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Look Up Say More Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Mashable Voices Trending Now Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series The following content is brought to you by Mashable partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. Give one Windows PC the full MS Office setup for just $54.99 Credit: Microsoft Deal pricing and availability subject to change after time of publication. Microsoft Office is useful precisely because it isn't exciting. Documents need writing, spreadsheets need analyzing, presentations need building, and email keeps arriving whether anyone invited it or not.

The country's machines can serve hot ramen, spot fake coins, and even withstand earthquakes. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. From post offices and parks to train stations and even the summit of Mount Fuji, Japan's vending machines are ubiquitous. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Creator Playbook Mashable Voices Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series Here's what to watch for As scammers become increasingly sophisticated, staying vigilant online has never been more important. The following content is brought to you by Mashable partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. PCMag.com is a leading authority on technology, delivering Labs-based, independent reviews of the latest products and services. Our expert industry analysis and practical solutions help you make better buying decisions and get more from technology.

British diplomats in six locations around the world, including Buenos Aires and Santiago in South America, will no longer get hardship payments for working in dangerous cities. Bonuses and respite breaks are granted to those working in cities considered risky, like Beirut in Lebanon, or with high crime, such as Pretoria in South Africa, or high air pollution in cities including New Delhi and Beijing. The UK government has removed Santiago, in Chile, Panama City in Panama, Pyongyang in North Korea, Bamako in Mali, and Sarajevo in Bosnia and Herzegovina, in addition to the two south American cities, according to the latest list published last week, external . The 144 locations still on the list include many trouble spots but also popular holiday destinations such as Bali and Rio De Janeiro, prompting criticism from the Conservatives. Tory chairman Kevin Hollin rake said civil servants receiving extra pay for working in such areas is unfair on the taxpayer. The Foreign Office has given civil servants extra payments for working in areas where there is a high level of danger, or high risks to health, for the past 30 years.

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 . Fox News AI Newsletter: IBM's AI warning sends'shockwave' Would you trust a tiny dental robot? Tesla helped save a driver; is your car ready? So why is your device showing ads? Would you pay $8,000 for a robot to fold laundry? Medical identity theft follows you into the doctor's office Energy secretary criticizes New York's data center ban amid AI race Trump says the late Sen Lindsey Graham's heart condition was'almost undetectable' Freedom of navigation is a'fundamental tenet' of the modern world: Ex-Naval CENTCOM commander Andrew Yang details support for Trump Accounts as program's rollout begins Fmr UN ambassador warns of Iranian drone weapons in Cuba, says strikes on US'highly possible' Uber CEO: This is about making'everyday life' better'Gutfeld!':

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 . NASA's Chandra telescope reveals Milky Way's outer reaches may stretch farther than previously known'Milestone': Scientists claim to build synthetic cell, raising concerns in step toward artificial life Artemis crew says they wanted to'connect with humanity,' show what can be done when they put their mind to it Scientists revive ancient 24,000-year-old'zombie worm' from Arctic ice -- then it reproduced'Gigantic' ancient octopus used jaws to crush prey and hunted alongside the dinosaurs 100M years ago: study Scientists uncover identity of mysterious'golden orb' discovered miles underwater in 2023 Perfectly preserved Inca potatoes offer rare glimpse into empire's food system Greg Gutfeld: These are the 2 things I don't want to think about'Seen and Unseen': Kamala Harris's word of the day is'hope' Is Spielberg's new UFO film more fact than fiction?

Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based on the quantity of submitted data are vulnerable to manipulation, as agents may submit fabricated or low-quality data to inflate their rewards. Prior work has proposed comparing each agent's data against others' to promote honesty: when others contribute genuine data, the best way to minimize discrepancy is to do the same. Yet prior implementations of this idea rely on very strong assumptions about the data distribution (e.g.

The next frontier of online advertising is revenue generation from LLM-generated content. We consider a setting where advertisers aim to influence the responses of an LLM, while platforms seek to maximize advertiser value and ensure user satisfaction. The challenge is that advertisers' preferences generally conflict with those of the user, and advertisers may misreport their preferences. To address this, we introduce MOSAIC, an auction mechanism that ensures that truthful reporting is a dominant strategy for advertisers and that aligns the utility of each advertiser with their contribution to social welfare. Importantly, the mechanism operates without LLM fine-tuning or access to model weights and provably converges to the output of the optimally fine-tuned LLM as computational resources increase. Additionally, it can incorporate contextual information about advertisers, which significantly improves social welfare. Via experiments with publicly available LLMs, we show that MOSAIC leads to high advertiser value and platform revenue with low computational costs. While our motivating application is online advertising, our mechanism can be applied in any setting with monetary transfers, making it a general-purpose solution for truthfully aggregating the preferences of selfinterested agents over LLM-generated replies.

We study the problem of designing procurement auctions for the strategic uncapacitated facility location problem: a company needs to procure a set of facility locations in order to serve its customers and each facility location is owned by a strategic agent. Each owner has a private cost for providing access to their facility (e.g., renting it or selling it to the company) and needs to be compensated accordingly. The goal is to design truthful auctions that decide which facilities the company should procure and how much to pay the corresponding owners, aiming to minimize the total cost, i.e., the monetary cost paid to the owners and the connection cost suffered by the customers (their distance to the nearest facility). We evaluate the performance of these auctions using the frugality ratio. We first analyze the performance of the classic VCG auction in this context and prove that its frugality ratio is exactly 3. We then leverage the learning-augmented framework and design auctions that are augmented with predictions regarding the owners' private costs. Specifically, we propose a family of learning-augmented auctions that achieve significant payment reductions when the predictions are accurate, leading to much better frugality ratios. At the same time, we demonstrate that these auctions remain robust even if the predictions are arbitrarily inaccurate, and maintain reasonable frugality ratios even under adversarially chosen predictions. We finally provide a family of "error-tolerant" auctions that maintain improved frugality ratios even if the predictions are only approximately accurate, and we provide upper bounds on their frugality ratio as a function of the prediction error.