD

  • Do a dungeon analytic with new posts and DM for Aaron – done!
  • Send email to Shimei for registration and meeting after grading is finished
  • Start review of Normal Accidents – started!
  • Debug NN code – in process. Very tricky figuring out the relationships between the layers in backpropagation
  • Sprint planning
  • NASA meeting
  • Talked to Zach about the tagging project. Looks good, but I wonder how much time we’ll have. Got a name though – TaggerML

Phil 12.11.18

7:00 – 4:30 ASRC PhD/NASA

mercator_projection

Somehow, this needs to get into a discussion of the trustworthiness of maps

  • I realized that we can hand-code these initial dungeons, learn a lot and make this a baseline part of the study. This means that we can compare human and machine data extraction for map making. My initial thoughts as to the sequence are:
    • Step 1: Finish running the initial dungeon
    • Step 2: researchers determine a set of common questions that would be appropriate for each room. Something like:
      • Who is the character?
      • Where is the character?
      • What is the character doing?
      • Why is the character doing this?
    • Each answer should also include a section of the text that the reader thinks answers that question. Once this has been worked out on paper, a simple survey website (simpler) can be built that automates this process and supports data collection at moderate scales.
    • Use answers to populate a “Trajectories” sheet in an xml file and build a map!
    • Step 3: Partially automate the extraction to give users a generated survey that lets them select the most likely answer/text for the who/where/what/why questions. Generate more maps!
    • Step 4: Full automation
  • Added these thoughts to the analysis section of the google doc
  • The 11th International Natural Language Generation Conference
    • The INLG conference is the main international forum for the presentation and discussion of all aspects of Natural Language Generation (NLG), including data-to-text, concept-to-text, text-to-text and vision to-text approaches. Special topics of interest for the 2018 edition included:
      • Generating Text with Affect, Style and Personality,
      • Conversational Interfaces, Chatbots and NLG, and
      • Data-driven NLG (including the E2E Generation Challenge)
  • Back to grokking DNNs
    • Still building a SimpleLayer class that will take a set of neurons and create a weight array that will point to the next layer
    • array formatting issues. Tricky
    • I think I’m done enough to start debugging. Tomorrow
  • Sprint review

Phil 12.7.18

7:00 – 4:30 ASRC NASA/PhD

Phil 12.6.18

7:00 – 4:00 ASRC PhD/NASA

  • Looks like Aaron has added two users
  • Create a “coherence” matrix, where the threshold is based on an average of one or more previous cells. The version shown below uses the tf-idf matrix as a source and checks to see if there are any non-zero values within an arbitrary span. If there are, then the target matrix (initialized with zeroes) is incremented by one on that span. This process iterates from a step of one (the default), to the specified step size. As a result, the more contiguous nonzero values are, the larger and more bell-curved the row sequences will be: spreadsheet3
  • Create a “details” sheet that has information about the database, query, parameters, etc. Done.
  • Set up a redirect so that users have to go through the IRB page if they come from outside the antibubbles site
  • It’s the End of News As We Know It (and Facebook Is Feeling Fine)
    • And as the platforms pumped headlines into your feed, they didn’t care whether the “news” was real. They didn’t want that responsibility or expense. Instead, they honed in on engagement—did you click or share, increasing value to advertisers?
      • Diversity (responsibility, expense), Stampede (engagement, share)
  • Finished Analyzing Discourse and Text Complexity for Learning and Collaborating, and created this entry for the notes.
  • Was looking at John Du Bois paper Towards a dialogic syntax, which looks really interesting, but seems like it might be more appropriate for spoken dialog. Instead, I think I’ll go to Claire Cardie‘s presentation on chat argument analysis at UMD tomorrow and see if that has better alignment.
    • Argument Mining with Structured SVMs and RNNs
      • We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.

