ASRC AIMS 7:00 – 4:00, PhD ML, 4:30 –
7:00 – 10:00 ASRC PhD. Fun, long day.
- Understanding BERT Transformer: Attention isn’t all you need
- Word Vectors and NLP Modeling from BoW to BERT
- Since the advent of word2vec, neural word embeddings have become a go to method for encapsulating distributional semantics in text applications. This series will review the strengths and weaknesses of using pre-trained word embeddings and demonstrate how to incorporate more complex semantic representation schemes such as Semantic Role Labeling, Abstract Meaning Representation and Semantic Dependency Parsing into your applications.
- Artificial Intelligence and Global Security Initiative Research Agenda
- The Center for a New American Security’s Artificial Intelligence and Global Security Initiative explores these and other issues surrounding the AI revolution. Current AI technology is powerful, but also has a number of vulnerabilities, including susceptibility to spoofing (false data) and control problems. An arms race in AI where nations and other actors rush to use this technology for their advantage without any concern for safety would be harmful to everyone. It is vitally important for the technology and policy communities to come together to better understand the implications of the AI revolution for global security and how best to navigate the challenges ahead.
- One more pass through Antonio’s paper this evening – done
- Working on getting the Slack chats into the database. It turns out that there can be threaded discussions within channels: `thread_ts`, `reply_count`, `reply_users_count`, `latest_reply`, `reply_users`, `replies` are the variables. It’s not critical now, but it would be nice to read these in as well.
- Added encoding=”utf8″ to the read statements
- We are over 10,000 rows!
- And it looks like the Google Keras team is going to run the dungeon
- Starting on SequenceAnalyzer. Not bat progress for a day
- Meeting with Wayne
“Slaying Monsters for Science”
A few from that infamous conference…
and the role of immersive play in rethinking modern religious expression….
7:00 – 5:00 ASRC Research
- Graph laplacian dissertation
- The spectrum of the normalized graph Laplacian can reveal structural properties of a network and can be an important tool to help solve the structural identification problem. From the spectrum, we attempt to develop a tool that helps us to understand the network structure on a deep level and to identify the source of the network to a greater extent. The information about different topological properties of a graph carried by the complete spectrum of the normalized graph Laplacian is explored. We investigate how and why structural properties are reflected by the spectrum and how the spectrum changes when compairing different networks from different sources.
- Universality classes in nonequilibrium lattice systems
- This article reviews our present knowledge of universality classes in nonequilibrium systems defined on regular lattices. The first section presents the most important critical exponents and relations, as well as the field-theoretical formalism used in the text. The second section briefly addresses the question of scaling behavior at first-order phase transitions. In Sec. III the author looks at dynamical extensions of basic static classes, showing the effects of mixing dynamics and of percolation. The main body of the review begins in Sec. IV, where genuine, dynamical universality classes specific to nonequilibrium systems are introduced. Section V considers such nonequilibrium classes in coupled, multicomponent systems. Most of the known nonequilibrium transition classes are explored in low dimensions between active and absorbing states of reaction-diffusion-type systems. However, by mapping they can be related to the universal behavior of interface growth models, which are treated in Sec. VI. The review ends with a summary of the classes of absorbing-state and mean-field systems and discusses some possible directions for future research.
- “The Government Spies Using Our Webcams:” The Language of Conspiracy Theories in Online Discussions
- Conspiracy theories are omnipresent in online discussions—whether to explain a late-breaking event that still lacks official report or to give voice to political dissent. Conspiracy theories evolve, multiply, and interconnect, further complicating efforts to limit their propagation. It is therefore crucial to develop scalable methods to examine the nature of conspiratorial discussions in online communities. What do users talk about when they discuss conspiracy theories online? What are the recurring elements in their discussions? What do these elements tell us about the way users think? This work answers these questions by analyzing over ten years of discussions in r/conspiracy—an online community on Reddit dedicated to conspiratorial discussions. We focus on the key elements of a conspiracy theory: the conspiratorial agents, the actions they perform, and their targets. By computationally detecting agent–action–target triplets in conspiratorial statements, and grouping them into semantically coherent clusters, we develop a notion of narrative-motif to detect recurring patterns of triplets. For example, a narrative-motif such as “governmental agency–controls–communications” appears in diverse conspiratorial statements alleging that governmental agencies control information to nefarious ends. Thus, narrative-motifs expose commonalities between multiple conspiracy theories even when they refer to different events or circumstances. In the process, these representations help us understand how users talk about conspiracy theories and offer us a means to interpret what they talk about. Our approach enables a population-scale study of conspiracy theories in alternative news and social media with implications for understanding their adoption and combating their spread
- Need to upload to ArXiv (try multiple tex files) – done!
- If I’m charging my 400 hours today, then start putting together text prediction. I’d like to try the Google prediction series to see what happens. Otherwise, there are two things I’d like to try with LSTMs, since they take 2 coordinates as inputs
- Use a 2D embedding space
- Use NLP to get a parts-of-speech (PoS) analysis of the text so that there can be a (PoS, Word) coordinate.
- Evaluate the 2 approaches on their ability to converge?
- Coordinating with Antonio about workshops. It’s the 2019 version of this: International Workshop on Massively Multi-Agent Systems (MMAS2018) in conjunction with IJCAI/ECAI/AAMAS/ICML 2018
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.