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

Fox News chief political analyst Brit Hume explains why President Donald Trump should not remove himself from the peace negotiations between Russia and Ukraine and more on'Special Report.' When Russia invaded Ukraine in February 2022, many experts predicted Kyiv's quick fall. When Ukraine pushed back overextended Russian forces, the same experts confidently said that Russia's mass -- a population almost four times larger than Ukraine -- would certainly grind Ukraine down. Triumph for Putin was inevitable. But, an odd thing happened on the way to Russia's victory parade: Ukraine is outfighting Russia.

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

Using a graph to represent the team behavior ensures that the relationship between Multi-Armed Bandits (MABs) are a class of reinforcement the agents are held. However, existing works either do learning problems where an agent is presented with a set of not consider the weight of each relationship (graph edges) arms (i.e., actions), with each arm giving a reward drawn (Madhushani and Leonard, 2020; Agarwal et al., 2021) or from a probability distribution unknown to the agent expect the user to manually set those weights (Moradipari (Lattimore and Szepesvári, 2020). The goal of the agent et al., 2022). is to maximize its total reward which requires balancing In this paper, we propose a new approach that combines exploration and exploitation. MABs offer a simple model graph optimization and MAMAB algorithms to enhance to simulate decision-making under uncertainty. Practical team performance by expediting the convergence to consensus applications of MAB algorithms include news recommendations of arm means. Our proposed approach: (Yang and Toni, 2018), online ad placement (Aramayo et al., 2022), dynamic pricing (Babaioff et al., 2015), improves team performance by optimizing the edge and adaptive experimental design (Rafferty et al., 2019). In weights in the graph representing the team structure contrast to single-agent cases, in certain applications such in large constrained teams, as search and rescue, a team of agents should cooperate does not require manual tuning of the graph weights, with each other to accomplish goals by maximizing team is independent of the MAMAB algorithm and only performance. Such problems are solved using Multi-Agent depends on the consensus formula, and Multi-Armed Bandit (MAMAB) algorithms (Xu et al., formulates the problem as a convex optimization, which 2020). Most existing algorithms rely on the presence of is computationally efficient for large teams.

Update, August 15, 5:50PM ET: The US Department of Homeland Security has issued a statement disputing some of MIT Technology Review's reporting. We've updated our post below with its statement and more details. The US Department of Homeland Security (DHS), which is looking to improve its facial recognition algorithms, is reportedly planning to use the facial data of migrant children entering the country for training. According to MIT Technology Review, the agency intends to collect and analyze facial captures of kids younger than 14. John Boyd, the assistant director of Homeland Security's Office of Biometric Identity Management who's involved in the development of biometric services for the government, told the publication that the collection will include children "down to the infant."

As Boyd explained at a conference in June, the key question for OBIM is, "If we pick up someone from Panama at the southern border at age four, say, and then pick them up at age six, are we going to recognize them?" Facial recognition technology (FRT) has traditionally not been applied to children, largely because training data sets of real children's faces are few and far between, and consist of either low-quality images drawn from the internet or small sample sizes with little diversity. Such limitations reflect the significant sensitivities regarding privacy and consent when it comes to minors. According to Syracuse University's Transactional Records Access Clearinghouse (TRAC), 339,234 children arrived at the US-Mexico border in 2022, the last year for which numbers are currently available. Of those children, 150,000 were unaccompanied--the highest annual number on record.

Personal Social Ontology (PSO), it is proposed, is how an individual perceives the ontological properties of terms. For example, an absolute fatalist would arguably use terms that remove any form of agency from a person. Such fatalism has the impact of ontologically defining acts such as winning, victory and success in a manner that is contrary to how a non-fatalist would ontologically define them. While both the said fatalist and non-fatalist would agree on the dictionary definition of these terms, they would differ on specifically how they can be brought about. This difference between the two individuals can be induced from their usage of these terms, i.e., the co-occurrence of these terms with other terms. As such a quantification of this such co-occurrence offers an avenue to characterise the social ontological views of the speaker. In this paper we ask, what specific term co-occurrence should be measured in order to obtain a valid and reliable psychometric measure of a persons social ontology? We consider the social psychology and social neuroscience literature to arrive at a list of social concepts that can be considered principal features of personal social ontology, and then propose an NLP pipeline to capture the articulation of these terms in language.

On Saturday, Iran launched more than 200 drones and cruise missiles at Israel. As the drones made their way across the Middle East en route to their target, Israel has invoked a number of defense systems to impede their progress. None will be more important than the Iron Dome. The Iron Dome, operational for well over a decade, comprises at least 10 missile-defense batteries strategically distributed around the country. When radar detects incoming objects, it sends that information back to a command-and-control center, which will track the threat to assess whether it's a false alarm, and where it might hit if it's not.

Los Angeles, California – The United States military shot down a flurry of objects this month: a large object it identified as a Chinese surveillance balloon followed by three smaller objects that the government said might be "benign". The airborne objects were drifting through airspace increasingly crowded with commercial and amateur balloons, drones and possible aerial surveillance craft belonging to adversaries. Their rising numbers pose a challenge to aviators and government agencies. Experts say that while heavy commercial balloons must meet strict Federal Aviation Administration (FAA) regulations, lighter amateur balloons are exempt from most rules, and the FAA might not be able to track them. Military and intelligence officials found no evidence that the three smaller objects were conducting surveillance for another country, and they were not sending communication signals, National Security Council spokesman John Kirby said at a White House briefing on Monday.

This morning, before checking in on my young son or making a coffee, I opened the Duolingo app on my phone and translated "They love smelling meat" into Italian. I've been starting my days like this for a few months now: wake up, wash face, grapple with the gerund. I usually spend between 10 and 20 minutes on it while the kettle boils or I load CBeebies or write some emails. Duolingo is a language learning app and pretty simple to use. After you've chosen which language you want to learn, you are presented with about 100 skill-sets divided by scenario or grammar (grocery shopping, the future tense and so on).