"Look with thine ears," Lear tells poor blind Gloucester, and that is exactly what the rest of us should do now we know that the majority of New York theatres will not open their doors until, at the most optimistic guesstimate, the middle of next year. Zoom fatigue set in months ago, but audio is stepping into the breach to take us places that glazed screen-gazing can't. The eyes tend toward the literal, while what we only hear can bloom, the way a novel does, in the privacy of the mind, as is the case with two new productions--one radical, one retro--that use audio to light a path forward for performance in the COVID era and beyond. "A Thousand Ways" (produced by the Brooklyn-based ArKtype) was created by the duo Abigail Browde and Michael Silverstone, who go by the moniker 600 Highwaymen and are known for devising inventive, sincere theatre of a kind that makes urbane audiences fatted on cynicism feel wonder afresh. In "This Great Country," from 2012, seventeen performers, some experienced, some green, acted out scenes from "Death of a Salesman," transforming that classic into something rich and strange; "Employee of the Year," staged in 2014, had five girls under the age of eleven tell the story of one woman's adulthood.

On a bright Tuesday afternoon in Paris last fall, Alex Karp was doing tai chi in the Luxembourg Gardens. He wore blue Nike sweatpants, a blue polo shirt, orange socks, charcoal-gray sneakers and white-framed sunglasses with red accents that inevitably drew attention to his most distinctive feature, a tangle of salt-and-pepper hair rising skyward from his head. Under a canopy of chestnut trees, Karp executed a series of elegant tai chi and qigong moves, shifting the pebbles and dirt gently under his feet as he twisted and turned. A group of teenagers watched in amusement. After 10 minutes or so, Karp walked to a nearby bench, where one of his bodyguards had placed a cooler and what looked like an instrument case. The cooler held several bottles of the nonalcoholic German beer that Karp drinks (he would crack one open on the way out of the park). The case contained a wooden sword, which he needed for the next part of his routine. "I brought a real sword the last time I was here, but the police stopped me," he said matter of factly as he began slashing the air with the sword. Those gendarmes evidently didn't know that Karp, far from being a public menace, was the chief executive of an American company whose software has been deployed on behalf of public safety in France. The company, Palantir Technologies, is named after the seeing stones in J.R.R. Tolkien's "The Lord of the Rings." Its two primary software programs, Gotham and Foundry, gather and process vast quantities of data in order to identify connections, patterns and trends that might elude human analysts. The stated goal of all this "data integration" is to help organizations make better decisions, and many of Palantir's customers consider its technology to be transformative. Karp claims a loftier ambition, however. "We built our company to support the West," he says. To that end, Palantir says it does not do business in countries that it considers adversarial to the U.S. and its allies, namely China and Russia. In the company's early days, Palantir employees, invoking Tolkien, described their mission as "saving the shire." The brainchild of Karp's friend and law-school classmate Peter Thiel, Palantir was founded in 2003. It was seeded in part by In-Q-Tel, the C.I.A.'s venture-capital arm, and the C.I.A. remains a client. Palantir's technology is rumored to have been used to track down Osama bin Laden -- a claim that has never been verified but one that has conferred an enduring mystique on the company. These days, Palantir is used for counterterrorism by a number of Western governments.

Theoretical computer science is everywhere, for TCS is concerned with the foundations of computing and computing is everywhere! In the last three decades, a vibrant Latin American TCS community has emerged: here, we describe and celebrate some of its many noteworthy achievements. Computer science became a distinct academic discipline in the 1950s and early 1960s. The first CS department in the U.S. was formed in 1962, and by the 1970s virtually every university in the U.S. had one. In contrast, by the late 1970s, just a handful of Latin American universities were actively conducting research in the area. Several CS departments were eventually established during the late 1980s. Often, theoreticians played a decisive role in the foundation of these departments. One key catalyst in articulating collaborations among the few but growing number of enthusiastic theoreticians who were active in the international academic arena was the foundation of regional conferences.

