'Gutfeld!' panel on how constant political correctness is hurting society This is a rush transcript of "Gutfeld!" on May 27, 2021. This copy may not be in its final form and may be updated. You did some fantastic and extensive reporting this weekend on Joe Biden, who he is. ASHLEY PARKER, MSNBC SENIOR POLITICAL ANALYST: Joe Biden, some of it, he has the taste of a five-year-old. It's PB&J chop salad with grilled chicken. He likes orange Gatorade, and he stacks the Oval Office with homemade chocolate chip cookies. That is some extensive reporting for the Food Network. You know in the old days, if you wanted an answer to something, you went to this. That's how I learned to play doctor. No worry it was with Raggedy Andy. But the one thing everybody had was a set of encyclopedias. Every time you had homework, you copy the answers word for word from their pages, what's now called the Biden method. Then as you got older, you discovered the library, a magical place filled with strange artifacts known as books. They were heavy, you turn the pages in order to read them. Of course, libraries are different now, they're not closed. You see -- you see a lot of massages going on in there. Hey, I don't make the rules. But that was really the first search engine, except there was no engine, just a librarian whose hair bun could stop a bullet. Now getting information seems easier. The whole world is in the palm of your hand. And yet for some reason, we still can't find the truth. Because even though we think we are in control, we aren't. We now have everything at our fingertips, but it's the tech giants who decide what we can and can't touch.

Last week, Google Research held an online workshop on the conceptual understanding of deep learning. The workshop, which featured presentations by award-winning computer scientists and neuroscientists, discussed how new findings in deep learning and neuroscience can help create better artificial intelligence systems. While all the presentations and discussions were worth watching (and I might revisit them again in the coming weeks), one in particular stood out for me: A talk on word representations in the brain by Christos Papadimitriou, professor of computer science at the University of Columbia. In his presentation, Papadimitriou, a recipient of the Gödel Prize and Knuth Prize, discussed how our growing understanding of information-processing mechanisms in the brain might help create algorithms that are more robust in understanding and engaging in conversations. Papadimitriou presented a simple and efficient model that explains how different areas of the brain inter-communicate to solve cognitive problems. "What is happening now is perhaps one of the world's greatest wonders," Papadimitriou said, referring to how he was communicating with the audience.

Over the decades, fashions in Computational Linguistics have changed again and again, with major shifts in motivations, methods and applications. When digital computers first appeared, linguistic analysis adopted the new methods of information theory, which accorded well with the ideas that dominated psychology and philosophy. Then came formal language theory and the idea of AI as applied logic, in sync with the development of cognitive science. That was followed by a revival of 1950s-style empiricism—AI as applied statistics—which in turn was followed by the age of deep nets. There are signs that the climate is changing again, and we offer some thoughts about paths forward, especially for younger researchers who will soon be the leaders.

And then I found Professor Bitcoin in tubing at the Max Planck Institute for biological cybernetics. And he had their junior research group they are back then and I was doing very very exciting research in, computational neuroscience and said that this is exactly what I wanted to do, so I wrote him an email and explain what I did before and what I want to do now and I was asking him for a for a PhD position and luckily he already did you just had a PhD position open for somebody with my my track record. So Started talking to him and me and then we decided okay sounds like a good match so I went to tune him and yeah started my academic career then into being.

Currently the CEO of AI chip company XMOS, Mark Lippett is an experienced business leader with over 25 years' experience in technology. If you look at the biggest technological breakthroughs of the past 100 years, what do you think of straight away? Whatever springs to mind at the moment, in about 20 years' time, you might want to think about adding the "artificial intelligence of things" to your list. The artificial intelligence of things (AIoT) promises to be one of the most exciting technology developments we've ever experienced. So much so, in fact, that it's likely to become a $3 trillion industry by 2024.

During the past few months, Microsoft Exchange servers have been like chum in a shark-feeding frenzy. Threat actors have attacked critical zero-day flaws in the email software: an unrelenting cyber campaign that the US government has described as "widespread domestic and international exploitation" that could affect hundreds of thousands of people worldwide. Gaining visibility into an issue like this requires a full understanding of all assets connected to a company's network. This type of continuous tracking of inventory doesn't scale with how humans work, but machines can handle it easily. For business executives with multiple, post-pandemic priorities, the time is now to start prioritizing security. "It's pretty much impossible these days to run almost any size company where if your IT goes down, your company is still able to run," observes Matt Kraning, chief technology officer and co-founder of Cortex Xpanse, an attack surface management software vendor recently acquired by Palo Alto Networks. You might ask why companies don't simply patch their systems and make these problems disappear. If only it were that simple. Unless businesses have implemented a way to find and keep track of their assets, that supposedly simple question is a head-scratcher. But businesses have a tough time answering what seems like a straightforward question: namely, how many routers, servers, or assets do they have? If cybersecurity executives don't know the answer, it's impossible to then convey an accurate level of vulnerability to the board of directors. And if the board doesn't understand the risk--and is blindsided by something even worse than the Exchange Server and 2020 SolarWinds attacks--well, the story almost writes itself. That's why Kraning thinks it's so important to create a minimum set of standards.

If you look at the biggest technological breakthroughs of the past 100 years, what do you think of straight away? Whatever springs to mind at the moment, in about 20 years' time, you might want to think about adding the "artificial intelligence of things" to your list. The artificial intelligence of things (AIoT) promises to be one of the most exciting technology developments we've ever experienced. So much so, in fact, that it's likely to become a $3 trillion industry by 2024. Functioning as the convergence of artificial intelligence (AI) and the internet of things (IoT), the AIoT represents a new way of delivering AI -- taking it out of the data centre and embedding it directly in the devices we are surrounded by daily.

As auto manufacturers continue developing tomorrow's self-driving cars, there's a parallel design process taking shape. Because as soon as engineers have perfected the autonomous vehicle, a new kind of passenger experience will take center stage, one that includes the sophisticated integration of rich, enveloping audio and video. Self-driving cars won't only be used for transportation. Conveniently, passengers will be able to work as they travel along, holding video meetings and conference calls. Or they will enjoy music, TV and movies much as they would in their own home theaters.

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.