Phil 3.14.19

ASRC AIMS 7:00 – 4:00, PhD ML, 4:30 –

Phil 3.11.19

7:00 – 10:00 ASRC PhD. Fun, long day.

• Understanding BERT Transformer: Attention isn’t all you need
• Word Vectors and NLP Modeling from BoW to BERT
• Since the advent of word2vec, neural word embeddings have become a go to method for encapsulating distributional semantics in text applications. This series will review the strengths and weaknesses of using pre-trained word embeddings and demonstrate how to incorporate more complex semantic representation schemes such as Semantic Role Labeling, Abstract Meaning Representation and Semantic Dependency Parsing into your applications.
• Artificial Intelligence and Global Security Initiative Research Agenda
• The Center for a New American Security’s Artificial Intelligence and Global Security Initiative explores these and other issues surrounding the AI revolution. Current AI technology is powerful, but also has a number of vulnerabilities, including susceptibility to spoofing (false data) and control problems. An arms race in AI where nations and other actors rush to use this technology for their advantage without any concern for safety would be harmful to everyone. It is vitally important for the technology and policy communities to come together to better understand the implications of the AI revolution for global security and how best to navigate the challenges ahead.
• One more pass through Antonio’s paper this evening – done
• Working on getting the Slack chats into the database. It turns out that there can be threaded discussions within channels: thread_ts, reply_count, reply_users_count, latest_reply, reply_users, replies are the variables. It’s not critical now, but it would be nice to read these in as well.
• We are over 10,000 rows!
• And it looks like the Google Keras team is going to run the dungeon
• Starting on SequenceAnalyzer. Not bat progress for a day
• Meeting with Wayne

Phil 3.1.19

7:00 – ASRC

• Got accepted to the TF dev conference. The flight out is expensive… Sent Eric V. a note asking for permission to go, but bought tix anyway given the short fuse
• Working on white paper. The single file was getting unwieldy, so I broke it up
• Found Speeding up Parliamentary Decision Making for Cyber Counter-Attack, which argues for the possibility of pre-authorizing automated response
• Up to six pages. IN the middle of the cyberdefense section

Phil 2.22.19

7:00 – 4:00 ASRC

• Running Ellen’s dungeon tonight
• Wondering what to do next. Look at text analytics? List is in this post.
• But before we do that, I need to extract from the DB posts as text. And now I have something to do!
• Sheesh – tried to update the database and had all kinds of weird problems. I wound up re-injesting everything from the Slack files. This seems to work fine, so I exported that to replace the .sql file that may have been causing all the trouble.
• Here’s a thing using the JAX library, which I’m becoming interested in: Meta-Learning in 50 Lines of JAX
• The focus of Machine Learning (ML) is to imbue computers with the ability to learn from data, so that they may accomplish tasks that humans have difficulty expressing in pure code. However, what most ML researchers call “learning” right now is but a very small subset of the vast range of behavioral adaptability encountered in biological life! Deep Learning models are powerful, but require a large amount of data and many iterations of stochastic gradient descent (SGD). This learning procedure is time-consuming and once a deep model is trained, its behavior is fairly rigid; at deployment time, one cannot really change the behavior of the system (e.g. correcting mistakes) without an expensive retraining process. Can we build systems that can learn faster, and with less data?
• Meta-Learning: Learning to Learn Fast
• A good machine learning model often requires training with a large number of samples. Humans, in contrast, learn new concepts and skills much faster and more efficiently. Kids who have seen cats and birds only a few times can quickly tell them apart. People who know how to ride a bike are likely to discover the way to ride a motorcycle fast with little or even no demonstration. Is it possible to design a machine learning model with similar properties — learning new concepts and skills fast with a few training examples? That’s essentially what meta-learning aims to solve.
• Meta learning is everywhere! Learning to Generalize from Sparse and Underspecified Rewards
• In “Learning to Generalize from Sparse and Underspecified Rewards“, we address the issue of underspecified rewards by developing Meta Reward Learning (MeRL), which provides more refined feedback to the agent by optimizing an auxiliary reward function. MeRL is combined with a memory buffer of successful trajectories collected using a novel exploration strategy to learn from sparse rewards.
• Lingvo: A TensorFlow Framework for Sequence Modeling
• While Lingvo started out with a focus on NLP, it is inherently very flexible, and models for tasks such as image segmentation and point cloud classification have been successfully implemented using the framework. Distillation, GANs, and multi-task models are also supported. At the same time, the framework does not compromise on speed, and features an optimized input pipeline and fast distributed training. Finally, Lingvo was put together with an eye towards easy productionisation, and there is even a well-defined path towards porting models for mobile inference.
• Working on white paper. Still reading Command Dysfunction and making notes. I think I’ll use the idea of C&C combat as the framing device of the paper. Started to write more bits
• What, if anything, can the Pentagon learn from this war simulator?
• It is August 2010, and Operation Glacier Mantis is struggling in the fictional Saffron Valley. Coalition forces moved into the valley nine years ago, but peace negotiations are breaking down after a series of airstrikes result in civilian casualties. Within a few months, the Coalition abandons Saffron Valley. Corruption sapped the reputation of the operation. Troops are called away to a different war. Operation Glacier Mantis ends in total defeat.
• Created a post for Command Dysfunction here. Finished.

