Complexity Science Hub, Metternichgasse 8, 1030, Vienna, Austria Strategic decision-making involves managing the tension between immediate opportunities and long-term objectives. We study this trade-off in chess by characterizing and comparing dynamics between human vs. human and AI vs. AI games. We propose a network-based metric of piece-to-piece interaction to quantify the ongoing strategic tension on the board. Its evolution in games reveals that the most competitive AI players sustain higher levels of strategic tension for longer durations than elite human players. Cumulative tension varies with algorithmic complexity for AI and correspondingly in human-played games increases abruptly with expertise at about 1600 Elo and again at 2300 Elo. The profiles reveal different approaches. Highly competitive AI tolerates interconnected positions balanced between offensive and defensive tactics over long periods. Human play, in contrast, limits tension and game complexity, which may reflect cognitive limitations and adaptive strategies. The difference may have implications for AI usage in complex, strategic environments. The aphorism that one may have won the battle but lost the war is encapsulated in the notion of a "Pyrrhic victory." Costly short-term wins must be balanced against the longer-term uncertainties, opportunities, or challenges that may emerge in competitive environments.

Watch the ondemand video of the Roundtables session: The Future of AI Games. Available only to MIT Alumni and subscribers. Featured speakers are Niall Firth, executive editor, and Allison Arieff, editorial director. Learn how generative AI is opening up new possibilities in gaming.

Battles between human and artificial intelligence are no longer science fiction. The strikes in Hollywood led by the united guilds of actors and screenwriters have a common, intangible enemy: the algorithms and computer-generated imagery that are increasingly programmed by studios to render them redundant. In New York last week, a new front in that stand-off was opened by a group of American novelists – including John Grisham, Jodi Picoult and Jonathan Franzen – who are suing OpenAI, the creators of the ChatGPT program. The legal case may help to define and protect those increasingly porous boundaries between human creativity and the robots that mimic it. In the meantime, Amazon, these days flooded by self-published books written by AI, has taken its first half-hearted steps to curtail that practice.

Last week felt like a lifetime in AI. Sure, the week before was fast-paced and so was the week before that, but this one -- really, a lifetime. And somehow I just couldn't let it go. While the rest of the world went about its business, I noodled about the implications of OpenAI's latest ChatGPT chess move. You've likely heard by now that OpenAI unveiled plugins that link its conversational AI ChatGPT to the real world.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Last week felt like a lifetime in AI. Sure, the week before was fast-paced and so was the week before that, but this one -- really, a lifetime. And somehow I just couldn't let it go. While the rest of the world went about its business, I noodled about the implications of OpenAI's latest ChatGPT chess move.

Artificial intelligence is evolving the landscape of every industry, and the gaming industry is no exception. Through innovation and growth, technology is exceeding our expectations every day. In gaming, artificial intelligence (AI) refers to responsive and flexible video game experiences. While artificial intelligence has long been present in video games, it is today seen as a burgeoning new frontier in how games are both created and played. AI games are progressively handing over control of the game experience to the player, whose actions influence the game experience.

The AI field is plagued by irrational optimism and irrational despair. In 1973, Sir James Lighthill was asked to compile a report on the then-present state of artificial intelligence. His report criticized the hype surrounding artificial intelligence research, suggesting that AI's best algorithms would always fail at solving real world problems and could really only work for solving "baby" problems. His report followed almost twenty-five years of fervent research into human-like algorithms. The AI "summer" between the 1950s and 1970s saw DARPA investing millions into undirected research that touched on natural language processing.

To stay on top of AI innovation, it's time to upgrade from multicore architecture. Join this VB Live event to learn how cutting-edge computer architecture can unlock new AI capabilities, from common use cases to real-world case studies and more. AI and machine learning demand new approaches to computer architecture -- but, of course, there are more factors. Large amounts of data, the arrival of industry-standard frameworks such as TensorFlow and PyTorch, and the death of Moore's Law, are all signs that it's time for the next generation of computing systems. And it's one of the biggest transitions that the computer industry has seen since the changes demanded by the Internet and online connectivity.

NetApp is changing the game for AI with its innovative enterprise solutions. FREMONT, CA: The idea of Artificial Intelligence (AI) has been around for years. But it was not until recently that AI stepped out of the realm of science fiction and became a critical part of modern business. From helping enterprises make faster, more accurate decisions to preventing fraud in real-time prove that AI is a crucial component of daily lives. AI helps organizations in all industries around the globe to innovate and to grow their businesses.

Google Cloud is rolling out an "AI Hub" supplying machine learning content ranging from data pipelines and TensorFlow modules. It also announced a new pipeline component for the Google-backed Kubeflow open-source project, the machine learning stack built on Kubernetes that among other things packages machine learning code for reuse. The AI marketplace and the Kubeflow pipeline are intended to accelerate development and deployment of AI applications, Google said Thursday (Nov. The new services follow related AI efforts such as expanding access to updated Tensor processing units (TPUs) on the Google Cloud. The AI Hub is described as a community for accessing "plug-and-play" machine learning content.