Category Archives: thesis

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 4.7.18

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 4.3.18

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 3.28.18

7:00 – 5:00 ASRC MKT

    • Aaron found this hyperparameter optimization service: Sigopt
      • Improve ML models 100x faster
      • SigOpt’s API tunes your model’s parameters through state-of-the-art Bayesian optimization.
      • Exponentially faster and more accurate than grid search. Faster, more stable, and easier to use than open source solutions.
      • Extracts additional revenue and performance left on the table by conventional tuning.
    • A Strategy for Ranking Optimization Methods using Multiple Criteria
      • An important component of a suitably automated machine learning process is the automation of the model selection which often contains some optimal selection of hyperparameters. The hyperparameter optimization process is often conducted with a black-box tool, but, because different tools may perform better in different circumstances, automating the machine learning workflow might involve choosing the appropriate optimization method for a given situation. This paper proposes a mechanism for comparing the performance of multiple optimization methods for multiple performance metrics across a range of optimization problems. Using nonparametric statistical tests to convert the metrics recorded for each problem into a partial ranking of optimization methods, results from each problem are then amalgamated through a voting mechanism to generate a final score for each optimization method. Mathematical analysis is provided to motivate decisions within this strategy, and sample results are provided to demonstrate the impact of certain ranking decisions
    • World Models: Can agents learn inside of their own dreams?
      • We explore building generative neural network models of popular reinforcement learning environments[1]. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment.
    • Tweaked the SingleNeuron spreadsheet
    • This came up again: A new optimizer using particle swarm theory (1995)
      • The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
      • New: Particle swarm optimization for hyper-parameter selection in deep neural networks
    • Working with the CIFAR10 data now. Tradeoff between filters and epochs: 
      NB_EPOCH = 10
NUM_FIRST_FILTERS = int(32/2)
NUM_MIDDLE_FILTERS = int(64/2)
OUTPUT_NEURONS = int(512/2)
Test score: 0.8670728429794311
Test accuracy: 0.6972
Elapsed time =  565.9446044602014

NB_EPOCH = 5
NUM_FIRST_FILTERS = int(32/1)
NUM_MIDDLE_FILTERS = int(64/1)
OUTPUT_NEURONS = int(512/1)
Test score: 0.8821897733688354
Test accuracy: 0.6849
Elapsed time =  514.1915690121759

NB_EPOCH = 10
NUM_FIRST_FILTERS = int(32/1)
NUM_MIDDLE_FILTERS = int(64/1)
OUTPUT_NEURONS = int(512/1)
Test score: 0.7007060846328735
Test accuracy: 0.765
Elapsed time =  1017.0974014300725

Augmented imagery
NB_EPOCH = 10
NUM_FIRST_FILTERS = int(32/1)
NUM_MIDDLE_FILTERS = int(64/1)
OUTPUT_NEURONS = int(512/1)
Test score: 0.7243581249237061
Test accuracy: 0.7514
Elapsed time =  1145.673343808471
    • And yet, something is clearly wrong: wrongPNG
    • Maybe try this version? samyzaf.com/ML/cifar10/cifar10.html

 

