Category Archives: Dissertation

Phil 12.13.19

7:00 – 5:00 ASRC Research & GOES

  • Dissertation. Ethics, ecologies, and Judas Goats
  • Installing project at work. It’s… very different since the last time I used it. Seems better, which is interesting
  • Working on setting up the NextGen AIMS, but making newbie mistakes
  • Submitting a Hadoop/Accumulo feature request for similarity queries

Phil 12.12.19

7:00 – 7:00 ASRC Research

  • 1st International Conference on Autonomic Computing and Self-Organizing Systems – ACSOS 2020
    • Washington DC from August 17 to August 21, 2020
    • Important Dates (tentative)
      • April 1, 2020: Abstract submission deadline
      • April 8, 2020: Paper submission deadline
      • June 8, 2020: Notification to authors
      • July 8, 2020: Camera Ready Deadline
  •  Dissertation
    • Starting on Ethics
    • A Framework for Making Ethical Decisions
      • Decisions about right and wrong permeate everyday life. Ethics should concern all levels of life: acting properly as individuals, creating responsible organizations and governments, and making our society as a whole more ethical. This document is designed as an introduction to making ethical decisions.  It recognizes that decisions about “right” and “wrong” can be difficult, and may be related to individual context. It first provides a summary of the major sources for ethical thinking, and then presents a framework for decision-making.
    • Archipelago-Wide Island Restoration in the Galápagos Islands: Reducing Costs of Invasive Mammal Eradication Programs and Reinvasion Risk
      • Invasive alien mammals are the major driver of biodiversity loss and ecosystem degradation on islands. Over the past three decades, invasive mammal eradication from islands has become one of society’s most powerful tools for preventing extinction of insular endemics and restoring insular ecosystems. As practitioners tackle larger islands for restoration, three factors will heavily influence success and outcomes: the degree of local support, the ability to mitigate for non-target impacts, and the ability to eradicate non-native species more cost-effectively. Investments in removing invasive species, however, must be weighed against the risk of reintroduction. One way to reduce reintroduction risks is to eradicate the target invasive species from an entire archipelago, and thus eliminate readily available sources. We illustrate the costs and benefits of this approach with the efforts to remove invasive goats from the Galapagos Islands. Project Isabela, the world’s largest island restoration effort to date, removed >140,000 goats from >500,000 ha for a cost of US$10.5 million. Leveraging the capacity built during Project Isabela, and given that goat reintroductions have been common over the past decade, we implemented an archipelago-wide goat eradication strategy.
    • Galápagos Monday: When Conservation Means Killing
    • Galápagos Redux: When Is It OK to Kill Goats?
  • Flynn’s proposal defense 11:30 – 1:30
    • Qualitative study of mental models with respect to security?
    • Limited qualitative studies in this area
    • How do you transfer a sophisticated user to a more naive one?
    • The profit model incentivised insecure design
    • Biometric adoption (what about legal?)
    • Experts are more disposed to use biometrics!
    • Government guidance is broad, technical, and hard to use
    • Commercial guidance is narrow and easier, but has a price
    • What was the sampling technique?
    • What does “technical” mean? Technospeak?
    • What about a validation study to show that the approach works more than untrained small business users? What about confounding variables, like whether companies that participate are more likely to be security aware

Phil 12.11.19

7:00 – 5:30 ASRC GOES

  • Call dentist – done!
  • Dissertation – finished designing for populations. Ethics are next


  • Evolver
    • Looking at Keras-Tuner (github) to compare Evolver against
    • Installing. Wow. Big. 355MB?
    • Installed the new optevolver whl. No more timeseriesml2 for tuning! Fixed many broken links in code that used timeseriesml2
    • Tried getting the keras-tuner package installed, but it seems to make the gpu invisible? Anyway, it broke everything and after figuring out that “cpu:0” worked just fine but “gpu:0” didn’t (which required setting up some quick code to prove all that), I cleaned out all the tf packages (tensorglow-gpu, tensorboard, and keras-tuner), and reinstalled tensorflow-gpu. Everything is humming happily again, but I need a less destructive Bayesian system.
    • Maybe this? An Introductory Example of Bayesian Optimization in Python with Hyperopt A hands-on example for learning the foundations of a powerful optimization framework
  • Meetings at Mission
    • Erik was stuck at a luncheon for the first meeting
    • Some new commits from Vadim, but he couldn’t make the meeting
    • Discussion about the Artificial Intelligence and Machine Learning, Technology Summit in April, and the AI Tech Connect Spring. Both are very aligned with industry (like AI + 3D Printing), which is not my thing, so I passed. I did suggest that IEEE ICTAI 2020 might be a good fit. Need to send info to John.
    • Still need to get started on the schedule for version 2 development. Include conferences and prep, and minimal assistance.

