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}

     

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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 4.6.18

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

Phil 4.4.18

7:00 – 5:00 ASRC MKT

  • From zero to research — An introduction to Meta-learning
    • Thomas Wolf Machine Learning, Natural Language Processing & Deep learning – Science Lead @ Huggingface  (We’re on a journey to build the first truly social artificial intelligence. Along the way, we contribute to the development of technology for the better.)
    • Over the last months, I have been playing and experimenting quite a lot with meta-learning models for Natural Language Processing and will be presenting some of this work at ICLR, next month in Vancouver 🇨🇦 — come say hi! 👋 In this post, I will start by making a very visual introduction to meta-learning, from zero to current research work. Then, we will code a meta-learning model in PyTorch from scratch and I will share some of the lessons learned on this project.

  • Google veteran Jeff Dean takes over as company’s AI chief
  • Add some MB framing words to the game theory part of the lit review – done
  • Work on the PSA writeup

Our research has indicated that an awareness of nomadic/explorer activity in belief space may help nudge stampeding groups away from a terminal trajectory and back towards “average” beliefs. Tajfel states that groups can exist “in opposition”, so providing counter-narratives may be ineffective. Rather, we think that a practical solution to online polarization is the injection of diversity into user’s feeds, be they social media, search results, videos, etc.  The infrastructure exists for this already in platform’s support of advertising. The precedent is the Public Service Announcement (PSA).

US Broadcasters since 1927, have been obligated to “serve the public interest” in exchange for spectrum rights. One way that this has been addressed is through the creation of the PSA, “the purpose of which is to improve the health, safety, welfare, or enhancement of people’s lives and the more effective and beneficial functioning of their community, state or region”

We believe that PSAs can be repurposed to support diversity injection through the following:

  • Random, non-political content designed to expand information horizons, analogous to clicking the “random article” link on Wikipedia.
  • Progressive levels of detail starting with an informative “hook” presented in social feeds or search results. Users should be able to explore as much or as little as they want.
  • Simultaneous presentation to large populations. Google has been approximating this with their “doodle” since 1998, with widespread positive feedback, which indicates that there may be good receptivity to common serendipitous information.
  • Format should reflect the medium, Text, images and videos.
  • Content should be easily verifiable, recognizable, and difficult to spoof.

We believe that such diversity injection mechanisms as described above can serve as a “first do no harm” first step in addressing the current crisis of misinformation. By nudging users towards an increased awareness of a wider world, which in turn interferes with the processes that lead to belief stampedes by increasing the number of dimensions, the awareness of different paths that others are taking. As we gain understanding of the mechanisms that influence group behaviors, it may be possible to further refine our designs and interfaces so that they no longer promote extremism while still providing value.

 

  • Done with first draft? Nope. Going to rework the implications section some more.

Phil 4.3.18

ASRC MKT 7:00 – 5:30

  • Integrating airplane notes on Influence of augmented humans in online interactions during voting events
  • Follow up on pointing logs
  • World Affairs Council (Part II. Part I is Jennifer Kavanagh and Tom Nichols: The End of Authority)
    • With so many forces undermining democratic institutions worldwide, we wanted a chance to take a step back and provide some perspective. Russian interference in elections here and in Europe, the rise in fake news and a decline in citizen trust worldwide pose a danger. In this second of a three part series, we look at the role of social media and the ways in which it was exploited for the purpose of sowing distrust. Janine Zacharia, former Jerusalem bureau chief and Middle East correspondent for The Washington Post, and Roger McNamee, managing director at Elevation Partners and an early stage investor in Google and Facebook, are in conversation with World Affairs CEO Jane Wales.
    • “The ultimate combination of propaganda and gambling … powered by machine learning”
  • The emergence of consensus: a primer (No Moscovici – odd)
    • The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges ‘spontaneously’ in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
  • Need to write up diversity injection proposal
    • Basically updated PSAs for social media
    • Intent is to expand the information horizon, not to counter anything in particular. So it’s not political
    • Presented in a variety of ways (maps, stories and lists)
    • Goes identically into everyone’s feed
    • Can be blocked, but blockers need to be studied
    • More injection as time on site goes up. Particularly with YouTube & FB
  • Working on SASO paper. Made it through discussion

Phil 4.2.18

7:00 – 5:00 ASRC MKT

  • Someone worked pretty hard on their April Fools joke
  • Started cleaning up my TF Dev Conf notes. Need to fill in speaker’s names and contacts – done
  • Contact Keith Bennet about “pointing” logs – done
  • Started editing the SASO flocking paper. Call is April 16!
    • Converted to LaTex and at 11 pages
  • But first – expense report…. Done! Forgot the parking though. Add tomorrow!
  • Four problems for news and democracy
    • To understand these four crises — addiction, economics, bad actors and known bugs — we have to look at how media has changed shape between the 1990s and today. A system that used to be linear and fairly predictable now features feedback loops that lead to complex and unintended consequences. The landscape that is emerging may be one no one completely understands, but it’s one that can be exploited even if not fully understood.
  • Humanitarianism’s other technology problem
    • Is social media affecting humanitarian crises and conflict in ways that kill people and may ultimately undermine humanitarian response?Fika. Meeting with Wajanat Friday to go over paper