Phil 3.27.18

7:00 – 6:00 ASRC MKT

  •  
  • Continuing with Keras
    • The training process can be stopped when a metric has stopped improving by using an appropriate callback: 
      keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')
    • How to download and install quiver
    • Tried to get Tensorboard working, but it doesn’t connect to the data right?
    • Spent several hours building a neuron that learns in Excel. I’m very happy with it. What?! SingleNeuron
  • This is a really interesting thread. Stonekettle provoked a response that can be measured for variance, and also for the people (and bots?) who participate.
  • Listening to the World Affairs Council on The End of Authority, about social influence and misinformation
    • 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 all pose a danger. In this first of a three-part series, we focus on the global erosion of trust. Jennifer Kavanagh, political scientist at the RAND Corporation and co-author of “Truth Decay”, and Tom Nichols, professor at the US Naval War college and author of “The Death of Expertise,” are in conversation with Ray Suarez, former chief national correspondent for PBS NewsHour.
  • Science maps for kids
    • Dominic Walliman has created science infographics and animated videos that explore how the fields of biology, chemistry, computer science, physics, and mathematics relate.
  • The More you Know (Wikipedia) might serve as a template for diversity injection
  • A list of the things that Google knows about you via Twitter
  • Collective movement ecology
    • The collective movement of animals is one of the great wonders of the natural world. Researchers and naturalists alike have long been fascinated by the coordinated movements of vast fish schools, bird flocks, insect swarms, ungulate herds and other animal groups that contain large numbers of individuals that move in a highly coordinated fashion ([1], figure 1). Vividly worded descriptions of the behaviour of animal groups feature prominently at the start of journal articles, book chapters and popular science reports that deal with the field of collective animal behaviour. These descriptions reflect the wide appeal of collective movement that leads us to the proximate question of how collective movement operates, and the ultimate question of why it occurs (sensu[2]). Collective animal behaviour researchers, in collaboration with physicists, computer scientists and engineers, have often focused on mechanistic questions [3–7] (see [8] for an early review). This interdisciplinary approach has enabled the field to make enormous progress and revealed fundamental insights into the mechanistic basis of many natural collective movement phenomena, from locust ‘marching bands’ [9] through starling murmurations [10,11].
  • Starting to read Influence of augmented humans in online interactions during voting events
    • Massimo Stella (Scholar)
    • Marco Cristoforetti (Scholar)
    • Marco Cristoforetti (Scholar)
    • Abstract: Overwhelming empirical evidence has shown that online social dynamics mirrors real-world events. Hence, understanding the mechanisms leading to social contagion in online ecosystems is fundamental for predicting, and even manouvering, human behavior. It has been shown that one of such mechanisms is based on fabricating armies of automated agents that are known as social bots. Using the recent Italian elections as an emblematic case study, here we provide evidence for the existence of a special class of highly influential users, that we name “augmented humans”. They exploit bots for enhancing both their visibility and influence, generating deep information cascades to the same extent of news media and other broadcasters. Augmented humans uniformly infiltrate across the full range of identified clusters of accounts, the latter reflecting political parties and their electoral ranks.
    • Bruter and Harrison [19] shift the focus on the psychological in uence that electoral arrangements exert on voters by altering their emotions and behavior. The investigation of voting from a cognitive perspective leads to the concept of electoral ergonomics: Understanding optimal ways in which voters emotionally cope with voting decisions and outcomes leads to a better prediction of the elections.
    • Most of the Twitter interactions are from humans to bots (46%); Humans tend to interact with bots in 56% of mentions, 41% of replies and 43% of retweets. Bots interact with humans roughly in 4% of the interactions, independently on interaction type. This indicates that bots play a passive role in the network but are rather highly mentioned/replied/retweeted by humans.
    • bots’ locations are distributed worldwide and they are present in areas where no human users are geo-localized such as Morocco.
    • Since the number of social interactions (i.e., the degree) of a given user is an important estimator of the in uence of user itself in online social networks [17, 22], we consider a null model fixing users’ degree while randomizing their connections, also known as configuration model [23, 24].
    • During the whole period, bot bot interactions are more likely than random (Δ > 0), indicating that bots tend to interact more with other bots rather than with humans (Δ < 0) during Italian elections. Since interactions often encode the spread of a given content online [16], the positive assortativity highlights that bots share contents mainly with each other and hence can resonate with the same content, be it news or spam.

Phil 3.26.18

But this occasional timidity is characteristic of almost all herding creatures. Though banding together in tens of thousands, the lion-maned buffaloes of the West have fled before a solitary horseman. Witness, too, all human beings, how when herded together in the sheepfold of a theatre’s pit, they will, at the slightest alarm of fire, rush helter-skelter for the outlets, crowding, trampling, jamming, and remorselessly dashing each other to death. Best, therefore, withhold any amazement at the strangely gallied whales before us, for there is no folly of the beasts of the earth which is not infinitely outdone by the madness of men.