Phil 12.5.18

7:00 – 4:30 ASRC PhD/NASA

Phil 11.29.18

7:00 – 4:30 ASRC PhD/NASA

    • Listening to repeat of America Abroad Sowing Chaos: Russia’s Disinformation Wars. My original notes are here
    • Finished World without End: The Delta Green Open Campaign Setting, by A. Scott Glancey
      • Overall, this describes the creation of the cannon of the Delta Green playspace. The goal as described was to root the work in existing fiction (Lovecraft’s Cthulhu) and historical fact. This provides the core of the space that players can move out from or fill in. Play does not produce more cannon, so it produces a trajectory that may have high influence for the actual players, but may not move beyond that. The article discusses Agent Angela, as an example of a thumbnail sketch that has become a mythical character, independent of the work of the authors with respect to Cannon. My guess is as the Agent Angela space became “stiffer” that it could also be shared more.
      • As a role-playing game, Delta Green’s narrative differs from the traditional narratives of literature, theater, and film because it offers only plot without characters to drive the story forward. It’s up to the role-players to provide the characters. Role-playing game settings are narratives not built around any specific protagonist, yet capable of accommodating multiple protagonists. Thus, role-playing games, particularly the classic paper-and-dice ones, are by their very nature vast narratives. (page 77)
      • During the designing of the Delta Green vast narrative it was decided that we would publish more open-ended source material than scenarios. Source material is usually built around an enemy of Delta Green with a particular agenda or set of goals, much like a traditional role-playing game scenario is set up, only without the framework of scenes and set pieces designed to channel the players through to a resolution of the scenario. The reason for emphasizing open ended source material over scenarios is that we were trying to encourage Keepers to design their own scenarios without pinning them down with too much canon. That is always a danger with creating a role-playing game background. You want to create a rich environment, but you don’t want to fill in so many details that there is nothing new for the players and Keepers to create with their own games. (Page 81)
      • If the players in a role-playing game campaign start to think that their characters are more disposable than the villain, they are going to feel marginalized After all, whose story is this-theirs or a non-player character’s? The fastest way to alienate a group of players is to give them the impression that they are not the center of the story. If they are not the ones driving the action forward, then what’s the point in playing a role-playing game? They might as well be watching a movie if they cannot affect the pacing, action, and outcome of a story. (Page 83)
    • Going to create a bag of words collection for post subjects and posts that are not from the DM, and then plot the use of the words over time (by sequential post). I think that once stop words are removed, that patterns might be visible.
      • Pulling out the words
      • Have the overall counts
      • Building the count mats
      • Stop words worked, needed to drop punctuation and caps
    • Yoast has an array that looks immediately usable:
      [ "a", "about", "above", "after", "again", "against", "all", "am", "an", "and", "any", "are", "as", "at", "be", "because", "been", "before", "being", "below", "between", "both", "but", "by", "could", "did", "do", "does", "doing", "down", "during", "each", "few", "for", "from", "further", "had", "has", "have", "having", "he", "he'd", "he'll", "he's", "her", "here", "here's", "hers", "herself", "him", "himself", "his", "how", "how's", "i", "i'd", "i'll", "i'm", "i've", "if", "in", "into", "is", "it", "it's", "its", "itself", "let's", "me", "more", "most", "my", "myself", "nor", "of", "on", "once", "only", "or", "other", "ought", "our", "ours", "ourselves", "out", "over", "own", "same", "she", "she'd", "she'll", "she's", "should", "so", "some", "such", "than", "that", "that's", "the", "their", "theirs", "them", "themselves", "then", "there", "there's", "these", "they", "they'd", "they'll", "they're", "they've", "this", "those", "through", "to", "too", "under", "until", "up", "very", "was", "we", "we'd", "we'll", "we're", "we've", "were", "what", "what's", "when", "when's", "where", "where's", "which", "while", "who", "who's", "whom", "why", "why's", "with", "would", "you", "you'd", "you'll", "you're", "you've", "your", "yours", "yourself", "yourselves" ]
    • Good, progress. I’m using TF-IDF to determine the importance of the term in the timeline. That’s ok, but not great. Here’s a plot: room_terms
    • You can see the three rooms, but they don’t stand out all that well. Maybe a low-pass filter on top of this? Anyway, done for the day.

 

Phil 11.28.18

7:00 – 4:00 ASRC PhD

    • Made so much progress yesterday that I’m not sure what to do next. Going to see if I can run queries against the DB in Python for a start, and then look at the Stanford tools.
      • installed pymysql (in lowercase. There is also a CamelCase version PyMySQL, that seems to be the same thing…)
      • Piece of cake! Here’s the test code:
        import pymysql
        
        class forum_reader:
            connection: pymysql.connections.Connection
        
            def __init__(self, user_name: str, user_password: str, db_name: str):
                print("initializing")
                self.connection = pymysql.connect(host='localhost', user=user_name, password=user_password, db=db_name)
        
            def read_data(self, sql_str: str) -> str:
                with self.connection.cursor() as cursor:
                    cursor.execute(sql_str)
                    result = cursor.fetchall()
                    return"{}".format(result)
        