     

Phil 3.8.18

7:00 – 5:00 ASRC

  • Another nice comment from Joanna Bryson on BBC Business Daily – The bias is seldom in the algorithm. Latent Semantic Indexing is simple arithmetic. The data contains the bias, and that’s from us. Fairness is a negotiated concept, which means that is is complicated. Requiring algorithmic fairness necessitates placing enormous power in the hands of those writing the algorithms.
  • The science of fake news (Science magazine)
    • The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.
  • Incorporating Sy’s comments into a new slide deck
  • More ONR
  • Meeting with Shimei
    • Definitely use the ONR-specified headings
    • Research is looking good and interesting! Had to spend quite a while explaining lexical trajectories.
  • Ran through the slides with Sy again. Mostly finalized?

Phil 3.7.18

7:00 – 5:00 ASRC MKT

  • Some surprising snow
  • Meeting with Sy at 1:30 slides
  • Meeting with Dr. DesJardins at 4:00
  • Nice chat with Wajanat about the presentation of the Saudi Female self in physical and virtual environments
  • Sprint planning
    • Finish ONR Proposal VP-331
    • CHIIR VP-332
    • Prep for TF dev conf VP-334
    • TF dev conf VP-334
  • Working on the ONR proposal
  • Oxford Internet Institute – Computational Propaganda Research Project
    • The Computational Propaganda Research Project (COMPROP) investigates the interaction of algorithms, automation and politics. This work includes analysis of how tools like social media bots are used to manipulate public opinion by amplifying or repressing political content, disinformation, hate speech, and junk news. We use perspectives from organizational sociology, human computer interaction, communication, information science, and political science to interpret and analyze the evidence we are gathering. Our project is based at the Oxford Internet Institute, University of Oxford.
    • Polarization, Partisanship and Junk News Consumption over Social Media in the US
      • What kinds of social media users read junk news? We examine the distribution of the most significant sources of junk news in the three months before President Donald Trump’s first State of the Union Address. Drawing on a list of sources that consistently publish political news and information that is extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news, we find that the distribution of such content is unevenly spread across the ideological spectrum. We demonstrate that (1) on Twitter, a network of Trump supporters shares the widest range of known junk news sources and circulates more junk news than all the other groups put together; (2) on Facebook, extreme hard right pages—distinct from Republican pages—share the widest range of known junk news sources and circulate more junk news than all the other audiences put together; (3) on average, the audiences for junk news on Twitter share a wider range of known junk news sources than audiences on Facebook’s public pages
      • Need to look at the variance in the articles. Are these topical stampedes? Or is this source-oriented?
  • Understanding and Addressing the Disinformation Ecosystem
    • This workshop brings together academics, journalists, fact-checkers, technologists, and funders to better understand the challenges produced by the current disinformation ecosystem. The facilitated discussions will highlight relevant research, share best-practices, identify key questions of scholarly and practical concern regarding the nature and implications of the disinformation ecosystem, and outline a potential research agenda designed to answer these questions.
  • More BIC
    • The psychology of group identity allows us to understand that group identification can be due to factors that have nothing to do with the individual preferences. Strong interdependence and other forms of common individual interest are one sort of favouring condition, but there are many others, such as comembership of some existing social group, sharing a birthday, and the artificial categories of the minimal group paradigm. (pg 150)
    • Wherever we may expect group identity we may also expect team reasoning. The effect of team reasoning on behavior is different from that of individualistic reasoning. We have already seen this for Hi-Lo. This has wide implications. It makes the theory of team reasoning a much more powerful explanatory and predictive theory than it would be if it came on line only in games with th3e right kind of common interest. To take just one example, if management brings it about so that the firm’s employees identify with the firm, we may expect for them to team-reason and so to make choices that are not predicted by the standard theories of rational choice. (pg 150)
    • As we have seen, the same person passes through many group identities in the flux of life, and even on a single occasion more than one of these identities may be stimulated. So we will need a model of identity in which the probability of a person’s identification is distributed over not just two alternatives-personal self-identity or identity with a fixed group-but, in principle, arbitrarily many. (pg 151)

