# Phil 8.12.18

7:00 – 4:00 ASRC MKT

• Having an interesting chat on recommenders with Robin Berjon on Twitter
• Long, but looks really good Neural Processes as distributions over functions
• Neural Processes (NPs) caught my attention as they essentially are a neural network (NN) based probabilistic model which can represent a distribution over stochastic processes. So NPs combine elements from two worlds:
• Deep Learning – neural networks are flexible non-linear functions which are straightforward to train
• Gaussian Processes – GPs offer a probabilistic framework for learning a distribution over a wide class of non-linear functions

Both have their advantages and drawbacks. In the limited data regime, GPs are preferable due to their probabilistic nature and ability to capture uncertainty. This differs from (non-Bayesian) neural networks which represent a single function rather than a distribution over functions. However the latter might be preferable in the presence of large amounts of data as training NNs is computationally much more scalable than inference for GPs. Neural Processes aim to combine the best of these two worlds.

• How The Internet Talks (Well, the mostly young and mostly male users of Reddit, anyway)
• To get a sense of the language used on Reddit, we parsed every comment since late 2007 and built the tool above, which enables you to search for a word or phrase to see how its popularity has changed over time. We’ve updated the tool to include all comments through the end of July 2017.
• Fix the DTW emergent population chart on the poster and in the slides. Print!
• Set up the LaTex Army BAA framework
• Slide walkthough. Good timing. Working on the poster some more

# Phil 6.6.18

7:00 – 4:30 ASRC MKT

• Finished the white paper
• Peer review of Dr. Li’s AIMS work
• Computational Propaganda in the United States of America: Manufacturing Consensus Online
• Do bots have the capacity to influence the flow of political information over social media? This working paper answers this question through two methodological avenues: A) a qualitative analysis of how political bots were used to support United States presidential candidates and campaigns during the 2016 election, and B) a network analysis of bot influence on Twitter during the same event. Political bots are automated software programs that operate on social media, written to mimic real people in order to manipulate public opinion. The qualitative findings are based upon nine months of fieldwork on the campaign trail, including interviews with bot makers, digital campaign strategists, security consultants, campaign staff, and party officials. During the 2016 campaign, a bipartisan range of domestic and international political actors made use of political bots. The Republican Party, including both self-proclaimed members of the “alt-right” and mainstream members, made particular use of these digital political tools throughout the election. Meanwhile, public conversation from campaigners and government representatives is inconsistent about the political influence of bots. This working paper provides ethnographic evidence that bots affect information flows in two key ways: 1) by “manufacturing consensus,” or giving the illusion of significant online popularity in order to build real political support, and 2) by democratizing propaganda through enabling nearly anyone to amplify online interactions for partisan ends. We supplement these findings with a quantitative network analysis of the influence bots achieved within retweet networks of over 17 million tweets, collected during the 2016 US election. The results of this analysis confirm that bots reached positions of measurable influence during the 2016 US election. Ultimately, therefore, we find that bots did affect the flow of information during this particular event. This mixed methods approach shows that bots are not only emerging as a widely-accepted tool of computational propaganda used by campaigners and citizens, but also that bots can influence political processes of global significance.

# Phil 6.5.18

7:00 – 6:00 ASRC

• Read the SASO comments. Most are pretty good. My reviewer #2 was #3 this time. There is some rework that’s needed. Most of the comments are good, even the angry ones from #3, which are mostly “where is particle swarm optimization???”
• Got an example quad chart from Helena that I’m going to base mine on
• Neat thing from Brian F:
• Lots. Of. White. Paper.

