Category Archives: Defense

Phil 8.5.20

Wajanat’s defense at 10:00!

Train your TensorFlow model on Google Cloud using TensorFlow Cloud

import

How QAnon Creates a Dangerous Alternate Reality

  • Game designer Adrian Hon says the conspiracy theory parallels the immersive worlds of alternate reality games.

GPT-2 Agents

  • Finish the results section – done!. Need to do Discussion (done!), Future Work (done!), and Conclusions(done!)
  • Looked on Scholar for “language model sociology GPT” and didn’t find anything, so I’m hopeful that this is still a pretty novel idea

Book

  • Add in more content to the Overleaf project

GOES

  • 2:00 Meeting

#COVID group 4:30

  • Write translator code for tomorrow and get that running

Read paper 5 – done. Started great but no results section!

Phil 3.12.20

7:00 – 6:00 ASRC GOES

Phil 2.20.20

7:00 – ?? ASRC GOES / PhD

  • Defense
    • Fixes as per Wayne
    • Walkthrough and timing
    • Order food (sandwiches, dessert, water)
    • 1:00 – 2:00pm ITE 459
  • Set up dev box
    • Intellij
    • Project
    • FF
    • GitHub desktop
    • Set up non-admin user
    • detach admin account from MS
  • Waikato meeting at 6:30

Phil 2.19.20

7:00 – 8:00 ASRC GOES

disinfoOps
  • Defense practice and tweaking
  • Continue setting up workstation
    • Java – done
    • Python – done
      • Pandas3d
      • Panda
      • Scikit-learn
      • TF 2.0
      • etc
    • TortoiseSVN – done
    • WinSCP – done
    • PuTTY – done
    • XAMPP – done
    • gVIM – done
    • MikTex – done
    • TexStudio – done
    • Adobe Creative Cloud – done
      • Acrobat- done
      • Illustrator- done
      • Photoshop – done
    • Intellij
    • Office- done
    • Project
    • Chrome- done
    • FF
    • GitHub desktop
    • Set up non-admin user
  • Mission Drive meetings

Phil 2.17.20

Pinged Aaron about getting together today – 3:00

  • Went through the talk he says it seems pretty solid
  • Also dropped by Don’s office to say hi

Generated a new map where a stampede stops when it hits the edge

Played around with the “Curse of Dimensionality” slide

Added some background on fact-checking to the RQ slide

Start fixing Alienware

  • Mount drive – done
  • Plug everything in and connect network – done
  • Copy files
  • Fix environment variables

Online Conspiracy Theories: The WIRED Guide

  • Everything you need to know about George Soros, Pizzagate, and the Berenstain Bears.

 

Phil 2.14.20

7:00 – 8:30 ASRC GOES

This document describes the Facebook Full URL shares dataset, resulting from a collaboration between Facebook and Social Science One. It is for Social Science One grantees and describes the dataset’s scope, structure, fields, and privacy-preserving characteristics. This is the second of two planned steps in the release of this “Full URLs dataset”, which we described at socialscience.one/blog/update-social-science-one.

Judging Truth

    • Deceptive claims surround us, embedded in fake news, advertisements, political propaganda, and rumors. How do people know what to believe? Truth judgments reflect inferences drawn from three types of information: base rates, feelings, and consistency with information retrieved from memory. First, people exhibit a bias to accept incoming information, because most claims in our environments are true. Second, people interpret feelings, like ease of processing, as evidence of truth. And third, people can (but do not always) consider whether assertions match facts and source information stored in memory. This three-part framework predicts specific illusions (e.g., truthiness, illusory truth), offers ways to correct stubborn misconceptions, and suggests the importance of converging cues in a post-truth world, where falsehoods travel further and faster than the truth.

       

 

  •  Dissertation
    • Practice! 52 minutes, 57 seconds
    • Maybe meeting with Wayne? Nope
  • Pack, move, unpack, setup
    • Bring ethernet cables! done
    • Moved out – done
    • Moved in – not done, but ready to unpack
  • Recovered my information for GSAW and TFDev
  • Write quick proposals for:
    • cybermap – done
    • Synthetic data as a service – done
    • White paper – kinda?

