7:00 – ASRC MKT
- Starting on the SASO slides. Found my diversity injection slide story:
- Max Hawkins
- (From NPR’s Invisibilia) “I just started thinking about these loops that we get into,” he says. “And about how the structure of your life … completely determines what happens in it.” Max’s once beautiful routine suddenly seemed unfulfilling. He felt like he was growing closer to people in his own bubble and becoming isolated from those outside of it. “There was something … that just made me feel trapped,” he says. “Like I was reading a story that I’d read before or I was playing out someone else’s script.” As any computer developer would do, Max turned to technology to craft his way out — a series of randomization applications.
- Reading Review: Totalitarianism: The Revised Standard Version
- …they have chosen to identify totalitarianism in terms of a set of six interrelated traits or characteristics-Fried- rich’s oft-referred-to “totalitarian syndrome” (9-io).25 The syndrome includes an official ideology (orientation), a single party typically led by one man (dimension reduction), a terroristic police (herding), a communications monopoly (social influence horizon), a weapons monopoly (??) and a centrally directed economy (dimension reduction)
- Continued to spin up on LSTM effort. Got my dev environment COMPLETELY up to date. Continued with Deep learning & Keras
3:00 – 5:00 Fika & meeting with Wayne
- Worked on the slides for PhD status. I realize that this is actually a good time to have demos with conclusions.
- Talked about options if IRAD falls through
- Need to think about what are the best ways for the work to have impact
Modeling relatedness and demography in social evolution
- With any theoretical model, the modeler must decide what kinds of detail to include and which simplifying assumptions to make. It could be assumed that models that include more detail are better, or more correct. However, no model is a perfect description of reality and the relative advantage of different levels of detail depends on the model’s empirical purpose. We consider the specific case of how relatedness is modeled in the field of social evolution. Different types of model either leave relatedness as an independent parameter (open models), or include detail for how demography and life cycle determine relatedness (closed models). We exploit the social evolution literature, especially work on the evolution of cooperation, to analyze how useful these different approaches have been in explaining the natural world. We find that each approach has been successful in different areas of research, and that more demographic detail is not always the most empirically useful strategy.
Listening to We Can’t Talk Anymore? Understanding the Structural Roots of Partisan Polarization and the Decline of Democratic Discourse in 21st Century America. Very Tajfel
- David Peritz
- Political polarization, accompanied by negative partisanship, are striking features of the current political landscape. Perhaps these trends were originally confined to politicians and the media, but we recently reached the point where the majority of Americans report they would consider it more objectionable if their children married across party lines than if they married someone of another faith. Where did this polarization come from? And what it is doing to American democracy, which is housed in institutions that were framed to encourage open deliberation, compromise and consensus formation? In this talk, Professor David Peritz will examine some of the deeper forces in the American economy, the public sphere and media, political institutions, and even moral psychology that best seem to account for the recent rise in popular polarization.
Sent out a Doodle to nail down the time for the PhD review
Went looking for something that talks about the cognitive load for TIT-FOR-TAT in the Iterated Prisoner’s Dilemma and can’t find anything. Did find this though, that is kind of interesting: New tack wins prisoner’s dilemma. It’s a collective intelligence approach:
- Teams could submit multiple strategies, or players, and the Southampton team submitted 60 programs. These, Jennings explained, were all slight variations on a theme and were designed to execute a known series of five to 10 moves by which they could recognize each other. Once two Southampton players recognized each other, they were designed to immediately assume “master and slave” roles – one would sacrifice itself so the other could win repeatedly.
- Nick Jennings
- Professor Jennings is an internationally-recognized authority in the areas of artificial intelligence, autonomous systems, cybersecurity and agent-based computing. His research covers both the science and the engineering of intelligent systems. He has undertaken fundamental research on automated bargaining, mechanism design, trust and reputation, coalition formation, human-agent collectives and crowd sourcing. He has also pioneered the application of multi-agent technology; developing real-world systems in domains such as business process management, smart energy systems, sensor networks, disaster response, telecommunications, citizen science and defence.
- Sarvapali D. (Gopal) Ramchurn
- I am a Professor of Artificial Intelligence in the Agents, Interaction, and Complexity Group (AIC), in the department of Electronics and Computer Science, at the University of Southampton and Chief Scientist for North Star, an AI startup. I am also the director of the newly created Centre for Machine Intelligence. I am interested in the development of autonomous agents and multi-agent systems and their application to Cyber Physical Systems (CPS) such as smart energy systems, the Internet of Things (IoT), and disaster response. My research combines a number of techniques from Machine learning, AI, Game theory, and HCI.
7:00 – 4:30 ASRC MKT
- SASO Travel request
- SASO Hotel – done! Aaaaand I booked for August rather than September. Sent a note to try and fix using their form. If nothing by COB try email.
- Potential DME repair?
- Starting Deep Learning with Keras. Done with chapter one
- Two seedbank lstm text examples:
- Generate Shakespeare using tf.keras
- This notebook demonstrates how to generate text using an RNN with tf.keras and eager execution.This notebook is an end-to-end example. When you run it, it will download a dataset of Shakespeare’s writing. The notebook will then train a model, and use it to generate sample output.
- This notebook will let you input a file containing the text you want your generator to mimic, train your model, see the results, and save it for future use all in one page.
“There was no colusion“…”
Anyone involved in that meddling to justice.“
Premises for Data Science Magical Realism
- What follows are some premises for data science magical realism stories based (very, very loosely) on experiences I’ve had or heard about — premises, that is, for stories about impossible, absurd, magical things happening to data scientists in ordinary data science situations. Enjoy!
- More from David Masad
Program Synthesis in 2017-18
- A high-level overview of the recent ideas and representative papers in program synthesis as of mid-2018.
- Alex (Oleksandr) Polozov, a researcher in the Deep Procedural Intelligence group at Microsoft Research AI, Redmond. I work on neural program synthesis from input-output examples and natural language, intersections of machine learning and software engineering, and neuro-symbolic architectures. I am particularly interested in combining neural and symbolic techniques to tackle the next generation of AI problems, including program synthesis, planning, and reasoning.
UMAP Uniform Manifold Approximation and Projection for Dimension Reduction | SciPy 2018 |(video) (paper)
- UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP as described has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.
- This could be nice for building maps
7:00 – 5:00 ASRC MKT
- Progress on getting my keys back!
- Got everyone’s response on the Doodle, but only 4 of the 5 line up…
- Finish first pass through PhD review slides
- Start SASO slides and poster?
- Continue with exporting terms from the sim and importing them into python. One of the things that will matter is the tagging of the data with the seed terms from the sim as well as the cell name so that reconstructions can be compared for accuracy.
- Added the cell location to each <sampleData> so that there can be some kind of tagging/ground truth about the maps we’re inferring.
- Working on iterating through the etree hierarchy. I can now read in the file, parse it and get elements that I’m looking for.
- Tomorrow will be pulling the seed words out of the code in an ordered list. Generated sentences will need to be timestamped to that conversations can be reconstructed. That being said, it could be interesting to take seed words out of a generated sentence and add them to the embedding seed words. Something to think about.