Phil 12.10.19

7:00 – ASRC GOES

  • Dissertation – got through the stories and games section. Then de-emphasizing lists, etc.
  • LMN prep (done) and demo
  • Evolver
    • Migrate to cookie cutter – done
    • Github – done
    • Try to make a package – done!
    • Start on paper/tutorial for IEEE ICTAI 2020. Need to compare against Bayesian system. Maybe just use the TF optimizer? Same models, same data, and they are very simple

Phil 12.9.19

7:00 – 8:00 ASRC

  • Saw this on Twitter this morning: Training Agents using Upside-Down Reinforcement Learning
    • Traditional Reinforcement Learning (RL) algorithms either predict rewards with value functions or maximize them using policy search. We study an alternative: Upside-Down Reinforcement Learning (Upside-Down RL or UDRL), that solves RL problems primarily using supervised learning techniques. Many of its main principles are outlined in a companion report [34]. Here we present the first concrete implementation of UDRL and demonstrate its feasibility on certain episodic learning problems. Experimental results show that its performance can be surprisingly competitive with, and even exceed that of traditional baseline algorithms developed over decades of research.
  • I wonder how it compares with Stuart Russell’s paper Cooperative Inverse Reinforcement Learning
    • For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition of the value alignment problem as cooperative inverse reinforcement learning (CIRL). A CIRL problem is a cooperative, partial- information game with two agents, human and robot; both are rewarded according to the human’s reward function, but the robot does not initially know what this is. In contrast to classical IRL, where the human is assumed to act optimally in isolation, optimal CIRL solutions produce behaviors such as active teaching, active learning, and communicative actions that are more effective in achieving value alignment. We show that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, prove that optimality in isolation is suboptimal in CIRL, and derive an approximate CIRL algorithm.
  • Dissertation
    • In the Ethics section, change ‘civilization’ to ‘culture’, and frame it in terms of the simulation – done
    • Last slide should be ‘Thanks for coming to my TED talk’
    • Ping Don’s composer and choreographer, if I can find them
    • Cool! A T-O style universe map (Unmismoobjetivo , via Wikipedia). The logarithmic distance effect is something that I need to look into: universe
  • Evolver
    • Quickstart
    • User’s guide
    • Finished commenting!
    • Flailing on geting the documentation tools to work.
  • ML Seminar
    • Double Crab Cake Platter (2) – 2 Vegetables – $34.00
    • Went over the Evolver. The Ensemble charts really make an impression, but overall, the code walkthrough is too difficult – there are two many moving parts. I need to write a paper with screengrabs that walk through the whole process. I’ll need to evaluate against Bayesian tuners, but I also have architecture search
    • The venue could be IEEE ICTAI 2020: The IEEE International Conference on Tools with Artificial Intelligence (ICTAI) is a leading Conference of AI in the Computer Society providing a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies. It will be in Baltimore, I think.
  • Meeting with Aaron. He thinks that part of the ethics discussion needs to be an addressing of the status quo

Phil 12.5.19

ASRC GOES 7:00 – 4:30, 6:30 – 7:00

  • Write up something for Erik and John?
  • Send gdoc link to Bruce – done
  • apply for TF Dev invite – done
  • Schedule physical! – done
  • Dissertation – more Designing for populations
  • Evolver
    • Comment EvolutionaryOptimizer – almost done
    • Comment ModelWriter
    • Quickstart
    • User’s guide
    • Comment the excel utils?
  • Waikato meeting with Alex and Panos

