We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations. An adversary in the environment observes the autonomous team's behavior to infer their objective and responds against the team. In this setting, we propose strategies for controlling the density of the autonomous team so that they can deceive the adversary regarding their objective while achieving the desired final resource allocation. We first develop a prediction algorithm based on the principle of maximum entropy to express the team's behavior expected by the adversary. Then, by measuring the deceptiveness via Kullback-Leibler divergence, we devise convex optimization-based planning algorithms that deceive the adversary by either exaggerating the behavior towards a decoy allocation strategy or creating ambiguity regarding the final allocation strategy. A user study with $320$ participants demonstrates that the proposed algorithms are effective for deception and reveal the inherent biases of participants towards proximate goals.

We consider the problem of learning a graph under the Laplacian constraint with a non-convex penalty: minimax concave penalty (MCP). For solving the MCP penalized graphical model, we design an inexact proximal difference-of-convex algorithm (DCA) and prove its convergence to critical points. We note that each subproblem of the proximal DCA enjoys the nice property that the objective function in its dual problem is continuously differentiable with a semismooth gradient. Therefore, we apply an efficient semismooth Newton method to subproblems of the proximal DCA. Numerical experiments on various synthetic and real data sets demonstrate the effectiveness of the non-convex penalty MCP in promoting sparsity. Compared with the existing state-of-the-art method, our method is demonstrated to be more efficient and reliable for learning graph Laplacian with MCP.

Existing approaches to algorithmic fairness aim to ensure equitable outcomes if human decision-makers comply perfectly with algorithmic decisions. However, perfect compliance with the algorithm is rarely a reality or even a desirable outcome in human-AI collaboration. Yet, recent studies have shown that selective compliance with fair algorithms can amplify discrimination relative to the prior human policy. As a consequence, ensuring equitable outcomes requires fundamentally different algorithmic design principles that ensure robustness to the decision-maker's (a priori unknown) compliance pattern. We define the notion of compliance-robustly fair algorithmic recommendations that are guaranteed to (weakly) improve fairness in decisions, regardless of the human's compliance pattern. We propose a simple optimization strategy to identify the best performance-improving compliance-robustly fair policy. However, we show that it may be infeasible to design algorithmic recommendations that are simultaneously fair in isolation, compliance-robustly fair, and more accurate than the human policy; thus, if our goal is to improve the equity and accuracy of human-AI collaboration, it may not be desirable to enforce traditional fairness constraints.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Sirens wailed across Russia and TV stations interrupted regular programming to broadcast warnings Wednesday as part of sweeping drills intended to test the readiness of the country's emergency responders amid the fighting in Ukraine. The exercise that started Tuesday follows Ukrainian drone attacks on Moscow and other cities. As the readiness drill went on, the Russian Defense Ministry said air defenses shot down 31 Ukrainian drones over border regions early Wednesday. The readiness of the public warning system is being tested!

Last month, I found myself in a particular seat. A few places to my left was Elon Musk. Down the table to my right sat Bill Gates. Across the room sat Satya Nadella, Microsoft's CEO, and not too far to his left was Eric Schmidt, the former CEO of Google. At the other end of the table sat Sam Altman, the head of OpenAI, the company responsible for ChatGPT.

White House correspondent Peter Doocy reports on how the White House push for Ukraine spending may be jeopardized after McCarthy ouster on'Special Report.' Russian military officials claim they fended off a massive Ukrainian drone attack overnight Wednesday, which would be the largest single cross-border drone assault reported by Moscow since it first invaded Ukraine 20 months ago. The Russian Defense Ministry said Wednesday its national air defenses shot down 31 Ukrainian drones launched by Kyiv's forces on border regions, but there were no immediate reports of any damage or casualties. The Russian Defense Ministry didn't provide any evidence for its claims about intercepting Ukrainian drones nor any details about any damage or casualties. Moscow also said Russian aircraft thwarted a Ukrainian attempt to deploy soldiers on Russian-annexed Crimea.

Russian and Ukrainian forces remained largely static on the battlefield in the 84th week of the war after a month of vigorous Ukrainian advances that saw Kyiv break through the first of three Russian lines of defence on the southern front. Still, Ukraine proved it could hold onto its gains against Russian counterattacks and even made a few advances. Russian forces appeared to have lost a kilometre-long (0.6-mile-long) trench west of Verbove, a village on the front line of the main Ukrainian thrust through Russian defences in the Zaporizhia region in southeastern Ukraine. Vladimir Rogov, an occupation official, said at least four companies of Ukrainian troops had launched an attack on the trench on September 26 supported by armoured fighting vehicles. Geolocated footage that Russian sources released the following day confirmed that Ukraine held the position it had stormed.

A drone called the T-600 - named after the Terminator - successfully launched a torpedo from the sky. BAE Systems demonstrated the feat during a NATO training exercise, which saw a human controller fly the quadcopter strapped with the torpedo from a dock and over the ocean, where it let the weapon drop. The electric-powered, car-sized T-600 has a payload capacity of 441 pounds, tops speeds of 87 miles per hour and has a range of up to 50 miles. The demonstration aimed to showcase the anti-submarine warfare capabilities of the T-600 and its potential for automated logistics, resupply, casualty and evacuation. The T-600 is the AI-powered machine that takes over the world in the iconic Terminator series and features a combat endoskeleton made of titanium alloy, sometimes covered in synthetic latex.

When you ask ChatGPT "What happened in China in 1989?" the bot describes how the Chinese army massacred thousands of pro-democracy protesters in Tiananmen Square. But ask the same question to Ernie and you get the simple answer that it does not have "relevant information." That's because Ernie is an AI chatbot developed by the China-based company Baidu. When OpenAI, Meta, Google, and Anthropic made their chatbots available around the world last year, millions of people initially used them to evade government censorship. For the 70 percent of the world's internet users who live in places where the state has blocked major social media platforms, independent news sites, or content about human rights and the LGBTQ community, these bots provided access to unfiltered information that can shape a person's view of their identity, community, and government.

The tan macaque with the hairless pink face could do little more than sit and shiver as her brain began to swell. The California National Primate Center staff observing her via livestream knew the signs. Whatever had been done had left her with a "severe neurological defect," and it was time to put the monkey to sleep. But the client protested; the Neuralink scientist whose experiment left the 7-year-old monkey's brain mutilated wanted to wait another day. As the attending staff sat back and observed, the monkey seized and vomited. Her pupils reacted less and less to the light.