Phil 4.21.18

Today’s ride

“Writing is Thinking”—an annotated twitter thread

  • Another in the series of State, Orientation and Velocity. In this case discussing the differences between stories and maps:
  • It is really incredible the amount of pushback I see from companies, startups to big, about writing. In particular around the notion that writing is the antithesis of agile. Writing ossifies and cements decision or plans that should change, it is said. My view is that agility comes from planning. Without plans, activities are just brownian motion. And you can’t have plans, especially shared plans, without writing.

Ervin Staub

  • In The Roots of Goodness and Resistance to Evil, Ervin Staub draws on his extensive experiences in scholarship and intervention to illuminate the socializing experiences, education, and trainings that lead children and adults to become helpers/active bystanders and rescuers, acting to prevent violence and create peaceful and harmonious societies. The book collects Staub’s most important and influential articles and essays in the field together with newly written chapters, with wide-ranging examples of helping behaviors as well as discussions of why we should help and not harm others. He addresses many examples of such behaviors, from helping people in everyday physical or psychological distress, to active bystandership in response to harmful actions by youth toward their peers (bullying), to endangering one’s life to save someone in immediate danger, or rescuing intended victims of genocide.
Advertisements

Phil 4.20.18

7:00 – ASRC MKT

  • Executing gradient descent on the earth
    • But the important question is: how well does gradient descent perform on the actual earth?
    • This is nice, because it suggests that we can compare GD algorithms on recognizable and visualizable terrains. Terrain locations can have multiple visualizable factors, height and luminance could be additional dimensions
  • Minds is the anti-facebook that pays you for your time
    • In a refreshing change from Facebook, Twitter, Instagram, and the rest of the major platforms, Minds has also retained a strictly reverse-chronological timeline. The core of the Minds experience, though, is that users receive “tokens” when others interact with their posts, or simply by spending time on the platform.
  • Continuing along with the Angular/PHP tutorial here. Nicely, there is also a Git repo
    • Had to add some styling to get the upload button to show
    • The HttpModule is deprecated, but sticking with it for now
    • Will need to connect/verify PHP server within IntelliJ, described here.
    • How to connect Apache, to IntelliJ
  • Installing and Configuring XAMPP with PhpStorm IDE. Don’t forget about deployment path: deploy

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

Phil 4.17.18

7:00 – ASRC MKT

  • Listening to an interview with Nial Ferguson this morning where he talks about how the Chinese IT model aligns more closely with developing countries because they have solved the payment problem. And the surveillance state apparatus comes along for free. A ML/AI trained in that population will provide even closer alignment and will feel more “native”.
  • A ML/AI trained in that population will feel more “native”, and increase the traction of the Chinese IT. The Chinese approach expands its footprint in the developing world because it feels better and solves problems.
  • This sets up a conflict between corporate systems in the US and EU and China? In sheer demographics that means that it’s more likely that the dominant ML/AI perspective would reflect the surveillance biases of the Chinese government.
  • Payment systems are Socio-cultural user interfaces
  • Submitted to SASO. Submission #32. Updated the ArXiv file too. ArXiv “forgets” all the attachments too, so the tarball approach is soooooo much nicer.
  • Alt text for screen readers using LaTex
    \documentclass{article}
    \usepackage{graphicx}
    \usepackage{pdfcomment}
    \pagestyle{empty}
    
    \begin{document}
    one two three
    
    \pdftooltip{\includegraphics{img.png}}{This is the ALT text}%
    
    four five six
    \end{document}

     

