The Department of Defense is considering organizational changes designed to create a more integrated approach to data and artificial intelligence, including the creation of a Chief Data and Artificial Intelligence Officer. If the reorganization occurs, the CDAO will oversee several pre-existing offices, including the office of the Chief Data Officer, the Joint Artificial Intelligence Center, and the Defense Digital Service. Consolidated oversight through creating an empowered CDAO could help ensure DoD has the tools it needs to excel and ensure U.S. defense innovation leadership moving forward. Technology leadership requires data and AI leadership, and right now DoD's data and AI efforts are splintered. For example, according to Govini, a decision science company based in Virginia, at least 15 separate institutions within DoD invest to some extent in artificial intelligence, AI adjacent technologies, foundational enabling capabilities for AI, or programs that use AI during development.

The break room is stocked with free sparkling water. The worktables are a lacquered, honey-blonde wood. And in the center of the gleaming workspace, under the scalding white ceiling of a two-story atrium, stood the solution to the southern border problem. Its four mechanical legs spread over the polished cement. A metal mast rose in bolted sections and at the top, level with the second floor's glass-walled offices, were two boxes.

The Artificial Intelligence industry should create a global community of hackers and "threat modellers" dedicated to stress-testing the harm potential of new AI products in order to earn the trust of governments and the public before it's too late. This is one of the recommendations made by an international team of risk and machine-learning experts, led by researchers at the University of Cambridge's Centre for the Study of Existential Risk (CSER), who have authored a new "call to action" published in the journal Science. They say that companies building intelligent technologies should harness techniques such as "red team" hacking, audit trails and "bias bounties" – paying out rewards for revealing ethical flaws – to prove their integrity before releasing AI for use on the wider public. Otherwise, the industry faces a "crisis of trust" in the systems that increasingly underpin our society, as public concern continues to mount over everything from driverless cars and autonomous drones to secret social media algorithms that spread misinformation and provoke political turmoil. The novelty and "black box" nature of AI systems, and ferocious competition in the race to the marketplace, has hindered development and adoption of auditing or third party analysis, according to lead author Dr Shahar Avin of CSER.

Ethiopia's 13-month war has seen yet another dramatic turn as the federal government's counteroffensive against fighters from the northern Tigray region has made substantial advances, reversing the spectacular gains made recently by the Tigrayan forces in their push southwards. State media said this week the country's "joint gallant security forces" had retaken the strategic towns of Dessie and Kombolcha, the latest in a series of battleground victories since Prime Minister Abiy Ahmed said last month he would head to the front line and urged Ethiopians to join the fight. As fighting drags on, the government, with its tiny air force of 22 combat-capable aircraft, seems to have also realised that air power and timely intelligence can make all the difference in a conflict – especially one fought over vast and often mountainous areas like in Ethiopia's north. Although there has been no official confirmation, analysts have pointed to credible reports saying Ahmed's government has reached out in recent months to manufacturers of cheap and efficient armed drones hoping that air power will turn the tide in its way. Photographic evidence has pointed to the presence of Chinese Wing Loong 2 Unarmed Aerial Vehicles or UAVs at Ethiopian military bases, while a Bellingcat investigation in August found strong indications that Iranian armed drones, along with their ground control stations, had been spotted at Semera Airport.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are often applied in cybersecurity, but their applications may not always work as intended. ISACA's new publication, AI Uses in Blue Team Security, looks at AI, ML and DL applications in cybersecurity to determine what is working, what is not, what looks encouraging for the future and what may be more hype than substance. Leveraging interviews with some of the engineers behind these technologies, firsthand examination and use of some of the related products, and observations of chief information security officers (CISOs) and chief information officers (CIOs), AI Uses in Blue Team Security seeks to determine whether marketing tactics obscure reality when it comes to new security technology. Of the 13 engineers who commented for this publication, none felt that the marketing associated with the products they were working on was completely accurate with respect to advertised capabilities. However, the engineers were optimistic about the direction they were heading and the technologies they would be creating as they relate to ML and DL.

There's greater accountability that is expected of data controllers or data processors, and this heralds the arrival of a compliance burden on entities. Join us to discuss: · The case for regulation of personal data usage · Expectations of the regulator · How do you comply with the regulations? Come, let's talk about #privacy. Pathways International and Jose Almeida will host a webinar on personal data protection, titled: Data Protection Act - Roadmap to Compliance.

To discover materials for better batteries, researchers must wade through a vast field of candidates. New research demonstrates a machine learning technique that could more quickly surface ones with the most desirable properties. The study could accelerate designs for solid-state batteries, a promising next-generation technology that has the potential to store more energy than lithium-ion batteries without the flammability concerns. However, solid-state batteries encounter problems when materials within the cell interact with each other in ways that degrade performance. Researchers from the National Renewable Energy Laboratory (NREL), the Colorado School of Mines, and the University of Illinois demonstrated a machine learning method that can accurately predict the properties of inorganic compounds.

A new study by the Oregon State University College of Engineering shows that machine learning techniques can offer powerful new tools for advancing personalized medicine, care that optimizes outcomes for individual patients based on unique aspects of their biology and disease features. The research with machine learning, a branch of artificial intelligence in which computer systems use algorithms and statistical models to look for trends in data, tackles long-unsolvable problems in biological systems at the cellular level, said Oregon State's Brian D. Wood, who conducted the study with then OSU Ph.D. student Ehsan Taghizadeh and Helen M. Byrne of the University of Oxford. "Those systems tend to have high complexity--first, because of the vast number of individual cells and second, because of the highly nonlinear way in which cells can behave," said Wood, a professor of environmental engineering. "Nonlinear systems present a challenge for upscaling methods, which is the primary means by which researchers can accurately model biological systems at the larger scales that are often the most relevant." A linear system in science or mathematics means any change to the system's input results in a proportional change to the output; a linear equation, for example, might describe a slope that gains 2 feet vertically for every foot of horizontal distance.

If the Pentagon is going to rely on algorithms and artificial intelligence, it's got to solve the problem of "brittle AI." A top Air Force official recently illustrated just how far there is to go. Gen. Daniel Simpson, assistant deputy chief of staff for intelligence, surveillance, and reconnaissance, said on Monday. Initially, the AI was fed data from a sensor that looked for a single surface-to-surface missile at an oblique angle, Simpson said. Then it was fed data from another sensor that looked for multiple missiles at a near-vertical angle.

Can artificial intelligence write its own programs? Is there AI in your TV remote control? Researchers at Seattle's Allen Institute for Artificial Intelligence say that knowing the right answers to such questions is an essential part of being literate in our tech-driven society -- and that most of us would get a failing grade. A national survey, involving 1,547 adult Americans who were given a 20-question quiz about AI's capabilities, found that only 16% of the test takers scored a passing grade of better than 60% on the quiz. "The majority of Americans are AI illiterate," Nicole DeCario and Oren Etzioni report today in a posting to PNW.ai, an information service provided by the institute, also known as AI2.