Phil 1.11.18

7:00 – 5:00 ASRC NASA

• The Philosopher Redefining Equality (New Yorker profile of Elizabeth Anderson)
• She takes great pleasure in arranging information in useful forms; if she weren’t a philosopher, she thinks, she’d like to be a mapmaker, or a curator of archeological displays in museums.
• Trolling the U.S.: Q&A on Russian Interference in the 2016 Presidential Election
• Ryan Boyd and researchers from Carnegie Mellon University and Microsoft Research analyzed Facebook ads and Twitter troll accounts run by Russia’s Internet Research Agency (IRA) to determine how people with differing political ideologies were targeted and pitted against each other through this “largely unsophisticated and low-budget” operation. To learn more about the study and its findings, we asked Boyd the following questions:
• Boyd is an interesting guy. Here’s his twitter profile: Social/Personality Psychologist, Computational Social Scientist, and Occasional Software Developer.
• Applied for an invite to the TF Dev summit
• Work on text analytics?
• Extract data by groups, group, user and start looking at cross-correlations
• Started modifying post_analyzer.py
• PHP “story” generator?
• Updating IntelliJ
• More DB work

Phil 11.6.18

7:00 – 2:00 ASRC PhD/BD

• Today’s big though: Maps are going top be easier than I thought. We’ve been doing  them for thousands of years with board games.
• Worked with Aaron on slides, including finding fault detection using our technologies. There is quite a bit, with pioneering work from NASA
• Called and left messages for Dr. Wilkins and Dr. Palazzolo. Need to send a follow-up email to Dr. Palazzolo and start on the short white papers
• Leaving early to vote
• The following two papers seem to be addressing edge stiffness
• Model of the Information Shock Waves in Social Network Based on the Special Continuum Neural Network
• The article proposes a special class of continuum neural network with varying activation thresholds and a specific neuronal interaction mechanism as a model of message distribution in social networks. Activation function for every neuron is fired as a decision of the specific systems of differential equations which describe the information distribution in the chain of the network graph. This class of models allows to take into account the specific mechanisms for transmitting messages, where individuals who, receiving a message, initially form their attitude towards it, and then decide on the further transmission of this message, provided that the corresponding potential of the interaction of two individuals exceeds a certain threshold level. The authors developed the original algorithm for calculating the time moments of message distribution in the corresponding chain, which comes to the solution of a series of Cauchy problems for systems of ordinary nonlinear differential equations.
• A cost-effective algorithm for inferring the trust between two individuals in social networks
• The popularity of social networks has significantly promoted online individual interaction in the society. In online individual interaction, trust plays a critical role. It is very important to infer the trust among individuals, especially for those who have not had direct contact previously in social networks. In this paper, a restricted traversal method is defined to identify the strong trust paths from the truster and the trustee. Then, these paths are aggregated to predict the trust rate between them. During the traversal on a social network, interest topics and topology features are comprehensively considered, where weighted interest topics are used to measure the semantic similarity between users. In addition, trust propagation ability of users is calculated to indicate micro topology information of the social network. In order to find the topk most trusted neighbors, two combination strategies for the above two factors are proposed in this paper. During trust inference, the traversal depth is constrained according to the heuristic rule based on the “small world” theory. Three versions of the trust rate inference algorithm are presented. The first algorithm merges interest topics and topology features into a hybrid measure for trusted neighbor selection. The other two algorithms consider these two factors in two different orders. For the purpose of performance analysis, experiments are conducted on a public and widely-used data set. The results show that our algorithms outperform the state-of-the-art algorithms in effectiveness. In the meantime, the efficiency of our algorithms is better than or comparable to those algorithms.
• Back to LSTMs. Made a numeric version of “all work and no play in the jack_torrance generator
• Reading in and writing out weight files. The predictions seems to be working well, but I have no insight into the arguments that go into the LSTM model. Going to revisit the Deep Learning with Keras book