Phil 2.13.20

7:00 – 4:00 ASRC (charge number?)

  • AI/ML workshop
  • Color code timeline – done
  • Pick up computer? – done.
    • It turns out that the alienware OEM power supply uses standard connectors in a non-standard way. When I had to use the OEM SATA low-profile connector, I tripped the power supply and also blew out the HD. Ordered a replacement SATA SSD
    • Rebuild travel folders
    • Copy laptop’s d: dev and program files folders onto new SSD

Phil 2.12.20

7:00 – 8:00pm ASRC PhD, GOES

  • Create figures that show an agent version of the dungeon
  • Replicate the methods and detailed methods of the cartography slides
  • Text for each group by room can be compared by the rank difference between them and the overall. Put that in a spreadsheet, plot and maybe determine the DTW value?
    • Add the sim version of the dungeon and the rank comparison to the dissertation
  • Put all ethics on one slide – done
  • Swapped out power supply, but now the box won’t start. Dropped off to get repaired
  • Corporate happy hour

Phil 2.11.20

7:00 – 9:00 ASRC GOES

The brains of birds synchronize when they sing duets

  • When a male or female white-browed sparrow-weaver begins its song, its partner joins in at a certain time. They duet with each other by singing in turn and precisely in tune. A team led by researchers from the Max Planck Institute for Ornithology in Seewiesen used mobile transmitters to simultaneously record neural and acoustic signals from pairs of birds singing duets in their natural habitat. They found that the nerve cell activity in the brain of the singing bird changes and synchronizes with its partner when the partner begins to sing. The brains of both animals then essentially function as one, which leads to the perfect duet. (original article: Duets recorded in the wild reveal that interindividually coordinated motor control enables cooperative behavior)

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

  • Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge

 

  •  Defense
    • Need to think about how to discuss maps like the T-O and belief space maps (flocking and stampeding projections?) are attention maps as well. Emphasizing well-triangulated but less-attended areas is a potential good. Compare to how maps opened up areas for exploration and exploitation, but this is constructive and not extractive
  • Admin -done
  • Walkthrough of Aaron’s slides
    • Showed him how to outline boxes and reduce the filesize
  • Shimei’s group
    • Walkthrough of the slides
    • Strengthen the connection between the sim and the human study

Phil 2.10.20

7:00 – 5:30 ASRC GOES

  • Defense
    • Slides and walkthrough
    • First pass is thirty minutes too long!
  • Trying to get back admin – maybe? Need to get the machine unlocked (again) tomorrow)

Phil 2.7.20

7:00 – 5:00 ASRC GOES

tacj

  •  Dissertation
    • Fixed some math
  •  Defense
    • Added a math slide for agent calculations – done
    • Need to add the sim justification slide, based on Aaron’s comments last night – done
    • Drop off signed papers at graduate school this morning – done
    • Working on the abstract – done and submitted!

Phil 2.6.20

7:00 – 4:00  ASRC GOES

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

  • Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have exposed the power of optimizing millions of synaptic weights over millions of observations to operate robustly in real-world contexts. These models do not learn simple, human-interpretable rules or representations of the world; rather, they use local computations to interpolate over task-relevant manifolds in a high-dimensional parameter space. Counterintuitively, similar to evolutionary processes, over-parameterized models can be simple and parsimonious, as they provide a versatile, robust solution for learning a diverse set of functions. This new family of direct-fit models present a radical challenge to many of the theoretical assumptions in psychology and neuroscience. At the same time, this shift in perspective establishes unexpected links with developmental and ecological psychology.

 

  •  Defense
    • Discussion slides
      • contributions – done
      • designing for populations 1 & 2- done
      • Diversity and resilience- done
      • Non-human agents- done
      • Reflection and reflex- done
      • Ethical considerations- done
      • Ethics of diversity injection
      • Ethics of belief space cartography
  • GOES
    • Status report
  • Get signature from Aaron at 7:30