—-Moby Dick, The Grand Armada

8:30 – 4:30 ASRC MKT

  • Finished BIC and put the notes on Phlog
  • Exposure to Opposing Views can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social Media
    • 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.
  • More Keras
  • hyperopt is a Python library for optimizing over awkward search spaces with real-valued, discrete, and conditional dimensions.
  • One Hidden Layer: 
    training label size =  60000
test label size =  10000
60000 train samples
10000 test samples
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 128)               100480    
_________________________________________________________________
activation_1 (Activation)    (None, 128)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 128)               16512     
_________________________________________________________________
activation_2 (Activation)    (None, 128)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 128)               16512     
_________________________________________________________________
activation_3 (Activation)    (None, 128)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 10)                1290      
_________________________________________________________________
activation_4 (Activation)    (None, 10)                0         
=================================================================
Total params: 134,794
Trainable params: 134,794
Non-trainable params: 0
  • Two hidden layers: 
    training label size =  60000
test label size =  10000
60000 train samples
10000 test samples
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 128)               100480    
_________________________________________________________________
activation_1 (Activation)    (None, 128)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 128)               16512     
_________________________________________________________________
activation_2 (Activation)    (None, 128)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 128)               16512     
_________________________________________________________________
activation_3 (Activation)    (None, 128)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 10)                1290      
_________________________________________________________________
activation_4 (Activation)    (None, 10)                0         
=================================================================
Total params: 134,794
Trainable params: 134,794
Non-trainable params: 0

Phil 3.23.18

7:00 – 5:00 ASRC MKT

  • Influence of augmented humans in online interactions during voting events
    • Overwhelming empirical evidence has shown that online social dynamics mirrors real-world events. Hence, understanding the mechanisms leading to social contagion in online ecosystems is fundamental for predicting, and even manouvering, human behavior. It has been shown that one of such mechanisms is based on fabricating armies of automated agents that are known as social bots. Using the recent Italian elections as an emblematic case study, here we provide evidence for the existence of a special class of highly influential users, that we name “augmented humans”. They exploit bots for enhancing both their visibility and influence, generating deep information cascades to the same extent of news media and other broadcasters. Augmented humans uniformly infiltrate across the full range of identified clusters of accounts, the latter reflecting political parties and their electoral ranks.
  • Reddit and the Struggle to Detoxify the Internet
    • “Does free speech mean literally anyone can say anything at any time?” Tidwell continued. “Or is it actually more conducive to the free exchange of ideas if we create a platform where women and people of color can say what they want without thousands of people screaming, ‘Fuck you, light yourself on fire, I know where you live’? If your entire answer to that very difficult question is ‘Free speech,’ then, I’m sorry, that tells me that you’re not really paying attention.”
    • This is the difference between discussion and stampede. That seems like it should be statistically detectable.
  • Metabolic Costs of Feeding Predictively Alter the Spatial Distribution of Individuals in Fish Schools
    • We examined individual positioning in groups of swimming fish after feeding
    • Fish that ate most subsequently shifted to more posterior positions within groups
    • Shifts in position were related to the remaining aerobic scope after feeding
    • Feeding-related constraints could affect leadership and group functioning
    • I wonder if this also keeps the hungrier fish at the front, increasing the effectiveness of gradient detections
  • Listening to Invisibilia: The Pattern Problem. There is a section on using machine learning for sociology. Listening to get the author of the ML and Sociology study. Predictions were not accurate. Not published?
  • The Coming Information Totalitarianism in China
    • The real-name system has two purposes. One is the chilling effect, and it works very well on average netizens but not so much on activists. The other and the main purpose is to be able to locate activists and eliminate them from certain information/opinion platforms, in the same way that opinions of dissident intellectuals are completely eradicated from the traditional media.
  • More BIC – Done! Need to assemble notes
    • It is a central component of resolute choice, as presented by McClennen, that (unless new information becomes available) later transient agents recognise the authority of plans made by earlier agents. Being resolute just is recognising that authority (although McClennen’ s arguments for the rationality and psychological feasibility of resoluteness apply only in cases in which the earlier agents’ plans further the common ends of earlier and later agents). This feature of resolute choice is similar to Bacharach’ s analysis of direction, explained in section 5. If the relationship between transient agents is modelled as a sequential game, resolute choice can be thought of as a form of direction, in which the first transient agent plays the role of director; the plan chosen by that agent can be thought of as a message sent by the director to the other agents. To the extent that each later agent is confident that this plan is in the best interests of the continuing person, that confidence derives from the belief that the first agent identified with the person and that she was sufficiently rational and informed to judge which sequence of actions would best serve the person’s objectives. (pg 197)
  • Meeting with celer scientific
  • More TF with Keras. Really good progress

Phil 3.22.18

7:00 – 5:00 ASRC MKT

  • The ONR proposal is in!
  • Promoted the Odyssey thoughts to Phlog
  • More BIC
    • The problem posed by Heads and Tails is not that the players lack a common understanding of salience; it is that game theory lacks an adequate explanation of how salience affects the decisions of rational players. All we gain by adding preplay communication to the model is the realisation that game theory also lacks an adequate explanation of how costless messages affect the decisions of rational players. (pg 180)
  • More TF crash course
    • Invert the ratio for train and validation
    • Add the check against test data
  • Get started on LSTM w/Aaron?

