Large language models (LLMs) are of paramount importance in today's technological landscape.

The music industry has been battling AI companies, alleging illegal use of their songs. The world's largest music companies have licensed their works to a music startup called Klay, which is building a streaming service that will allow users to remake songs using artificial intelligence tools. Klay is the first music AI service to reach a deal with all three major record labels, Universal, Sony and Warner, according to people familiar with the deals. Klay plans to announce its agreements in the coming days, said the people, who asked not to be identified discussing confidential plans. Klay is building a product that will offer the features of a streaming service like Spotify, amplified by AI technology that will let users remake songs in different styles.

A drone explodes during a Russian drone strike in Kyiv on Nov. 14. LONDON - Explosive weapons killed or injured children at record levels last year, as wars increasingly move into urban areas, Save the Children said in a report published Thursday. Nearly 12,000 children were killed or injured in conflict last year worldwide, said the U.K.-based charity, citing U.N. figures. This is the highest number since records began in 2006, and is 42% higher than the 2020 total. Previously, children in war zones were more likely to die from malnutrition, disease or failing health systems. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

NASA's Perseverance rover discovers a shiny metallic rock nicknamed 'Phippsaksla' on Mars that scientists believe could be a meteorite from an ancient asteroid.

HTTP/3 is a new application layer protocol supported by most browsers. It uses QUIC as an underlying transport protocol. QUIC provides multiple benefits, like faster connection establishment, reduced latency, and improved connection migration.

World's biggest company Nvidia stuns Wall Street as it gives biggest clue yet to state of US economy'Triple whammy' will decide if Wall Street crashes within the next day A senior White House official has told me the REAL threat to Trump. Epstein is a humiliating distraction. But he's losing grip fast... this could be fatal: ANDREW NEIL Secret reasons Ronaldo was desperate to meet Trump... and what he REALLY wants from the president Melania Trump delivers'dystopian' speech to troops sparking meltdown Kevin Spacey reveals he is currently homeless and'living in hotels' as he admits his financial situation is'not great' - two years after he was cleared of sexual assault allegations Female health inspector sparks internet firestorm over video of her pouring BLEACH all over unlicensed taco vendor's food Haunting final words of boy, 12, 'tortured by lesbian wives' until he shrunk and died Nancy Mace leaks wild sexts about Republican colleague: 'You will be a good girl' Full-faced Britney Spears looks unrecognizable as she carries Champagne flute from wine bar, then drives away... AGAIN: Family speak out on'nightmare' spiral'The Mamdani effect' goes berserk: Desperate New Yorkers fight over multimillion-dollar homes outside city... prices jump 24% in five DAYS All the scandals of the 1939 Wizard of Oz: How Judy Garland was drugged and starved in an'iron corset', actors DIED and one had an eyelid burned off... not to mention the drunken orgies SARAH VINE: Meghan the Domestic Goddess is back - and she's in full festive flow. Meghan Markle goes barefaced as she poses on cover of Harper's Bazaar magazine Doctors warn'overprescribed' medical test use has DOUBLED despite raising the risk of cancer by three times Deep red state of Utah will see its population swell by TWO MILLION by 2065 thanks to'net-in migration' READ MORE: Can you spot the AI-generated faces? Britain boasts some of the best authors in the world - but they could soon be replaced by AI, a disturbing report reveals.

The central task of interpretability research is to explain the functions that AI systems learn from data. Investigating these functions requires experimentation with trained models, using tools that incorporate varying degrees of human input. Hand-tooled approaches that rely on close manual inspection [Zeiler and Fergus, 2014, Zhou et al., 2014, Mahendran and V edaldi, 2015, Olah et al., 2017, 2020, Elhage et al., 2021] or search for predefined phenomena [Wang et al., 2022, Nanda

Recent advances in generative AI for music have achieved remarkable fidelity and stylistic diversity, yet these systems often fail to align with nuanced human preferences due to the specific loss functions they use. This paper advocates for the systematic application of preference alignment techniques to music generation, addressing the fundamental gap between computational optimization and human musical appreciation. Drawing on recent breakthroughs including MusicRL's large-scale preference learning, multi-preference alignment frameworks like diffusion-based preference optimization in DiffRhythm+, and inference-time optimization techniques like Text2midi-InferAlign, we discuss how these techniques can address music's unique challenges: temporal coherence, harmonic consistency, and subjective quality assessment. We identify key research challenges including scalability to long-form compositions, reliability amongst others in preference modelling. Looking forward, we envision preference-aligned music generation enabling transformative applications in interactive composition tools and personalized music services. This work calls for sustained interdisciplinary research combining advances in machine learning, music-theory to create music AI systems that truly serve human creative and experiential needs.

The law draws a sharp distinction between objects and persons, and between two kinds of persons, the ''fictional'' kind (i.e. corporations), and the ''non-fictional'' kind (individual or ''natural'' persons). This paper will assess whether we maximize overall long-term legal coherence by (A) maintaining an object classification for all future AI systems, (B) creating fictional legal persons associated with suitably advanced, individuated AI systems (giving these fictional legal persons derogable rights and duties associated with certified groups of existing persons, potentially including free speech, contract rights, and standing to sue ''on behalf of'' the AI system), or (C) recognizing non-fictional legal personhood through legal identity for suitably advanced, individuated AI systems (recognizing them as entities meriting legal standing with non-derogable rights which for the human case include life, due process, habeas corpus, freedom from slavery, and freedom of conscience). We will clarify the meaning and implications of each option along the way, considering liability, copyright, family law, fundamental rights, civil rights, citizenship, and AI safety regulation. We will tentatively find that the non-fictional personhood approach may be best from a coherence perspective, for at least some advanced AI systems. An object approach may prove untenable for sufficiently humanoid advanced systems, though we suggest that it is adequate for currently existing systems as of 2025. While fictional personhood would resolve some coherence issues for future systems, it would create others and provide solutions that are neither durable nor fit for purpose. Finally, our review will suggest that ''hybrid'' approaches are likely to fail and lead to further incoherence: the choice between object, fictional person and non-fictional person is unavoidable.

Understanding customer attitudes has become a critical component of decision-making due to the growing influence of social media and e-commerce. Text-based opinions are the most structured, hence playing an important role in sentiment analysis. Most of the existing methods, which include lexicon-based approaches and traditional machine learning techniques, are insufficient for handling contextual nuances and scalability. While the latter has limitations in model performance and generalization, deep learning (DL) has achieved improvement, especially on semantic relationship capturing with recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The aim of the study is to enhance opinion mining by introducing a hybrid deep neural network model that combines a bidirectional gated recurrent unit (BGRU) and long short-term memory (LSTM) layers to improve sentiment analysis, particularly addressing challenges such as contextual nuance, scalability, and class imbalance. To substantiate the efficacy of the proposed model, we conducted comprehensive experiments utilizing benchmark datasets, encompassing IMDB movie critiques and Amazon product evaluations. The introduced hybrid BGRULSTM (HBGRU-LSTM) architecture attained a testing accuracy of 95%, exceeding the performance of traditional DL frameworks such as LSTM (93.06%), CNN+LSTM (93.31%), and GRU+LSTM (92.20%). Moreover, our model exhibited a noteworthy enhancement in recall for negative sentiments, escalating from 86% (unbalanced dataset) to 96% (balanced dataset), thereby ensuring a more equitable and just sentiment classification. Furthermore, the model diminished misclassification loss from 20.24% for unbalanced to 13.3% for balanced dataset, signifying enhanced generalization and resilience.