More than 30 performing arts leaders in the UK, including the bosses of the National Theatre, Opera North and the Royal Albert Hall, have joined the chorus of creative industry concern about the government's plans to let artificial intelligence companies use artists' work without permission. They also urged the government to support the "moral and economic rights" of the creative community in music, dance, drama and opera. The 35 signatories of the statement include the chief executives of the Sadler's Wells dance theatre, the Royal Shakespeare Company, the City of Birmingham Symphony Orchestra and the Leeds Playhouse. The performing arts bosses added that they embraced advances in technology and were "participants" in innovation, but stated the government's plans risked undermining their ability to participate in the development and deployment of AI. Critics of the opt out plan have described it as unfair and impractical.

Li et al. (2023) used the Othello board game as a test case for the ability of GPT-2 to induce world models, and were followed up by Nanda et al. (2023b). We briefly discuss the original experiments, expanding them to include more language models with more comprehensive probing. Specifically, we analyze sequences of Othello board states and train the model to predict the next move based on previous moves. We evaluate seven language models (GPT-2, T5, Bart, Flan-T5, Mistral, LLaMA-2, and Qwen2.5) on the Othello task and conclude that these models not only learn to play Othello, but also induce the Othello board layout. We find that all models achieve up to 99% accuracy in unsupervised grounding and exhibit high similarity in the board features they learned. This provides considerably stronger evidence for the Othello World Model Hypothesis than previous works. Li et al. (2023) used the Othello board game to probe the ability of LLMs to induce world models. Their network had a 60-word input vocabulary, corresponding to the 64 tiles of an Othello board, except for the four that are already filled at the start. They trained the network on two datasets: one on about 140,000 real Othello games and another on millions of synthetic games. They then trained 64 independent non-linear probes (two-layer MLP classifiers) to classify each of the 64 tiles into three states: black, blank, and white, using internal representations from Othello-GPT as input.

Recent sparse multi-view scene reconstruction advances like DUSt3R and MASt3R no longer require camera calibration and camera pose estimation. However, they only process a pair of views at a time to infer pixel-aligned pointmaps. When dealing with more than two views, a combinatorial number of error prone pairwise reconstructions are usually followed by an expensive global optimization, which often fails to rectify the pairwise reconstruction errors. To handle more views, reduce errors, and improve inference time, we propose the fast single-stage feed-forward network MV-DUSt3R. At its core are multi-view decoder blocks which exchange information across any number of views while considering one reference view. To make our method robust to reference view selection, we further propose MV-DUSt3R+, which employs cross-reference-view blocks to fuse information across different reference view choices. To further enable novel view synthesis, we extend both by adding and jointly training Gaussian splatting heads. Experiments on multi-view stereo reconstruction, multi-view pose estimation, and novel view synthesis confirm that our methods improve significantly upon prior art. Code will be released.

The game of Othello is one of the world's most complex and popular games that has yet to be computationally solved. Othello has roughly ten octodecillion (10 to the 58th power) possible game records and ten octillion (10 to the 28th power) possible game positions. The challenge of solving Othello, determining the outcome of a game with no mistake made by either player, has long been a grand challenge in computer science. This paper announces a significant milestone: Othello is now solved. It is computationally proved that perfect play by both players lead to a draw. Strong Othello software has long been built using heuristically designed search techniques. Solving a game provides a solution that enables the software to play the game perfectly.

Foundation models exhibit significant capabilities in decision-making and logical deductions. Nonetheless, a continuing discourse persists regarding their genuine understanding of the world as opposed to mere stochastic mimicry. This paper meticulously examines a simple transformer trained for Othello, extending prior research to enhance comprehension of the emergent world model of Othello-GPT. The investigation reveals that Othello-GPT encapsulates a linear representation of opposing pieces, a factor that causally steers its decision-making process. This paper further elucidates the interplay between the linear world representation and causal decision-making, and their dependence on layer depth and model complexity. We have made the code public.

Valerie Harper is an excellent actress with a strong background and a long list of accomplishments. She has demonstrated her ability and variety as an actress over the course of a career spanning more than six decades. Do you want to learn more about her greatest professional successes and achievements? Just a few of her accomplishments are listed below: - Harper played Rhoda Morgenstern in the popular television series "The Mary Tyler Moore Show." The seven-season program was a numerous award winner, with Harper winning three Golden Globes for Best Supporting Actress in a Comedy Series.

The multi-talented entertainer Tracey Ullman has compiled an outstanding résumé throughout the course of her career. For your upcoming production, are you looking for a dynamic and adaptable entertainer? The most notable feature of Tracey's career is her aptitude at switching between many mediums with ease. Tracey has repeatedly shown that she is a genuine chameleon of the entertainment business, from her early days as a stand-up comedian on stage to her success on television with her own sketch comedy shows to her more recent work in film and theater. Tracey is a talented writer who has written and produced a number of her own television shows in addition to penning several novels.

Artificial intelligence (AI) has become an integral part of our everyday lives, found in everything from social media algorithms to e-commerce and navigation, but not everyone is comfortable with the idea. The key to winning over a skeptical public could be a family of robot "sisters." Sophia, Grace and Desdemona are humanoid robots, each programmed with sophisticated AI. The oldest of the three, Sophia, was first activated in 2016 and gained widespread attention, mostly for her looks. Whereas most artificial intelligence operates discretely out of sight, powering things like software and smartphones, Sophia is designed to look like a young woman and gained celebrity status as the face of AI.

Desdemona uses AI technology to draw from a library of great artists, poets, scientists and writers to produce spontaneous AI-generated poetry using natural language processing-oriented neural networks.

"In our field of risk analytics, as in many others, artificial intelligence and machine learning will make the type of information that we see as valuable …