# Phil 6.4.18

7:00 – 4:00 ASRC MKT

• Got accepted to SASO!
• Listening to a show about energy in bitcoin mining. There are ramifications to AI, since that’s also expensive processing.
• Thinking about the ramifications of ‘defect always’ emerging in a society.
• More Bit by Bit
• Fika

# Phil 6.1.18

7:00 – 6:00 ASRC MKT

• Bot stampede reaction to “evolution” in a thread about UNIX. This is in this case posting scentiment against the wrong thing. There are layers here though. It can also be advertising. Sort of the dark side of diversity injection.
• Seems like an explore/exploit morning
• Autism on “The Leap”: Neurotypical and Neurodivergent (Neurodiversity)
• From a BBC Business Daily show on Elon Musk
• Thomas Astebro (Decision Science): The return to independent invention: evidence of unrealistic optimism, risk seeking or skewness loving?
• Examining a sample of 1,091 inventions I investigate the magnitude and distribution of the pre‐tax internal rate of return (IRR) to inventive activity. The average IRR on a portfolio investment in these inventions is 11.4%. This is higher than the risk‐free rate but lower than the long‐run return on high‐risk securities and the long‐run return on early‐stage venture capital funds. The portfolio IRR is significantly higher, for some ex anteidentifiable classes of inventions. The distribution of return is skew: only between 7‐9% reach the market. Of the 75 inventions that did, six realised returns above 1400%, 60% obtained negative returns and the median was negative.
• Myth of first mover advantage
• Conventional wisdom would have us believe that it is always beneficial to be first – first in, first to market, first in class. The popular business literature is full of support for being first and legions of would-be business leaders, steeped in the Jack Welch school of business strategy, will argue this to be the case. The advantages accorded to those who are first to market defines the concept of First Mover Advantage (FMA). We outline why this is not the case, and in fact, that there are conditions of applicability in order for FMA to hold (and these conditions often do not hold). We also show that while there can be advantages to being first, from an economic perspective, the costs generally exceed the benefits, and the full economics of FMA are usually a losing proposition. Finally, we show that increasingly, we live in a world where FMA is eclipsed by innovation and format change, rendering the FMA concept obsolete (i.e. strategic obsolescence).
• More Bit by Bit
• Investigating the Effects of Google’s Search Engine Result Page in Evaluating the Credibility of Online News Sources
• Recent research has suggested that young users are not particularly skilled in assessing the credibility of online content. A follow up study comparing students to fact checkers noticed that students spend too much time on the page itself, while fact checkers performed “lateral reading”, searching other sources. We have taken this line of research one step further and designed a study in which participants were instructed to do lateral reading for credibility assessment by inspecting Google’s search engine result page (SERP) of unfamiliar news sources. In this paper, we summarize findings from interviews with 30 participants. A component of the SERP noticed regularly by the participants is the so-called Knowledge Panel, which provides contextual information about the news source being searched. While this is expected, there are other parts of the SERP that participants use to assess the credibility of the source, for example, the freshness of top stories, the panel of recent tweets, or a verified Twitter account. Given the importance attached to the presence of the Knowledge Panel, we discuss how variability in its content affected participants’ opinions. Additionally, we perform data collection of the SERP page for a large number of online news sources and compare them. Our results indicate that there are widespread inconsistencies in the coverage and quality of information included in Knowledge Panels.
• White paper
• Note that belief maps are cultural artifacts, so comparing someone from one belief space to others in a shared physical belief environment can be roughly equivalent to taking the dot product of the belief space vectors that you need to compare. This could produce a global “alignment map” that can suggest how aligned, opposed, or indifferent a population might be with respect to an intervention, ranging from medical (Ebola teams) to military (special forces operations).
• Similar maps related to wealth in Rwanda based on phone metadata: Blumenstock, Joshua E., Gabriel Cadamuro, and Robert On. 2015. “Predicting Poverty and Wealth from Mobile Phone Metadata.” Science350 (6264):1073–6. https://doi.org/10.1126/science.aac4420
• Added a section about how mapping belief maps would afford prediction about local belief, since overall state, orientation and velocity could be found for some individuals who are geolocated to that area and then extrapolated over the region.