            def close(self):
                self.connection.close()
        if __name__ == '__main__':
            fr = forum_reader("some_user", "some_pswd", "some_db")
            print(fr.read_data("select topic_id, forum_id, topic_title from phpbb_topics"))
      • And here’s the result:
        initializing
        ((4, 14, 'SUBJECT: 3 Room Linear Dungeon Test 1'),)
      • Note that this is not an object db, which I prefer, but since this is a pre-existing schema, that’s what I’ll be doing. Going to look for a way to turn a query into an object anyway. But it turns out that you can do this:
        self.connection = pymysql.connect(
            host='localhost', user=user_name, password=user_password, db=db_name,
            cursorclass=pymysql.cursors.DictCursor)
      • Which returns as an array of JSON objects:
        [{'topic_id': 4, 'forum_id': 14, 'topic_title': 'SUBJECT: 3 Room Linear Dungeon Test 1'}]
    • Built a MySQL view to get all the data back in one shot:
      CREATE or REPLACE VIEW post_view AS
      SELECT p.post_id, FROM_UNIXTIME(p.post_time) as post_time, p.topic_id, t.topic_title, t.forum_id, f.forum_name, u.username, p.poster_ip, p.post_subject, p.post_text
        FROM phpbb_posts p
        INNER JOIN phpbb.phpbb_forums f ON p.forum_id=f.forum_id
        INNER JOIN phpbb.phpbb_topics t ON p.topic_id=t.topic_id
        INNER JOIN phpbb.phpbb_users u ON p.poster_id=u.user_id;
    • And that works like a charm in the Python code:
      [{
      	'post_id': 4,
      	'post_time': datetime.datetime(2018, 11, 27, 16, 0, 27),
      	'topic_id': 4,
      	'topic_title': 'SUBJECT: 3 Room Linear Dungeon Test 1',
      	'forum_id': 14,
      	'forum_name': 'DB Test',
      	'username': 'dungeon_master1',
      	'poster_ip': '71.244.249.217',
      	'post_subject': 'SUBJECT: 3 Room Linear Dungeon Test 1',
      	'post_text': 'POST: dungeon_master1 says that you are about to take on a 3-room linear dungeon.'
      }]

       

  • Tricia Wang thick data <- add some discussion about this with respect to gathering RPG data
  • Spend some time Grokking as well. Need to nail down backpropagation. Not today
  • Long discussions with Aaron about the structure of TimeSeriesML. Including looking at FFTs for the initial analytics.
  • A2P/AIMS meeting
    • Terrabytes of AIMS data?

Progress for today 🙂 ide

Phil 11.27.18

7:00 – 5:00 ASRC PhD

  • Statistical physics of liquid brains
    • Liquid neural networks (or ”liquid brains”) are a widespread class of cognitive living networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (”solid brains”), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting to external signals.
  • Amazon is releasing a robot cloud dev environment with simulators:
    • AWS RoboMaker’s robotics simulation makes it easy to set up large-scale and parallel simulations with pre-built worlds, such as indoor rooms, retail stores, and racing tracks, so developers can test their applications on-demand and run multiple simulations in parallel. AWS RoboMaker’s fleet management integrates with AWS Greengrass and supports over-the-air (OTA) deployment of robotics applications from the development environment onto the robot. 
  • Working on script generator. Here’s the initial output:
    SUBJECT: dungeon_master1's introduction to the dungeon
    	POST: dungeon_master1 says that you are about to take on a 3-room linear dungeon.
    
    SUBJECT: dungeon_master1's introduction to room_0
    	 POST: dungeon_master1 says, The party now finds itself in room_0. There is a troll here.
    	 SUBJECT: Asra_Rogueplayer's move in room_0
    		 POST: Asra_Rogueplayer runs from the troll in room_0.
    	 SUBJECT: Ping_Clericplayer's move in room_0
    		 POST: Ping_Clericplayer walks towards the troll in room_0.
    	 SUBJECT: Valen_Fighterplayer's move in room_0
    		 POST: Valen_Fighterplayer reasons with the troll in room_0.
    	 SUBJECT: Emmi_MonkPlayer's move in room_0
    		 POST: Emmi_MonkPlayer walks towards the troll in room_0.
    	 SUBJECT: Avia_Bardplayer's move in room_0
    		 POST: Avia_Bardplayer casts a spell at the troll in room_0.
    	 SUBJECT: Mirek_Thiefplayer's move in room_0
    		 POST: Mirek_Thiefplayer casts a spell at the troll in room_0.
    	 SUBJECT: Lino_Magicplayer's move in room_0
    		 POST: Lino_Magicplayer casts a spell at the troll in room_0.
    SUBJECT: dungeon_master1's conclusion for room_0
    	 POST: dungeon_master1 says that you have triumphed in the challenge of room_0.
    