Phil 3.2.18

7:00 – 5:00 ASRC MKT

  • Got Wayne’s comments. Will integrate and see if EasyChair will take it
  • Work on ONR WhitePaper
  • Twitter proposal?
  • Society for Personality and Social Psychology
    • The mission of SPSP is to advance the scienceteaching, and application of social and personality psychology. SPSP members aspire to understand individuals in their social contexts for the benefit of all people.
    • Social psychology is the scientific study of how people’s thoughts, feelings, and behaviors are influenced by the actual, imagined, or implied presence of others.
  • Rebecca Hofstein Grady
    • I am interested in the ways that bias and motivation can affect our reasoning and memory to influence the judgments and decisions that we make.  In particular, I am currently studying how these biases apply to real-world situations, such as political conflicts, hiring decisions, and legal decision-making.  I explore not only how biases affect decision-making but what people think about their own biases and the best ways to help correct them.
    • Data from a pre-publication independent replication initiative examining ten moral judgement effects

Phil 3.1.18

7:00 – 4:30 ASRC MKT

  • Anonymize (done) and submit paper – done!
  • Finish T’s timeline approach? Finished my version. I think I like it.
  • This may be important: https://twitter.com/jack/status/969234275420655616
    • We’re committing Twitter to help increase the collective health, openness, and civility of public conversation, and to hold ourselves publicly accountable towards progress.11:33 AM – 1 Mar 2018 from San Francisco, CA
      Our friends at @cortico and @socialmachines introduced us to the concept of measuring conversational health. They came up with four indicators: shared attention, shared reality, variety of opinion, and receptivity. Read about their work here: https://www.cortico.ai/blog/2018/2/29/public-sphere-health-indicators
    • We simply can’t and don’t want to do this alone. So we’re seeking help by opening up an RFP process to cast the widest net possible for great ideas and implementations. This will take time, and we’re committed to providing all the necessary resources. RFP: https://blog.twitter.com/official/en_us/topics/company/2018/twitter-health-metrics-proposal-submission.html

     

  • Interactive topic hierarchy revision for exploring a collection of online conversations
    • In the last decade, there has been an exponential growth of asynchronous online conversations (e.g. blogs), thanks to the rise of social media. Analyzing and gaining insights from such discussions can be quite challenging for a user, especially when the user deals with hundreds of comments that are scattered around multiple different conversations. A promising solution to this problem is to automatically mine the major topics from conversations and organize them into a hierarchical structure. However, the resultant topic hierarchy can be noisy and/or it may not match the user’s current information needs. To address this problem, we introduce a novel human-in-the-loop approach that allows the user to revise the topic hierarchy based on her feedback. We incorporate this approach within a visual text analytics system that helps users in analyzing and getting insights from conversations by exploring and revising the topic hierarchy. We evaluated the resulting system with real users in a lab-based study. The results from the user study, when compared to its counterpart that does not support interactive revisions of a hierarchical topic model, provide empirical evidence of the potential utility of our system in terms of both performance and subjective measures. Finally, we summarize generalizable lessons for introducing human-in-the-loop computation within a visual text analytics system
  • Understanding the Promise and Limits of Automated Fact-Checking
    • The furor over so-called ‘fake news’ has exacerbated long-standing concerns about political lying and online rumors in a fragmented media environment, drawing attention to the potential of various automated fact-checking (AFC) technologies to combat online misinformation. This factsheet gives an overview of current efforts to automatically police false claims and misleading content online. Based on a review of recent research and interviews with both fact-checkers and computer scientists working in this area, we find that:
      • Much of the terrain covered by human fact-checkers requires a kind of judgement and sensitivity to context that remains far out of reach for fully automated verification. 
      • Despite progress in automatic verification of a narrow range of simple factual claims, AFC systems will require human supervision for the foreseeable future.
      • The promise of AFC technologies for now lies in tools to assist fact-checkers to identify and investigate claims, and to deliver their conclusions, as effectively as possible.
  • More BIC
    • Now it is the case, and increasingly widely recognized to be, that in games in general there’s no way players can rationally deliberate to a Nash equilibrium. Rather, classical canons of rationality do not in general support playing in Nash equilibria. So it looks as though shared intentions cannot, in the general run of games, by classical canons, be rationally formed! And that means in the general run of life as well. This is highly paradoxical if you think that rational people can have shared intentions. The paradox is not resolved by the thought that when they do, the context is not a game: any situation in which people have to make the sorts of decisions that issue in shared intentions must be a game, which is, after all, just a situation in which combinations of actions matter to the combining parties. (pg 139)
    • Turn to the idea that a joint intention to do (x,y) is rationally produced in 1 and 2 by common knowledge of two conditional intentions: Pl has the intention expressed by ‘I’ll do x if and only if she does y’, and P2 the counterpart one. Clearly P1 doesn’t have the intention to do x if and. only if P2 in fact does y whether or not Pl believes P2 will do y; the right condition must be along the lines of:
      (C1) P1 intends to do x if and only if she believes P2 will do y. (pg 139)