# Phil 5.31.18

7:00 – ASRC MKT

• Via BBC Business Daily, found this interesting post on diversity injection through lunch table size:
• KQED is playing America Abroad – today on russian disinfo ops:
• Sowing Chaos: Russia’s Disinformation Wars
• Revelations of Russian meddling in the 2016 US presidential election were a shock to Americans. But it wasn’t quite as surprising to people in former Soviet states and the EU. For years they’ve been exposed to Russian disinformation and slanted state media; before that Soviet propaganda filtered into the mainstream. We don’t know how effective Russian information warfare was in swaying the US election. But we do know these tactics have roots going back decades and will most likely be used for years to come. This hour, we’ll hear stories of Russian disinformation and attempts to sow chaos in Europe and the United States. We’ll learn how Russia uses its state-run media to give a platform to conspiracy theorists and how it invites viewers to doubt the accuracy of other news outlets. And we’ll look at the evolution of internet trolling from individuals to large troll farms. And — finally — what can be done to counter all this?
• Some interesting papers on the “Naming Game“, a form of coordination where individuals have to agree on a name for something. This means that there is some kind of dimension reduction involved from all the naming possibilities to the agreed-on name.
• The Grounded Colour Naming Game
• Colour naming games are idealised communicative interactions within a population of artificial agents in which a speaker uses a single colour term to draw the attention of a hearer to a particular object in a shared context. Through a series of such games, a colour lexicon can be developed that is sufficiently shared to allow for successful communication, even when the agents start out without any predefined categories. In previous models of colour naming games, the shared context was typically artificially generated from a set of colour stimuli and both agents in the interaction perceive this environment in an identical way. In this paper, we investigate the dynamics of the colour naming game in a robotic setup in which humanoid robots perceive a set of colourful objects from their own perspective. We compare the resulting colour ontologies to those found in human languages and show how these ontologies reflect the environment in which they were developed.
• Group-size Regulation in Self-Organised Aggregation through the Naming Game
• In this paper, we study the interaction effect between the naming game and one of the simplest, yet most important collective behaviour studied in swarm robotics: self-organised aggregation. This collective behaviour can be seen as the building blocks for many others, as it is required in order to gather robots, unable to sense their global position, at a single location. Achieving this collective behaviour is particularly challenging, especially in environments without landmarks. Here, we augment a classical aggregation algorithm with a naming game model. Experiments reveal that this combination extends the capabilities of the naming game as well as of aggregation: It allows the emergence of more than one word, and allows aggregation to form a controllable number of groups. These results are very promising in the context of collective exploration, as it allows robots to divide the environment in different portions and at the same time give a name to each portion, which can be used for more advanced subsequent collective behaviours.
• More Bit by Bit. Could use some worked examples. Also a login so I’m not nagged to buy a book I own.
• Descriptive and injunctive norms – The transsituational influence of social norms.
• Three studies examined the behavioral implications of a conceptual distinction between 2 types of social norms: descriptive norms, which specify what is typically done in a given setting, and injunctive norms, which specify what is typically approved in society. Using the social norm against littering, injunctive norm salience procedures were more robust in their behavioral impact across situations than were descriptive norm salience procedures. Focusing Ss on the injunctive norm suppressed littering regardless of whether the environment was clean or littered (Study 1) and regardless of whether the environment in which Ss could litter was the same as or different from that in which the norm was evoked (Studies 2 and 3). The impact of focusing Ss on the descriptive norm was much less general. Conceptual implications for a focus theory of normative conduct are discussed along with practical implications for increasing socially desirable behavior.
• Construct validity centers around the match between the data and the theoretical constructs. As discussed in chapter 2, constructs are abstract concepts that social scientists reason about. Unfortunately, these abstract concepts don’t always have clear definitions and measurements.
• Simulation is a way of implementing theoretical constructs that are measurable and testable.
• Hyperparameter Optimization with Keras
• Recognizing images from parts Kaggle winner
• White paper
• Storyboard meeting
• The advanced analytics division(?) needs a modeling and simulation department that builds models that feed ML systems.
• Meeting with Steve Specht – adding geospatial to white paper

# Phil 5.30.18

7:15 – 6:00 ASRC MKT

• More Bit by Bit
• An interesting tweet about the dichotomy between individual and herd behaviors.
• More white paper. Add something about awareness horizon, and how maps change that from a personal to a shared reality (cite understanding ignorance?)
• Great discussion with Aaron about incorporating adversarial herding. I think that there will be three areas
• Thunderdome – affords adversarial herding. Users have to state their intent before joining a discussion group. Bots and sock puppets allowed
• Clubhouse – affords discussion with chosen individuals. THis is what I thought JuryRoom was
• JuryRoom – fully randomized members and topics, based on activity in the Clubhouse and Thunderdome