     

Phil 3.20.18

7:00 – 3:00 ASRC MKT

  • What (satirical) denying a map looks like. Nice application of believability.
  • Need to make a folder with all the CUDA bits and Visual Studio to get all my boxes working with GPU tensorflow
  • Assemble one-page resume for ONR proposal
  • More BIC
    • The fundamental principle of this morality is that what each agent ought to do is to co-operate, with whoever else is co-operating, in the production of the best consequences possible given the behaviour of non-co-operators’ (Regan 1980, p. 124). (pg 167)
    • Ordered On Social Facts
      • Are social groups real in any sense that is independent of the thoughts, actions, and beliefs of the individuals making up the group? Using methods of philosophy to examine such longstanding sociological questions, Margaret Gilbert gives a general characterization of the core phenomena at issue in the domain of human social life.

Back to the TF crash course

    • Had to update my numpy from Christoph Gohlke’s Unofficial Windows Binaries for Python Extension Packages. It’s wonderful, but WHY???
    • Also had this problem updating numpy 
      D:\installed>pip3 install "numpy-1.14.2+mkl-cp37-cp37m-win_amd64.whl"
numpy-1.14.2+mkl-cp37-cp37m-win_amd64.whl is not a supported wheel on this platform.
    • That was solved by installing numpy-1.14.2+mkl-cp36-cp36m-win_amd64.whl. Why cp36 works and cp 37 doesn’t is beyond me.
    • Discussions with Aaron about tasks between now and the TFDS
    • Left early due to snow

 

Phil 3.19.18

7:00 – 5:00 ASRC MKT

    • The Perfect Selfishness of Mapping Apps
      • Apps like Waze, Google Maps, and Apple Maps may make traffic conditions worse in some areas, new research suggests.
    • Cambridge Social Decision-Making Lab
    • More BIC
      • Schema 3: Team reasoning (from a group viewpoint) pg 153
        • We are the members of S.
        • Each of us identifies with S.
        • Each of us wants the value of U to be maximized.
        • A uniquely maximizes U.
        • Each of us should choose her component of A.
      • Schema 4: Team reasoning (from an individual viewpoint) pg 159
        • I am a member of S.
        • It is common knowledge in S that each member of S identifies
          with S.
        • It is common knowledge in S that each member of S wants the
          value of U to be maximized.
        • It is common knowledge in S that A uniquely maximizes U.
        • I should choose my component of A.
      • Schema 7: Basic team reasoning pg 161
        • I am a member of S.
        • It is common knowledge in S that each member of S identifies
          with S.
        • It is common knowledge in S that each member of S wants the
          value of U to be maximized.
        • It is common knowledge in S that each member of S knows his
          component of the profile that uniquely maximizes U.
        • I should choose my component of the profile that uniquely
          maximizes U.