Phil 4.16.18

9:00 – ASRC MKT

  • Finished up and submitted the CI 2018 and also put up on ArXive. Probably 90 minutes total?
  • SASO deadlines got extended:
    • Abstract submission (extended)  April 23, 2018
    • Submission (extended) April 30, 2018
  • Some diversity injection: Report for America Supports Journalism Where Cutbacks Hit Hard
    • Report for America, a nonprofit organization modeled after AmeriCorps, aims to install 1,000 journalists in understaffed newsrooms by 2022. Now in its pilot stage, the initiative has placed three reporters in Appalachia. It has chosen nine more, from 740 applicants, to be deployed across the country in June.
  • An information-theoretic, all-scales approach to comparing networks
    • As network research becomes more sophisticated, it is more common than ever for researchers to find themselves not studying a single network but needing to analyze sets of networks. An important task when working with sets of networks is network comparison, developing a similarity or distance measure between networks so that meaningful comparisons can be drawn. The best means to accomplish this task remains an open area of research. Here we introduce a new measure to compare networks, the Portrait Divergence, that is mathematically principled, incorporates the topological characteristics of networks at all structural scales, and is general-purpose and applicable to all types of networks. An important feature of our measure that enables many of its useful properties is that it is based on a graph invariant, the network portrait. We test our measure on both synthetic graphs and real world networks taken from protein interaction data, neuroscience, and computational social science applications. The Portrait Divergence reveals important characteristics of multilayer and temporal networks extracted from data.

3:00 – 4:00 Fika

Phil 4.14.18

Text Embedding Models Contain Bias. Here’s Why That Matters.

  • It occurs to me that bias may be a way of measuring societal dimension reduction. Need to read this carefully.
  • Neural network models can be quite powerful, effectively helping to identify patterns and uncover structure in a variety of different tasks, from language translation to pathology to playing games. At the same time, neural models (as well as other kinds of machine learning models) can contain problematic biases in many forms. For example, classifiers trained to detect rude, disrespectful, or unreasonable comments may be more likely to flag the sentence “I am gay” than “I am straight” [1]; face classification models may not perform as well for women of color [2]; speech transcription may have higher error rates for African Americans than White Americans [3].

Visual Analytics for Explainable Deep Learning

  • Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper, we review visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discuss potential challenges and future research directions.

Submitted final version of the CI 2018 paper and also put a copy up on ArXive. Turns out that you can bundle everything into a tar file and upload once.

Phil 4.13.18

7:00 – ASRC MKT/BD

  • That Politico article on “news deserts” doesn’t really show what it claims to show
    • Its heart is in the right place, and the decline of local news really is a big threat to democratic governance.
  • Firing up the JuryRoom effort again
    • Unsurprisingly, there are updates
    • And a lot of fixing plugins. Big update
    • Ok, back to having PHP and MySQL working. Need to see how to integrate it with the Angular CLI
      • Updated CLI as per stackoverflow
        • In order to update the angular-cli package installed globally in your system, you need to run:

          npm uninstall -g angular-cli
          npm cache clean
          npm install -g @angular/cli@latest
          

          Depending on your system, you may need to prefix the above commands with sudo.

          Also, most likely you want to also update your local project version, because inside your project directory it will be selected with higher priority than the global one:

          rm -rf node_modules
          npm uninstall --save-dev angular-cli
          npm install --save-dev @angular/cli@latest
          npm install
          

          thanks grizzm0 for pointing this out on GitHub.

           

        • Updated my work environment too. Some PHP issues, and the Angular CLI wouldn’t update until I turned on the VPN. Duh.
      • Angular 4 + PHP: Setting Up Angular And Bootstrap – Part 2
    • Back to proposal writing

Phil 4.12.18

7:00 – 5:00 ASRC MKT/BD

  • Downloaded my FB DB today. Honestly, the only thing that seems excessive is the contact information
  • Interactive Semantic Alignment Model: Social Influence and Local Transmission Bottleneck
    • Dariusz Kalociński
    • Marcin Mostowski
    • Nina Gierasimczuk
    • We provide a computational model of semantic alignment among communicating agents constrained by social and cognitive pressures. We use our model to analyze the effects of social stratification and a local transmission bottleneck on the coordination of meaning in isolated dyads. The analysis suggests that the traditional approach to learning—understood as inferring prescribed meaning from observations—can be viewed as a special case of semantic alignment, manifesting itself in the behaviour of socially imbalanced dyads put under mild pressure of a local transmission bottleneck. Other parametrizations of the model yield different long-term effects, including lack of convergence or convergence on simple meanings only.
  • Starting to get back to the JuryRoom app. I need a better way to get the data parts up and running. This tutorial seems to have a minimal piece that works with PHP. That may be for the best since this looks like a solo effort for the foreseeable future
  • Proposal
    • Cut implementation down to proof-of-concept?
    • We are keeping the ASRC format
    • Got Dr. Lee’s contribution
    • And a lot of writing and figuring out of things

