Umpire Dan Bellino's baffling foul tip call on Seiya Suzuki renews calls for robot review in MLB Dakich: sports media has created an'industry' out of complaining about white athletes like Caitlin Clark Greg Sankey insists SEC is'strongest league' despite Big Ten winning three straight national championships Phillies look to upset Dodgers behind Zack Wheeler as Philadelphia's turnaround continues in LA Joey McGuire calls Steve Sarkisian's bluff, dares Texas to play Texas Tech in Week 1 Rams troublemaker WR Puka Nacua says he's a changed man after biting incident and stint in rehab Chiefs have no plans to release Rashee Rice and see jail time as a'life lesson' opportunity Diamondbacks fans catch same player's home run on back-to-back nights after showing up on the wrong date Father Mike Schmitz: Pope Leo XIV wants this world view in line with humanity's good Pompeo warns Iran will rebuild nuclear facilities'the moment' it gets the chance Purple Heart recipient speaks out after Graham Platner's controversial remarks'Chipotle Karen' caught hurling burrito bowl at worker's face Jaxson Dart spoke to reporters about his well-chronicled introduction of President Donald Trump at a rally last week and after he was done, it was Abdul Carter's turn to make his way to the microphone. As the two New York Giants teammates passed each other, they embraced. The Giants want the world to know there is no beef inside their locker room in general, or between the two players in particular despite the fact the starting quarterback supports Trump and the blossoming defensive lineman has an obvious distaste for the president. But there's a catch amid all this understanding: While these two guys may not dislike each other, they do not agree. Giants linebacker Abdul Quarter says there is no beef between him and quarterback Jaxson Dart after he made an appearance at an event featuring President Donald Trump.

It's been instructive to see "Ballerina," which opens this week, so soon after the new "Mission: Impossible" installment. In the latter, it's hard to top Tom Cruise's intrepid stunt work, which reaches its zenith in a pair of extended sequences (one in a submarine, the other on biplanes), but the story, involving a diabolical scheme using A.I. to commandeer and launch the world's nuclear weaponry, is a mere pretext. Going to "Mission: Impossible" for the story is like going to Casablanca for the waters. In contrast, "Ballerina"--like the four John Wick films that it's spun off from--is, strangely, far better at story than at action. The first John Wick film is the weakest, because the framework for the franchise was still unformed: a retired hit man (Keanu Reeves) gets back into action to respond to a mobster's attacks.

While large language models (LLMs) equipped with techniques like chain-of-thought prompting have demonstrated impressive capabilities, they still fall short in their ability to reason robustly in complex settings. However, evaluating LLM reasoning is challenging because system capabilities continue to grow while benchmark datasets for tasks like logical deduction have remained static. We introduce MuSR, a dataset for evaluating language models on multistep soft reasoning tasks specified in a natural language narrative. This dataset has two crucial features. First, it is created through a novel neurosymbolic synthetic-to-natural generation algorithm, enabling the construction of complex reasoning instances that challenge GPT-4 (e.g., murder mysteries roughly 1000 words in length) and which can be scaled further as more capable LLMs are released. Second, our dataset instances are free text narratives corresponding to real-world domains of reasoning; this makes it simultaneously much more challenging than other synthetically-crafted benchmarks while remaining realistic and tractable for human annotators to solve with high accuracy. We evaluate a range of LLMs and prompting techniques on this dataset and characterize the gaps that remain for techniques like chain-of-thought to perform robust reasoning.

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The 44th International Conference on Software Engineering(ICSE 2022) was held in person from May 22 to May 27, 2022 in Pittsburgh, PA, USA. Since ICSE was held as a solely virtual conference for the last two years, the opportunity to interact with other members of the software engineering community in person and to engage in insightful discussions in a physical room was greatly welcomed. Each day was organized into paper sessions, poster sessions, and Birds of a Feather(BoF) sessions, in addition to plenty of time for networking. Each paper session consisted of around 6-10 5 minute talks and time for questions for the authors. The Birds of a Feather sessions allowed for a broader discussion on a topic; the sessions varied in terms of topics and format. In this document, we summarize themes of research that we observed at the conference.

Turn-taking is the ability for agents to lead or follow in social interactions. Turn-taking between humans and intelligent agents has been studied in human-robot interaction but has not been applied to improvisational, dance-based interactions. User understanding and experience of turn-taking in an improvisational, dance-based system known as LuminAI was investigated in a preliminary study of 11 participants. The results showed a trend towards users understanding the difference between turn-taking and non-turn-taking versions of LuminAI but reduced user experience in the turn-taking version.

A hundred years have passed since the Bipedal Event of 2065... An international ban on unofficial use of super artificial intelligence is enacted as the Earth adjusts to life with non-human races (now called bipeds despite humans sharing the classification). One day, while sifting through an abandoned government warehouse in space, Winston, a punk canine biped, finds Grant, a programmer from New York who's been cryogenically frozen since the 2030s. Together, they hang out in Winston's spaceship and eat donuts. There may also be some nunchaku wielding mechs, quantum encrypted black holes, and a little occult stuff sprinkled in.

In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or visualizations. However, as shown in cognitive science as well as in early AI research, concept understanding can also be improved by the alignment of a given instance for a concept with a similar counterexample. Contrasting a given instance with a structurally similar example which does not belong to the concept highlights what characteristics are necessary for concept membership. Such near misses have been proposed by Winston (1970) as efficient guidance for learning in relational domains. We introduce an explanation generation algorithm for relational concepts learned with Inductive Logic Programming (\textsc{GeNME}). The algorithm identifies near miss examples from a given set of instances and ranks these examples by their degree of closeness to a specific positive instance. A modified rule which covers the near miss but not the original instance is given as an explanation. We illustrate \textsc{GeNME} with the well known family domain consisting of kinship relations, the visual relational Winston arches domain and a real-world domain dealing with file management. We also present a psychological experiment comparing human preferences of rule-based, example-based, and near miss explanations in the family and the arches domains.

A GPT-3-powered bot has been caught posing as a human on Reddit after more than a week of rampant posting on one of the site's most popular subreddits. Under the username of thegentlemetre, the bot had been churning out a post per minute on /r/AskReddit, a sub with more than 30 million users. That behavior raised the suspicions of writer Philip Winston. "I read through some of the posts and they reminded me of text I'd seen from OpenAI's language model GPT-3," Winston wrote on his blog. Winston shared his theory on the subreddit /r/GPT3. Another Redditor named Wiskkey noticed that the structure of its writing was similar to that used by the Philosopher AI, a controversial text generator powered by GPT-3.

What is it exactly that makes humans so smart? In his seminal 1950 paper, "Computer Machinery and Intelligence," Alan Turing argued human intelligence was the result of complex symbolic reasoning. Philosopher Marvin Minsky, cofounder of the artificial intelligence lab at the Massachusetts Institute of Technology, also maintained that reasoning--the ability to think in a multiplicity of ways that are hierarchical--was what made humans human. Patrick Henry Winston begged to differ. "I think Turing and Minsky were wrong," he told me in 2017. "We forgive them because they were smart and mathematicians, but like most mathematicians, they thought reasoning is the key, not the byproduct." Winston, a professor of computer science at MIT, and a former director of its AI lab, was convinced the key to human intelligence was storytelling. "My belief is the distinguishing characteristic of humanity is this keystone ability to have descriptions with which we construct stories. I think stories are what make us different from chimpanzees and Neanderthals. And if story-understanding is really where it's at, we can't understand our intelligence until we understand that aspect of it."