          • Bacharach notes to himself the ‘hunch’ that this schema is ‘the basic rational capacity’ which leads to high in Hi-Lo, and that it ‘seems to be indispensable if a group is ever to choose the best plan in the most ordinary organizational circumstances’. Notice that Schema 7 does not require that the individual who uses it know everyone’s component of the profile that maximizes U.
      • His hypothesis is that group identification is an individual’s psychological response to the stimulus of a particular decision situation. It is not in itself a group action. (To treat it as a group action would, in Bacharach’ s framework, lead to an infinite regress.) In the theory of circumspect team reasoning, the parameter w is interpreted as a property of a psychological mechanism-the probability that a person who confronts the relevant stimulus will respond by framing the situation as a problem ‘for us’. The idea is that, in coming to frame the situation as a problem ‘for us’, an individual also gains some sense of how likely it is that another individual would frame it in the same way; in this way, the value of w becomes common knowledge among those who use this frame. (Compare the case of the large cube in the game of Large and Small Cubes, discussed in section 4 of the introduction.) Given this model, it seems that the ‘us’ in terms of which the problem is framed must be determined by how the decision situation first appears to each individual. Thus, except in the special case in which w == 1, we must distinguish S (the group with which individuals are liable to identify, given the nature of the decision situation) from T (the set of individuals who in fact identify with S). pg 163
    • Starting with the updates 
      C:\WINDOWS\system32>pip3 install --upgrade tensorflow-gpu
Collecting tensorflow-gpu
  Downloading tensorflow_gpu-1.6.0-cp36-cp36m-win_amd64.whl (85.9MB)
    100% |████████████████████████████████| 85.9MB 17kB/s
Collecting termcolor>=1.1.0 (from tensorflow-gpu)
  Downloading termcolor-1.1.0.tar.gz
Collecting absl-py>=0.1.6 (from tensorflow-gpu)
  Downloading absl-py-0.1.11.tar.gz (80kB)
    100% |████████████████████████████████| 81kB 6.1MB/s
Collecting grpcio>=1.8.6 (from tensorflow-gpu)
  Downloading grpcio-1.10.0-cp36-cp36m-win_amd64.whl (1.3MB)
    100% |████████████████████████████████| 1.3MB 1.1MB/s
Collecting numpy>=1.13.3 (from tensorflow-gpu)
  Downloading numpy-1.14.2-cp36-none-win_amd64.whl (13.4MB)
    100% |████████████████████████████████| 13.4MB 121kB/s
Collecting astor>=0.6.0 (from tensorflow-gpu)
  Downloading astor-0.6.2-py2.py3-none-any.whl
Requirement already up-to-date: six>=1.10.0 in c:\program files\python36\lib\site-packages (from tensorflow-gpu)
Collecting tensorboard<1.7.0,>=1.6.0 (from tensorflow-gpu)
  Downloading tensorboard-1.6.0-py3-none-any.whl (3.0MB)
    100% |████████████████████████████████| 3.1MB 503kB/s
Collecting protobuf>=3.4.0 (from tensorflow-gpu)
  Downloading protobuf-3.5.2.post1-cp36-cp36m-win_amd64.whl (958kB)
    100% |████████████████████████████████| 962kB 1.3MB/s
Collecting gast>=0.2.0 (from tensorflow-gpu)
  Downloading gast-0.2.0.tar.gz
Requirement already up-to-date: wheel>=0.26 in c:\program files\python36\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: html5lib==0.9999999 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
Requirement already up-to-date: bleach==1.5.0 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
Requirement already up-to-date: markdown>=2.6.8 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
Requirement already up-to-date: werkzeug>=0.11.10 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
Collecting setuptools (from protobuf>=3.4.0->tensorflow-gpu)
  Downloading setuptools-39.0.1-py2.py3-none-any.whl (569kB)
    100% |████████████████████████████████| 573kB 2.3MB/s
Building wheels for collected packages: termcolor, absl-py, gast
  Running setup.py bdist_wheel for termcolor ... done
  Stored in directory: C:\Users\philip.feldman\AppData\Local\pip\Cache\wheels\de\f7\bf\1bcac7bf30549e6a4957382e2ecab04c88e513117207067b03
  Running setup.py bdist_wheel for absl-py ... done
  Stored in directory: C:\Users\philip.feldman\AppData\Local\pip\Cache\wheels\3c\0f\0a\6c94612a8c26070755559045612ca3645fea91c11f2148363e
  Running setup.py bdist_wheel for gast ... done
  Stored in directory: C:\Users\philip.feldman\AppData\Local\pip\Cache\wheels\8e\fa\d6\77dd17d18ea23fd7b860e02623d27c1be451521af40dd4a13e
Successfully built termcolor absl-py gast
Installing collected packages: termcolor, absl-py, setuptools, protobuf, grpcio, numpy, astor, tensorboard, gast, tensorflow-gpu
  Found existing installation: setuptools 38.4.0
    Uninstalling setuptools-38.4.0:
      Successfully uninstalled setuptools-38.4.0
  Found existing installation: protobuf 3.5.1
    Uninstalling protobuf-3.5.1:
      Successfully uninstalled protobuf-3.5.1
  Found existing installation: numpy 1.13.0+mkl
    Uninstalling numpy-1.13.0+mkl:
      Successfully uninstalled numpy-1.13.0+mkl
  Found existing installation: tensorflow-gpu 1.4.0
    Uninstalling tensorflow-gpu-1.4.0:
      Successfully uninstalled tensorflow-gpu-1.4.0
Successfully installed absl-py-0.1.11 astor-0.6.2 gast-0.2.0 grpcio-1.10.0 numpy-1.14.2 protobuf-3.5.2.post1 setuptools-39.0.1 tensorboard-1.6.0 tensorflow-gpu-1.6.0 termcolor-1.1.0
    • That caused the following items to break when I tried running “fully_connected.py” 
      "C:\Program Files\Python36\python.exe" D:/Development/Sandboxes/TensorflowPlayground/HelloPackage/fully_connected_feed.py
Traceback (most recent call last):
  File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\platform\self_check.py", line 75, in preload_check
    ctypes.WinDLL(build_info.cudart_dll_name)
  File "C:\Program Files\Python36\lib\ctypes\__init__.py", line 348, in __init__
    self._handle = _dlopen(self._name, mode)
OSError: [WinError 126] The specified module could not be found