# Phil 5.29.18

Insane, catastrophic rain this weekend. That’s the top of a guardrail in the middle of the scene below:

7:00 – 4:30 ASRC MKT

• The Neural Representation of Social Networks
• The computational demands associated with navigating large, complexly bonded social groups are thought to have significantly shaped human brain evolution. Yet, research on social network representation and cognitive neuroscience have progressed largely independently. Thus, little is known about how the human brain encodes the structure of the social networks in which it is embedded. This review highlights recent work seeking to bridge this gap in understanding. While the majority of research linking social network analysis and neuroimaging has focused on relating neuroanatomy to social network size, researchers have begun to define the neural architecture that encodes social network structure, cognitive and behavioral consequences of encoding this information, and individual differences in how people represent the structure of their social world.
• This website is amazing, linear algebra with interactive examples. Vectors, matrix, dot product, etc, cool resource for learning
• Web Literacy for Student Fact-Checkers: …and other people who care about facts.
• Author: Mike Caulfield
• We Should Put Fact-Checking Tools In the Core Browser
• Years ago when the web was young, Netscape (Google it, noobs!) decided on its metaphor for the browser: it was a “navigator”. <—— this!!!!
• Navigator: a person who directs the route or course of a ship, aircraft, or other form of transportation, especially by using instruments and maps.
• Browser: a person who looks casually through books or magazines or at things for sale.
• Deep Learning Hunts for Signals Among the Noise
• Interesting article that indicates that deep learning generalizes through some form of compression. If that’s true, then the teurons and layers are learning how to coordinate (who recognizes what), which means dimension reduction and localized alignment (what are the features that make a person vs. a ship). Hmmm.
• More Bit by Bit
• Really enjoying Casualties of Cool, btw. Lovely sound layering. Reminds me of Dark Side of the Moon / Wish you were here Pink Floyd
• Why you need to improve your training data, and how to do it
• No scrum today
• Travel briefing – charge to conference code
• Complexity Explorables
• Ride my Kuramotocycle!
• This explorable illustrates the Kuramoto model for phase coupled oscillators. This model is used to describe synchronization phenomena in natural systems, e.g. the flash synchronization of fire flies or wall-mounted clocks. The model is defined as a system of NN oscillators. Each oscillator has a phase variable θn(t)θn(t) (illustrated by the angular position on a circle below), and an angular frequency ωnωn that captures how fast the oscillator moves around the circle.
• Into the Dark
• This explorable illustrates how a school of fish can collectively find an optimal location, e.g. a dark, unexposed region in their environment simply by light-dependent speed control. The explorable is based on the model discussed in Flock’n Roll, which you may want to explore first. This is how it works: The swarm here consists of 100 individuals. Each individual moves around at a constant speed and changes direction according to three rules
• More cool software: Kepler.gl is a powerful open source geospatial analysis tool for large-scale data sets.
• White paper. Good progress! I like the conclusions

# Phil 5.25.18

7:00 – 4:30 ASRC MKT

• Reading more Bit by Bit. At the end of chapter two, Salganik mentions inference graphs, which made me think of Markov Chains which led me to Judea Pearl. Interesting morning.
• Sprint review
• More white paper writing

# Phil 5.24.18

7:00 ASRC

• Is Bitcoin alive? Local organization and global entropy:
• Tweaked my terms page a bit
• Continuing Bit by Bit. Nicely written. currently reading about the pros and cons of using big data. It’s making me think about how to structure the Jury Room data so that it lends itself better to prolonged research.
• A gentle introduction to Doc2Vec
• In this post you will learn what is doc2vec, how it’s built, how it’s related to word2vec, what can you do with it, hopefully with no mathematic formulas.
• The combination of tags and paragraph/document ID could make this very nice for JuryRoom
• 1:30 CoE meeting
• 2:00 Meeting with Anton

