Welcome to ALT Highlights, a series of blog posts spotlighting various happenings at the recent conference ALT 2021, including plenary talks, tutorials, trends in learning theory, and more! To reach a broad audience, the series will be disseminated as guest posts on different blogs in machine learning and theoretical computer science. John has been kind enough to host the first post in the series. This initiative is organized by the Learning Theory Alliance, and overseen by Gautam Kamath. All posts in ALT Highlights are indexed on the official Learning Theory Alliance blog.

A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages in handling high dimensional network data. Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to both academics and practitioners. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. The survey also summarizes the popular benchmark data sets, model evaluation metrics, and open-source implementations to address experimentation settings. We then discuss the practical applications of community detection in various domains and point to implementation scenarios. Finally, we outline future directions by suggesting challenging topics in this fast-growing deep learning field.

Swayed by IBM's Watson boasts, McKinsey predicted a 30–50 percent productivity improvement for nurses, a 5–9 percent reduction in health care costs, and health care savings in developed countries equal to up to 2 percent of GDP. The Wall Street Journal published a cautionary article in 2017, and soon others were questioning the hype. A 2019 article in IEEE Spectrum concluded that Watson had "overpromised and underdelivered." Soon afterward, IBM pulled Watson from drug discovery, and media enthusiasm waned as bad news about A.I. health care accumulated. For example, a 2020 Mayo Clinic and Harvard survey of clinical staff who were using A.I.-based clinical decision support to improve glycemic control in patients with diabetes gave the program a median score of 11 on a scale of 0 to 100, with only 14 percent saying that they would recommend the system to other clinics.

ACM fellow Jack Minker passed away on April 9, 2021, at the age of 93. Minker was a leader in the development of automating logistic reasoning, including deductive databases, logic programming, and artificial intelligence, but he is perhaps best known for his efforts to promote the social responsibility of scientists and human rights. In 1972, Minker was invited to join the newly constituted Committee of Concerned Scientists. He was asked to help identify Soviet computer scientists whose human rights were under attack by their government, frequently because of their career choices or because they had requested permission to emigrate from the Soviet Union. "It was something I could not refuse to do," said Jack in 2011.

It's wonderful to be in an area like computer science because, as we expand its reach, we encounter problems for which we don't even have the appropriate abstractions to be able to think about them. When you imagine the future of the field, what areas do you think hold the most promise? AHO: That is a great question. Particularly with fields like AI, we're starting to replace people who do routine cognitive jobs with computer programs. What will the job market of the future be with this increasing capacity and power of computing?

If you have ever owned an Apple TV streaming device, then you know how easy it is to lose its remote. Its thin frame becomes its downfall when confronted with the comfy spaces between couch cushions. Naturally, when Apple recently introduced AirTags, a tiny disc capable of tracking your stuff through the iPhone's Find My app, some people joked we could finally keep track of that easy-to-lose Apple TV remote. During an interview with the website MobileSyrup, Tim Twerdahl, Apple's vice president of product marketing for home and audio, said upgrades to the size of the Siri-enabled Apple TV remote were enough to make it less likely to lose without incorporating technology similar to AirTags. "With the changes we've made to the Siri Remote – including making it a bit thicker so it won't fall in your couch cushions as much – that need to have all these other network devices find it seems a little bit lower," said Twerdahl.

As a computer scientist, I don't take it lightly when I say that computer programs are failing us at an alarming rate. There was that time a Google image search program accidentally thought Black people were gorillas. Or that time Microsoft built an AI chat-bot that became a Hitler fan in less than a day. Also, that time Amazon built a resume ranking program that had no interest in hiring women. Why does this kind of head-scratching, potentially life-altering, but also seemingly avoidable outcome happen so frequently with computer programs? I think it has something to do with something I call Frankenstein bias.

In February of 2020, I participated in the "On Consciousness" podcast with Bernie Baars and David Edelman. We talked about my work at The Neurosciences Institute (NSI) in La Jolla, California on the Darwin series of Brain-Based Devices, as well as my current research in neurorobotics. Unsurprisingly, the conversation turned to consciousness. I happened to mention that a page from my old lab notebook, which is pinned to a bulletin board in my office at UC Irvine, outlines a roadmap towards the creation of a Conscious Artifact. The key steps in this roadmap were laid out by Gerald Edelman, who was the director of the NSI at the time I was a research fellow there.

These days, Natural Language Processing applications such as chatbots, speech recognition, and text summarization are gaining a lot of attraction. Countless companies have integrated or looking to integrate smart chatbot services to better serve customers. Researches are pushing the boundaries on improving natural interpretation accuracy in hopes that machines can one day reach and surpass human interpretation of natural languages. "Alexa, how much time is left on the pizza timer?" It's trivial for Alexa to respond to a question like that.

Despite the many ominous connotations trumpeted in works of fiction, the adoption and growth of AI can is simply another phase of the technological advance that has marked the development of human society. Yet, because we associate intelligence with living creatures, particularly our own species, the idea of machines that possess that faculty excites some trepidation. AI agents may turn out to be as unpredictable and perverse as any intelligent human. No such worry is evident in Silicon Valley. Sundar Pichai, Google's chief, speaking at the World Economic Forum in Davos, Switzerland, enthused about the technology: "AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire," he said. Google is a major participant in an AI market that is clipping along at a five-year compound annual growth rate (CAGR) of 17.5%. Globally, the industry is projected to swell to $554.3 billion by 2024. Other players of note are IBM, Intuit, Microsoft, OpenText, Palantir, SAS, and Slack.