PodQueue

Guillermo del Toro believes in one universal truth: We all get to a moment in our lives when we see ourselves for who we really are. That belief not only guides his own life, it guides the characters through his many films. Guillermo and Marc talk about how he takes on the dark forces of the world in his movies, including his latest, Nightmare Alley. They also discuss his friendship with fellow Oscar-winning directors Alejandro González Iñárritu and Alfonso Cuarón, his expansive collection of oddities, and his strong identification with outsider characters and monsters.

Neil Gaiman’s writing - from The Sandman to American Gods to Good Omens to hundreds of comics, novels and screenplays - uses fantasy to help explain modern reality. Marc talks with Neil about how his early work sidestepped the pre-adolescent male power fantasies of most contemporary comic books and helped connect with a broad and enduring fanbase. They also talk about the new adaptation of The Sandman for Netflix and why Neil believes his past experiences in TV led him to make the ideal filmed version of his work.

Singer-songwriter Sharon Van Etten creates personal, deeply felt songs that also provide her with relief. Sharon talks with Marc about her stream of consciousness writing style and how it helps her process the difficult parts of her life. They also get into how Sharon’s new album emerged from pandemic isolation, how hard it was to tour in the Era of Covid, how her friendship with collaborator Angel Olsen got started, and why she continues to study psychology in the pursuit of a degree.

Abstract: The energy transition has to happen imminently towards net-zero. There, the distributed paradigm toward more flexible operation of the electricity system may find its new foundation in the active real-time self-management of the many distributed energy resources. There, interestingly, the recent progress of developing AI-based algorithms for distributed self-organized agents part of the system can consider some system stability constraints, and manage network congestion through their energy market participation. However, reinforced AI-based control may lack verifiable guarantees, transparency, and privacy, and can have inherent inaccuracies. In response, the system operators finally responsible for the reliable operation of the grid need advanced state estimation techniques using collected measurements so they can prevent instabilities. Processing these collected measurements is unfortunately not straightforward as the amount of data is increasing exponentially which is why advanced data processing techniques are currently in development. There, also for system operators, AI-based algorithms are very promising in the estimation of system states, the detection of active devices without intrusion, and the forecasts of some state variables such as demand or distributed renewable power injection. In this context, this keynote address will put forward a vision for developing tools for the energy transition with AI methods such as multi-agent reinforcement learning and deep learning.

Dr. Jochen Cremer is the Co-Director of the TU Delft AI Energy Lab and an Assistant Professor at the Faculty of Electrical Engineering, Mathematics, and Computer Science. He is also Research Associate at Imperial College London. Jochen’s research focuses on applying machine learning and data analytics to energy systems operation and control. The TU Delft AI Energy lab team develops new computational methods for system operation and control that combine statistical machine learning and mathematical optimisation.

Abstract: Automatic analysis of human affective and social signals brought computer science closer to social sciences and, in particular, enabled collaborations between computer scientists and behavioral scientists. In this talk, I will highlight the main research areas in this burgeoning interdisciplinary area, and provide an overview of the opportunities and challenges. Drawing on examples from our recent research, such as automatic analysis of interactive play therapy sessions with children and diagnosis of bipolar disorder from multimodal cues, as well as relying on recent examples from the growing literature, I will explore the potential of human-AI collaboration, where AI systems do not replace, but support monitoring and human decision making in behavioral and clinical sciences.

Albert Ali Salah is Professor of Social and Affective Computing at Utrecht University and Adjunct Professor with the Computer Engineering Department at Boğaziçi University. He works on computer analysis of human behaviour, multimodal interaction, and affective computing. He has written over 200 scientific works in these areas, including an edited book "Computer Analysis of Human Behavior". He is an associate editor of IEEE Trans. on Affective Computing, IEEE Trans. on Cognitive and Developmental Systems, Pattern Recognition, and Int. Journal of Human-Computer Studies, serves on the steering board of eNTERFACE, ACM ICMI, and IEEE FG, and is a senior member of IEEE and ACM.

Prof. Dr. Paul Lukowicz is Full Professor of AI at the Technical University of Kaiserslautern in Germany where he heads the Embedded Intelligence group at DFKI. His research focusses on context aware ubiquitous and wearable systems including sensing, pattern recognition, system architectures, models of large scale self organized systems, and applications. Paul Lukowicz coordinates the HumanE AI project, acts as editor for various scientific publcations, and has served on the TPCs (including TPC Chair) of all the main conferences within his research area.

Abstract: Advances in computer systems and their performance have played an important role in the ongoing AI revolution. A good example is high-performance GPU systems that have helped to enable the training of increasingly deep neural networks. Since then, the AI revolution has also acted as a source of inspiration for a lot of computer systems research focused on either better handling of AI-based workloads or deploying AI methods to design and implement better systems. This trend can also be seen in embedded computer systems, which are ubiquitous computing systems that can be found in many application domains, such as consumer electronics, industrial systems, healthcare, automotive, and avionics. In this talk, I will highlight a few of such research efforts in the area of neural-network model inference on resource-constrained edge devices as well as in the domain of applying AI techniques for improving the robustness of industrial cyber-physical systems.