Phil 12.4.19

7:00 – 8:00 ASRC GOES

  • Dissertation – back to designing for populations
  • Timesheet revisions
  • Applying for MS Project
  • Evolver – more documentation
  • GOES Meeting
    • Bought a copy of MS Project for $15
    • Send Erik a note about permission to charge for TF Dev Conf
    • Good chat with Bruce about many things, including CASSIE as a Cloud service
    • Re-send links to common satellite dictionary
    • Vadim got a pendulum working
  • Meeting with Roger
    • Got a tour of the new building
    • Lots of VR discussion
    • Some academic future options

Phil 12.3.19

7:00 – 4:00 ASRC GOES

  • Dissertation – reworked the last paragraph of the Reflection and reflex section
  • Evolver – more documentation
  • Send this out to the HCC mailing list: The introvert’s academic “alternative networking” guide
  • Arpita’s proposal defense
    • Stanford: Open information extraction (open IE) refers to the extraction of relation tuples, typically binary relations, from plain text, such as (Mark Zuckerberg; founded; Facebook). The central difference from other information extraction is that the schema for these relations does not need to be specified in advance; typically the relation name is just the text linking two arguments. For example, Barack Obama was born in Hawaii would create a triple (Barack Obama; was born in; Hawaii), corresponding to the open domain relation was-born-in(Barack-Obama, Hawaii).
    • Open Information Extraction 5
    • UKG Open Information Extraction
    • Supervised Ensemble of Open IE
    • Datasets
      • AW-OIE
      • AW-OIE-C
      • WEB
      • NYT
      • PENN
    • Why the choice of 100 dimensins for your symentic embedding? How does it compare to other dimensions?
    • Contextual embedding for NLP?
    • Input-Output Hidden Markov Model (version on GitHub)

Phil 12.2.19

December! Yikes!

7:00 – 8:00 ASRC GOES

  • Dissertation
    • Designing for populations
  • Evolver
    • Oh, boy – big IDE updates. Hoping nothing breaks
      • Had to connect back to python
      • TF still works!
    • Commenting and documenting
      • Finished
      • Starting
  • ML seminar (food fro La Madeleine!)
  • Meeting with Aaron M

Phil 11.29.19

ALIFE 2020

  • July 13-18 2020
  • Centre Mont-Royal, Montrial, Quebec
  • Call for papers (Due March 1, 2020)
  • Topics
    • Complex dynamical systems and networks
    • Artificial chemistry, origins of life, computational biology
    • Synthetic biology, protocells and wet artificial life
    • Ecology and evolution
    • Bio-inspired, cognitive and evolutionary robotics, swarms
    • Artificial intelligence and machine learning
    • Perception, cognition, behavior
    • Social systems, artificial and alternative societies
    • Evolution of language, computational linguistics
    • Philosophy of mind, philosophy of science
    • Artificial-life-based art
    • Artificial Life in education
    • For this edition of the conference the special theme is “New Frontiers in AI: What can ALife offer AI?

AI and Compute

  • We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period).[1]

     Since 2012, this metric has grown by more than 300,000x (a 2-year doubling period would yield only a 7x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it’s worth preparing for the implications of systems far outside today’s capabilities

Does play matter? Functional and evolutionary aspects of animal and human play

  • In this paper I suggest that play is a distinctive behavioural category whose adaptive significance calls for explanation. Play primarily affords juveniles practice toward the exercise of later skills. Its benefits exceed its costs when sufficient practice would otherwise be unlikely or unsafe, as is particularly true with physical skills and socially competitive ones. Manipulative play with objects is a byproduct of increased intelligence, specifically selected for only in a few advanced primates, notably the chimpanzee.

Dissertation – slooooooow going setting up the reflection and reflex section. Found some nice stuff on developing skills through training an play though

Phil 11.27.19

7:00 – 3:00 ASRC GOES

  • Dissertation – Added a bit at the beginning of the discussion section to explain why this should fit in the HCI universe. Started working on the Non-human agents part, and am explaining why things like the GPT-2 create their own low dimensional spaces due to the cost of implementation and the incentives of research
  • Evolver – Commenting and tweaking
    • Done with, which contains
      • class ValueAxisType(Enum):
      • class ValueAxis:
      • class EvolveAxis:
      • Example usage, evaluation and class exercising code using
        if __name__ == '__main__':
  • Ran out of space on my primary drive and had to drop everything and fix that

