ESPN's Mad Dog Russo melts down over'U-S-A' chants at the RBC Heritage A piece of the UFC White House event's setup is sitting in Pennsylvania Amish country Viral Ottawa Senators fan blamed for team's 0-2 playoff start banished to Taiwan'First Take' host acts disgusted when she has to cover Vrabel-Russini drama Edward Cabrera's strikeout prop is the play as struggling Phillies face surging Cubs today Nuggets vs Timberwolves Game 3 pick hinges on Jaden McDaniels calling out Denver's entire defense Charles Barkley was disgusted by Magic's highly questionable pregame handshake ChatGPT predicted the first round of the NFL Draft and here's what it said Curt Cignetti was so focused this offseason, he turned down all external requests: 'I'm 95% football' California governor's race intensifies as six candidates face off Trump: US Navy to'shoot and kill' any boat placing mines in Hormuz Virginia court blocks Democrats' redistricting effort, Florida next Trump weighs in on Iran's internal power struggle and Strait of Hormuz control Hasan Piker justifies'social murder' of CEO Fox News celebrates'Bring Your Kids to Work Day' WWE's Kit Wilson backs Cody Rhodes after scathing Pat McAfee promo WWE star Kit Wilson tells Fox News Digital why he's forgiven Cody Rhodes for taking his anger out on him. WWE star Seth Rollins has had a tough week. He lost his WrestleMania 42 match against Gunther after a brutal ambush from Bron Breakker, and on Monday Night Raw, Breakker ambushed him again. It has been rough for The Visionary and on Thursday he didn't appear to be in the mood for any teasing. NFL Network host Kyle Brandt appears during the Cincinnati Bengals game against the Los Angeles Rams in Super Bowl LVI at SoFi Stadium in Inglewood, Calif., on Feb. 13, 2022.

Reinforcement Learning from Human Feedback (RLHF), the standard for aligning Large Language Models (LLMs) with human values, is known to fail to satisfy properties that are intuitively desirable, such as respecting the preferences of the majority \cite{ge2024axioms}. To overcome these issues, we propose the use of a probabilistic Social Choice rule called \emph{maximal lotteries} as a replacement for RLHF. We show that a family of alignment techniques, namely Nash Learning from Human Feedback (NLHF) \cite{munos2023nash} and variants, approximate maximal lottery outcomes and thus inherit its beneficial properties. We confirm experimentally that our proposed methodology handles situations that arise when working with preferences more robustly than standard RLHF, including supporting the preferences of the majority, providing principled ways of handling non-transitivities in the preference data, and robustness to irrelevant alternatives. This results in systems that better incorporate human values and respect human intentions.

The Intelligence Community Campus-Bethesda, a vast office complex covered in vertical panels of maroon siding and mirrored glass, sits on a cliff overlooking the Potomac, surrounded by a forty-acre lawn and a tall wrought-iron fence. Roughly three thousand employees of various United States spy agencies work there. About two dozen of them are assigned to the Foreign Malign Influence Center--the command hub of the battle to protect the Presidential election from manipulation by foreign powers. The center, which opened in 2022, is responsible for deciphering, and defeating, surreptitious efforts to rig or tilt the American vote. The October before an election is the busy season.

Three years ago, Google set an ambitious plan to address climate change by going "net zero", meaning it would release no more climate-changing gases into the air than it removes, by 2030. But a report from the company on Tuesday showed it is nowhere near meeting that goal. Rather than declining, its emissions grew 13 percent in 2023 over the year before. Compared with its baseline year of 2019, emissions have soared 48 percent. Google cited artificial intelligence and the demand it puts on data centres, which require massive amounts of electricity, for last year's growth.