# Phil 5.25.18

7:00 – 6:00 ASRC MKT

• Starting Bit by Bit
• I realized the hook for the white paper is the military importance of maps. I found A Revolution in Military Cartography?: Europe 1650-1815
• Military cartography is studied in order to approach the role of information in war. This serves as an opportunity to reconsider the Military Revolution and in particular changes in the eighteenth century. Mapping is approached not only in tactical, operational and strategic terms, but also with reference to the mapping of war for public interest. Shifts in the latter reflect changes in the geography of European conflict.
• Reconnoitering sketch from Instructions in the duties of cavalry reconnoitring an enemy; marches; outposts; and reconnaissance of a country; for the use of military cavalry. 1876 (pg 83)
• rutter is a mariner’s handbook of written sailing directions. Before the advent of nautical charts, rutters were the primary store of geographic information for maritime navigation.
• It was known as a periplus (“sailing-around” book) in classical antiquity and a portolano (“port book”) to medieval Italian sailors in the Mediterranean Sea. Portuguese navigators of the 16th century called it a roteiro, the French a routier, from which the English word “rutter” is derived. In Dutch, it was called a leeskarte (“reading chart”), in German a Seebuch (“sea book”), and in Spanish a derroterro
• Example from ancient Greece:
• From the mouth of the Ister called Psilon to the second mouth is sixty stadia.
• Thence to the mouth called Calon forty stadia.
• From Calon to Naracum, which last is the name of the fourth mouth of the Ister, sixty stadia.
• Hence to the fifth mouth a hundred and twenty stadia.
• Hence to the city of Istria five hundred stadia.
• From Istria to the city of Tomea three hundred stadia.
• From Tomea to the city of Callantra, where there is a port, three hundred stadia
• Battlespace
• Cyber-Human Systems (CHS)
• In a world in which computers and networks are increasingly ubiquitous, computing, information, and computation play a central role in how humans work, learn, live, discover, and communicate. Technology is increasingly embedded throughout society, and is becoming commonplace in almost everything we do. The boundaries between humans and technology are shrinking to the point where socio-technical systems are becoming natural extensions to our human experience – second nature, helping us, caring for us, and enhancing us. As a result, computing technologies and human lives, organizations, and societies are co-evolving, transforming each other in the process. Cyber-Human Systems (CHS) research explores potentially transformative and disruptive ideas, novel theories, and technological innovations in computer and information science that accelerate both the creation and understanding of the complex and increasingly coupled relationships between humans and technology with the broad goal of advancing human capabilities: perceptual and cognitive, physical and virtual, social and societal.
• Reworked Section 1 to incorporate all this in a single paragraph
• Long discussion about all of the above with Aaron
• Worked on getting the CoE together by CoB
• Do Diffusion Protocols Govern Cascade Growth?
• Continuing with creating the Simplest LSTM ever
• All work and no play makes jack a dull boy indexes alphabetically as :

# Phil 5.22.18

8:00 – 5:00 ASRC MKT

• EAMS meeting
• Rational
• Sensitivity knn. Marching cubes, or write into space. Pos lat/lon altitude speed lat lon (4 dimensions)
• Do they have flight path?
• Memory
• Retraining (batch)
• inference real time
• How will time be used
• Much discussion of simulation
• End-to-end Machine Learning with Tensorflow on GCP
• In this workshop, we walk through the process of building a complete machine learning pipeline covering ingest, exploration, training, evaluation, deployment, and prediction. Along the way, we will discuss how to explore and split large data sets correctly using BigQuery and Cloud Datalab. The machine learning model in TensorFlow will be developed on a small sample locally. The preprocessing operations will be implemented in Cloud Dataflow, so that the same preprocessing can be applied in streaming mode as well. The training of the model will then be distributed and scaled out on Cloud ML Engine. The trained model will be deployed as a microservice and predictions invoked from a web application. This lab consists of 7 parts and will take you about 3 hours. It goes along with this slide deck
• Slides
• Codelab
• Added in JuryRoom Text rough. Next is Research Browser
• Worked with Aaron on LSTM some more. More ndarray slicing experience:
import numpy as np
dimension = 3
size = 10
dataset1 = np.ndarray(shape=(size, dimension))
dataset2 = np.ndarray(shape=(size, dimension))
for x in range(size):
for y in range(dimension):
val = (y+1) * 10 + x +1
dataset1[x,y] = val
val = (y+1) * 100 + x +1
dataset2[x,y] = val

