You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Queen Elizabeth's in-flight requests revealed - from a favourite drink to her secret weapon to fight jet lag The down-to-earth Cotswolds pub - serving pizzas and Negronis - that has its much fancier rivals truly'Stumped' Here's how to book with confidence and make the most of your package holiday Austria's 007 heaven: As the unveiling of the next Bond edges closer, how the film franchise's coolest attraction lets fans relive Daniel Craig's thrilling Spectre chase at 3,000M Would YOU go on a first date abroad? Singletons reveal what happened when they agreed to holiday with someone they'd just met The tiny European island with incredible beaches set for a tourism boost from The Odyssey - and it's not in Greece Is this the five-bed'secret' retreat - boasting its own pool, terrace and 60 acres - that Tom Holland and Zendaya stayed in on the lavish Beaverbrook estate after tying the knot (again)? The secret UK getaways where you can escape Britain's summer crowds, from glorious Welsh mountains to sleepy Norfolk villages Forget the overcrowded Canary Islands, this is why GREECE is the best place to visit this summer: Delicious food, gorgeous beaches, the friendliest locals. I've stayed in countless Greek hotels over the past 40 years - here are my top ten... Is this the world's most luxurious cruise ship?

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Large Language Models (LLMs) are being increasingly used as a building block in data systems to process large text datasets. To do so, LLM model providers offer multiple LLMs with different sizes, spanning various cost-quality trade-offs when processing text at scale. Top-of-the-line LLMs (e.g., GPT-4o, Claude Sonnet) operate with high accuracy but are prohibitively expensive when processing many records. To avoid high costs, more affordable but lower quality LLMs (e.g., GPT-4o-mini, Claude Haiku) can be used to process records, but we need to ensure that the overall accuracy does not deviate substantially from that of the top-of-the-line LLMs. The model cascade framework provides a blueprint to manage this trade-off, by using the confidence of LLMs in their output (e.g., log-probabilities) to decide on which records to use the affordable LLM. However, existing solutions following this framework provide only marginal cost savings and weak theoretical guarantees because of poor estimation of the quality of the affordable LLM's outputs. We present BARGAIN, a method that judiciously uses affordable LLMs in data processing to significantly reduce cost while providing strong theoretical guarantees on the solution quality. BARGAIN employs a novel adaptive sampling strategy and statistical estimation procedure that uses data and task characteristics and builds on recent statistical tools to make accurate estimations with tight theoretical guarantees. Variants of BARGAIN can support guarantees on accuracy, precision, or recall of the output. Experimental results across 8 real-world datasets show that BARGAIN reduces cost, on average, by up to 86% more than state-of-the-art, while providing stronger theoretical guarantees on accuracy of output, with similar gains when guaranteeing a desired level of precision or recall.

Policy Space Response Oracles (PSRO) interleaves empirical game-theoretic analysis with deep reinforcement learning (DRL) to solve games too complex for traditional analytic methods. Tree-exploiting PSRO (TE-PSRO) is a variant of this approach that iteratively builds a coarsened empirical game model in extensive form using data obtained from querying a simulator that represents a detailed description of the game. We make two main methodological advances to TE-PSRO that enhance its applicability to complex games of imperfect information. First, we introduce a scalable representation for the empirical game tree where edges correspond to implicit policies learned through DRL. These policies cover conditions in the underlying game abstracted in the game model, supporting sustainable growth of the tree over epochs. Second, we leverage extensive form in the empirical model by employing refined Nash equilibria to direct strategy exploration. To enable this, we give a modular and scalable algorithm based on generalized backward induction for computing a subgame perfect equilibrium (SPE) in an imperfect-information game. We experimentally evaluate our approach on a suite of games including an alternating-offer bargaining game with outside offers; our results demonstrate that TE-PSRO converges toward equilibrium faster when new strategies are generated based on SPE rather than Nash equilibrium, and with reasonable time/memory requirements for the growing empirical model.

Earlier today, The Atlantic's CEO, Nicholas Thompson, announced in an internal email that the company has entered into a business partnership with OpenAI, the creator of ChatGPT. Editorial content from this publication will soon be directly referenced in response to queries in OpenAI products. In practice, this means that users of ChatGPT, say, might type in a question and receive an answer that briefly quotes an Atlantic story; according to Anna Bross, The Atlantic's senior vice president of communications, it will be accompanied by a citation and a link to the original source. Other companies, such as Axel Springer, the publisher of Business Insider and Politico, have made similar arrangements. It does all feel a bit like publishers are making a deal with--well, can I say it?

Google announced Friday that it is releasing a course aimed at teaching one million Americans how to use artificial intelligence tools. As part of the rollout, the tech giant also announced that its charitable arm, Google.org, The new AI skills course will be available for 49 on Coursera, a for-profit online course provider. The announcement comes after Google scrapped its rules requiring suppliers and staffing firms it works with to provide good pay and benefits to their employees - along with laying off thousands of employees despite turning record profits. Google announced two new initiatives: One is a self-paced course on AI skills, the other is a grant program for AI job skills training.

When the board of OpenAI staged a bum mutiny last November, throwing out the company's leadership only to have the bosses return while board members were pressured to resign, something seemed rotten in the state of effective altruism. Nominally, OpenAI's mission had been to ensure that AI "benefits all of humanity." Fiduciarily, OpenAI's mission is to benefit the subset of humanity with a stake in OpenAI. And then, of course, there was Sam Bankman-Fried, the felonious altruist who argued in court last fall that his sordid crypto exchange was in fact a noble exercise in earning-to-give--making Midas money, sure, but only to funnel it to the global poor. This week he's facing a prison sentence of up to 50 years, which his legal team has complained paints him as a "depraved super-villain."

Kevin Sorbo and wife Sam Sorbo shared their concerns about artificial intelligence and why the actors strike is necessary as negotiations continue to be at a standstill between SAG-AFTRA and the AMPTP.

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Sometimes, those who would forget history are condemned to repeat it. For those of us with long memories, the current fuss – nay hysteria – surrounding ChatGPT (and "generative AI" generally) rings a bell. We have been here before: in January 1966, to be precise. That was the moment when Joseph Weizenbaum, a computer scientist at MIT, unveiled Eliza, which would have been called the world's first chatbot if that term had existed at the time. Weizenbaum wrote the software (in a programming language intriguingly called MAD-SLIP) to demonstrate that communications between humans and computers were inevitably superficial.