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "D:/Development/Sandboxes/TensorflowPlayground/HelloPackage/fully_connected_feed.py", line 28, in 
    import tensorflow as tf
  File "C:\Program Files\Python36\lib\site-packages\tensorflow\__init__.py", line 24, in 
    from tensorflow.python import *
  File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\__init__.py", line 49, in 
    from tensorflow.python import pywrap_tensorflow
  File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 30, in 
    self_check.preload_check()
  File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\platform\self_check.py", line 82, in preload_check
    % (build_info.cudart_dll_name, build_info.cuda_version_number))
ImportError: Could not find 'cudart64_90.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable. Download and install CUDA 9.0 from this URL: https://developer.nvidia.com/cuda-toolkit
    • Installing Visual Studio for the DLLs before I install the Cuda parts
    • Downloading cuda_9.0.176_win10.exe from here There are also two patches
    • Next set of errors 
      Traceback (most recent call last):
  File "D:/Development/Sandboxes/TensorflowPlayground/HelloPackage/fully_connected_feed.py", line 28, in 
    import tensorflow as tf
  File "C:\Program Files\Python36\lib\site-packages\tensorflow\__init__.py", line 24, in 
    from tensorflow.python import *
  File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\__init__.py", line 49, in 
    from tensorflow.python import pywrap_tensorflow
  File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 30, in 
    self_check.preload_check()
  File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\platform\self_check.py", line 97, in preload_check
    % (build_info.cudnn_dll_name, build_info.cudnn_version_number))
ImportError: Could not find 'cudnn64_7.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable. Note that installing cuDNN is a separate step from installing CUDA, and this DLL is often found in a different directory from the CUDA DLLs. You may install the necessary DLL by downloading cuDNN 7 from this URL: https://developer.nvidia.com/cudnn
  • Looking for cudnn64_7.dll here?
  • Aaaand that seems to be working!
  • Tweaked ONR proposal with Aaron. Discovered that there is one page per PI, so we need to make one-page resumes.

 

 

