Aryna Sabalenka has produced a consistency at the Grand Slam tournaments which is unrivalled in the women's game. With a powerful style translating across court surfaces, the world number one has made 12 semi-final appearances in her past 13 major tournaments - with the latest coming when she faces Jessica Pegula at the US Open on Thursday. But here's the kicker - Sabalenka's tendency to combust at a critical juncture means only three of those have resulted in titles. None of the 27-year-old Belarusian's runs at this year's majors have ended with the trophy. In the Australian Open final, French Open final and Wimbledon semi-finals, an erratic level has led to devastating defeats.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Formula 1 and Amazon Web Services (AWS) have been partners for more than six years. But, that longstanding partnership is now set to reach new heights as the popular sports league and the leading tech company will leverage AWS tools to develop a generative artificial intelligence-designed trophy for the upcoming Canadian Grand Prix. The first-of-its-kind approach to the trophy for the highly-anticipated event is expected to help increase creativity.

Game development is an expensive and time-consuming business. Right now, 2,000 people are working on the next instalment in Ubisoft's blockbuster Assassin's Creed series, across 18 studios around the globe, and it's a project that will take 2 to 3 years. Imagine how any of those people might feel to learn that last year, a self-taught programmer racked up nearly 280,000 from a series of games he made while sitting in his pants on hot days in a two-bedroom flat in Harlesden. And that each one took him about 30 minutes. "The first one, I'll be honest, probably took seven or eight hours," says TJ Gardner.

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.

Human beings are social creatures. We routinely reason about other agents, and a crucial component of this social reasoning is inferring people's goals as we learn about their actions. In many settings, we can perform intuitive but reliable goal inference from language descriptions of agents, actions, and the background environments. In this paper, we study this process of language driving and influencing social reasoning in a probabilistic goal inference domain. We propose a neuro-symbolic model that carries out goal inference from linguistic inputs of agent scenarios. The "neuro" part is a large language model (LLM) that translates language descriptions to code representations, and the "symbolic" part is a Bayesian inverse planning engine. To test our model, we design and run a human experiment on a linguistic goal inference task. Our model closely matches human response patterns and better predicts human judgements than using an LLM alone.

Language models have become very popular recently and many claims have been made about their abilities, including for commonsense reasoning. Given the increasingly better results of current language models on previous static benchmarks for commonsense reasoning, we explore an alternative dialectical evaluation. The goal of this kind of evaluation is not to obtain an aggregate performance value but to find failures and map the boundaries of the system. Dialoguing with the system gives the opportunity to check for consistency and get more reassurance of these boundaries beyond anecdotal evidence. In this paper we conduct some qualitative investigations of this kind of evaluation for the particular case of spatial reasoning (which is a fundamental aspect of commonsense reasoning). We conclude with some suggestions for future work both to improve the capabilities of language models and to systematise this kind of dialectical evaluation.

The second day of the World Cup 2022 semifinals will pit two-time champions and holders France against Morocco. Kashef, our artificial intelligence (AI) robot, has analysed more than 200 metrics, including the number of wins, goals scored and FIFA rankings, from matches played over the past century to see who is most likely to win on Wednesday. Morocco are currently unbeaten in this tournament and have stunned the footballing world – not once or twice but three times – by beating Belgium, Spain and Portugal. On every occasion, Kashef was left stunned. The only remaining Arab and African team are now just two wins away from lifting the World Cup trophy.

I'm burning rubber as I approach the motel parking lot. The tires squeal in protest as I spin the handlebars hard, waiting for the motorbike to start drifting before I hit the nitro. I'm doing a donut, not too tight, just clearing the parked cars. Surely this time I'm going to make it. I only need five seconds, four, three, two … and a zombie slams into me out of nowhere, sending me sprawling onto the asphalt.

When I hear news about "AI" these days, what is often meant are methods for pattern recognition and approximations of complex functions, most importantly in the form of Machine Learning. It is true that we have seen impressive applications of Machine Learning systems in a number of different industries such as product personalization, fraud detection, credit risk modeling, insurance pricing, medical image analysis, or self-driving cars. What is the origin of intelligent behavior? Intelligent behavior is the capability of using one's knowledge about the world to make decisions in novel situations: people act intelligently if the use what they know to get what they want. The premise of AI research is that this type of intelligence is fundamentally computational in nature, and that we can therefore find ways to replicate it in machines.

Australian Open champion Naomi Osaka says she had to be a "robot" and turn off her feelings to hold her nerve and win the final against Petra Kvitova. The Japanese, 21, had tears in her eyes after having three match points saved by her Czech opponent in the second set - before winning 7-6 (7-2) 5-7 6-4. "You know how some people get worked up about things? That's a very human thing to do," said Osaka. "Sometimes I feel like I don't want to waste my energy doing stuff like that."