    SUBJECT: dungeon_master1's introduction to room_1
    	 POST: dungeon_master1 says, The party now finds itself in room_1. There is an idol here.
    	 SUBJECT: Asra_Rogueplayer's move in room_1
    		 POST: Asra_Rogueplayer knocks out the idol in room_1.
    	 SUBJECT: Ping_Clericplayer's move in room_1
    		 POST: Ping_Clericplayer walks towards the idol in room_1.
    	 SUBJECT: Valen_Fighterplayer's move in room_1
    		 POST: Valen_Fighterplayer casts a spell at the idol in room_1.
    	 SUBJECT: Emmi_MonkPlayer's move in room_1
    		 POST: Emmi_MonkPlayer examines the idol in room_1.
    	 SUBJECT: Avia_Bardplayer's move in room_1
    		 POST: Avia_Bardplayer sneaks by the idol in room_1.
    	 SUBJECT: Mirek_Thiefplayer's move in room_1
    		 POST: Mirek_Thiefplayer sneaks by the idol in room_1.
    	 SUBJECT: Lino_Magicplayer's move in room_1
    		 POST: Lino_Magicplayer runs from the idol in room_1.
    SUBJECT: dungeon_master1's conclusion for room_1
    	 POST: dungeon_master1 says that you have triumphed in the challenge of room_1.
    
    SUBJECT: dungeon_master1's introduction to room_2
    	 POST: dungeon_master1 says, The party now finds itself in room_2. There is an orc here.
    	 SUBJECT: Asra_Rogueplayer's move in room_2
    		 POST: Asra_Rogueplayer casts a spell at the orc in room_2.
    	 SUBJECT: Ping_Clericplayer's move in room_2
    		 POST: Ping_Clericplayer reasons with the orc in room_2.
    	 SUBJECT: Valen_Fighterplayer's move in room_2
    		 POST: Valen_Fighterplayer knocks out the orc in room_2.
    	 SUBJECT: Emmi_MonkPlayer's move in room_2
    		 POST: Emmi_MonkPlayer runs from the orc in room_2.
    	 SUBJECT: Avia_Bardplayer's move in room_2
    		 POST: Avia_Bardplayer walks towards the orc in room_2.
    	 SUBJECT: Mirek_Thiefplayer's move in room_2
    		 POST: Mirek_Thiefplayer distracts the orc in room_2.
    	 SUBJECT: Lino_Magicplayer's move in room_2
    		 POST: Lino_Magicplayer examines the orc in room_2.
    SUBJECT: dungeon_master1's conclusion for room_2
    	 POST: dungeon_master1 says that you have triumphed in the challenge of room_2.
    
    SUBJECT: dungeon_master1's conclusion
    	POST: dungeon_master1 says that you have triumphed in the challenge of the 3-room linear dungeon.
  • And here are the users. We’ll have to have multiple browsers running anonymous mode to have all these active simultaneously. users
  • Data! data.PNG

Phil 11.26.18

7:00 – 5:00ASRC PhD

  • Had a thought that simulation plus diversity might be an effective way of increasing system resilience. This is based on the discussion of Apollo 13 in Normal Accidents
  • Start folding in content from simulation papers. Don’t worry about coherence yet
  • Start figuring out PHPbb
    • Working on the IRB form – done
    • Set user creation to admin-approved – done
    • Create easily identifiable players
      • Asra Rogueplayer
      • Ping Clericplayer
      • Valen Fighterplayer
      • Emmi MonkPlayer
      • Avia Bardplayer
      • Mirek Thiefplayer
      • Lino Magicplayer
      • Daz Dmplayer
    • Some notes on play by post
    • Added Aaron as a founder. He’s set up the overall structure: dungeon
    • Add easily identifiable content. Working. Set up the AntibubblesDungeon as a python project. I’m going to write a script generator that we will then use to paste in content. Then back up and download the database and run queries on it locally.

