Loud eaters and phones nearly spoiled my cinema trip - and it's not just me The cinema lights are low and you're cocooned in your seat, ready for the film to transport you to another world. But just as you settle in, you're jolted back to reality. Audience members around you are scrolling on their phones, talking and munching loudly. Cinemas do clearly ask everyone not to disturb those around them - through the use of adverts, announcements and signs - but is behaviour in getting worse? I experienced disruption a few weeks ago while watching Ryan Gosling's sci-fi movie, Project Hail Mary, at a cinema in London.

There are many entities that disseminate in the physical space - information, gossip, mood, innovation etc. Personal spaces are also entities that disperse and interplay. In this work we study the emergence of configurations formed by participants when choosing a place to sit in a rectangular auditorium. Based on experimental questionnaire data we design several models and assess their relevancy to a real time-lapse footage of lecture hall being filled up. The main focus is to compare the evolution of entropy of occupied seat configurations in time. Even though the process of choosing a seat is complex and could depend on various properties of participants or environment, some of the developed models can capture at least basic essence of the real processes. After introducing the problem of seat selection and related results in close research areas, we introduce preliminary collected data and build models of seat selection based on them. We compare the resulting models to the real observational data and discuss areas of future research directions.

This work highlights a critical shortcoming in text-based Large Language Models (LLMs) used for human-robot interaction, demonstrating that text alone as a conversation modality falls short in such applications. While LLMs excel in processing text in these human conversations, they struggle with the nuances of verbal instructions in scenarios like social navigation, where ambiguity and uncertainty can erode trust in robotic and other AI systems. We can address this shortcoming by moving beyond text and additionally focusing on the paralinguistic features of these audio responses. These features are the aspects of spoken communication that do not involve the literal wording (lexical content) but convey meaning and nuance through how something is said. We present "Beyond Text"; an approach that improves LLM decision-making by integrating audio transcription along with a subsection of these features, which focus on the affect and more relevant in human-robot conversations. This approach not only achieves a 70.26% winning rate, outperforming existing LLMs by 48.30%, but also enhances robustness against token manipulation adversarial attacks, highlighted by a 22.44% less decrease ratio than the text-only language model in winning rate. "Beyond Text" marks an advancement in social robot navigation and broader Human-Robot interactions, seamlessly integrating text-based guidance with human-audio-informed language models.

In May of 1997, Garry Kasparov sat down at a chess board in a Manhattan skyscraper. Kasparov, considered the best chess player of all time, wasn't challenging another grandmaster. He was playing with an AI called Deep Blue. Deep Blue was one of the world's most powerful supercomputers, built by IBM with a specific goal in mind: to beat humanity at its own game. For IBM, billions of dollars worth of business clout was on the table, and to a certain extent, Kasparov was playing for the fate of chess itself. He had never lost a multi-game match in his entire career. Could a machine beat him? Newsweek ran a cover story with his picture alongside the words "The Brain's Last Stand." As Kasparov joked years later, "No pressure." Thanks to ChatGPT, once hypothetical questions about the future of work, art, and disinformation are now immediate concerns.

On the evening of 9 August 1956, a couple of hundred people squeezed into a student union lounge for a concert recital at the University of Illinois Urbana-Champaign, about 130 miles outside Chicago. Student performances didn't usually attract so many people, but this was an exceptional case, the debut of the Illiac Suite: String Quartet No 4, that a member of the chemistry faculty, Lejaren Hiller Jr, had devised with the school's one and only computer, the Illiac I. Decades before today's artificial intelligence pop stars, Auto-Tune and deepfake compositions was Hiller's piece, described by the New York Times in his 1994 obituary as "the first substantial piece of music composed on a computer" – and indeed by a computer. One of the four musicians who performed the piece that night was George Andrix, a violist and composition student at the university. Now 89, Andrix remembers an auditorium packed with people "who showed up to see what this monster of a computer could do." The Illiac I, short for Illinois Automatic Computer, was the first supercomputer to be housed by an academic institution.

