Phil 11.19.18

6:00 – 2:30 ASRC PhD, NASA

  • Antonio didn’t make much in the way of modifications, so I think the paper is now done. Ask tomorrow if it’s alright to put this version on ArXive.
  • ‘Nothing on this page is real’: How lies become truth in online America
    • A new message popped onto Blair’s screen from a friend who helped with his website. “What viral insanity should we spread this morning?” the friend asked. “The more extreme we become, the more people believe it,” Blair replied.
    • “No matter how racist, how bigoted, how offensive, how obviously fake we get, people keep coming back,” Blair once wrote, on his own personal Facebook page. “Where is the edge? Is there ever a point where people realize they’re being fed garbage and decide to return to reality?”
  • Blind appears to be the LinkedIn version of Secret/Whisper
    • Blind is an anonymous social networking platform for professionals. Work email-verified professionals can connect with coworkers and other company/industry professionals by holding meaningful conversations on a variety of different topics.
  • Started reading Third Person. It really does look like the literature is thin:
    • A crucial consideration when editing our previous volume, Second Person, was to give close attention to the underexamined area of tabletop role-playing games. Generally speaking, what scholarly consideration these games have received has cast them as of historical interest, as forerunners of today’s digital games. In his chapter here, Ken Rolston-the designer of major computer role-playing games such as the Elder Scrolls titles Morrowind and Oblivion-says that his strongest genre influences are tabletop RPGs and live-action role-playing (IARP) games. He considers nonwired RPGs to be a continuing vital force, and so do we. (Page 7)
  • Quick meeting with Wayne
    • CHI Play 2019
      • CHI PLAY is the international and interdisciplinary conference (by ACM SIGCHI) for researchers and professionals across all areas of play, games and human-computer interaction (HCI). We call this area “player-computer interaction.”  22–25 October 2019
    • Conversation Map: An Interface for Very-Large-Scale Conversations
      • Very large-scale conversation (VLSC) involves the exchange of thousands of electronic mail (e-mail) messages among hundreds or thousands of people. Usenet newsgroups are good examples (but not the only examples) of online sites where VLSCs take place. To facilitate understanding of the social and semantic structure of VLSCs, two tools from the social sciences—social networks and semantic networks—have been extended for the purposes of interface design. As interface devices, social and semantic networks need to be flexible, layered representations that are useful as a means for summarizing, exploring, and cross-indexing the large volumes of messages that constitute the archives of VLSCs. This paper discusses the design criteria necessary for transforming these social scientific representations into interface devices. The discussion is illustrated with the description of the Conversation Map system, an implemented system for browsing and navigating VLSCs.
    • Terra Nova blog
    • Nic Ducheneaut
      • My research pioneered the use of large-scale, server-side data for modeling behavior in video games. At Xerox PARC I founded the PlayOn project, which conducted the longest and largest quantitative study of user behavior in World of Warcraft (500,000+ players observed over 5 years). At Ubisoft, I translated my findings into practical recommendations for both video game designers and business leaders. Today, as the co-founder and technical lead of Quantic Foundry, I help game companies bridge analytics and game design to maximize player engagement and retention.
    • Nick Yee
      • I’m the co-founder and analytics lead of Quantic Foundry, a consulting practice around game analytics. I combine social science, data science, and an understanding of the psychology of gamers to generate actionable insights in gameplay and game design.
    • Celia pierce
      • Celia Pearce is a game designer, artist, author, curator, teacher, and researcher specializing in multiplayer gaming and virtual worlds, independent, art, and alternative game genres, as well as games and gender. 
    • T. L. Taylor
      • T.L. Taylor is is a qualitative sociologist who has focused on internet and game studies for over two decades. Her research explores the interrelations between culture and technology in online leisure environments. 
    • MIT10: A Reprise – Democracy and Digital Media
      • Paper proposals might address the following topics/issues:
        • politics of truth/lies, alternative facts
        • media, authoritarianism, and polarization
        • diversity in gaming / livestreaming / esports
        • making or breaking publics with algorithmic cultures/machine learning/AI
        • environmental media (from medium theory to climate change) and activism
        • media infrastructures as public utilities or utility publics?
        • social media, creating consensus, and bursting filter bubbles
        • designing media technologies for inclusion
        • the #metoo movement and its impact
        • social media platforms (FaceBook, Twitter, Instagram, etc), politics, and civic responsibility
        • Twitter, viral videos, and the new realities of political advertising
      • Please submit individual paper proposals, which should include a title, author(s) name, affiliation, 250-word abstract, and 75-word biographical statement to this email address: media-in-transition@mit.edu — by February 1, 2019. Early submissions are encouraged and we will review them on a rolling basis. Full panel proposals of 3 to 4 speakers can also be submitted, and should include a panel title and the details listed above for each paper, as well as a panel moderator. We notify you of the status of your proposals by February 15, 2019 at the latest.
  • Continuing Characterizing Online Public Discussions through Patterns of Participant Interactions. Sheesh, that’s a long article. 21 pages!
  • More Grokking: Here’s a very simple full NN:
    # based on https://github.com/iamtrask/Grokking-Deep-Learning/blob/master/Chapter6%20-%20Intro%20to%20Backpropagation%20-%20Building%20Your%20First%20DEEP%20Neural%20Network.ipynb
    import numpy as np
    import matplotlib.pyplot as plt
    
    # variables ------------------------------------------
    
    # one weight for each column (or light - the things we're sampling)
    weight_array = np.random.rand(3)
    alpha = 0.1
    
    # the samples. Columns are the things we're sampling
    streetlights_array = np.array([[1, 0, 1],
                                   [ 0, 1, 1 ],
                                   [ 0, 0, 1 ],
                                   [ 1, 1, 1 ],
                                   [ 0, 1, 1 ],
                                   [ 1, 0, 1 ]])
    
    # The data set we want to map to. Each entry in the array matches the corresponding streetlights_array roe
    walk_vs_stop_array = np.array([0, 1, 0, 1, 1, 0])
    
    error_plot_mat = [] # for drawing plots
    weight_plot_mat = [] # for drawing plots
    iter = 0
    max_iter = 1000
    epsilon = 0.001
    total_error = 2 * epsilon
    
    while total_error > epsilon:
        total_error = 0
        for row_index in range(len(walk_vs_stop_array)):
            input_array = streetlights_array[row_index]
            goal_prediction = walk_vs_stop_array[row_index]
    
            prediction = input_array.dot(weight_array)
            error = (goal_prediction - prediction) ** 2
            total_error += error
    
            delta = prediction - goal_prediction
            weight_array = weight_array - (alpha * (input_array * delta))
    
            print("[{}] Error: {}, Weights: {}".format(iter, total_error, weight_array))
            error_plot_mat.append([total_error, error])
            weight_plot_mat.append(weight_array.copy())
    
            iter += 1
            if iter > max_iter:
                break
    
    
    f1 = plt.figure(1)
    plt.plot(error_plot_mat)
    plt.title("error")
    plt.legend(["total_error", "error"])
    #f1.show()
    
    f2 = plt.figure(2)
    plt.plot(weight_plot_mat)
    names = []
    for i in range(len(weight_array)):
        names.append("weight[{}]".format(i))
    plt.legend(names)
    plt.title("weights")
    
    #f2.show()
    plt.show()
  • And here is it learning

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