Miniature people have been a staple of science fiction and fantasy going all the way back to Jonathan Swift's, and shrunken characters have taken the spotlight in everything from classic Hollywood movies like and to family-friendly blockbusters like and . References to these movies and others are strewn throughout the new Peacock limited series, but the drawn-out, 10-episode show isn't a particularly worthwhile addition to the sci-fi shrinking canon. Taking only the title and basic premise from Manuel Gonzales's 2014 short story, stars Elizabeth Banks as Lindy Littlejohn, a once-prominent author who now works as a university professor and has been overshadowed by her scientist husband Les (Matthew Macfadyen). Lindy, you see, feels metaphorically small in both her personal and professional lives, and is about to become literally small following an accident - or it? The most pressing problem for Lindy is that Les has yet to develop a stable antidote to his formula, and everything that he has attempted to return to its original size thus far has almost immediately exploded.

Luminous by Silvia Park and Ode to the Half-Broken by Suzanne Palmer are both thoughtful and well-written science fiction novels, featuring robots in richly realised worlds. But there the similarities end, says Emily H. Wilson Do we relate better to stories about robots with faces and bodies? Robots and whether they will one day deserve to be treated like people - or destroy humanity, or both - have interested writers for well over a century now. In the real world, the robot threat appears to involve the uses of artificial intelligence in misinformation and more direct forms of warfare such as drone attacks. In the world of literature, however, many writers focus on individual robots.

He told Mission Control that they saw'an island of terrain completely surrounded by darkness.' 'Up to the north, there is a very nice double crater. It looks like a snowman just sitting there,' he continued. 'On the southern edge, there is a hole.

Accurate uncertainty quantification is crucial for making reliable decisions in various supervised learning scenarios, particularly when dealing with complex, multimodal data such as images and text. Current approaches often face notable limitations, including rigid assumptions and limited generalizability, constraining their effectiveness across diverse supervised learning tasks. To overcome these limitations, we introduce Generative Score Inference (GSI), a flexible inference framework capable of constructing statistically valid and informative prediction and confidence sets across a wide range of multimodal learning problems. GSI utilizes synthetic samples generated by deep generative models to approximate conditional score distributions, facilitating precise uncertainty quantification without imposing restrictive assumptions about the data or tasks. We empirically validate GSI's capabilities through two representative scenarios: hallucination detection in large language models and uncertainty estimation in image captioning. Our method achieves state-of-the-art performance in hallucination detection and robust predictive uncertainty in image captioning, and its performance is positively influenced by the quality of the underlying generative model. These findings underscore the potential of GSI as a versatile inference framework, significantly enhancing uncertainty quantification and trustworthiness in multimodal learning.

Motifs often recur in musical works in altered forms, preserving aspects of their identity while undergoing local variation. This paper investigates how such motivic transformations occur within their musical context in symbolic music. To support this analysis, we develop a probabilistic framework for modeling motivic transformations and apply it to Beethoven's piano sonatas by integrating multiple datasets that provide melodic, rhythmic, harmonic, and motivic information within a unified analytical representation. Motif transformations are represented as multilabel variables by comparing each motif instance to a designated reference occurrence within its local context, ensuring consistent labeling across transformation families. We introduce a multilabel Conditional Random Field to model how motif-level musical features influence the occurrence of transformations and how different transformation families tend to co-occur. Our goal is to provide an interpretable, distributional analysis of motivic transformation patterns, enabling the study of their structural relationships and stylistic variation. By linking computational modeling with music-theoretical interpretation, the proposed framework supports quantitative investigation of musical structure and complexity in symbolic corpora and may facilitate the analysis of broader compositional patterns and writing practices.

Why is Anthropic sponsoring an exhibition about Monet? Shower thoughts are typically best left in the shower. Such as: What might Claude the AI chatbot have to say about Claude Monet? Earlier this month, San Francisco's de Young Museum unveiled its newest exhibition, "Monet and Venice," which is dedicated to the impressionist painter's beautiful and meditative canvases of the floating city. And Anthropic, perhaps having seized on a marketing opportunity, is one of the show's lead sponsors.

Tech firm'says goodbye' to Sora, made publicly available in 2024, just six months after its launch of a stand-alone app In an abrupt announcement on Tuesday, OpenAI said it was "saying goodbye" to its AI video generator Sora. The move comes just six months after the company's splashy launch of a stand-alone app with which people could make and share hyper-realistic AI videos in a scrolling social feed. "To everyone who created with Sora, shared it, and built community around it: thank you," the company wrote in a post on X . "What you made with Sora mattered, and we know this news is disappointing." OpenAI first made Sora publicly available in late 2024, but it wasn't until the company launched Sora 2 and its stand-alone app last September that the video generator reached mainstream attention.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Neural Information Processing Systems http://nips.cc/

Services such as Google TV and Amazon's Fire TV have recommendation systems, as well as search functions, that may prioritise some content over others. Services such as Google TV and Amazon's Fire TV have recommendation systems, as well as search functions, that may prioritise some content over others. World's broadcasters urge EU to tighten rules for big tech in smart TV battle The world's largest broadcasters have pushed for the EU to enforce its toughest regulations against virtual TVs and smart assistants built by Google, Amazon, Apple and Samsung . The call came in a letter from the Association of Commercial Television and Video on Demand Services in Europe (ACT), whose members include Canal+, RTL, Mediaset, ITV, Paramount+, NBCUniversal, Walt Disney, Warner Bros Discovery, Sky and TF1 Groupe. The letter argues that big tech companies have growing control over the operating systems of smart TVs and voice assistants, allowing them to act as "gatekeepers" funnelling users towards some content and away from others.