Monthly Archives: May 2019

Phil 5.31.19

7:00 – 3:00 NASA GEOS

  • Got a proposal from Panos and his group. Michael Mayo is interested in running Google’s Universal Sentence Encoder on the data
  • Defending Against Neural Fake News
    • Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural fake news: targeted propaganda that closely mimics the style of real news. 
      Modern computer security relies on careful threat modeling: identifying potential threats and vulnerabilities from an adversary’s point of view, and exploring potential mitigations to these threats. Likewise, developing robust defenses against neural fake news requires us first to carefully investigate and characterize the risks of these models. We thus present a model for controllable text generation called Grover. Given a headline like `Link Found Between Vaccines and Autism,’ Grover can generate the rest of the article; humans find these generations to be more trustworthy than human-written disinformation. 
    • Developing robust verification techniques against generators like Grover is critical. We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data. Counterintuitively, the best defense against Grover turns out to be Grover itself, with 92% accuracy, demonstrating the importance of public release of strong generators. We investigate these results further, showing that exposure bias — and sampling strategies that alleviate its effects — both leave artifacts that similar discriminators can pick up on. We conclude by discussing ethical issues regarding the technology, and plan to release Grover publicly, helping pave the way for better detection of neural fake news.
  • Retooling CHIPLAY for GROUP. Deadline is June 21
  • More JASS tweaking:
    • Switch the urls in the paper to antibubbles to anonymize – done

Phil 5.30.19

7:00 – 2:30 NASA GEOS

  • CHI Play reviews should come back today!
    • Darn – rejected. From the reviews, it looks like we are in the same space, but going a different direction – an alignment problem. Need to read the reviews in detail though.
    • Some discussion with Wayne about GROUP
  • More JASSS paper
    • Added some broader thoughts to the conclusion and punched up the subjective/objective map difference
  • Start writing proposal for Bruce
    • Simple simulation baseline for model building
    • Develop models for
      • Extrapolating multivariate (family) values, including error conditions
      • Classify errors
      • Explainable model, that has sensor inputs drive the controls of the model that produce outputs that are evaluated against the original inputs using RL
      • “Safer” ML using Sanhedrin approach
  • EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling
    • In our ICML 2019 paper, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, we propose a novel model scaling method that uses a simple yet highly effective compound coefficient to scale up CNNs in a more structured manner. Unlike conventional approaches that arbitrarily scale network dimensions, such as width, depth and resolution, our method uniformly scales each dimension with a fixed set of scaling coefficients. Powered by this novel scaling method and recent progress on AutoML, we have developed a family of models, called EfficientNets, which superpass state-of-the-art accuracy with up to 10x better efficiency (smaller and faster). EfficientNet

Phil 5.28.19

Phil 7:00 – 5:00 ASRC NASA GEOS

  • Factors Motivating Customization and Echo Chamber Creation Within Digital News Environments
    • With the influx of content being shared through social media, mobile apps, and other digital sources – including fake news and misinformation – most news consumers experience some degree of information overload. To combat these feelings of unease associated with the sheer volume of news content, some consumers tailor their news ecosystems and purposefully include or exclude content from specific sources or individuals. This study explores customization on social media and news platforms through a survey (N = 317) of adults regarding their digital news habits. Findings suggest that consumers who diversify their online news streams report lower levels of anxiety related to current events and highlight differences in reported anxiety levels and customization practices across the political spectrum. This study provides important insights into how perceived information overload, anxiety around current events, political affiliations and partisanship, and demographic characteristics may contribute to tailoring practices related to news consumption in social media environments. We discuss these findings in terms of their implications for industry, policy, and theory
  • More JASSS paper
  • Installing new IntelliJ and re-indexing
  • Discovered a few bugs with the JsonUtils.find. Fixed and submitted a version to StackOverflow. Eeeep!

Phil 5.26.19

Tikkun olam (Hebrew for “world repair”) has come to connote social action and the pursuit of social justice. The phrase has origins in classical rabbinic literature and in Lurianic kabbalah, a major strand of Jewish mysticism originating with the work of the 16th-century kabbalist Isaac Luria.

Cooperation in large-scale human societies — What, if anything, makes it unique, and how did it evolve?