Phil 10.31.18

7:00 – ASRC PhD

• Read this carefully today: Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees
• Today, we’re excited to share AdaNet, a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on our recent reinforcement learning and evolutionary-based AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models.
• What about data from simulation?
• Github repo
• AdaNet is a lightweight and scalable TensorFlow AutoML framework for training and deploying adaptive neural networks using the AdaNet algorithm [Cortes et al. ICML 2017]. AdaNet combines several learned subnetworks in order to mitigate the complexity inherent in designing effective neural networks. This is not an official Google product.
• Tutorials: for understanding the AdaNet algorithm and learning to use this package
• Welcome to adanet! For a tour of this python package’s capabilities, please work through the following notebooks:
• This looks like it’s based deeply the cloud AI and Machine Learning products, including cloud-based hyperparameter tuning.
• Time series prediction is here as well, though treated in a more BigQuery manner
• In this blog post we show how to build a forecast-generating model using TensorFlow’s DNNRegressor class. The objective of the model is the following: Given FX rates in the last 10 minutes, predict FX rate one minute later.
• Text generation:
• Cloud poetry: training and hyperparameter tuning custom text models on Cloud ML Engine
• Let’s say we want to train a machine learning model to complete poems. Given one line of verse, the model should generate the next line. This is a hard problem—poetry is a sophisticated form of composition and wordplay. It seems harder than translation because there is no one-to-one relationship between the input (first line of a poem) and the output (the second line of the poem). It is somewhat similar to a model that provides answers to questions, except that we’re asking the model to be a lot more creative.
• Codelab: Google Developers Codelabs provide a guided, tutorial, hands-on coding experience. Most codelabs will step you through the process of building a small application, or adding a new feature to an existing application. They cover a wide range of topics such as Android Wear, Google Compute Engine, Project Tango, and Google APIs on iOS.
Codelab tools on GitHub

• Add the Range and Length section in my notes to the DARPA measurement section. Done. I need to start putting together the dissertation using these parts
• Read Open Source, Open Science, and the Replication Crisis in HCI. Broadly, it seems true, but trying to piggyback on GitHub seems like a shallow solution that repurposes something for coding – an ephemeral activity, to science, which is archival for a reason. Thought needs to be given to an integrated (collection, raw data, cleaned data, analysis, raw results, paper (with reviews?), slides, and possibly a recording of the talk with questions. What would it take to make this work across all science, from critical ethnographies to particle physics? How will it be accessible in 100 years? 500? 1,000? This is very much an HCI problem. It is about designing a useful socio-cultural interface. Some really good questions would be “how do we use our HCI tools to solve this problem?”, and, “does this point out the need for new/different tools?”.
• NASA AIMS meeting. Demo in 2 weeks. AIMS is “time series prediction”, A2P is “unstructured data”. Proove that we can actually do ML, as opposed to saying things.
• How about cross-point correlation? Could show in a sim?
• Meeting on Friday with a package
• We’ve solved A, here’s the vision for B – Z and a roadmap. JPSS is a near-term customer (JPSS Data)
• Getting actionable intelligence from the system logs
• Application portfolios for machine learning
• Umbrella of capabilities for Rich Burns
• New architectural framework for TTNC
• Software Engineering Division/Code 580
• A2P as a toolbox, but needs to have NASA-relevant analytic capabilities
• GMSEC overview

