Understanding and solving complex reasoning tasks is vital for addressing the information needs of a user. Although dense neural models learn contextualised embeddings, they still underperform on queries containing negation. To understand this phenomenon, we study negation in both traditional neural information retrieval and LLM-based models. We (1) introduce a taxonomy of negation that derives from philosophical, linguistic, and logical definitions; (2) generate two benchmark datasets that can be used to evaluate the performance of neural information retrieval models and to fine-tune models for a more robust performance on negation; and (3) propose a logic-based classification mechanism that can be used to analyze the performance of retrieval models on existing datasets. Our taxonomy produces a balanced data distribution over negation types, providing a better training setup that leads to faster convergence on the NevIR dataset. Moreover, we propose a classification schema that reveals the coverage of negation types in existing datasets, offering insights into the factors that might affect the generalization of fine-tuned models on negation.

A great thing about catching a cold in December, as a critic, is that it's a perfect time to play NyQuil-induced catch-up with all the screeners I'd yet to watch. Cynthia Erivo is as good as everyone says in Wicked. Hundreds of Beavers is funny and incredibly well calculated, astute in its ability to shape-shift just enough to never get tedious. The Wild Robot is emotionally satisfying--but it made me lament a world in which even a robot has to have her programming overridden by the American social imperative to be a "mother." The Remarkable Life of Ibelin is a worthy reminder of what the old internet, the internet of my own upbringing, used to feel like: communal, social, mysterious.

Recent advancements in Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language understanding and generation. While these models excel in general complex reasoning tasks, they still face challenges in mathematical problem-solving and logical reasoning. To address these limitations, researchers have explored function calling abilities, allowing LLMs to execute provided functions and utilize their outputs for task completion. However, concentrating on specific tasks can be very inefficient for large-scale LLMs to be used, because of the expensive cost of training and inference stages they need in terms of computational resources. This study introduces a novel framework for training smaller language models in function calling, focusing on specific logical and mathematical reasoning tasks. The approach aims to improve performances of small-scale models for these tasks using function calling, ensuring a high level of accuracy. Our framework employs an agent that, given a problem and a set of callable functions, queries the LLM by injecting a description and examples of the usable functions into the prompt and managing their calls in a step-by-step reasoning chain. This process is used to create a dataset of correct and incorrect reasoning chain chat completions from a large-scale LLM. This dataset is used to train a smaller LLM using Reinforcement Learning from Human Feedback (RLHF), specifically employing the Direct Preference Optimization (DPO) technique. Experimental results demonstrate how the proposed approach balances the trade-off between model size and performance, improving the ability of function calling for reasoning tasks, in smaller models.

CyberGuy explains how to use Name Drop to share your contact information with other iPhone users. Imagine scrolling through social media only to stumble upon a version of yourself promoting some random brand, or maybe starring in a commercial you've never seen, or perhaps even endorsing a political stance you've never taken. This eerie scenario isn't far off for Tom Hanks, who recently found his AI-generated twin making a pitch for a dental plan. With an uncanny resemblance to Hanks, this digital doppelganger was seen zealously promoting a dental plan that promises a smile as captivating as the actor's. The AI-generated Hanks seemed to have taken on the role of a dental specialist, making promises of pristine pearly whites.

Robin Williams' daughter, Zelda, spoke out amid the actors strike about her father's voice being used by AI without his consent as experts weigh in on the technology's use in Hollywood.

Fake AI pictures and videos will be nearly impossible to discern from real images as the technology behind deepfakes advances, University of California, Berkeley professor says. American television personality Gayle King has warned her followers about the dangers of artificial intelligence (AI) after she became the victim of a manipulated video. A video of King has circulated on Instagram in which she appeared to promote various weight loss products from a company known as Artipet. The sponsored post appeared on the feed of many of the "CBS Mornings'" host's one million followers. "Ladies, honestly, I did not expect my weight loss to spark so many questions. My direct messages on Instagram are overflowing," King can be heard saying in the video.

Tom Hanks and wife Rita Wilson walked the Pre-Grammy GALA red carpet discussing what they believe is the success to a great relationship, after being married for 34 years. Tom Hanks is warning fans about a potential AI-generated scam. The "Forrest Gump" actor says his name and likeness are being used without his consent in a dental promotion, and that users should "beware." I have nothing to do with it," he wrote, signing his name in a post on Instagram. Tom Hanks condemned a dental promotion using his name and likeness to promote their plan. A representative for Hanks did not immediately return Fox News Digital's request for comment. It is unclear where the image originated. Hanks recently gave his own two cents on artificial intelligence – noting that its use in the industry is nothing new but has "always been" lingering. "The first time we did a movie that had a huge amount of our own data locked in a computer, literally what we looked like, was a movie called'The Polar Express,'" Hanks said on "The Adam Buxton Podcast" about his 2004 animated film that used the technology. Film preparation for "The Polar Express" included motion capture. "And we saw this coming.

With the Writers Guild of America currently on strike and the Screen Actors Guild's contract up at the end of the month, the Alliance of Motion Picture and Television Producers has turned to the more affordable screenwriting talents of artificial intelligence to produce a lineup of extremely cost-effective TV shows. Every night of television requires a procedural drama. After processing thousands of scripts from top-performing criminal/medical/legal TV programs, a highly advanced artificial-intelligence script generator determined that audiences love to watch teams of detectives, scientists, and/or lawyers band together to solve and prosecute crimes. But do audiences really need all the inefficient and costly dialogue between characters, when there could be a show centered on just one character who performs all the jobs? Artificially generated network procedurals are one thing, but people have remained skeptical of A.I.'s ability to write original comedies. Let's get to the other side.

Tom Hanks and wife Rita Wilson walked the Pre-Grammy GALA red carpet and discussed what they believe is the success to a great relationship after being married for 34 years. Hollywood heavy-hitter Tom Hanks is weighing in on the use of artificial intelligence in films, especially as he grows older and his name and likeness are still coveted. The "A Man Called Otto" actor gave his opinion on AI – noting that its use in the industry is nothing new and has rather "always been" lingering. "The first time we did a movie that had a huge amount of our own data locked in a computer, literally what we looked like, was a movie called'The Polar Express,'" Hanks said on "The Adam Buxton Podcast" about his 2004 animated film. "And we saw this coming. We saw that there was going to be this ability to take 0s and 1s inside a computer and turn it into a face and a character. Now, that is only grown a billion-fold since then, and we see it everywhere. And I can tell you that there is discussions going on in all of the guilds, all of the agencies and all of legal firms in order to come up with the legal ramifications of my face and my voice – and everybody else's – being our intellectual property," he added.

"I can tell you that there is discussions going on in all of the guilds, all of the agencies, and all of the legal firms in order to come up with the legal ramifications of my face and my voice and everybody else's being our intellectual property," Hanks added.