However, associating small molecules with changes in cell state is challenging.

Online Budgeted Matching (OBM) is a classic problem with important applications in online advertising, online service matching, revenue management, and beyond. Traditional online algorithms typically assume a small bid setting, where the maximum bid-to-budget ratio (κ) is infinitesimally small. While recent algorithms have tried to address scenarios with non-small or general bids, they often rely on the Fractional Last Matching (FLM) assumption, which allows for accepting partial bids when the remaining budget is insufficient. This assumption, however, does not hold for many applications with indivisible bids. In this paper, we remove the FLM assumption and tackle the open problem of OBM with general bids. We first establish an upper bound of 1 κ on the competitive ratio for any deterministic online algorithm. We then propose a novel meta algorithm, called MetaAd, which reduces to different algorithms with first known provable competitive ratios parameterized by the maximum bid-to-budget ratio κ [0, 1]. As a by-product, we extend MetaAd to the FLM setting and get provable competitive algorithms. Finally, we apply our competitive analysis to the design learningaugmented algorithms.

Neural Information Processing Systems http://nips.cc/

Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant energy and contribute to climate change. This makes sustainable data center control a priority. In this paper, we introduce SustainDC, a set of Python environments for benchmarking multiagent reinforcement learning (MARL) algorithms for data centers (DC). SustainDC supports custom DC configurations and tasks such as workload scheduling, cooling optimization, and auxiliary battery management, with multiple agents managing these operations while accounting for the effects of each other. We evaluate various MARL algorithms on SustainDC, showing their performance across diverse DC designs, locations, weather conditions, grid carbon intensity, and workload requirements. Our results highlight significant opportunities to improve data center operations using MARL algorithms. Given the increasing use of DC due to AI, SustainDC provides a crucial platform for developing and benchmarking advanced algorithms essential for achieving sustainable computing and addressing other heterogeneous real-world challenges.

In the high desert east of Reno, Nevada, construction crews are flattening the golden foothills of the Virginia Range, laying the foundations of a data center city. Google, Tract, Switch, EdgeCore, Novva, Vantage, and PowerHouse are all operating, building, or expanding huge facilities nearby. Meanwhile, Microsoft has acquired more than 225 acres of undeveloped property, and Apple is expanding its existing data center just across the Truckee River from the industrial park. The corporate race to amass computing resources to train and run artificial intelligence models and store information in the cloud has sparked a data center boom in the desert--and it's just far enough away from Nevada's communities to elude wide notice and, some fear, adequate scrutiny. This story is part of Power Hungry: AI and our energy future--our new series shining a light on the energy demands and carbon costs of the artificial intelligence revolution.

Do you want allegedly useful "artificial intelligence" features in your face in every single service and tool you use, constantly, unceasingly, and demanding you pay more for it? The latest perpetrator is Adobe, who's now raising the price of its priciest Creative Cloud plans next month and justifying it by bundling in a bunch of generative AI tools. The Creative Cloud All Apps plan is being renamed Creative Cloud Pro, because apparently tools that cost hundreds of dollars a year and aren't available as full purchases aren't for "professionals" unless they're paying the maximum amount. If you're in the US, Canada, or Mexico, and if you're currently subscribed to All Apps, you'll be moved over to the Pro plan starting on June 17th… with a price bump from 60 per month to 70 per month for standard, yearly-subscribed users in the US. Month-to-month prices will jump from the already-sky-high 90 per month to 105 per month.

Innovation in IT got us to this point. Graphics processing units (GPUs) that power the computing behind AI have fallen in cost by 99% since 2006. There was similar concern about the energy use of data centers in the early 2010s, with wild projections of growth in electricity demand. But gains in computing power and energy efficiency not only proved these projections wrong but enabled a 550% increase in global computing capability from 2010 to 2018 with only minimal increases in energy use. In the late 2010s, however, the trends that had saved us began to break.

Over the course of a three-day trip to the Middle East, President Trump and his emissaries from Silicon Valley have transformed the Persian Gulf from an artificial-intelligence neophyte into an A.I. power broker. They have reached an enormous deal with the United Arab Emirates to deliver hundreds of thousands of today's most advanced chips from Nvidia annually to build one of the world's largest data center hubs in the region, three people familiar with the talks said. The shipments would begin this year, and include roughly 100,000 chips for G42, an Emirati A.I. firm, with the rest going to U.S. cloud service providers. The administration revealed the agreement on Thursday in an announcement unveiling a new A.I. campus in Abu Dhabi supported by 5 gigawatts of electrical power. It would the largest such project outside of the United States and help U.S. companies serve customers in Africa, Europe and Asia, the administration said.

Sumitomo and SBI Holdings will each acquire a 20% stake in a unit of Vietnam's software and telecommunications conglomerate FPT to foster artificial intelligence adoption in Japan, according to a statement. Sumitomo and SBI will invest in FPT Smart Cloud Japan, which oversees FPT's Japan AI data center, according to a statement from the Vietnamese technology firm. FPT will remain the unit's major stakeholder, it said. SBI Holdings late last year signed a memorandum of understanding to acquire as much as a 35% stake in FPT's Japan cloud unit. FPT is setting up a Japan AI data center, with an initial investment of 200 million.