dataset1[:, 0:1] = dataset2[:, -1:]
print(dataset1)
print(dataset2)
• Results in:
[[301.  21.  31.]
[302.  22.  32.]
[303.  23.  33.]
[304.  24.  34.]
[305.  25.  35.]
[306.  26.  36.]
[307.  27.  37.]
[308.  28.  38.]
[309.  29.  39.]
[310.  30.  40.]]
[[101. 201. 301.]
[102. 202. 302.]
[103. 203. 303.]
[104. 204. 304.]
[105. 205. 305.]
[106. 206. 306.]
[107. 207. 307.]
[108. 208. 308.]
[109. 209. 309.]
[110. 210. 310.]]

# Phil 5.21.18

8:00 – 5:00 ASRC MKT

• Working through the BAA and transposing all the critical terms to the RFI
• A lot of time with Aaron unpacking text-based LSTM an ddoing stupid Python things

# Phil 4.19.18

8:00 – ASRC MKT/BD

• Good discussion with Aaron about the agents navigating embedding space. This would be a great example of creating “more realistic” data from simulation that bridges the gap between simulation and human data. This becomes the basis for work producing text for inputs such as DHS input streams.
• Get the embedding space from the Jack London corpora (crawl here)
• Train a classifier that recognizes JL using the embedding vectors instead of the words. This allows for contextual closeness. Additionally, it might allow a corpus to be trained “at once” as a pattern in the embedding space using CNNs.
• Train an NN(what type?) to produce sentences that contain words sent by agents that fool the classifier
• Record the sentences as the trajectories
• Reconstruct trajectories from the sentences and compare to the input
• Some thoughts WRT generating Twitter data
• Closely aligned agents can retweet (alignment measure?)
• Less closely aligned agents can mention/respond, and also add their tweet
• Handed off the proposal to Red Team. Still need to rework the Exec Summary. Nope. Doesn’t matter that the current exec summary does not comply with the requirements.
• A dog with high social influence creates an adorable stampede:
• Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
• This is a paper that describes how ML can be used to predict the behavior of chaotic systems. An implication is that this technique could be used for early classification of nomadic/flocking/stampede behavior
• Visualizing a Thinker’s Life
• This paper presents a visualization framework that aids readers in understanding and analyzing the contents of medium-sized text collections that are typical for the opus of a single or few authors.We contribute several document-based visualization techniques to facilitate the exploration of the work of the German author Bazon Brock by depicting various aspects of its texts, such as the TextGenetics that shows the structure of the collection along with its chronology. The ConceptCircuit augments the TextGenetics with entities – persons and locations that were crucial to his work. All visualizations are sensitive to a wildcard-based phrase search that allows complex requests towards the author’s work. Further development, as well as expert reviews and discussions with the author Bazon Brock, focused on the assessment and comparison of visualizations based on automatic topic extraction against ones that are based on expert knowledge.

# Phil 4.18.18

7:00 – 6:30 ASRC MKT/BD

• Meeting with James Foulds. We talked about building an embedding space for a literature body (The works of Jack London, for example) that agents can then navigate across. At the same time, train an LSTM on the same corpora so that the ML system, when given the vector of terms from the embedding (with probabilities/similarities?), produce a line that could be from the work that incorporates those terms. This provides a much more realistic model of the agent output that could be used for mapping. Nice paper to continue the current work while JuryRoom comes up to speed.
• Recurrent Neural Networks for Multivariate Time Series with Missing Values
• Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRUD, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.
•  The fall of RNN / LSTM
• We fell for Recurrent neural networks (RNN), Long-short term memory (LSTM), and all their variants. Now it is time to drop them!
• JuryRoom
• Back to proposal writing
• Done with section 5! LaTex FTW!
• Clean up Abstract, Exec Summary and Transformative Impact tomorrow