Phil 11.23.18

8:00 – 3:00 ASRC PhD

  • A Map of Knowledge
    • Knowledge representation has gained in relevance as data from the ubiquitous digitization of behaviors amass and academia and industry seek methods to understand and reason about the information they encode. Success in this pursuit has emerged with data from natural language, where skip-grams and other linear connectionist models of distributed representation have surfaced scrutable relational structures which have also served as artifacts of anthropological interest. Natural language is, however, only a fraction of the big data deluge. Here we show that latent semantic structure, comprised of elements from digital records of our interactions, can be informed by behavioral data and that domain knowledge can be extracted from this structure through visualization and a novel mapping of the literal descriptions of elements onto this behaviorally informed representation. We use the course enrollment behaviors of 124,000 students at a public university to learn vector representations of its courses. From these behaviorally informed representations, a notable 88% of course attribute information were recovered (e.g., department and division), as well as 40% of course relationships constructed from prior domain knowledge and evaluated by analogy (e.g., Math 1B is to Math H1B as Physics 7B is to Physics H7B). To aid in interpretation of the learned structure, we create a semantic interpolation, translating course vectors to a bag-of-words of their respective catalog descriptions. We find that the representations learned from enrollments resolved course vectors to a level of semantic fidelity exceeding that of their catalog descriptions, depicting a vector space of high conceptual rationality. We end with a discussion of the possible mechanisms by which this knowledge structure may be informed and its implications for data science.
  • Set up PHP BB and see how accessible the data is.
  • Found an error in the iConf paper standalone/complex/monolithic figure. Fixed for ArXive
  • Set up the dissertation document in LaTex so that I can start putting things in it. Done! In subversion. Used the UMD template here: Thesis & Dissertation Filing, which is the same as the UMBC format listed here: Thesis & Dissertation

Phil 11.5.18

7:00- 4:30 ASRC PhD

  • Make integer generator by scaling and shifting the floating point generator to the desired values and then truncating. It would be fun to read in a token list and have the waveform be words
    • Done with the int waveform. This is an integer waveform of the function
      math.sin(xx)*math.sin(xx/2.0)*math.cos(xx/4.0)

      set on a range from 0 – 100:

    •  IntWaves
    • And here’s the unmodified floating-point version of the same function:
    • FloatWaves
    • Here’s the same function as words:
      #confg: {"function":math.sin(xx)*math.sin(xx/2.0)*math.cos(xx/4.0), "rows":100, "sequence_length":20, "step":1, "delta":0.4, "type":"floating_point"}
      routed, traps, thrashing, fifteen, ultimately, dealt, anyway, apprehensions, boats, job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, 
      traps, thrashing, fifteen, ultimately, dealt, anyway, apprehensions, boats, job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, 
      thrashing, fifteen, ultimately, dealt, anyway, apprehensions, boats, job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, 
      fifteen, ultimately, dealt, anyway, apprehensions, boats, job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, 
      ultimately, dealt, anyway, apprehensions, boats, job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, 
      dealt, anyway, apprehensions, boats, job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, 
      anyway, apprehensions, boats, job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', 
      apprehensions, boats, job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, 
      boats, job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, 
      job, descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, 
      descended, tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, 
      tongue, dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, 
      dripping, adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, 
      adoration, boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, 
      boats, routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, guarded, 
      routed, routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, guarded, tempers, 
      routed, strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, guarded, tempers, partnership, 
      strokes, cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, guarded, tempers, partnership, bare, 
      cheerful, charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, guarded, tempers, partnership, bare, count, 
      charleses, travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, guarded, tempers, partnership, bare, count, descended, 
      travellers, unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, guarded, tempers, partnership, bare, count, descended, dashed, 
      unsuspected, malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, guarded, tempers, partnership, bare, count, descended, dashed, ears, 
      malingerer, respect, aback, vair', wraith, bare, creek, descended, assortment, flashed, reputation, guarded, tempers, partnership, bare, count, descended, dashed, ears, q, 
      

       