      • So this is in belief space, and belief is based on awareness and trust
    • There are two obstacles to showing this, one superable, the other not, I think. First, there are two Nash equilibria, and nothing in the setup to suggest that some standard refinement (strengthening) of the Nash equilibrium condition will eliminate one. However, I suspect that my description of the situation could be refined without ‘changing the subject’. Perhaps the conditional intention Cl should really be ‘I’ll do x if and only if she’ll do y, and that’s what I would like best’. For example, if x and y are the two obligations in a contract being discussed, it is natural to suppose that Pl thinks that both signing would be better than neither signing. If we accept this gloss then the payoff structure becomes a Stag Hunt – Hi-Lo if both are worse off out of equilibrium than in the poor equilibrium (x’ ,y’). To help the cause of rationally deriving the joint intention (x,y), assume the Hi-Lo case. What are the prospects now? As I have shown in chapter 1, there is no chance of deriving (x,y) by the classical canons, and the only (so far proposed) way of doing to is by team reasoning. (pg 140)
    • The nature of team reasoning, and of the conditions under which it is likely to be primed in individual agents, has a consequence that gives further support to this claim. This is that joint intentions arrived at by the route of team reasoning involve, in the individual agents, a ‘sense of collectivity’. The nature of team reasoning has this effect, because the team reasoner asks herself not ‘What should I do?’ but ‘What should we do?’ So, to team-reason, you must already be in a frame in which first-person plural concepts are activated. The priming conditions for team reasoning have this effect because, as we shall see later in this chapter, team reasoning, for a shared objective, is likely to arise spontaneously in an individual who is in the psychological state of group-identifying with the set of interdependent actors; and to self-identify as a member of a group essentially involves a sense of collectivity. (pg 141)
  • Starting on ONR white paper – first pass banged together
    • Need to add figures and references
  • discovered pandoc, which converts nicely between many files, including LaTex and word. The command that matters is:
    pandoc -s foo.tex -o foo.docx

Phil 2.25.18

Looks like I need to update the DC and the CI 2018 paper with a new reference:

Dynamic Word Embeddings for Evolving Semantic Discovery

  • Zijun YaoYifan Sun, Weicong Ding, Nikhil RaoHui Xiong
  • Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer social trends and language constructs over different periods of human history. However, traditional techniques such as word representation learning do not adequately capture the evolving language structure and vocabulary. In this paper, we develop a dynamic statistical model to learn time-aware word vector representation. We propose a model that simultaneously learns time-aware embeddings and solves the resulting “alignment problem”. This model is trained on a crawled NYTimes dataset. Additionally, we develop multiple intuitive evaluation strategies of temporal word embeddings. Our qualitative and quantitative tests indicate that our method not only reliably captures this evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.
  • Embeddings

 

Phil 2.23.18

6:30 – 8:30, 11:00 – 5:00 ASRC MKT

  • Graphstream with javafx? https://github.com/graphstream/gs-ui-javafx
  • Learning to Cooperate, Compete, and Communicate
    • Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two useful properties: first, there is a natural curriculum — the difficulty of the environment is determined by the skill of your competitors (and if you’re competing against clones of yourself, the environment exactly matches your skill level). Second, a multiagent environment has no stable equilibrium: no matter how smart an agent is, there’s always pressure to get smarter. These environments have a very different feel from traditional environments, and it’ll take a lot more research before we become good at them.
  • Storytelling and Politics: How History, Myths and Narratives Drive Our Decisions (video)
    • A narrative with historical overtones, an emotive connection and credibility not only convinces people, it frames the points of reference they use to evaluate the decision they are being asked to make.
    • Logos Pathos Ethos?
  • Continuing with rewrite. Had to fire up the MiKTex admin console to install wrapfig. Permissions issue?
    • Need to take the description of the maps at the end of the results section and turn into a paragraph.
  • Walk through of presentation this afternoon. Need to set up a skype session and bridge. Went well, I need to make a few fixes. Most importantly I need to put together a 2×2 payoff matrix that covers nomad/flock/stampede

Phil 2.22.18

7:00 – ASRC MKT

  • Long chat with Wajant about the CI 2018 paper. going to work up a new version
    • Started in Docs, but wound up saving out and reworking the LaTex version to keep track of the length.
  • Coincidentally, ONR is soliciting white papers for theoretically-based decision making tools. Five pages plus references for the paper, and a one-page resume.
    • The 5-page body of the white paper shall include the following information:
      • Principal Investigator(s);
      • Relevance of the proposed effort to the research areas described in Section II; (Topic 2, Research Focus Area 1)
        • relationship of the proposed work to current state of art.
      • Technical objective of the proposed effort;
      • Technical approach that will be pursued to meet the objective;
      • A summary of recent relevant technical breakthroughs; and
      • A funding plan showing requested funding per fiscal year.
  • Need to register for TF conference when Aaron gets in. Got hotel and $$ approval.
  • More dimension reduction and belief vectors on twitter