Audio is also available on Apple Podcasts and Spotify. On this episode of the Workday Podcast, I talked with Trenton Cycholl, vice president of business technology at Citrix, about how the organization is using machine learning to close the skills gap. You can also find more podcast episodes here. Josh Krist: Before we get started, can you tell me a little bit about your background and your current role at Citrix? I started my career working with structured data sources. I didn't know at the time that those data sources would later be the foundational pillars and centers of intelligence that companies would use to build future technology innovations, like AI.

We all love the fact that computers can execute annoying work for us. Work we already know how to do, work that is repeatable and, often, also repetitive. For the past few decades, new processes such as procedural generation have been helping us achieve diverse results with minimal input, leaving us to focus on being creative. Be it in the shape of procedural level generation of early rogue-like games, procedural nature such as Speedtree or, lately, the vast possibilities of procedural texturing with noise procedurality as seen in Substance Designer. Neural networks that generate new data and in the case of so called StyleGAN's it creates images or sequences. These machine learning frameworks are making two AI's play against each other to test and learn what would be considered to be a realistic result. This is based on the library you are feeding the network.

Tim Althoff is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His research advances computational methods that leverage large-scale behavioral data to extract actionable insights about our lives, health and happiness through combining techniques from data science, social network analysis, and natural language processing. Tim holds Ph.D. and M.S. degrees from the Computer Science Department at Stanford University, where he worked with Jure Leskovec. Prior to his PhD, Tim obtained M.S. and B.S. degrees from the University of Kaiserslautern, Germany. He has received several fellowships and awards including the SAP Stanford Graduate Fellowship, Fulbright scholarship, German Academic Exchange Service scholarship, the German National Merit Foundation scholarship, a Best Paper Award by the International Medical Informatics Association, and the SIGKDD Dissertation Award 2019.

Mr. Howell was offended by Mr. Wheeler's characterization of his project but relieved he could keep working on it. "There's a lot of excessive force here in Portland," he said in a phone interview. "Knowing who the officers are seems like a baseline." Mr. Howell, 42, is a lifelong protester and self-taught coder; in graduate school, he started working with neural net technology, an artificial intelligence that learns to make decisions from data it is fed, such as images. He said that the police had tear-gassed him during a midday protest in June, and that he had begun researching how to build a facial recognition product that could defeat officers' attempts to shield their identity.

This paper shows how to construct knowledge graphs (KGs) from pre-trained language models (e.g., BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are built in either a supervised or semi-supervised manner, requiring humans to create knowledge. Recent deep language models automatically acquire knowledge from large-scale corpora via pre-training. The stored knowledge has enabled the language models to improve downstream NLP tasks, e.g., answering questions, and writing code and articles. In this paper, we propose an unsupervised method to cast the knowledge contained within language models into KGs. We show that KGs are constructed with a single forward pass of the pre-trained language models (without fine-tuning) over the corpora. We demonstrate the quality of the constructed KGs by comparing to two KGs (Wikidata, TAC KBP) created by humans. Our KGs also provide open factual knowledge that is new in the existing KGs. Our code and KGs will be made publicly available.

William Isaac is a senior research scientist on the ethics and society team at DeepMind, an AI startup that Google acquired in 2014. I asked him about the current and potential challenges facing AI development--as well as the solutions. A: I want to shift the question. The threats overlap, whether it's predictive policing and risk assessment in the near term, or more scaled and advanced systems in the longer term. Many of these issues also have a basis in history. So potential risks and ways to approach them are not as abstract as we think.

Eight technologies developed by MIT Lincoln Laboratory researchers, either wholly or in collaboration with researchers from other organizations, were among the winners of the 2020 R&D 100 Awards. Annually since 1963, these international R&D awards recognize 100 technologies that a panel of expert judges selects as the most revolutionary of the past year. Six of the laboratory's winning technologies are software systems, a number of which take advantage of artificial intelligence techniques. The software technologies are solutions to difficulties inherent in analyzing large volumes of data and to problems in maintaining cybersecurity. Another technology is a process designed to assure secure fabrication of integrated circuits, and the eighth winner is an optical communications technology that may enable future space missions to transmit error-free data to Earth at significantly higher rates than currently possible.