Andy D. Pimentel is full professor at the University of Amsterdam where he chairs the Parallel Computing Systems group. His research centers around system-level modeling, simulation and exploration of (embedded) multicore and manycore computer systems with the purpose of efficiently and effectively designing and programming these systems. He has an MSc and PhD in computer science from the University of Amsterdam. He is a cofounder of the International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS). He has (co-)authored more than 130 scientific publications and is an Associate Editor of the Simulation Modelling Practice and Theory journal as well as the Journal of Signal Processing Systems. He served as the General Chair of the HIPEAC 2015 conference, as Local Organization Co-Chair of Embedded Systems Week 2015, and as Program Chair of CODES+ISSS in 2016 and 2017. Furthermore, he has served on the TPC of many leading (embedded) computer systems design conferences, such as DAC, DATE, CODES+ISSS, ICCD, ICCAD, FPL, and LCTES.

Abstract: "Rationality" is the principle that humans make decisions on the basis of step-by-step (algorithmic) reasoning using systematic rules of logic. An ideal "explanation" for a decision is a chronicle of the steps used to arrive at the decision. Herb Simon’s "bounded rationality" is the observation that the ability of a human brain to handle algorithmic complexity and data is limited. As a consequence, human decision making in complex cases mixes some rationality with a great deal of intuition, relying more on Daniel Kahneman's "System 1" than "System 2." A DNN-based AI, similarly, does not arrive at a decision through a rational process in this sense. An understanding of the mechanisms of the DNN yields little or no insight into any rational explanation for its decisions. The DNN is operating in a manner more like System 1 than System 2. Humans, however, are quite good at constructing post-facto rationalizations of their intuitive decisions. If we demand rational explanations for AI decisions, engineers will inevitably develop AIs that are very effective at constructing such post-facto rationalizations. With their ability to handle to handle vast amounts of data, the AIs will learn to build rationalizations using many more precedents than any human could, thereby constructing rationalizations for ANY decision that will become very hard to refute. The demand for explanations, therefore, could backfire, resulting in effectively ceding to the AIs much more power. In this talk, I will discuss similarities and differences between human and AI decision making and will speculate on how, as a society, we might be able to proceed to leverage AIs in ways that benefit humans.

Edward Ashford Lee has been working on software systems for 40 years. He currently divides his time between between software systems research and studies of philosophical and societal implications of technology. After education at Yale, MIT, and Bell Labs, he landed at Berkeley, where he is now Professor of the Graduate School in Electrical Engineering and Computer Sciences. His software research focuses on cyber-physical systems, which integrate computing with the physical world. He is author of several textbooks and two general-audience books, The Coevolution: The Entwined Futures and Humans and Machines (2020) and Plato and the Nerd: The Creative Partnership of Humans and Technology (2017).

https://www.writersvoice.net/wp-content/uploads/podcasts/WV-2020-08-19.mp3Podcast: Play in new window | DownloadSubscribe: Apple Podcasts | RSSWe spend the hour with Phillip A. Neel talking about his book Hinterland: America’s New Landscape Of Class And Conflict. It’s about how America’s class structure is recomposing itself in new geographies of race, poverty, and production.

Writer’s Voice — in depth conversation with writers of all genres, on the air since 2004. Rate us on iTunes or whatever podcast app you use!

Like us on Facebook at Writers Voice with Francesca Rheannon or find us on twitter @WritersVoice. Phillip A. Neel

Driven by an ever-expanding socioeconomic crisis (now exacerbated by the covid19 pandemic) America’s class structure is recomposing itself in new geographies of race, poverty, and production. Over the last forty years, the human landscape of the United States has been fundamentally transformed.

The metamorphosis is partially visible in the ascendance of glittering, coastal hubs for finance, infotech, and the so-called creative class. But this is only the tip of an economic iceberg, the bulk of which lies in the darkness of the declining heartland or on the dimly lit fringe of sprawling cities.

This is America’s hinterland, populated by towering grain threshers and hunched farmworkers, where laborers drawn from every corner of the world crowd into factories and “fulfillment centers” and where cold storage trailers are filled with fentanyl-bloated corpses when the morgues cannot contain the dead.

By means of memoir, analysis of the political geography of class and sheer descriptive power, Philip Neel’s book Hinterland is a guidebook to America’s new heart of darkness.

Neel teaches geography at the University of Washington. He was raised in a mobile home in the Siskiyou Mountains, on the border of California and Oregon.

“Wageless Life”: Review of Hinterland by Sarah Brouillette

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