Three decades and hundreds of millions of lines of computer code after graduating from the Milwaukee School of Engineering, NVIDIA's Dwight Diercks returned today to celebrate a donation that will put his alma mater at the forefront of AI undergraduate education. Diercks, who grew up the son of a mailman, working on his family's pig farm in Red Wing, Minnesota, came to NVIDIA as its 22nd employee. Today, he oversees a team of some 5,000 software engineers around the world who ship tens of millions of lines of code each month that help accelerate the world's computing. Diercks' $34 million gift, the largest from an alum in MSOE's 116-year history, is the keystone in the school's efforts to infuse its engineering program with artificial intelligence. Two years ago, MSOE became one of the very few programs, together with Carnegie Mellon, to offer a computer science degree focused on AI.

This paper presents an explainable AI (XAI) system that provides explanations for its predictions. The system consists of two key components - namely, the prediction And-Or graph (AOG) model for recognizing and localizing concepts of interest in input data, and the XAI model for providing explanations to the user about the AOG's predictions. In this work, we Figure 1: Two frames (scenes) of a video: (a) focus on the XAI model specified to interact top-left image (scene1) shows two persons sitting with the user in natural language, at the reception and others entering the auditorium whereas the AOG's predictions are considered and (b) top-right (scene2) image people running given and represented by the corresponding out of an auditorium. Bottom-left shows the parse graphs (pg's) of the AOG. AOG parse graph (pg) for the top-left image and Our XAI model takes pg's as input and Bottom-right shows the pg for the top-right image provides answers to the user's questions using the following types of reasoning: direct evidence (e.g., detection scores), Consider for example, two frames (scenes) of part-based inference (e.g., detected parts a video shown in Figure 1. An action detection provide evidence for the concept asked), model might predict that two people in the scene1 and other evidences from spatiotemporal are in sitting posture. User might be interested context (e.g., constraints from the spatiotemporal to know more details about the prediction such surround). We identify several as: Why do the model think the people are in sitting correlations between user's questions posture? Why not standing instead of sitting? and the XAI answers using Youtube Action Why two persons are sitting instead of one?

It's not quite the question one expected during the Q&A session at the end of the 2019 BCS Turing Talk on Artificial Intelligence. The event was held earlier this week at the swanky IET building in London's Savoy Place and the audience comprised academics, developers and tech professionals. You'd think such an interjection was akin to someone grabbing the microphone in the main auditorium during a cryptocurrency conference and blurting "So… there aren't any actual coins?" Surely this was a cue for the auditorium to resound with an unpleasant cacophony of forehead-slapping and eye-rolling. And let me tell you, the ugly wet sound of hundreds of people rolling their eyes at the same time is the stuff of Japanese body horror nightmares.

Babylon Health founder Ali Parsa presenting the results of his AI-powered medical information software, which took a standard doctor's exam.Photo by Parmy Olson The lights were dimmed in an auditorium packed with doctors on Wednesday night at London's Royal College of Physicians. They were there to find out how AI might fundamentally change the way they work. On stage Dr. Mobasher Butt, a director at digital healthcare startup Babylon Health, stood before a podium to read out the results of an exam taken by his company's carefully trained AI doctor. The average passmark for the MRCGP exam, which trainee general practitioners take to test their ability to diagnose, has been 72% over the past five years. "How did Babylon Health do?" he asked, before waiting a beat.

Babylon Health founder Ali Parsa presenting the results of his AI-powered medical information software, which took a standard doctor's exam. The lights were dimmed in an auditorium packed with doctors on Wednesday night at London's Royal College of Physicians. They were there to find out how AI might fundamentally change the way they work. On stage Dr. Mobasher Butt, a director at digital healthcare startup Babylon Health, stood before a podium to read out the results of an exam taken by his company's carefully trained AI doctor. The average passmark for the MRCGP exam, which trainee general practitioners take to test their ability to diagnose, has been 72% over the past five years.