Collaborating Authors


The most watched movies of the week: New releases and some unexpected classics


Wondering what everyone's been watching this week? Well, spring is in the air and so is action, action, action! Every week, the popularity of movies across streaming might be determined by promotions, star power, critic raves, social media buzz, good old-fashioned word of mouth, or a new addition to a beloved franchise. While the reasons may vary, you can't argue with the numbers that streaming aggregator Reelgood collected from hundreds of streaming services in the U.S. and UK. As it has for weeks, The Batman continues to reign supreme.

Raising Robovoices

Communications of the ACM

In a critical episode of The Mandalorian, a TV series set in the Star Wars universe, a mysterious Jedi fights his way through a horde of evil robots. As the heroes of the show wait anxiously to learn the identity of their cloaked savior, he lowers his hood, and--spoiler alert-- they meet a young Luke Skywalker. Actually, what we see is an animated, de-aged version of the Jedi. Then Luke speaks, in a voice that sounds very much like the 1980s-era rendition of the character, thanks to the use of an advanced machine learning model developed by the voice technology startup Respeecher. "No one noticed that it was generated by a machine," says Dmytro Bielievtsov, chief technology officer at Respeecher.

The Historical Case for a Gay Bridgerton


It's simple math, really: In a family with eight children, it stands to reason, surely one of them must be queer. Bridgerton has defied other expectations of a Regency-era love story: It is set in an alternate universe where the upper class is fully integrated and race is not an issue. The show's first two seasons focus on interracial romances, and the second season at least obliquely references the history of British colonialism in India. There's one obvious candidate for such a storyline: On the show, Eloise is the most outspoken, most feminist Bridgerton sibling. She is not interested in becoming a debutante, delaying her appearance to pursue another year of studies. She often dismisses marriage, questioning why a husband and children are all that are waiting in store for women.

Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

'Gutfeld' on COVID warnings for New Year's Eve, 2021 in review

FOX News

'Gutfeld!' panel discusses the year in review as 2021 comes to a close. This is a rush transcript from "Gutfeld!," December 30, 2021. This copy may not be in its final form and may be updated. EMILY COMPAGNO, FOX NEWS CHANNEL HOST: I know what you're thinking. Greg's never looked at this good in a dress. Like a tiny Ghost of Christmas Present, because I'm celebrating the holiday today. Because this year COVID robbed me of Christmas with my family. COVID robbed us of our studio audience. And it robbed me of my Christmas Eve Feast of the Seven Fishes. So to make up for it, we are having a feast tonight. COMPAGNO: In New Year's Eve news, Omicron fear mongers are warning people to stay away from New York's Times Square celebration. Even though previous crowds were exposed to something much worse. Thank God it'll be me hosting in Time Square this year. See you at 10:00 p.m. Eastern on Fox News. Germany's also banned large group gatherings. But you know who's never bans large gatherings of Germans? China's Wuhan Institute of virology recently hosted a conference on lab safety, to which the world responded a little (BLEEP) late, guys. In a recent segment on COVID Safety, CNN's Dr. Leana Wen admitted cloth masks don't stop transmission of the virus. Today in New York Mayor Bill de Blasio said he doesn't believe in shutdowns despite having shut down the city for months. He then added "I also oppose letting criminals roam free to murder people." Chris Tucker turned down a $10 million payday for a sequel to the awesome movie Friday, saying he's too mature to be seen behaving badly on screen anymore.

Hong Kong Machine Learning Season 4 Episode 4


We are looking to organize online x in-person meetups on HK island going forward. Thanks to our sponsor Darwinex to help us supporting the various costs. Abstract: We introduce a class of interpretable tree-based models (P-Trees) for analyzing panel data, with iterative and global (instead of recursive and local) splitting criteria to avoid overfitting and improve model performance. We apply P-Tree to generate a stochastic discount factor model and test assets for cross-sectional asset pricing. Unlike other tree algorithms, P-Trees accommodate imbalanced panels of asset returns and grow under the no-arbitrage condition.

Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs Artificial Intelligence

Do language models have beliefs about the world? Dennett (1995) famously argues that even thermostats have beliefs, on the view that a belief is simply an informational state decoupled from any motivational state. In this paper, we discuss approaches to detecting when models have beliefs about the world, and we improve on methods for updating model beliefs to be more truthful, with a focus on methods based on learned optimizers or hypernetworks. Our main contributions include: (1) new metrics for evaluating belief-updating methods that focus on the logical consistency of beliefs, (2) a training objective for Sequential, Local, and Generalizing model updates (SLAG) that improves the performance of learned optimizers, and (3) the introduction of the belief graph, which is a new form of interface with language models that shows the interdependencies between model beliefs. Our experiments suggest that models possess belief-like qualities to only a limited extent, but update methods can both fix incorrect model beliefs and greatly improve their consistency. Although off-the-shelf optimizers are surprisingly strong belief-updating baselines, our learned optimizers can outperform them in more difficult settings than have been considered in past work. Code is available at

Yuval Harari warns humans will be "hacked" if artificial intelligence is not globally regulated


The future could see the world's human data, delivered through the rising power and reach of artificial intelligence, in the hands of a powerful few - a recipe for a dystopian tomorrow populated by "hacked humans," says Yuval Noah Harari. The world-renowned author tells Anderson Cooper nations must begin cooperating to prevent this by regulating artificial intelligence and the collection of data across all nations. The interview with Harari will be broadcast on 60 Minutes, Sunday October 31, at 7 p.m. ET/PT on CBS. Harari says the countries and companies that control the most data will control the world. "The world is increasingly kind of cut up into spheres of data collection, of data harvesting. In the Cold War, you had the Iron Curtain. Now we have the Silicon Curtain, that the world is increasingly divided between the USA and China," Harari tells Cooper.

Apple's first Korean TV series is about brain hacking


Apple TV will expand into a new territory when it launches in South Korea on November 4th. In a case of perfect timing, the streaming service's first series from the country will debut worldwide on the same day (or a day earlier if you're in the US). The brilliantly titled Dr. Brain is a sci-fi drama based on a webtoon of the same name. Lee Sun-kyun of Parasite fame (he played Park Dong-ik, the father of the well-off family) stars as brain scientist Sewon. After Sewon's family suffers a mysterious accident, he hacks the brains of the deceased to access their memories and learn clues about what happened.

Transferring Domain-Agnostic Knowledge in Video Question Answering Artificial Intelligence

Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip. The current available large-scale datasets have made it possible to formulate VideoQA as the joint understanding of visual and language information. However, this training procedure is costly and still less competent with human performance. In this paper, we investigate a transfer learning method by the introduction of domain-agnostic knowledge and domain-specific knowledge. First, we develop a novel transfer learning framework, which finetunes the pre-trained model by applying domain-agnostic knowledge as the medium. Second, we construct a new VideoQA dataset with 21,412 human-generated question-answer samples for comparable transfer of knowledge. Our experiments show that: (i) domain-agnostic knowledge is transferable and (ii) our proposed transfer learning framework can boost VideoQA performance effectively.