WASHINGTON – About a month ago, Afghanistan's Ministry of Defense posted photographs on social media of seven brand-new helicopters arriving in Kabul, delivered by the United States. "They'll continue to see a steady drumbeat of that kind of support going forward," U.S. Defense Secretary Lloyd Austin told reporters a few days later at the Pentagon. In a matter of weeks, however, the Taliban had seized most of the country, as well as any weapons and equipment left behind by fleeing Afghan forces. Video showed the advancing insurgents inspecting long lines of vehicles and opening crates of new firearms, communications gear and even military drones. "Everything that hasn't been destroyed is the Taliban's now," said one U.S. official, speaking on the condition of anonymity.

In the new "AI era," she says, sophisticated intelligence can come from almost anywhere -- armchair researchers, private technology companies, commercial satellites and ordinary citizens who livestream on Facebook. Zegart served on a task force of top experts who warned that the U.S. intelligence community -- a collection of 18 spy agencies across the government -- "enters 2021 flatly behind the technology curve." Published earlier this year by the Center for Strategic and International Studies, the report warned that U.S. intelligence agencies have become "risk-averse" and too wedded to traditional espionage. "Many of these problems stem from an [intelligence community] culture that is resistant to change, reliant on traditional tradecraft and -- ironically, given the popular perception -- averse to risk-taking, particularly to acquiring and adopting new technologies." So imagine a new intelligence cycle where you begin by using open-source information to surface key issues, and then get human sources to dig deeper into them.

The Defense Ministry will seek another record budget of over ¥5.4 trillion ($49 billion) for fiscal 2022, aiming to beef up its capabilities around remote southwestern islands to counter China's growing naval activities, government sources have said. The request would exceed the ministry's highest-ever ¥5.3 trillion initial budget for fiscal 2021, which started in April, and also reflects an increase in the cost to develop cutting-edge technologies, such as unmanned aircraft using artificial intelligence, the sources said Thursday. The defense budget could further expand, possibly topping 1% of Japan's gross domestic product, when it is finalized in December, as the request excludes outlays linked to hosting U.S. military bases. Japan's defense budget has long stayed at around 1% of its GDP, in light of the country's postwar pacifist Constitution and since the Cabinet decided in 1976 that the outlays should not exceed 1%. The last time the defense expenditure exceeded 1% was in fiscal 2010, when the GDP shrank sharply following the 2008-2009 global financial crisis.

Musk said building a humanoid robot is a logical next step for Tesla, since, he said, it's already "the world's biggest robotics company," with its cars basically robots. The humanoid robot will use all the tools in Tesla's vehicles -- sensors, cameras, neural networks, etc. -- to autonomously navigate the outside world. "We're making the pieces that would be useful for a humanoid robot, so we should probably make it. If we don't, someone else will -- and we want to make sure it's safe," Musk said. "I think this will be quite profound," Musk added, speculating that the robot could eventually change how the world works.

Michael Williams' wife pleaded with him to remember their fishing trips with the grandchildren, how he used to braid her hair, anything to jar him back to his world outside the concrete walls of Cook County Jail. His three daily calls to her had become a lifeline, but when they dwindled to two, then one, then only a few a week, the 65-year-old Williams felt he couldn't go on. He made plans to take his life with a stash of pills he had stockpiled in his dormitory. Williams was jailed last August, accused of killing a young man from the neighborhood who asked him for a ride during a night of unrest over police brutality in May. But the key evidence against Williams didn't come from an eyewitness or an informant; it came from a clip of noiseless security video showing a car driving through an intersection, and a loud bang picked up by a network of surveillance microphones. Prosecutors said technology powered by a secret algorithm that analyzed noises detected by the sensors indicated Williams shot and killed the man. "I kept trying to figure out, how can they get away with using the technology like that against me?" said Williams, speaking publicly for the first time about his ordeal. Williams sat behind bars for nearly a year before a judge dismissed the case against him last month at the request of prosecutors, who said they had insufficient evidence.

