'Bella the robot waitress won't replace our staff' 4 days agoShareSaveSophie CridlandReporting fromPortlandShareSaveBBCMike Deadman, from The View Cafe and Bar, said Bella was not being used to replace staff Bella carries multiple trays packed with food and drinks, deftly swerving any obstacles and delivering orders day in and day out to her customers. This is the latest recruit at The View Cafe and Bar at Portland's Heights hotel in Dorset. But Bella is no normal member of the waiting staff - she is a state-of-the art robot programmed to serve and even interact with the eatery's patrons. And costing a little under 9,000, it is hoped it can be an economical idea, as well as a novel one. But assistant manager Mike Deadman insists Bella - built by Chinese technology company Pudu - will not result in any job losses.

Debate is a commonly used form of human communication catered towards problem-solving because of its efficiency. Debate fundamentally allows multiple viewpoints to be brought up in problem-solving, and for complex problems, each viewpoint opens a new path for problem-solving. In this work, we apply this concept to LLM decision-making by proposing town hall-style debate prompting (THDP), a prompting method that splices a language model into multiple personas that will debate one another to reach a conclusion. Our experimental pipeline varies both the number of personas and the personality types of each persona to find the optimum town hall size and personality for benchmark performance as measured by ZebraLogic bench, a reasoning-intensive benchmark characterized by both multiple-choice and fill-in-the-blank questions. Our experimental results demonstrate that a town hall size of 5 personas with LLM-determined personality types performs optimally on ZebraLogic, achieving a 13\% improvement over one-shot CoT baselines in per-cell accuracy in GPT-4o, 9% puzzle accuracy increase in Claude 3.5 Sonnet, and an improvement in hard puzzle accuracy from 10-15%.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? How should we guard against cheating with AI? Long explanation: My 13-year-old rising 8th grader had minimal summer homework to complete. The homework was reading with related writing and it was not difficult. One of the books he had to read was The Sea of Monsters by Rick Riordan.

Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks. Despite demonstrations of significant merits such as improved robustness and resilience to unseen or out-of-distribution inputs over their non- Bayesian counterparts, their practical use has faded to near insignificance. In this study, we introduce an innovative framework to mitigate the computational burden of Bayesian neural networks (BNNs). Our approach follows the principle of Bayesian techniques based on deep ensembles, but significantly reduces their cost via multiple low-rank perturbations of parameters arising from a pre-trained neural network. Both vanilla version of ensembles as well as more sophisticated schemes such as Bayesian learning with Stein Variational Gradient Descent (SVGD), previously deemed impractical for large models, can be seamlessly implemented within the proposed framework, called Bayesian Low-Rank LeArning (Bella). In a nutshell, i) Bella achieves a dramatic reduction in the number of trainable parameters required to approximate a Bayesian posterior; and ii) it not only maintains, but in some instances, surpasses the performance of conventional Bayesian learning methods and non-Bayesian baselines. Our results with large-scale tasks such as ImageNet, CAMELYON17, DomainNet, VQA with CLIP, LLaVA demonstrate the effectiveness and versatility of Bella in building highly scalable and practical Bayesian deep models for real-world applications.

Greek director Yorgos Lanthimos has made a handful of very different movies over the past decade and a half, but his pet themes have a way of recurring in every one. To take a just a few examples: His breakthrough movie, 2009's Dogtooth, was a hermetic fable about a tyrannical couple who keep their three grown children trapped in a locked compound, feeding them lies about the world beyond their gates. The Lobster, from 2015, took place in an allegorical alternate reality where single adults who fail to find a romantic partner are legally compelled to be transformed into animals. The Favourite, Lanthimos' biggest international hit and the movie that won Olivia Colman a Best Actress Oscar in 2019, was a hyperstylized historical drama that played 18th-century court intrigue for the blackest of comedy. Poor Things, Lanthimos' adaptation of a 1992 novel by the Scottish writer Alasdair Gray (the screenplay is by Tony McNamara, who also co-wrote The Favourite), can be seen as the culminating expression of the filmmaker's longtime obsessions: the horror of being trapped in a closed system, the individual's often self-destructive quest to break free from said bondage, the warping effects of intergenerational trauma, and the capacity of the human body for transformation. Poor Things is a feminist recasting of the Frankenstein myth, a gorgeously designed setting for the jewel that is Emma Stone's lead performance, and not just my favorite Lanthimos movie I've seen yet but maybe the only one of his I've really liked.

In recent years, understanding the decision-making process of black-box models has become not only a legal requirement but also an additional way to assess their performance. However, the state of the art post-hoc interpretation approaches rely on synthetic data generation. This introduces uncertainty and can hurt the reliability of the interpretations. Furthermore, they tend to produce explanations that apply to only very few data points. This makes the explanations brittle and limited in scope. Finally, they provide scores that have no direct verifiable meaning. In this paper, we present BELLA, a deterministic model-agnostic post-hoc approach for explaining the individual predictions of regression black-box models. BELLA provides explanations in the form of a linear model trained in the feature space. Thus, its coefficients can be used directly to compute the predicted value from the feature values. Furthermore, BELLA maximizes the size of the neighborhood to which the linear model applies, so that the explanations are accurate, simple, general, and robust. BELLA can produce both factual and counterfactual explanations. Our user study confirms the importance of the desiderata we optimize, and our experiments show that BELLA outperforms the state-of-the-art approaches on these desiderata.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? This may seem like a low-stakes question, but I am truly concerned. My 15-year-old daughter is an extreme introvert, and strongly dislikes big groups of people and large events. She finds it difficult to make conversation and is seemingly uncomfortable even with talking with some of her classmates, even those she has known for years.

"Who put Bella down the Wych Elm - Hagley Wood?" A famous unsolved murder mystery memorialized by graffiti in England, I ran across it twice this month. The first instance of this graffiti was seen on the wall in Birmingham Fruit Market, in chalk, on March 30, 1944. Then it morphed into "Who put Luebella in the Wych Elm" on March 31 (reddit source). In 1999, a version appeared on the obelisk on Wychbury Hill as seen above and has remained ever since, even after restoration of the obelisk. The graffiti, by unknown writers, evidently refers to the body of a murdered woman found in an elm in Hagley Wood in April 1943. Robert Hart, of Wollescote, Stourbridge, told the Coroner and jury how at midday on Sunday, 18 April, he and three other lads when birdsnesting in the wood.

There have been many kinds of books, with many kinds of meanings. This one book was special because it was the first fictional story produced via artificial intelligence. It was the first book in the sense that its contents made sense. Before this book, all other attempts of letting an AI write a book had produced things that were pastiches of randomness. A couples of sentences here and there surrounded by text that made no sense.