  • Started LSTMs again, using this example using Alice in Wonderland
  • Aaron and T in all day discussions with Kevin about NASA/NOAA. Dropped in a few times. NASA is airgapped, but you can bring code in and out. Bringing code in requires a review.
  • Call the Army BAA people. We need white paper templates and a response for Dr. Palazzolo.
  • Finish and submit 810 reviews tonight. Done.
  • This is important for the DARPA and Army BAAs: The geographic embedding of online echo chambers: Evidence from the Brexit campaign
    • This study explores the geographic dependencies of echo-chamber communication on Twitter during the Brexit campaign. We review the evidence positing that online interactions lead to filter bubbles to test whether echo chambers are restricted to online patterns of interaction or are associated with physical, in-person interaction. We identify the location of users, estimate their partisan affiliation, and finally calculate the distance between sender and receiver of @-mentions and retweets. We show that polarized online echo-chambers map onto geographically situated social networks. More specifically, our results reveal that echo chambers in the Leave campaign are associated with geographic proximity and that the reverse relationship holds true for the Remain campaign. The study concludes with a discussion of primary and secondary effects arising from the interaction between existing physical ties and online interactions and argues that the collapsing of distances brought by internet technologies may foreground the role of geography within one’s social network.
  • Also important:
    • How to Write a Successful Level I DHAG Proposal
      • The idea behind a Level I project is that it can be “high risk/high reward.” Put another way, we are looking for interesting, innovative, experimental, new ideas, even if they have a high potential to fail. It’s an opportunity to figure things out so you are better prepared to tackle a big project. Because of the relatively low dollar amount (no more than $50K), we are willing to take on more risk for an idea with lots of potential. By contrast, at the Level II and especially at the Level III, there is a much lower risk tolerance; the peer reviewers expect that you’ve already completed an earlier start-up or prototyping phase and will want you to convince them your project is ready to succeed.
  • Tracing a Meme From the Internet’s Fringe to a Republican Slogan
    • This feedback loop is how #JobsNotMobs came to be. In less than two weeks, the three-word phrase expanded from corners of the right-wing internet onto some of the most prominent political stages in the country, days before the midterm elections.
  • Effectiveness of gaming for communicating and teaching climate change
    • Games are increasingly proposed as an innovative way to convey scientific insights on the climate-economic system to students, non-experts, and the wider public. Yet, it is not clear if games can meet such expectations. We present quantitative evidence on the effectiveness of a simulation game for communicating and teaching international climate politics. We use a sample of over 200 students from Germany playing the simulation game KEEP COOL. We combine pre- and postgame surveys on climate politics with data on individual in-game decisions. Our key findings are that gaming increases the sense of personal responsibility, the confidence in politics for climate change mitigation, and makes more optimistic about international cooperation in climate politics. Furthermore, players that do cooperate less in the game become more optimistic about international cooperation but less confident about politics. These results are relevant for the design of future games, showing that effective climate games do not require climate-friendly in-game behavior as a winning condition. We conclude that simulation games can facilitate experiential learning about the difficulties of international climate politics and thereby complement both conventional communication and teaching methods.
    • This reinforces the my recent thinking that games may be a fourth, distinct form of human sociocultural communication

Phil 11.1.18

7:00 – 4:30 ASRC PhD

  • Quick thought. Stampedes may be recognized not just from low variance (density of connections), but also the speed that a new term moves into the lexicon (stiffness)
  • The Junk News Aggregator, the Visual Junk News Aggregator and the Top 10 Junk News Aggregator are research projects of the Computational Propaganda group (COMPROP) of the Oxford Internet Institute (OII)at the University of Oxford.These aggregators are intended as tools to help researchers, journalists, and the public see what English language junk news stories are being shared and engaged with on Facebook, ahead of the 2018 US midterm elections on November 6, 2018.The aggregators show junk news posts along with how many reactions they received, for all eight types of post reactions available on Facebook, namely: Likes, Comments, Shares, and the five emoji reactions: Love, Haha, Wow, Angry, and Sad.
  • Reading Charles Perrow’s Normal Accidents. Riveting. All about dense, tightly connected networks with hidden information
    • From The Montreal Review
      • Normal Accident drew attention to two different forms of organizational structure that Herbert Simon had pointed to years before, vertical integration, and what we now call modularity. Examining risky systems in the Accident book, I focused upon the unexpected interactions of different parts of the system that no designer could have expected and no operator comprehend or be able to interdict. Reading Charles Perrow’s Normal Accidents. Riveting. All about dense, tightly connected networks with hidden information
  • Building generators.
    • Need to change the “stepsize” in the Torrance generator to be variable – done. Here’s my little ode to The Shining:
      #confg: {"rows":100, "sequence_length":26, "step":26, "type":"words"}
      all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work 
      and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a 
      dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no 
      play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy 
      all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work 
      and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a 
      dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no 
      play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy 
      all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work 
      and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a 
      