Phil 10.2.18

7:00 – 5:00 ASRC Research

• Graph laplacian dissertation
• The spectrum of the normalized graph Laplacian can reveal structural properties of a network and can be an important tool to help solve the structural identification problem. From the spectrum, we attempt to develop a tool that helps us to understand the network structure on a deep level and to identify the source of the network to a greater extent. The information about different topological properties of a graph carried by the complete spectrum of the normalized graph Laplacian is explored. We investigate how and why structural properties are reflected by the spectrum and how the spectrum changes when compairing different networks from different sources.
• Universality classes in nonequilibrium lattice systems
• This article reviews our present knowledge of universality classes in nonequilibrium systems defined on regular lattices. The first section presents the most important critical exponents and relations, as well as the field-theoretical formalism used in the text. The second section briefly addresses the question of scaling behavior at first-order phase transitions. In Sec. III the author looks at dynamical extensions of basic static classes, showing the effects of mixing dynamics and of percolation. The main body of the review begins in Sec. IV, where genuine, dynamical universality classes specific to nonequilibrium systems are introduced. Section V considers such nonequilibrium classes in coupled, multicomponent systems. Most of the known nonequilibrium transition classes are explored in low dimensions between active and absorbing states of reaction-diffusion-type systems. However, by mapping they can be related to the universal behavior of interface growth models, which are treated in Sec. VI. The review ends with a summary of the classes of absorbing-state and mean-field systems and discusses some possible directions for future research.
• “The Government Spies Using Our Webcams:” The Language of Conspiracy Theories in Online Discussions
• Conspiracy theories are omnipresent in online discussions—whether to explain a late-breaking event that still lacks official report or to give voice to political dissent. Conspiracy theories evolve, multiply, and interconnect, further complicating efforts to limit their propagation. It is therefore crucial to develop scalable methods to examine the nature of conspiratorial discussions in online communities. What do users talk about when they discuss conspiracy theories online? What are the recurring elements in their discussions? What do these elements tell us about the way users think? This work answers these questions by analyzing over ten years of discussions in r/conspiracy—an online community on Reddit dedicated to conspiratorial discussions. We focus on the key elements of a conspiracy theory: the conspiratorial agents, the actions they perform, and their targets. By computationally detecting agent–action–target triplets in conspiratorial statements, and grouping them into semantically coherent clusters, we develop a notion of narrative-motif to detect recurring patterns of triplets. For example, a narrative-motif such as “governmental agency–controls–communications” appears in diverse conspiratorial statements alleging that governmental agencies control information to nefarious ends. Thus, narrative-motifs expose commonalities between multiple conspiracy theories even when they refer to different events or circumstances. In the process, these representations help us understand how users talk about conspiracy theories and offer us a means to interpret what they talk about. Our approach enables a population-scale study of conspiracy theories in alternative news and social media with implications for understanding their adoption and combating their spread
• Need to upload to ArXiv (try multiple tex files) – done!
• If I’m charging my 400 hours today, then start putting together text prediction. I’d like to try the Google prediction series to see what happens. Otherwise, there are two things I’d like to try with LSTMs, since they take 2 coordinates as inputs
• Use a 2D embedding space
• Use NLP to get a parts-of-speech (PoS) analysis of the text so that there can be a (PoS, Word) coordinate.
• Evaluate the 2 approaches on their ability to converge?
• Coordinating with Antonio about workshops. It’s the 2019 version of this: International Workshop on Massively Multi-Agent Systems (MMAS2018) in conjunction with IJCAI/ECAI/AAMAS/ICML 2018

Phil 9.21.18

7:00 – 4:00 ASRC MKT

• “Who’s idea was it to connect every idiot on the internet with every other idiot” PJ O’Rourke, Commonwealth Club, 2018
• Running Programs In Reverse for Deeper A.I.” by Zenna Tavares
• In this talk I show that inverse simulation, i.e., running programs in reverse from output to input, lies at the heart of the hardest problems in both human cognition and artificial intelligence. How humans are able to reconstruct the rich 3D structure of the world from 2D images; how we predict that it is safe to cross a street just by watching others walk, and even how we play, and sometimes win at Jenga, are all solvable by running programs backwards. The idea of program inversion is old, but I will present one of the first approaches to take it literally. Our tool ReverseFlow combines deep-learning and our theory of parametric inversion to compile the source code of a program (e.g., a TensorFlow graph) into its inverse, even when it is not conventionally invertible. This framework offers a unified and practical approach to both understand and solve the aforementioned problems in vision, planning and inference for both humans and machines.
• Bot-ivistm: Assessing Information Manipulation in Social Media Using Network Analytics
• Matthew Benigni
• Kenneth Joseph
• Kathleen M. Carley (Scholar)
• Social influence bot networks are used to effect discussions in social media. While traditional social network methods have been used in assessing social media data, they are insufficient to identify and characterize social influence bots, the networks in which they reside and their behavior. However, these bots can be identified, their prevalence assessed, and their impact on groups assessed using high dimensional network analytics. This is illustrated using data from three different activist communities on Twitter—the “alt-right,” ISIS sympathizers in the Syrian revolution, and activists of the Euromaidan movement. We observe a new kind of behavior that social influence bots engage in—repetitive @mentions of each other. This behavior is used to manipulate complex network metrics, artificially inflating the influence of particular users and specific agendas. We show that this bot behavior can affect network measures by as much as 60% for accounts that are promoted by these bots. This requires a new method to differentiate “promoted accounts” from actual influencers. We present this method. We also present a method to identify social influence bot “sub-communities.” We show how an array of sub-communities across our datasets are used to promote different agendas, from more traditional foci (e.g., influence marketing) to more nefarious goals (e.g., promoting particular political ideologies).
• Pinged Aaron M. about writing an article
• More iConf paper. Got a first draft on everything but the discussion section