Phil 10.26.19

7:00 – 3:30 ASRC GOES

  • Russian Trolls Aren’t Actually Persuading Americans on Twitter, Study Finds
    • New research highlights a surprising barrier to hacking our democracy: filter bubbles
    • The Duke Polarization Lab is a group of seven faculty members, 21 graduate students, and four undergraduate students who are working to develop new technology to combat political polarization online.
    • Source Article: Assessing the Russian Internet Research Agency’s impact on the political attitudes and behaviors of American Twitter users in late 2017
      • There is widespread concern that Russia and other countries have launched social-media campaigns designed to increase political divisions in the United States. Though a growing number of studies analyze the strategy of such campaigns, it is not yet known how these efforts shaped the political attitudes and behaviors of Americans. We study this question using longitudinal data that describe the attitudes and online behaviors of 1,239 Republican and Democratic Twitter users from late 2017 merged with nonpublic data about the Russian Internet Research Agency (IRA) from Twitter. Using Bayesian regression tree models, we find no evidence that interaction with IRA accounts substantially impacted 6 distinctive measures of political attitudes and behaviors over a 1-mo period. We also find that interaction with IRA accounts were most common among respondents with strong ideological homophily within their Twitter network, high interest in politics, and high frequency of Twitter usage. Together, these findings suggest that Russian trolls might have failed to sow discord because they mostly interacted with those who were already highly polarized. We conclude by discussing several important limitations of our study—especially our inability to determine whether IRA accounts influenced the 2016 presidential election—as well as its implications for future research on social media influence campaigns, political polarization, and computational social science.
    • This makes sense to me, as we are most responsive to those that we align with and least responsive to those that we are opposed to. The problem is that I don’t think the Russians are interested in persuasion. They are interested in sowing discord using polarization, which this technique works splendidly for
  • Dissertation – finished the resilience section
  • Evolver. Undo all the indexing crap – done! And it’s working. Here’s the chart of the exhaustive [X Y] search (1600 possibilities), vs the evolved [X Y Zfunc] search (640,000 possibilities). And it’s actually 30 evolution steps: many_paramaters
  • Here’s all the steps. The most recent is on top. Note that it discovers the mult function early on and never looks back: ExcelEvolve
  • Now I need to fix all the code I broke and write some documentation

Phil 11.25.19

7:00 – 7:00 ASRC GOES

  • Dissertation – more discussion
    • Added Clark’s Grounding in communication to the lit review
    • Added more to the diversity section. Need to fold ecosystem thinking in
  • Evolver – get copied state nailed down
    • That seems to be working in the test harness:
      vzfunc[0]: Zfunc
      d1={'Zfunc': 2.5, 'Zfunc_function': 'plus_func', 'Zvals1': 1.0, 'Zvals2': 1.5}
      d2={'Zfunc': 2.5, 'Zfunc_function': 'plus_func', 'Zvals1': 1.0, 'Zvals2': 1.5}
      vzfunc[1]: Zfunc
      d1={'Zfunc': 4.5, 'Zfunc_function': 'div_func', 'Zvals1': 4.5, 'Zvals2': 1.0}
      d2={'Zfunc': 4.5, 'Zfunc_function': 'div_func', 'Zvals1': 4.5, 'Zvals2': 1.0}
      vzfunc[2]: Zfunc
      d1={'Zfunc': 3.5, 'Zfunc_function': 'mult_func', 'Zvals1': 1.0, 'Zvals2': 3.5}
      d2={'Zfunc': 3.5, 'Zfunc_function': 'mult_func', 'Zvals1': 1.0, 'Zvals2': 3.5}
      vzfunc[3]: Zfunc
      d1={'Zfunc': 7.5, 'Zfunc_function': 'plus_func', 'Zvals1': 3.5, 'Zvals2': 4.0}
      d2={'Zfunc': 7.5, 'Zfunc_function': 'plus_func', 'Zvals1': 3.5, 'Zvals2': 4.0}
    • Still not setting the values of the EvolveAxis History_list correctly when breeding genomes, I think
  • Fika – slides are done-ish
  • ML – seminar
    • Good point – I need to visit with each of the committee to walk them through the dissertation (possibly with slides?) some time in January. Also, use the conclusions to build a TL;DR version.
  • Meeting with Aaron – nope