    • Need to be able to turn out a numeric equivalent. Done with floating point. This:
      #confg: {"function":math.sin(xx)*math.sin(xx/2.0)*math.cos(xx/4.0), "rows":100, "sequence_length":20, "step":1, "delta":0.4, "type":"floating_point"}
      0.0,0.07697897630719268,0.27378318599563484,0.5027638400821064,0.6604469814238397,0.6714800165989514,0.519596709539434,0.2524851001382131,-0.04065231596017931,-0.2678812526747579,-0.37181365763470914,-0.34898182120310306,-0.24382057359778858,-0.12182487479311599,-0.035942415169752356,-0.0027892469005274916,0.00019865778200507415,0.016268713740310237,0.07979661440830532,0.19146155036709192,
      0.07697897630719312,0.2737831859956355,0.5027638400821071,0.6604469814238401,0.6714800165989512,0.5195967095394334,0.2524851001382121,-0.04065231596018022,-0.26788125267475843,-0.37181365763470925,-0.3489818212031028,-0.24382057359778805,-0.12182487479311552,-0.0359424151697521,-0.0027892469005274395,0.0001986577820050832,0.016268713740310397,0.07979661440830574,0.19146155036709248,0.31158944024296154,
      0.2737831859956368,0.502763840082108,0.6604469814238405,0.6714800165989508,0.5195967095394324,0.25248510013821085,-0.04065231596018143,-0.2678812526747592,-0.37181365763470936,-0.34898182120310245,-0.24382057359778747,-0.12182487479311502,-0.03594241516975184,-0.002789246900527388,0.00019865778200509222,0.01626871374031056,0.07979661440830614,0.191461550367093,0.311589440242962,0.3760334615921674,
      0.5027638400821092,0.6604469814238411,0.6714800165989505,0.5195967095394312,0.25248510013820913,-0.040652315960182955,-0.26788125267476015,-0.37181365763470964,-0.348981821203102,-0.24382057359778667,-0.12182487479311428,-0.03594241516975145,-0.0027892469005273107,0.00019865778200510578,0.016268713740310803,0.07979661440830675,0.1914615503670939,0.3115894402429629,0.3760334615921675,0.3275646734005755,
      0.660446981423842,0.6714800165989498,0.5195967095394289,0.2524851001382062,-0.04065231596018568,-0.2678812526747618,-0.37181365763471,-0.34898182120310123,-0.24382057359778553,-0.1218248747931133,-0.03594241516975093,-0.0027892469005272066,0.00019865778200512388,0.016268713740311122,0.07979661440830756,0.19146155036709495,0.31158944024296387,0.3760334615921676,0.3275646734005745,0.1475692800414062,
      0.671480016598949,0.5195967095394267,0.25248510013820324,-0.04065231596018842,-0.2678812526747636,-0.3718136576347104,-0.34898182120310045,-0.24382057359778414,-0.12182487479311209,-0.03594241516975028,-0.002789246900527077,0.0001986577820051465,0.016268713740311528,0.07979661440830856,0.19146155036709636,0.3115894402429648,0.37603346159216783,0.32756467340057344,0.1475692800414041,-0.12805444308254293,
      0.5195967095394245,0.2524851001382003,-0.04065231596019116,-0.2678812526747653,-0.3718136576347107,-0.3489818212030998,-0.24382057359778303,-0.12182487479311109,-0.03594241516974975,-0.0027892469005269733,0.00019865778200516457,0.016268713740311847,0.07979661440830936,0.19146155036709747,0.3115894402429657,0.37603346159216794,0.32756467340057244,0.147569280041402,-0.1280544430825456,-0.41793663502550105,
      0.2524851001381973,-0.04065231596019389,-0.26788125267476703,-0.3718136576347111,-0.3489818212030989,-0.2438205735977817,-0.12182487479310988,-0.0359424151697491,-0.002789246900526843,0.00019865778200518717,0.01626871374031225,0.07979661440831039,0.1914615503670989,0.3115894402429671,0.3760334615921681,0.3275646734005709,0.14756928004139883,-0.1280544430825496,-0.41793663502550454,-0.6266831461371138,
      
    • Gives this: Waves
    • Need to write a generator that reads in text (words and characters) and produces data tables with stepsizes
    • Need to write a generator that takes an equation as a waveform
  • USPTO Meeting. Use NN to produce multiple centrality / laplacians that user interact with
  • Working on my 810 tasks
    • Potentially useful for mapmaking: Learning the Preferences of Ignorant, Inconsistent Agents
      • An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people’s past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.
    • An Overview of the Schwartz Theory of Basic Values (Added to normative map making) Schwartz
      • This article presents an overview of the Schwartz theory of basic human values. It discusses the nature of values and spells out the features that are common to all values and what distinguishes one value from another. The theory identifies ten basic personal values that are recognized across cultures and explains where they come from. At the heart of the theory is the idea that values form a circular structure that reflects the motivations each value expresses. This circular structure, that captures the conflicts and compatibility among the ten values is apparently culturally universal. The article elucidates the psychological principles that give rise to it. Next, it presents the two major methods developed to measure the basic values, the Schwartz Value Survey and the Portrait Values Questionnaire. Findings from 82 countries, based on these and other methods, provide evidence for the validity of the theory across cultures. The findings reveal substantial differences in the value priorities of individuals. Surprisingly, however, the average value priorities of most societal groups exhibit a similar hierarchical order whose existence the article explains. The last section of the article clarifies how values differ from other concepts used to explain behavior—attitudes, beliefs, norms, and traits.