Phil 3.16.18

7:00 – 4:00 ASRC MKT

    • Umwelt
      • In the semiotic theories of Jakob von Uexküll and Thomas A. Sebeok, umwelt (plural: umwelten; from the German Umwelt meaning “environment” or “surroundings”) is the “biological foundations that lie at the very epicenter of the study of both communication and signification in the human [and non-human] animal”.[1] The term is usually translated as “self-centered world”.[2] Uexküll theorised that organisms can have different umwelten, even though they share the same environment. The subject of umwelt and Uexküll’s work is described by Dorion Sagan in an introduction to a collection of translations.[3] The term umwelt, together with companion terms umgebungand innenwelt, have special relevance for cognitive philosophers, roboticists and cyberneticians, since they offer a solution to the conundrum of the infinite regress of the Cartesian Theater.
    • Benjamin Kuipers
      • How Can We Trust a Robot? (video)
        • Advances in artificial intelligence (AI) and robotics have raised concerns about the impact on our society of intelligent robots, unconstrained by morality or ethics
      • Socially-Aware Navigation Using Topological Maps and Social Norm Learning
        • We present socially-aware navigation for an intelligent robot wheelchair in an environment with many pedestrians. The robot learns social norms by observing the behaviors of human pedestrians, interpreting detected biases as social norms, and incorporating those norms into its motion planning. We compare our socially-aware motion planner with a baseline motion planner that produces safe, collision-free motion. The ability of our robot to learn generalizable social norms depends on our use of a topological map abstraction, so that a practical number of observations can allow learning of a social norm applicable in a wide variety of circumstances. We show that the robot can detect biases in observed human behavior that support learning the social norm of driving on the right. Furthermore, we show that when the robot follows these social norms, its behavior influences the behavior of pedestrians around it, increasing their adherence to the same norms. We conjecture that the legibility of the robot’s normative behavior improves human pedestrians’ ability to predict the robot’s future behavior, making them more likely to follow the same norm.
    • Erin’s defense
      • Nice slides!
      • Slide 4 – narrowing from big question to dissertation topic. Nice way to set up framing
      • Intellectual function vs. adaptive behavior
      • Loss of self-determination
      • Maker culture as a way of having your own high-dimensional vector? Does this mean that the maker culture is inherently more exploratory when compared to …?
      • “Frustration is an easy way to end up in off-task behavior”
      • Peer learning as gradient descent?
      • Emic ethnography
      • Pervasive technology in education
      • Turn-taking
      • Antecedent behavior consequence theory
      • Reducing the burden on the educators. Low-level detection and to draw attention to the educator and annotate. Capturing and labeling
      • Helina – bring the conclusions back to the core questions
      • Diversity injection works! Mainstream students gained broader appreciation of students with disability
      • Q: Does it make more sense to focus on potentially charismatic technologies that will include the more difficult outliers even if it requires a breakthrough? Or to make incremental improvements that can improve accessibility to some people with disabilities faster?
      • Boris analytic software

 

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 3.6.18

7:00 – 4:00 ASRC MKT

  • Endless tweaking of the presentation
    • Pinged Sy – Looks like something on Wednesday. Yep his place around 1:30
  • More BIC
    • The explanatory potential of team reasoning is not confined to pure coordination games like Hi-Lo. Team reasoning is assuredly important for its role in explaining the mystery facts about Hi-Lo; but I think we have stumbled on something bigger than a new theory of behaviour in pure coordination games. The key to endogenous group identification is not identity of interest but common interest giving rise to strong interdependence. There is common interest in Stag Hunts, Battles of the Sexes, bargaining games and even Prisoner’s Dilemmas. Indeed, in any interaction modelable as a ‘mixed motive’ game there is an element of common interest. Moreover, in most of the landmark cases, including the Prisoner’s Dilemma, the common interest is of the kind that creates strong interdependence, and so on the account of chapter 2 creates pressure for group identification. And given group identification, we should expect team reasoning. (pg 144)
    • There is a second evolutionary argument in favour of the spontaneous team-reasoning hypothesis. Suppose there are two alternative mental mechanisms that, given common interest, would lead humans to act to further that interest. Other things being equal, the cognitively cheapest reliable mechanism will be favoured by selection. As Sober and Wilson (1998) put it, mechanisms will be selected that score well on availability, reliability and energy efficiency. Team reasoning meets these criteria; more exactly, it does better on them than the alternative heuristics suggested in the game theory and psychology literature for the efficient solution of common-interest games. (pg 146)
    • BIC_pg 149 (pg 149)
  • Educational resources from machine learning experts at Google
    • We’re working to make AI accessible by providing lessons, tutorials and hands-on exercises for people at all experience levels. Filter the resources below to start learning, building and problem-solving.
  • A Structured Response to Misinformation: Defining and Annotating Credibility Indicators in News Articles
    • The proliferation of misinformation in online news and its amplification by platforms are a growing concern, leading to numerous efforts to improve the detection of and response to misinformation. Given the variety of approaches, collective agreement on the indicators that signify credible content could allow for greater collaboration and data-sharing across initiatives. In this paper, we present an initial set of indicators for article credibility defined by a diverse coalition of experts. These indicators originate from both within an article’s text as well as from external sources or article metadata. As a proof-of-concept, we present a dataset of 40 articles of varying credibility annotated with our indicators by 6 trained annotators using specialized platforms. We discuss future steps including expanding annotation, broadening the set of indicators, and considering their use by platforms and the public, towards the development of interoperable standards for content credibility.
    • Slide deck for above
  • Sprint review
    • Presented on Talk, CI2018 paper, JuryRoom, and ONR proposal.
  • ONR proposal
    • Send annotated copy to Wayne, along with the current draft. Basic question is “is this how it should look? Done
    • Ask folks at school for format help?