Phil 4.11.18

7:00 – 5:00 ASRC MKT

  • Fixed the quotes in Simon’s Anthill
  • Ordered Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations by Yoav Shoham.
  • Read more about SNM detection
  • Meeting with Aaron and T about aligning dev plan
  • More writing. We got a week extension!
    • Triaged exec summary
    • Triaged Transformational
  • Introducing TensorFlow Probability
    • At the 2018 TensorFlow Developer Summit, we announced TensorFlow Probability: a probabilistic programming toolbox for machine learning researchers and practitioners to quickly and reliably build sophisticated models that leverage state-of-the-art hardware. You should use TensorFlow Probability if:
      • You want to build a generative model of data, reasoning about its hidden processes.
      • You need to quantify the uncertainty in your predictions, as opposed to predicting a single value.
      • Your training set has a large number of features relative to the number of data points.
      • Your data is structured — for example, with groups, space, graphs, or language semantics — and you’d like to capture this structure with prior information.
      • You have an inverse problem — see this TFDS’18 talk for reconstructing fusion plasmas from measurements.
    • TensorFlow Probability gives you the tools to solve these problems. In addition, it inherits the strengths of TensorFlow such as automatic differentiation and the ability to scale performance across a variety of platforms: CPUs, GPUs, and TPUs.

Phil 4.10.18

7:00 – 5:00 ASRC MKT

  • Incorporating Wajanat’s changes
  • Discovered the csquotes package!
    \usepackage[autostyle]{csquotes}
    
    \begin{document}
    
    \enquote{Thanks!}
    
    \end{document}
  • Meeting with Drew
    • Nice chat. Basically, “use the databases!”
    • Also found this:
      • A Mechanism for Reasoning about Time and Belief
        • Hideki Isozaki
        • Yoav Shoham (Twitter)
        • Several computational frameworks have been proposed to maintain information about the evolving world, which embody a default persistence mechanism; examples include time maps and the event calculus. In multi-agent environments, time and belief both play essential roles. Belief interacts with time in two ways: there is the time at which something is believed, and the time about which it is believed. We augment the default mechanisms proposed for the purely temporal case so as to maintain information not only about the objective world but also about the evolution of beliefs. In the simplest case, this yields a two dimensional map of time, with persistence along each dimension. Since beliefs themselves may refer to other beliefs, we have to think of a statement referring to an agent’s temporal belief about another agent’s temporal belief ( a nested temporal belief statement). It poses both semantical and algorithmic problems. In this paper, we concentrate on the algorithmic aspect of the problems. The general case involves multi-dimensional maps of time called Temporal Belief Maps.
  • Register for CI 2018 – done
  • Finalize and submit paper by April 27, 2018
  • Did not get a go ahead for ONR
  • More work on the DHS proposal. Thinking about having a discussion about using latent values and clustering as the initial detection approach, and using ML as the initial simulation approach.
  • Then much banging away at keyboards. Good progress, I think
  • Neural Artistic Style Transfer: A Comprehensive Look