Phil 8.30.18

7:00 – 5:00  ASRC MKT

• Target Blue Sky paper for iSchool/iConference 2019: The chairs are particularly looking for “Blue Sky Ideas” that are open-ended, possibly even “outrageous” or “wacky,” and present new problems, new application domains, or new methodologies that are likely to stimulate significant new research.
• I’m thinking that a paper that works through the ramifications of this diagram as it relates to people and machines. With humans that are slow responding with spongy, switched networks the flocking area is large. With a monolithic densely connected system it’s going to be a straight line from nomadic to stampede.
• Length: Up to 4 pages (excluding references)
• Submission deadline: October 1, 2018
• Final versions due: December 14, 2018
• First versions will be submitted using .pdf. Final versions must be submitted in .doc, .docx or La Tex.
• More good stuff on BBC Business Daily Trolling for Cash
• Anger and animosity is prevalent online, with some people even seeking it out. It’s present on social media of course as well as many online forums. But now outrage has spread to mainstream media outlets and even the advertising industry. So why is it so lucrative? Bonny Brooks, a writer and researcher at Newcastle University explains who is making money from outrage. Neuroscientist Dr Dean Burnett describes what happens to our brains when we see a comment designed to provoke us. And Curtis Silver, a tech writer for KnowTechie and ForbesTech, gives his thoughts on what we need to do to defend ourselves from this onslaught of outrage.
• Exposure to Opposing Views can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social Media
• Christopher Bail (Scholar)
• There is mounting concern that social media sites contribute to political polarization by creating “echo chambers” that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for one month that exposed them to messages produced by elected officials, organizations, and other opinion leaders with opposing political ideologies. Respondents were re-surveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative post-treatment, and Democrats who followed a conservative Twitter bot became slightly more liberal post-treatment. These findings have important implications for the interdisciplinary literature on political polarization as well as the emerging field of computational social science.
• Setup gcloud tools on laptop – done
• Setup Tensorflow on laptop. Gave up un using CUDA 9.1, but got tf doing ‘hello, tensorflow’
• Marcom meeting – 2:00
• Get the concept of behaviors being a more scalable, dependable way of vetting information.
• Eg Watching the DISI of outrage as manifested in trolling
• “Uh. . . . not to be nitpicky,,,,,but…the past tense of drag is dragged, not drug.”: An overview of trolling strategies
• Dr Claire Hardaker (Scholar) (Blog)
• I primarily research aggression, deception, and manipulation in computer-mediated communication (CMC), including phenomena such as flaming, trolling, cyberbullying, and online grooming. I tend to take a forensic linguistic approach, based on a corpus linguistic methodology, but due to the multidisciplinary nature of my research, I also inevitably branch out into areas such as psychology, law, and computer science.
• This paper investigates the phenomenon known as trolling — the behaviour of being deliberately antagonistic or offensive via computer-mediated communication (CMC), typically for amusement’s sake. Having previously started to answer the question, what is trolling? (Hardaker 2010), this paper seeks to answer the next question, how is trolling carried out? To do this, I use software to extract 3,727 examples of user discussions and accusations of trolling from an eighty-six million word Usenet corpus. Initial findings suggest that trolling is perceived to broadly fall across a cline with covert strategies and overt strategies at each pole. I create a working taxonomy of perceived strategies that occur at different points along this cline, and conclude by refining my trolling definition.
• Citing papers
• FireAnt (Filter, Identify, Report, and Export Analysis Toolkit) is a freeware social media and data analysis toolkit with built-in visualization tools including time-series, geo-position (map), and network (graph) plotting.
• Fix marquee – done
• Export to ppt – done!
• include videos – done
• Center title in ppt:
• model considerations – done
• diversity injection – done
• Got the laptop running Python and Tensorflow. Had a stupid problem where I accidentally made a virtual environment and keras wouldn’t work. Removed, re-connected and restarted IntelliJ and everything is working!