  • There is much controversy about whether the cooperative behaviours underlying the functioning of human societies can be explained by individual self-interest. Confusion over this has frustrated the understanding of how large-scale societies could ever have evolved and be maintained. To clarify this situation, we here show that two questions need to be disentangled and resolved. First, how exactly do individual social interactions in small- and large-scale societies differ? We address this question by analysing whether the exchange and collective action dilemmas in large-scale societies differ qualitatively from those in small-scale societies, or whether the difference is only quantitative. Second, are the decision-making mechanisms used by individuals to choose their cooperative actions driven by self-interest? We address this question by extracting three types of individual decision-making mechanism (three type of “minds”) that have been assumed in the literature, and compare the extent to which these decision-making mechanisms are sensitive to individual material payoff. After addressing the above questions, we ask: what was the key change from other primates that allowed for cooperative behaviours to be maintained as the scale of societies grew? We conclude that if individuals are not able to refine the social interaction mechanisms underpinning cooperation, i.e change the rules of exchange and collective action dilemmas, then new mechanisms of transmission of traits between individuals are necessary. Examples are conformity-biased or prestige-biased social learning, as stressed by the cultural group selection hypothesis. But if individuals can refine and adjust their social interaction mechanisms, then no new transmission mechanisms are necessary and cooperative acts can be sustained in large-scale societies entirely by way of self-interest, as stressed by the institutional path hypothesis. Overall, our analysis contributes to the theoretical foundation of the evolution of human social behaviour.

Phil 5.24.19

7:00 – 3:30 ASRC GEOS

Phil5.23.19

7:00 – 5:00 ASRC GEOS

  • Saw 4×3000 with David and Roger last night. The VR lab seems to be a thing. Need to go down and have a chat, possibly about lists, stories, maps and games
  • Found the OECD Principles on Artificial Intelligence. I haven’t had a chance to actually *read* any of it, but I did create a “Treaty Lit” folder in the Sanhedrin folder and put pdf versions of them. I ran my tool over them and got the following probe:
    • state cyber cybercrime agree united china mechanism
  • Putting that into Google Scholar returns some good hits as well, though I haven’t gotten a chance to do anything beyond that.
  • JASSS paper
    • Changing “consensus” to “alignment”, and breaking many paragraphs. I think the setup of the space in the introduction is better now.
  • Got caught up on NESDIS. Worked some on the slide deck, which I finally got back. Scheduled a walkthrough with T tomorrow
  • GEOS AI/ML meeting at NSOF. Still trying to figure out roles and responsibilities. I think the Sanhedrin concept will help Bruce formalize our contributions.

Phil 5.16.18

7:00 – 9:00 ASRC GEOS/AIMES

  • Worked on the slides a bit
  • Adding changes to the JASSS paper
  • Waiting for meeting
  • Meeting went well, I think? Funding appears to be solid, and I’m now a “Futurist”
  • Meeting with Shimei’s group. Fatima might be interested in ML summer work
  • Meeting with Aaron. Fleshed out the Sanhedrin-17a concept

Phil 5.14.19

7:00 – 8:00 ASRC NASA GEOS-R

  • More Dissertation
  • Break out the network slides to “island” (initial state), “star” (radio) “cyclic star” (talk radio), “dense” social media
  • MatrixScalar
  • 7:30 Waikato meeting.
    • Walked through today’s version, which is looking very nice
    • Went over tasking spreadsheets

Phil 5.13.19

7:00 – 3:00 ASRC NASA GEOS-R

Phil 5.10.19

7:00 – 4:00 ASRC NASA GOES

  • Tensorflow Graphics? TF-Graphics
  • An End-to-End AutoML Solution for Tabular Data at KaggleDays
  • More dissertation writing. Added a bit on The Sorcerer’s Apprentice and finished my first pass at Moby-Dick
  • Add pickling to MatrixScalar – done! 
    def save_class(the_class, filename:str):
    print("save_class")
    # Its important to use binary mode
    dbfile = open(filename, 'ab')

    # source, destination
    pickle.dump(the_class, dbfile)
    dbfile.close()


def restore_class(filename:str) -> MatrixScalar:
    print("restore_class")
    # for reading also binary mode is important
    dbfile = open(filename, 'rb')
    db = pickle.load(dbfile)
    dbfile.close()
    return db
  • Added flag to allow unlimited input buffer cols. It automatically sizes to the max if no arg for input_size
  • NOTE: Add a “notes” dict that is added to the setup tab for run information