Phil 4.9.18

7:00 – ASRC MKT / BD

  • The Collective Intelligence 2018 paper was accepted! Now I need to start thinking about the presentation. And lodging, travel, etc.
  • Tweaking the SASO paper
  • The reasonably current version is on ArXive! Will update after submission to SASO this week.
  • This One Simple Trick Disrupts Digital Communities 
    • This paper describes an agent based simulation used to model human actions in belief space, a high-dimensional subset of information space associated with opinions. Using insights from animal collective behavior, we are able to simulate and identify behavior patterns that are similar to nomadic, flocking and stampeding patterns of animal groups. These behaviors have analogous manifestations in human interaction, emerging as solitary explorers, the fashion-conscious, and members of polarized echo chambers. We demonstrate that a small portion of nomadic agents that widely traverse belief space can disrupt a larger population of stampeding agents. Extending the model, we introduce the concept of Adversarial Herding, where bad actors can exploit properties of technologically mediated communication to artificially create self sustaining runaway polarization. We call this condition the Pishkin Effect as it recalls the large scale buffalo stampedes that could be created by native Americans hunters. We then discuss opportunities for system design that could leverage the ability to recognize these negative patterns, and discuss affordances that may disrupt the formation of natural and deliberate echo chambers.
  • Kind of between things, so I wrote up my notes on Influence of augmented humans in online interactions during voting events
  • Looks important: Lessons Learned Reproducing a Deep Reinforcement Learning Paper
  • Proposal all day today probably
  • Fika
  • add something about base model
  • echo chamber, bad actor

Phil 4.7.18

A Tale of Two Movements: Egypt During the Arab Spring and Occupy Wall Street

  • Social media provides flexible platforms that play key roles in energizing collective action in movements like Arab Spring (AS) and Occupy Wall Street (OWS). By enabling individuals to display emotions broadly, social media amplify sentiments defined as shared collective emotion to supply the forces that drive change in society. This study describes how one platform, Facebook, contributed to these two different examples of political activism. Using social network analytics and text mining, we examine how Fan Page posts during the life of the movements influenced the formation of social ties by using sentimental messaging. We hypothesize a set of relationships between group cohesion and polarity of sentiments in explaining involvement. We find that the strength of social ties formed through exchanges of posts and comments influence participation, but its effect differs across two movements. We also find that negative sentiments are associated with more participation for Egypt during the AS than OWS. Our results suggest cultural differences play a major role in participation behaviors. Social media is important in engineering management, because someone who has a negative reaction to a project or a product can use these media to reach thousands of individuals and potentially turn sentiment against a project.

Prefrontal cortex as a meta-reinforcement learning system

  • Over the past twenty years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine ‘stamps in’ associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. In the present work, we draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine system trains another part of the brain, the prefrontal cortex, to operate as its own free-standing learning system. This new perspective accommodates the findings that motivated the standard model, but also deals gracefully with a wider range of observations, providing a fresh foundation for future research.

Blade Runner And The Synthetic Panopticon

  •  There are already thousands of articles on misinformation, disinformation, and journalism flying by us every day in the US, in this very strange year, 2017. Rather than add to that, I simply intend to make several big picture observations that seem to be getting very little attention. Our present journalistic crisis comes to be not because people are merely misinformed about the truth, but because of a fundamental misunderstanding about how social power determines the construction of truth.

The disinformation order: Disruptive communication and the decline of democratic institutions

  • Many democratic nations are experiencing increased levels of false information circulating through social media and political websites that mimic journalism formats. In many cases, this disinformation is associated with the efforts of movements and parties on the radical right to mobilize supporters against centre parties and the mainstream press that carries their messages. The spread of disinformation can be traced to growing legitimacy problems in many democracies. Declining citizen confidence in institutions undermines the credibility of official information in the news and opens publics to alternative information sources. Those sources are often associated with both nationalist (primarily radical right) and foreign (commonly Russian) strategies to undermine institutional legitimacy and destabilize centre parties, governments and elections. The Brexit campaign in the United Kingdom and the election of Donald Trump in the United States are among the most prominent examples of disinformation campaigns intended to disrupt normal democratic order, but many other nations display signs of disinformation and democratic disruption. The origins of these problems and their implications for political communication research are explored.