Phil 8.10.18

7:00 – ASRC MKT

• Finished the first pass through the SASO slides. Need to start working on timing (25 min + 5 min questions)
• Start on poster (A0 size)
• Sent Wayne a note to get permission for 899
• Started setting up laptop. I hate this part. Google drive took hours to synchronize
• Java
• Python/Nvidia/Tensorflow
• Intellij
• Visual Studio
• MikTex
• TexStudio
• Xampp
• Vim
• TortoiseSVN
• WinSCP
• 7-zip
• Creative Cloud
• Acrobat
• Illustrator
• Photoshop
• Microsoft suite
• Express VPN

Phil 8.3.18

7:00 – 3:30 ASRC MKT

• Slides and walkthrough – done!
• Ramping up on SASO
• Textricator is a tool for extracting text from computer-generated PDFs and generating structured data (CSV or JSON). If you have a bunch of PDFs with the same format (or one big, consistently formatted PDF) and you want to extract the data to CSV or JSON, _Textricator_ can help! It can even work on OCR’ed documents!
• LSTM links for getting back to things later
• Who handles misinformation outbreaks?
• Misinformation attacks— the deliberate and sustained creation and amplification of false information at scale — are a problem. Some of them start as jokes (the ever-present street sharks in disasters) or attempts to push an agenda (e.g. right-wing brigading); some are there to make money (the “Macedonian teens”), or part of ongoing attempts to destabilise countries including the US, UK and Canada (e.g. Russia’s Internet Research Agency using troll and bot amplification of divisive messages).

Enough people are writing about why misinformation attacks happen, what they look like and what motivates attackers. Fewer people are activelycountering attacks. Here are some of them, roughly categorised as:

• Journalists and data scientists: Make misinformation visible
• Platforms and governments: Reduce misinformation spread
• Communities: directly engage misinformation
• Adtech: Remove or reduce misinformation rewards

Phil 7.26.18

7:00 – 5:30 ASRC

• This could be interesting. Includes predictive analytics: BigQuery ML
• Working on slides
• Working on RNNs and LSTMS. I would love to build a simple, explanatory model in Excel, but can’t find one.
• Helped Aaron flail on getting tab dates into the A2P GUI

Phil 7.20.18

• David Peritz
• Political polarization, accompanied by negative partisanship, are striking features of the current political landscape. Perhaps these trends were originally confined to politicians and the media, but we recently reached the point where the majority of Americans report they would consider it more objectionable if their children married across party lines than if they married someone of another faith. Where did this polarization come from? And what it is doing to American democracy, which is housed in institutions that were framed to encourage open deliberation, compromise and consensus formation? In this talk, Professor David Peritz will examine some of the deeper forces in the American economy, the public sphere and media, political institutions, and even moral psychology that best seem to account for the recent rise in popular polarization.

Sent out a Doodle to nail down the time for the PhD review

Went looking for something that talks about the cognitive load for TIT-FOR-TAT in the Iterated Prisoner’s Dilemma and can’t find anything. Did find this though, that is kind of interesting: New tack wins prisoner’s dilemma. It’s a collective intelligence approach:

• Teams could submit multiple strategies, or players, and the Southampton team submitted 60 programs. These, Jennings explained, were all slight variations on a theme and were designed to execute a known series of five to 10 moves by which they could recognize each other. Once two Southampton players recognized each other, they were designed to immediately assume “master and slave” roles – one would sacrifice itself so the other could win repeatedly.
• Nick Jennings
• Professor Jennings is an internationally-recognized authority in the areas of artificial intelligence, autonomous systems, cybersecurity and agent-based computing. His research covers both the science and the engineering of intelligent systems. He has undertaken fundamental research on automated bargaining, mechanism design, trust and reputation, coalition formation, human-agent collectives and crowd sourcing. He has also pioneered the application of multi-agent technology; developing real-world systems in domains such as business process management, smart energy systems, sensor networks, disaster response, telecommunications, citizen science and defence.
• Sarvapali D. (Gopal) Ramchurn
• I am a Professor of Artificial Intelligence in the Agents, Interaction, and Complexity Group (AIC), in the department of Electronics and Computer Science, at the University of Southampton and Chief Scientist for North Star, an AI startup.  I am also the director of the newly created Centre for Machine Intelligence.  I am interested in the development of autonomous agents and multi-agent systems and their application to Cyber Physical Systems (CPS) such as smart energy systems, the Internet of Things (IoT), and disaster response. My research combines a number of techniques from Machine learning, AI, Game theory, and HCI.

