Breakthroughs, discoveries, and DIY tips sent every weekday. A famous prehistoric cave site in Belgium has yielded the oldest multifunctional tool of its kind. This Ice Age "Swiss Army knife" wasn't crafted by early Homo sapiens, however. Instead, the handy accessory came from our evolutionary cousin, the Neanderthal. The findings are detailed in a study published in Scientific Reports.

The heterogeneity issue in federated learning (FL) has attracted increasing attention, which is attempted to be addressed by most existing methods. Currently, due to systems and objectives heterogeneity, enabling clients to hold models of different architectures and tasks of different demands has become an important direction in FL. Most existing FL methods are based on the homogeneity assumption, namely, different clients have the same architectural models with the same tasks, which are unable to handle complex and multivariate data and tasks. To flexibly address these heterogeneity limitations, we propose a novel federated multi-task learning framework with the help of tensor trace norm, FedSAK. Specifically, it treats each client as a task and splits the local model into a feature extractor and a prediction head.

Forthcoming in Philipp Hacker, Andreas Engel, Sarah Hammer and Brent Mittelstadt (eds) The Oxford Handbook on the Foundations and Regulation of Generative AI (Oxford University Press) Abstract Foundation models and generative artificial intelligence (AI) exacerbate a core regulatory challenge associated with AI: its heterogeneity. By their very nature, foundation models and generative AI can perform multiple functions for their users, thus presenting a vast array of different risks. This multifunctionality means that prescriptive, one-size-fits-all regulation will not be a viable option. Even performance standards and ex post liability--regulatory approaches that usually afford flexibility--are unlikely to be strong candidates for responding to multifunctional AI's risks, given challenges in monitoring and enforcement. Regulators will do well instead to promote proactive risk management on the part of developers and users by using management-based regulation, an approach that has proven effective in other contexts of heterogeneity. Regulators will also need to maintain ongoing vigilance and agility. More than in other contexts, regulators of multifunctional AI will need sufficient resources, top human talent and leadership, and organizational cultures committed to regulatory excellence. Consider one of humanity's most primal of tools: the knife [30]. The knife is not a singular tool; rather, it comes in many different varieties that serve many functions, each of which can generate value for society. Knives are used in the kitchen to prepare delicious meals, and then they are used by diners to consume those same meals. Knives carve objects, cut rope, and open packages. They clear paths through forests and jungles, and they help in harvesting seasonal crops. Knives can be used, of course, to injure or kill people. But in the hands of surgeons, knives are routinely used to save lives. And even though knives take many different forms and are often designed for many different purposes--think of, for example, the many types and sizes of surgical scalpels, woodcarver's chisels, and kitchen implements, among others--knives designed for one purpose also can be adapted for different uses, as anyone who has used a dinner knife to open a postal letter can attest. Many knives, though, are deliberately intended to serve multiple functions, as is the case with a simple pocketknife or, even more emblematically, the classic Swiss army knife, some models of which boast a combination of more than 30 different tools in one. The proliferation of functions performed by different knives has led over the years to different forms and sources of rules governing their manufacture, sale, and deployment.

Samsung's latest smartphone packs a plethora of the latest flashy AI tools in an attempt to improve text, images, video and search – with both hits and misses. The new Galaxy S24 Ultra comes equipped with a combination of Samsung and Google's latest AI layered on top of one of the most capable phones on the market, filled to the brim with competition-beating specs. All this capability comes at a steep 1,249 ( 1,469/ 1,299.99/A The overall design differs only iteratively from predecessors, with a fully flat rather than curved screen and new titanium sides, similar to the iPhone 15 Pro Max, which gives it a gripper finish and in theory makes it stronger and more durable. The screen has Corning's latest Gorilla Armor glass that is more scratch-resistant and much less reflective, significantly reducing glare which, combined with the super-bright screen, makes using it outdoors as easy as indoors even on the sunniest of days. The S24 Ultra has the latest top-of-the-range chip from Qualcomm, the Snapdragon 8 Gen 3, offering a 20% speed increase, 26% faster graphics and greater power efficiency on previous generations.

Another good example for AI-powered decisions that reduce emissions at scale is Google's partnership with electricityMap. By utilizing electricityMap – an AI-powered platform that shows in real-time how clean electricity is around the world and provides past, current and forecasted carbon footprint data for electricity by country – Google manages to align computing tasks with times of low-carbon electricity supply in the grid and, thus, reduces CO2e emissions from electricity consumption.

Amongst the major challenges that come up when discussing how to implement artificial intelligence, and any type of advanced analytics, into insurance processes is the issue of how it will impact on employees and skills. Is AI a string of code, a process, a department, a way of thinking, or a part of the product design and delivery? How does it fit into the concept of the lifetime value of the customer? Where should it sit in an insurance company? Looking around at recent developments, the technology has reached a point where there is a lot of exploration with a certain amount of hype, and just a few examples of real machine learning going into services. Allianz is launching a proof of concept for automating the estimation of motor repairs from photos at the accident scene.

Silicon Valley – and Wall Street – have a new king. Alphabet, the company formerly known as Google, looks set to become the world's largest publicly traded company on Tuesday thanks to a spike in its share price, following exceptionally good results and a decision to come clean on how its makes and spends its money. Google's Alphabet set to overtake Apple as world's most valuable company Less than a year after it stormed past Berkshire Hathaway, ExxonMobil and Microsoft on its way to the top, the company's value has passed Apple. Tuesday will see whether it can hold on to those gains but the battle is on. In the past six months alone, since Google restructured to become Alphabet, its cap has risen by $200bn (£139bn), almost doubling its total value.

This package contains the mlpack Library development files. . Machine Learning Pack (mlpack) is an intuitive, fast, scalable C machine learning library, meant to be a machine learning analog to LAPACK. It aims to implement a wide array of machine learning methods and function as a "swiss army knife" for machine learning researchers. This package contains the mlpack Library runtime files. . Machine Learning Pack (mlpack) is an intuitive, fast, scalable C machine learning library, meant to be a machine learning analog to LAPACK.

When Anne Taylor walks into a room, she wants to know the same things that any person would. Where is there an empty seat? Who is walking up to me, and is that person smiling or frowning? What does that sign say? For Taylor, who is blind, there aren't always easy ways to get this information. Perhaps another person can direct her to her seat, describe her surroundings or make an introduction.

When Anne Taylor walks into a room, she wants to know the same things that any person would. Where is there an empty seat? Who is walking up to me, and is that person smiling or frowning? What does that sign say? For Taylor, who is blind, there aren't always easy ways to get this information. Perhaps another person can direct her to her seat, describe her surroundings or make an introduction.