Society could soon see transformative artificial intelligence (TAI). Models of competition for TAI show firms face strong competitive pressure to deploy TAI systems before they are safe. This paper explores a proposed solution to this problem, a Windfall Clause, where developers commit to donating a significant portion of any eventual extremely large profits to good causes. However, a key challenge for a Windfall Clause is that firms must have reason to join one. Firms must also believe these commitments are credible. We extend a model of TAI competition with a Windfall Clause to show how firms and policymakers can design a Windfall Clause which overcomes these challenges. Encouragingly, firms benefit from joining a Windfall Clause under a wide range of scenarios. We also find that firms join the Windfall Clause more often when the competition is more dangerous. Even when firms learn each other's capabilities, firms rarely wish to withdraw their support for the Windfall Clause. These three findings strengthen the case for using a Windfall Clause to promote the safe development of TAI.

To present the biodiversity information, a semantic model is required that connects all kinds of data about living creatures and their habitats. The model must be able to encode human knowledge for machines to be understood. Ontology offers the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in the biodiversity domain. Various ontologies are developed for the biodiversity domain however a review of the current landscape shows that these ontologies are not capable to define the Indian biodiversity information though India is one of the megadiverse countries. To semantically analyze the Indian biodiversity information, it is crucial to build an ontology that describes all the essential terms of this domain from the unstructured format of the data available on the web. Since, the curation of the ontologies heavily depends on the domain where these are implemented hence there is no ideal methodology is defined yet to be ready for universal use. The aim of this article is to develop an ontology that semantically encodes all the terms of Indian biodiversity information in all its dimensions based on the proposed methodology. The comprehensive evaluation of the proposed ontology depicts that ontology is well built in the specified domain.

There is burgeoning interest in designing AI-based systems to assist humans in designing computing systems, including tools that automatically generate computer code. The most notable of these comes in the form of the first self-described `AI pair programmer', GitHub Copilot, a language model trained over open-source GitHub code. However, code often contains bugs - and so, given the vast quantity of unvetted code that Copilot has processed, it is certain that the language model will have learned from exploitable, buggy code. This raises concerns on the security of Copilot's code contributions. In this work, we systematically investigate the prevalence and conditions that can cause GitHub Copilot to recommend insecure code. To perform this analysis we prompt Copilot to generate code in scenarios relevant to high-risk CWEs (e.g. those from MITRE's "Top 25" list). We explore Copilot's performance on three distinct code generation axes -- examining how it performs given diversity of weaknesses, diversity of prompts, and diversity of domains. In total, we produce 89 different scenarios for Copilot to complete, producing 1,692 programs. Of these, we found approximately 40% to be vulnerable.

Broad Explainable Artificial Intelligence moves away from interpreting individual decisions based on a single datum and aims to provide integrated explanations from multiple machine learning algorithms into a coherent explanation of an agent's behaviour that is aligned to the communication needs of the explainee. Reinforcement Learning (RL) methods, we propose, provide a potential backbone for the cognitive model required for the development of Broad-XAI. RL represents a suite of approaches that have had increasing success in solving a range of sequential decision-making problems. However, these algorithms all operate as black-box problem solvers, where they obfuscate their decision-making policy through a complex array of values and functions. EXplainable RL (XRL) is relatively recent field of research that aims to develop techniques to extract concepts from the agent's: perception of the environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and objectives. This paper aims to introduce a conceptual framework, called the Causal XRL Framework (CXF), that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI. Additionally, we recognise that RL methods have the ability to incorporate a range of technologies to allow agents to adapt to their environment. CXF is designed for the incorporation of many standard RL extensions and integrated with external ontologies and communication facilities so that the agent can answer questions that explain outcomes and justify its decisions.

Artificial intelligence helped co-pilot a U-2 "Dragon Lady" spy plane during a test flight Tuesday, the first time artificial intelligence has been used in such a way aboard a US military aircraft. Mastering artificial intelligence or "AI" is increasingly seen as critical to the future of warfare and Air Force officials said Tuesday's training flight represented a major milestone. "The Air Force flew artificial intelligence as a working aircrew member onboard a military aircraft for the first time, December 15," the Air Force said in a statement, saying the flight signaled "a major leap forward for national defense in the digital age." The Artificial Intelligence algorithm, known as "ARTUµ," was developed by researchers at the Air Force's Air Combat Command U-2 Federal Laboratory. The AI system has been "trained ... to execute specific in-flight tasks that otherwise would be done by the pilot," the statement said.