Phil 10.30.18

7:00 – 3:30 ASRC PhD

  • Search as embodies in the “Ten Blue Links” meets the requirements of a Parrow “Normal Accident”
    • The search results are densely connected. That’s how PageRank works. Even latent connections matter.
    • The change in popularity of a page rapidly affects the rank. So the connections are stiff
    • The relationships of the returned links both to each other and to the broader information landscape in general is hidden.
    • An additional density and stiffness issue is that everyone uses Google, so there is a dense, stiff connection between the search engine and the population of users
  • Write up something about how
    • ML can make maps, which decrease the likelihood of IR contributing to normal accidents
    • AI can use these maps to understand the shape of human belief space, and where the positive regions and dangerous sinks are.
  • Two measures for maps are the concepts or Range and length. Range is the distance that a trajectory can be placed on the map and remain contiguous. Length is the total distance that a trajectory travels, independent of the map its placed on.
  • Write up the basic algorithm of ML to map production
    • Take a set of trajectories that are known to be in the same belief region (why JuryRoom is needed) as the input
    • Generate an N-dimensional coordinate frame that best preserves length over the greatest range.
    • What is used as the basis for the trajectory may matter. The range (at a minimum), can go from letters to high-level topics. I think any map reconstruction based on letters would be a tangle, with clumps around TH, ER, ON, and AN. At the other end, an all-encompassing meta-topic, like WORDS would be a single, accurate, but useless single point. So the map reconstruction will become possible somewhere between these two extremes.
  • The Nietzsche text is pretty good. In particular, check out the way the sentences form based on the seed  “s when one is being cursed.
    • the fact that the spirit of the spirit of the body and still the stands of the world
    • the fact that the last is a prostion of the conceal the investion, there is our grust
    • the fact them strongests! it is incoke when it is liuderan of human particiay
    • the fact that she could as eudop bkems to overcore and dogmofuld
    • In this case, the first 2-3 words are the same, and random, semi-structured text. That’s promising, since the compare would be on the seed plus the generated text.
  • Today, see how fast a “Shining” (All work and no play makes Jack a dull boy.) text can be learned and then try each keyword as a start. As we move through the sentence, the probability of the next words should change.
    • Generate the text set
    • Train the Nietzsche model on the new text. Done. Here are examples with one epoch and a batch size of 32, with a temperature of 1.0:
      ----- diversity: 0.2
      ----- Generating with seed: "es jack a 
      dull boy all work and no play"
      es jack a 
      dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      
      ----- diversity: 0.5
      ----- Generating with seed: "es jack a 
      dull boy all work and no play"
      es jack a 
      dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      
      ----- diversity: 1.0
      ----- Generating with seed: "es jack a 
      dull boy all work and no play"
      es jack a 
      dull boy all work and no play makes jack a dull boy anl wory and no play makes jand no play makes jack a dull boy all work and no play makes jack a 
      
      ----- diversity: 1.2
      ----- Generating with seed: "es jack a 
      dull boy all work and no play"
      es jack a 
      dull boy all work and no play makes jack a pull boy all work and no play makes jack andull boy all work and no play makes jack a dull work and no play makes jack andull

      Note that the errors start with a temperature of 1.0 or greater

    • Rewrite the last part of the code to generate text based on each word in the sentence.
      • So I tried that and got gobbledygook. The issues is that the prediction only works on waveform-sized chunks. To verify this, I created a seed from the input text, truncating it to maxlen (20 in this case):
        sentence = "all work and no play makes jack a dull boy"[:maxlen]

        That worked, but it means that the character-based approach isn’t going to work

        ----- temperature: 0.2
        ----- Generating with seed: [all work and no play]
        all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
        
        ----- temperature: 0.5
        ----- Generating with seed: [all work and no play]
        all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
        
        ----- temperature: 1.0
        ----- Generating with seed: [all work and no play]
        all work and no play makes jack a dull boy all work and no play makes jack a dull boy pllwwork wnd no play makes 
        
        ----- temperature: 1.2
        ----- Generating with seed: [all work and no play]
        all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes

         

    • Based on this result and the ensuing chat with Aaron, we’re going to revisit the whole LSTM with numbers and build out a process that will support words instead of characters.
  • Looking for CMAC models, I found Self Organizing Feature Maps at NeuPy.com:
  • Here’s How Much Bots Drive Conversation During News Events
    • Late last week, about 60 percent of the conversation was driven by likely bots. Over the weekend, even as the conversation about the caravan was overshadowed by more recent tragedies, bots were still driving nearly 40 percent of the caravan conversation on Twitter. That’s according to an assessment by Robhat Labs, a startup founded by two UC Berkeley students that builds tools to detect bots online. The team’s first product, a Chrome extension called BotCheck.me, allows users to see which accounts in their Twitter timelines are most likely bots. Now it’s launching a new tool aimed at news organizations called FactCheck.me, which allows journalists to see how much bot activity there is across an entire topic or hashtag