This enterprise proved so amusing that the pair, in collaboration with composer Dave Soldier, repeated the experiment with popular music, releasing the "most wanted" and "least wanted" songs together on a CD with a cover photo of all three men wearing white lab coats and pointing at a calculator. Sadly, the pair stopped short of what I view as the greatest challenge: producing novels that reflect what Americans like and dislike in fiction. Now, at last, with People's Choice Literature, by the writer/artist/composer Tom Comitta, a new "scientist" has taken up the task. People's Choice Literature offers its readers two novels for the price of one. The first is a thriller whose heroine tries to prevent her boss, a new age–y tech mogul, from launching a quantum computing network that will bring about a total surveillance state.

Amazon Prime Day 2025 is fast approaching, and the sale is already underway on some items. To help you find the best early Prime Day deals, we've scoured Amazon for deals on the tech we love. As always, every deal we recommend here is on a product our reviewers have personally tested and approved--you won't find any shoddy dupes or mystery brands here. This year Prime Day runs for four days, July 8-11, rather than the usual two. That means there's twice as long to suffer save. Be sure to read our explainer on all the Amazon Prime perks you should be taking advantage of.

'The CyberGuy' Kurt Knutsson joins'Fox & Friends Weekend' to discuss the potential effects of artificial intelligence software like ChatGPT on the brain. Massachusetts Institute of Technology researchers are studying ChatGPT's effects on the brain. BRAIN DANGER: Using ChatGPT on a long-term basis could have negative effects on brain function. That's according to a study led by the Massachusetts Institute of Technology (MIT), which found that using a large language model (LLM) to write multiple essays over a four-month period could hamper cognitive abilities. 'ERRATIC': Videos taken this week by passengers showed Tesla robotaxis – which are Model Y vehicles with advanced software – braking suddenly, speeding, conducting improper drop-offs, entering the wrong lane and driving over a curb, according to Reuters.

Corey Feldman discusses his movie "The Birthday," which wrapped in 2004. "The Goonies" star Corey Feldman is concerned that the rise of artificial intelligence could ruin the "magic" of Hollywood filmmaking. In a new interview with Fox News Digital, the entertainer talked about his decades of being part of the film industry and what he thinks of it today compared to how it was when he was starring in beloved 80s classics like "Goonies," "The Lost Boys" and "The Burbs." When asked if he believes modern Hollywood can still conjure up the same "magic" that led to the creation of these iconic films, he said he wasn't so sure. "Well, I share the opinion that there is a lot of the magic that's been lost because of A.I., because of CGI, because of, you know, these things kind of taking over from the good stories, the great characters that we draw, the great writing," Feldman said.

The taglines on M3GAN 2.0 posters read like text messages from an overconfident tween: "HEY, QUEENS." "MISS ME?" "I'M STILL THAT B." (Another that apparently exists, though I haven't seen in the wild, hilariously reads: "THIS BITCH.") Next to them, the titular robot who looks like an uncanny-valley Olsen twin peers from above circular sunglasses. This character that, per her 2023 film debut, will kill you and your little dog, too, is now being marketed with big child-star energy. While she always had more to offer than malice (her late-movie dance break went viral from its trailer alone), this moment marks a clear pivot on M3GAN's Mary Janes.

As the very first image of devil doll sequel M3gan 2.0 emerges on screen, of a desert with the words "somewhere on the Turkish-Iranian border" popping up like it's a Bond movie, you'd be forgiven for double-checking if you're in the right cinema. The original, a grabby artificial intelligence (AI) riff on Child's Play and Annabelle, was a brisk, by-the-numbers domestic horror, released on the first weekend of 2023, a slot usually given to the very worst genre films. M3gan was smarter than most, often sly and frequently funny and introducing what's now become a rarity, an almost instant non-IP pop culture icon, whose virality exploded the film into a surprise smash (raking in over 180m from a 12m budget). Like the films it was inspired by, a franchise was inevitable although where we're taken in M3gan 2.0 was far less of a given. For the follow-up, writer-director Gerard Johnstone has swerved from horror to action while retaining and tweaking the comedy with a release date that's been upgraded to summer blockbuster territory. It doesn't always work – a two-hour runtime that's a little too long, world-saving stakes that are a little too big, funny lines that are a little too not funny – but it's a mostly watchable second-tier event movie that, in a world of inconsequential sequels that fail to justify their existence, will do.

On June 18, AI image-generation company Midjourney released a tool that lets users create short video clips using their own images as a template. Days later, Reddit cofounder Alexis Ohanian posted on X about how he used the tech to animate a photo of his late mother, which shows him as a child wrapped in her embrace. In the artificial video, she laughs and smiles before rocking him in her arms. "Damn, I wasn't ready for how this would feel," he wrote. "This is how she hugged me.

Language models (LMs) have demonstrated remarkable capabilities in NLP, yet adapting them efficiently and robustly to specific tasks remains challenging. As their scale and complexity grow, fine-tuning LMs on labelled data often underutilizes available unlabelled data, leads to overfitting on small task-specific sets, and imposes significant computational costs. These limitations hamper their application to the open-ended landscape of real-world language tasks. This thesis proposes a series of methods to better adapt LMs to downstream applications. First, we explore strategies for extracting task-relevant knowledge from unlabelled data, introducing a novel continued pre-training technique that outperforms state-of-the-art semi-supervised approaches. Next, we present a parameter-efficient fine-tuning method that substantially reduces memory and compute costs while maintaining competitive performance. We also introduce improved supervised fine-tuning methods that enable LMs to better follow instructions, especially when labelled data is scarce, enhancing their performance across a range of NLP tasks, including open-ended generation. Finally, we develop new evaluation methods and benchmarks, such as multi-hop spatial reasoning tasks, to assess LM capabilities and adaptation more comprehensively. Through extensive empirical studies across diverse NLP tasks, our results demonstrate that these approaches substantially improve LM robustness, efficiency, and generalization, making them more adaptable to a broad range of applications. These advances mark a significant step towards more robust and efficient LMs, bringing us closer to the goal of artificial general intelligence.

Large Language Models (LLMs) excel in understanding and generating text but struggle with providing professional literary criticism for works with profound thoughts and complex narratives. This paper proposes GLASS (Greimas Literary Analysis via Semiotic Square), a structured analytical framework based on Greimas Semiotic Square (GSS), to enhance LLMs' ability to conduct in-depth literary analysis. GLASS facilitates the rapid dissection of narrative structures and deep meanings in narrative works. We propose the first dataset for GSS-based literary criticism, featuring detailed analyses of 48 works. Then we propose quantitative metrics for GSS-based literary criticism using the LLM-as-a-judge paradigm. Our framework's results, compared with expert criticism across multiple works and LLMs, show high performance. Finally, we applied GLASS to 39 classic works, producing original and high-quality analyses that address existing research gaps. This research provides an AI-based tool for literary research and education, offering insights into the cognitive mechanisms underlying literary engagement.

Text-guided diffusion models have become essential for high-quality image synthesis, enabling dynamic image editing. In image editing, two crucial aspects are editability, which determines the extent of modification, and faithfulness, which reflects how well unaltered elements are preserved. However, achieving optimal results is challenging because of the inherent trade-off between editability and faithfulness. To address this, we propose Faithfulness Guidance and Scheduling (FGS), which enhances faithfulness with minimal impact on editability. FGS incorporates faithfulness guidance to strengthen the preservation of input image information and introduces a scheduling strategy to resolve misalignment between editability and faithfulness. Experimental results demonstrate that FGS achieves superior faithfulness while maintaining editability. Moreover, its compatibility with various editing methods enables precise, high-quality image edits across diverse tasks.