Designing private voting rules is an important and pressing problem for trustworthy democracy. In this paper, under the framework of differential privacy, we propose a novel famliy of randomized voting rules based on the well-known Condorcet method, and focus on three classes of voting rules in this family: Laplacian Condorcet method ($\CMLAP_\lambda$), exponential Condorcet method ($\CMEXP_\lambda$), and randomized response Condorcet method ($\CMRR_\lambda$), where $\lambda$ represents the level of noise. We prove that all of our rules satisfy absolute monotonicity, lexi-participation, probabilistic Pareto efficiency, approximate probabilistic Condorcet criterion, and approximate SD-strategyproofness. In addition, $\CMRR_\lambda$ satisfies (non-approximate) probabilistic Condorcet criterion, while $\CMLAP_\lambda$ and $\CMEXP_\lambda$ satisfy strong lexi-participation. Finally, we regard differential privacy as a voting axiom, and discuss its relations to other axioms.

Social choice functions (SCFs) map the preferences of a group of agents over some set of alternatives to a non-empty subset of alternatives. The Gibbard-Satterthwaite theorem has shown that only extremely restrictive SCFs are strategyproof when there are more than two alternatives. For set-valued SCFs, or so-called social choice correspondences, the situation is less clear. There are miscellaneous -- mostly negative -- results using a variety of strategyproofness notions and additional requirements. The simple and intuitive notion of Kelly-strategyproofness has turned out to be particularly compelling because it is weak enough to still allow for positive results. For example, the Pareto rule is strategyproof even when preferences are weak, and a number of attractive SCFs (such as the top cycle, the uncovered set, and the essential set) are strategyproof for strict preferences. In this paper, we show that, for weak preferences, only indecisive SCFs can satisfy strategyproofness. In particular, (i) every strategyproof rank-based SCF violates Pareto-optimality, (ii) every strategyproof support-based SCF (which generalize Fishburn's C2 SCFs) that satisfies Pareto-optimality returns at least one most preferred alternative of every voter, and (iii) every strategyproof non-imposing SCF returns the Condorcet loser in at least one profile. We also discuss the consequences of these results for randomized social choice.

Satellite imagery of the Sahara desert presents an arid expanse, the endless rolling dunes we know from movies. The thing is, normal satellite images don't show individual trees, but that doesn't necessarily mean they're not there. Researchers from the University of Copenhagen and NASA taught artificial intelligence about trees and had them take another look. It turns out there is lots of vegetation in the Western Sahara: an estimated 1.8 billion trees and shrubs. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert, because up until now, most people thought that virtually none existed," says lead author Martin Brandt of the university's Department of Geosciences and Natural Resource Management.

A combination of high-resolution satellite imaging and'deep learning' has identified more than 1.8 billion trees across the West African Sahara, Sahel and sub-humid zone – significantly more trees than were previously thought to exist in the region. The collaboration between NASA and several geoscience departments across the world used 11,128 satellite images from four satellites to count individual trees across 1.3 million square kilometres. The deep-learning approach has, for the first time, allowed researchers to identify individual trees across the dryland expanse. Because of the absence of closed canopies, many parts of the Sahara and the Sahel have previously been mapped with zero per cent tree cover. 'You need high-resolution satellite images to be able to detect individual trees and not just to make estimations based on identified areas of canopy cover,' says Martin Brandt from the University of Copenhagen.

Poorly timed traffic lights don't just waste precious minutes. Like Google's chief sustainability officer Kate Brandt pointed out at a media event yesterday, they're also bad for the environment and public health. The company unveiled a slew of sustainability-centric products and updates today that aim to help users make more informed, environmentally friendly decisions. But it's also been working on a project that could use AI to make traffic lights more efficient and, as a result, decrease pollution in general. When your vehicle stops at an intersection, that idling time leads to wasted fuel and "more street-level air pollution," Brandt said.

When a team of international scientists set out to count every tree in a large swathe of west Africa using AI, satellite images and one of the world's most powerful supercomputers, their expectations were modest. Previously, the area had registered as having little or no tree cover. The biggest surprise, says Martin Brandt, assistant professor of geography at the University of Copenhagen, is that the part of the Sahara that the study covered, roughly 10%, "where no one would expect to find many trees", actually had "quite a few hundred million". Trees are crucial to our long-term survival, as they absorb and store the carbon dioxide emissions that cause global heating. But we still do not know how many there are.