7:00 – 4:30 ASRC MKT

• SASO Travel request
• SASO Hotel – done! Aaaaand I booked for August rather than September. Sent a note to try and fix using their form. If nothing by COB try email.
• Potential DME repair?
• Starting Deep Learning with Keras. Done with chapter one
• Two seedbank lstm text examples:
• Generate Shakespeare using tf.keras
• This notebook demonstrates how to generate text using an RNN with tf.keras and eager execution.This notebook is an end-to-end example. When you run it, it will download a dataset of Shakespeare’s writing. The notebook will then train a model, and use it to generate sample output.
• CharRNN
• This notebook will let you input a file containing the text you want your generator to mimic, train your model, see the results, and save it for future use all in one page.

Phil 7.19.18

7:00 – 3:00 ASRC MKT

• More on augmented athletics: Pinarello Nytro electric road bike review
• WhatsApp Research Awards for Social Science and Misinformation (\$50k – Applications are due by August 12, 2018, 11:59pm PST)
• Setting up meeting with Don for 3:30 Tuesday the 24th. He also gave me some nice leads on potential people for Dance my PhD:
• Dr. Linda Dusman
• Linda Dusman’s compositions and sonic art explore the richness of contemporary life, from the personal to the political. Her work has been awarded by the International Alliance for Women in Music, Meet the Composer, the Swiss Women’s Music Forum, the American Composers Forum, the International Electroacoustic Music Festival of Sao Paulo, Brazil, the Ucross Foundation, and the State of Maryland in 2004, 2006, and 2011 (in both the Music: Composition and the Visual Arts: Media categories). In 2009 she was honored as a Mid- Atlantic Arts Foundation Fellow for a residency at the Virginia Center for the Creative Arts. She was invited to serve as composer in residence at the New England Conservatory’s Summer Institute for Contemporary Piano in 2003. In the fall of 2006 Dr. Dusman was a Visiting Professor at the Conservatorio di musica “G. Nicolini” in Piacenza, Italy, and while there also lectured at the Conservatorio di musica “G. Verdi” in Milano. She recently received a Maryland Innovation Initiative grant for her development of Octava, a real-time program note system (octavaonline.com).
• Doug Hamby
• A choreographer who specializes in works created in collaboration with dancers, composers, visual artists and engineers. Before coming to UMBC he performed in several New York dance companies including the Martha Graham Dance Company and Doug Hamby Dance. He is the co-artistic director of Baltimore Dance Project, a professional dance company in residence at UMBC. Hamby’s work has been presented in New York City at Lincoln Center Out-of-Doors, Riverside Dance Festival, New York International Fringe Festival and in Brooklyn’s Prospect Park. His work has also been seen at Fringe Festivals in Philadelphia, Edinburgh, Scotland and Vancouver, British Columbia, as well as in Alaska. He has received choreography awards from the National Endowment for the Arts, Maryland State Arts Council, New York State Council for the Arts, Arts Council of Montgomery County, and the Baltimore Mayor’s Advisory Committee on Arts and Culture. He has appeared on national television as a giant slice of American Cheese.
• Sent out a note with dates and agenda to the committee for the PhD review thing. Thom can open up August 6th
• Continuing extraction of seed terms for the sentence generation. And it looks like my tasking for next sprint will be to put together a nice framework for plugging in predictive patterns systems like LSTM and multi-layer perceptrons.
• This seems to be working:
agentRelationships GreenFlockSh_1
sampleData 0.0
cell cell_[4, 6]
influences AGENT
influence GreenFlockSh_0 val =  0.8778825396520958
influence GreenFlockSh_2 val =  0.8859173062045552
influence GreenFlockSh_3 val =  0.9390368569108515
influence GreenFlockSh_4 val =  0.9774328763377834
influences SOURCE
influence UL_point val =  0.032906293611796644
• Sprint planning
• VP-613: Develop general TensorFlow/Keras NN format
• LSTM
• MLP
• CNN
• VP-616: SASO Preparation
• Slides
• Poster
• Demo