With artificial intelligence putting productivity on hyperspeed, the painstaking but often slow nature of dealing with other countries, as well as policymaking, is also forced to speed up. But a panel at the forefront of these changes at the BRIDGE Summit in Abu Dhabi--which convenes creators, policymakers, investors, technologists, media institutions, and cultural leaders around the world to discuss the future of media--said that breaking things fast is not without consequences. "Decision makers are being asked to make decisions very quickly on the basis of information that may not be verified or verifiable," Elizabeth Churchill, a professor of Human-Computer Interaction from the Mohamed Bin Zayed University of Artificial Intelligence, told moderator Nikhil Kumar, an executive editor at TIME, which is a media partner of the BRIDGE Summit. Churchill, who held senior roles in firms like Google and Yahoo, said she returned to academia to explore transparent and "interrogable" AI tools and content that is effectively watermarked--so that decision-makers know at a glance if information is trustworthy. She said current shortfalls in information quality are "very much a design problem that sits at the surface of all of the tools that we use and in diplomacy conversations many different people are using."

Question answering over temporal knowledge graphs (TKGs) is crucial for understanding evolving facts and relationships, yet its development is hindered by limited datasets and difficulties in generating custom QA pairs. We propose a novel categorization framework based on timeline-context relationships, along with \textbf{TimelineKGQA}, a universal temporal QA generator applicable to any TKGs. The code is available at: \url{https://github.com/PascalSun/TimelineKGQA} as an open source Python package.

'The Big Weekend Show' analyzes the possibilities of artificial intelligence when it comes to influencing voters. NEW YORK, NEW YORK - MAY 02: Katy Perry attends The 2022 Met Gala Celebrating "In America: An Anthology of Fashion" at The Metropolitan Museum of Art on May 02, 2022 in New York City. IT'S SUPERNATURAL: A picture of Perry at the bottom of the Met steps circulated online, leading fans to believe the "Wide Awake" singer was attending the event. In the picture, Perry is wearing an off-white ball gown adorned with roses and moss. GROWING WITH AI: Over 3,000 micro business owners were surveyed by Venture Forward, GoDaddy's international research initiative, in February 2024 about leveraging generative artificial intelligence to compete with large brands and level the playing field across a multitude of industries.

Dyson is firmly of the view that we need to do less of this cleaning nonsense. "Our future vision is of a home that can look after itself," says Dyson's chief technology officer John Churchill. "Our engineers continue to employ technologies to reduce the cognitive burden on our owners, saving time, energy, and effort … a true set-and-forget mindset." Perhaps this is bad time to mention that according to a recent study in Neurology, completing household chores may actually lower the risk of dementia? Churchill is holding court at Dyson's global headquarters, St James Power Station, in Singapore, during a splashy three-day press trip in April.

Computers can now mimic everything from literary characters to Nirvana songs.

In recent years, real-time strategy (RTS) games have gained interest in the AI research community for their multitude of challenging subproblems -- such as collaborative pathfinding, effective resource allocation and unit targeting, to name a few. In this paper we consider the build order problem in RTS games in which we need to find concurrent action sequences that, constrained by unit dependencies and resource availability, create a certain number of units and structures in the shortest possible time span. We present abstractions and heuristics that speed up the search for approximative solutions considerably in the game of StarCraft, and show the efficacy of our method by comparing its real-time performance with that of professional StarCraft players.

Real-time strategy (RTS) games are known to be one of the most complex game genres for humans to play, as well as one of the most difficult games for computer AI agents to play well. To tackle the task of applying AI to RTS games, recent techniques have focused on a divide-and-conquer approach, splitting the game into strategic components, and developing separate systems to solve each. This trend gives rise to a new problem: how to tie these systems together into a functional real-time strategy game playing agent. In this paper we discuss the architecture of UAlbertaBot, our entry into the 2011/2012 StarCraft AI competitions, and the techniques used to include heuristic search based AI systems for the intelligent automation of both build order planning and unit control for combat scenarios.

Heuristic search has been very successful in abstract game domains such as Chess and Go. In video games, however, adoption has been slow due to the fact that state and move spaces are much larger, real-time constraints are harsher, and constraints on computational resources are tighter. In this paper we present a fast search method -- Alpha-Beta search for durative moves-- that can defeat commonly used AI scripts in RTS game combat scenarios of up to 8 vs. 8 units running on a single core in under 5ms per search episode. This performance is achieved by using standard search enhancements such as transposition tables and iterative deepening, and novel usage of combat AI scripts for sorting moves and state evaluation via playouts. We also present evidence that commonly used combat scripts are highly exploitable -- opening the door for a promising line of research on opponent combat modelling.

Online strategy video games offer several unique challenges to the field of AI research. Due to their large state and action spaces, existing search algorithms have difficulties in making strategically strong decisions. Additionally, the nature of competitive on-line video games adds the requirement that game designers be able to tweak game properties regularly when strategic imbalances are found. This means that an AI system for a game like this needs to be robust to such changes and less reliant on expert knowledge. This paper makes two main contributions to advancing the state of the art for AI in modern strategy video games which have large state and action spaces.

Real-Time Strategy games have become a popular test-bed for modern AI system due to their real-time computational constraints, complex multi-unit control problems, and imperfect information. One of the most important aspects of any RTS AI system is the efficient control of units in complex combat scenarios, also known as micromanagement. Recently, a model-based heuristic search technique called Portfolio Greedy Search (PGS) has shown promisingpaper we present the first integration of PGS into the StarCraft game engine, and compare its performance to the current state-of-the-art deep reinforcement learning method in several benchmark combat scenarios. We then perform theperformance for providing real-time decision making in RTS combat scenarios, but has so far only been tested in SparCraft: an RTS combat simulator. In this same experiments within the SparCraft simulator in order to investigate any differences between PGS performance in the simulator and in the actual game. Lastly, we investigate how varying parameters of the SparCraft simulator affect the performance of PGS in the StarCraft game engine. We demonstrate that the performance of PGS relies heavily on the accuracy of the underlying model, outperforming other techniques only for scenarios where the SparCraft simulation model more accurately matches the StarCraft game engine.