Phil 4.6.18

7:00 – 9:00 ASRC MKT

  • Heard a San Francisco comedian refer to Google as “Mordor” to knowing laughter in the audience. That says a lot about the relationship between the SF folks and their technology nation-states to the south. It also makes me rethink what Mordor actually was…
  • More ArXive submission
    • Tips for submitting to ArXive for the first time
    • Make sure that only the used pix are uploaded
      • AdversarialHerding
      • EchoChamberAngle
      • Explore-Exploit
      • directionpreserving
      • SlewAngle
      • Explorer
      • coloredFlocking
      • stampede
      • RunawayTrace
      • populations
      • HerdingImpact
    • It may be possible to submit as a single zipped (.gz? .tar?)  package. Will try that next time
    • Submitted and pending approval.
  • Start on DHS proposal
    • Built LaTex document
    • The templates provided by ASRC are completely wrong. Fixed in the LaTex template
    • Lots of discussion and negotiation on the form of the concept. I think we’re ready to start Monday
  • Nice chat with Wajanat about the paper and then her work. It’s interesting to hear how references and metaphors that I think are common get missed when they are read by a non-native english speaker from a different cultural frame. For example, I refer to a “plague of locusts” , which I had to explain as one of the biblical plagues of Egypt. Once explained, Wajanat immediately got it, and mentioned the Arabic word طاعون, We then asked Ali, who’s Iranian. He didn’t know about plagues either, but by using طاعون, he was able to get the entire context. She also suggested improving the screenshot at the beginning of the paper and expanding the transition to the intelligent vehicle stampede section.
  • Then a meandering and fun chat with Shimei, mostly about psychology and AI ethics. Left at 9:00

Phil 4.5.18

7:00 – 5:00 ASRC MKT

  • More car stampedes: On one of L.A.’s steepest streets, an app-driven frenzy of spinouts, confusion and crashes
  • Working on the first draft of the paper. I think(?) I’m reasonably happy with it.
  • Trying to determine the submission guidelines. Are IEEE paper anonymized? If they are, here’s the post on how to do it and my implementation:
    \usepackage{xcolor}
    \usepackage{soul}
    
    \sethlcolor{black}
    \makeatletter
    \newif\if@blind
    \@blindfalse %use \@blindtrue to anonymize, \@blindfalse on final version
    \if@blind \sethlcolor{black}\else
    	\let\hl\relax
    \fi
    
    \begin{document}
    this text is \hl{redacted}
    \end{document}
    
    
  • So this clever solution doesn’t work, because you can select under the highlight. This is my much simpler solution:
    %\newcommand*{\ANON}{}
    \ifdefined\ANON
    	\author{\IEEEauthorblockN{Anonymous Author(s)}
    	\IEEEauthorblockA{\textit{this line kept for formatting} \\
    		\textit{this line kept for formatting}\\
    		this line kept for formatting \\
    		this line kept for formatting}
    }
    \else
    	\author{\IEEEauthorblockN{Philip Feldman}
    	\IEEEauthorblockA{\textit{ASRC Federal} \\
    	Columbia, USA \\
    	philip.feldman@asrcfederal.com}
    	}
    \fi
  • Submitting to Arxive
  • Boy, this hit home: The Swamp of Sadness
    • Even with Arteyu pulling on his bridle, Artex still had to start walking and keep walking to survive, and so do you. You have to pull yourself out of the swamp. This sucks, because it’s difficult, slow, hand-over-hand, gritty, horrible work, and you will end up very muddy. But I think the muddier the swamp, the better the learning really. I suspect the best kinds of teachers have themselves walked through very horrible swamps.
  • You have found the cui2vec explorer. This website will let you interact with embeddings for over 108,000 medical concepts. These embeddings were created using insurance claims for 60 million americans, 1.7 million full-text PubMed articles, and clinical notes from 20 million patients at Stanford. More information about the methods used to create these embeddings can be found in our preprint: https://arxiv.org/abs/1804.01486 
  • Going to James Foulds’ lecture on Mixed Membership Word Embeddings for Computational Social Science. Send email for meeting! Such JuryRoom! Done!
  • Kickoff meeting for the DHS proposal. We have until the 20th to write everything. Sheesh