The Defense Department has pinned its hopes on someday putting artificial intelligence tools in the hands of warfighters to help them make data-driven decisions on the battlefield, but given the current state of the technology and the dearth of training data that algorithms need, that goal appears difficult to achieve in the short-term. The defense community, including the Defense AI Center stood up last year, have rolled out AI pilots on everything from predictive maintenance of aircraft and vehicles to autonomous ships. For all of DoD's aspirational projects, AI tools tend not to fare well in situations where data is spare or not structured in a way that the algorithm can't process. Ranjeev Mittu, the head of the Naval Research Lab's information management and decision architectures branch, said the AI algorithms of today are starved for reliable training data to make informed decisions in the real world. "It's not really clear how much data is needed under what scenarios, for what kinds of problems yet, and I think it's kind of emerging. There's a lot of research going on, but I think fundamentally there's still a lot more research that needs to be done in the relationship between data and training, and what the right tradeoffs are for the different kinds of problems," Mittu said in an interview with Federal News Network.

Combining his background in laboratory experiments with his expertise in data analytics, Dr. Burns contributes to projects along the entire data science lifecycle. After receiving a Ph.D. based at the CERN Large Hadron Collider (LHC) on a team that contributed to the discovery of the Higgs boson, Dustin currently leads a multidisciplinary team of consultants across many industries and government agencies to respond to the world's most impactful problems and evaluate emerging technologies using AI.

Fox News Flash top headlines for Oct. 21 are here. Check out what's clicking on Foxnews.com When confronted with a swarming drone attack, defenders need to operate with the understanding that each mini-drone could itself be an incoming explosive, a surveillance "node" for a larger weapons system or even an electronic warfare weapon intended to disrupt vital command and control systems. Defenders under drone attack from medium and large drones need to recognize that the attacking platform can be poised to launch missiles or find targets for long-range ground based missiles, air assets or even approaching forces. Modern technology enables drones to use high-resolution sensors and targeting systems to both find and attack targets at very long ranges, thus compounding the threat.

Hill Knowlton Strategies is joining forces with AI pioneers Advanced Symbolics Inc. to conduct in-depth analysis of 28 key federal ridings in #ELXN43. This series is called Ridings to Watch. Over the course of the election we will add blocks of seven strategically important ridings. Below you will find all 28 Ridings to Watch! The team at ASI created "Polly," an AI that predicts voter intentions based on publicly available social media data.

In January, Liz O'Sullivan wrote a letter to her boss at artificial intelligence startup Clarifai, asking him to set ethical limits on its Pentagon contracts. WIRED had previously revealed that the company worked on a controversial project processing drone imagery. O'Sullivan urged CEO Matthew Zeiler to pledge the company would not contribute to the development of weapons that decide for themselves whom to harm or kill. At a company meeting a few days later, O'Sullivan says, Zeiler rebuffed the plea, telling staff he saw no problems with contributing to autonomous weapons. Clarifai did not respond to a request for comment.

Financial firms are working to identify potential fraud by analyzing how customers hold their phones, how fast they type and other information about mobile interactions--and the strategy is yielding results. Using artificial-intelligence tools to crunch behavioral data is often a more secure way to verify customers than traditional means such as passcodes, experts say. The Federal Bureau of Investigation warned companies last month that cybercriminals can circumvent typical multifactor-authentication techniques. One way is by calling a telecommunications company, posing as a customer and getting a service agent to switch that person's phone number to the criminal's device. The fraudster can then have the individual's bank send a one-time passcode to the phone and gain access to the target's bank account.

"Bias in AI" refers to situations where machine learning-based data analytics systems discriminate against particular groups of people. This discrimination usually follows our own societal biases regarding race, gender, biological sex, nationality, or age (more on this later). Just this past week, for example, researchers showed that Google's AI-based hate speech detector is biased against black people. In this article, I'll explain two types of bias in artificial intelligence and machine learning: algorithmic/data bias and societal bias. I'll explain how they occur, highlight some examples of AI bias in the news, and show how you can fight back by becoming more aware.

MIT neuroscientists have now identified two brain circuits that help tune out distracting sensory information, and they have found a way to reverse noise hypersensitivity in mice by boosting the activity of those circuits. One of the circuits the researchers identified is involved in filtering noise, while the other exerts top-down control by allowing the brain to switch its attention between different sensory inputs. The researchers showed that restoring the function of both circuits worked much better than treating either circuit alone. This demonstrates the benefits of mapping and targeting multiple circuits involved in neurological disorders, says Michael Halassa, an assistant professor of brain and cognitive sciences and a member of MIT's McGovern Institute for Brain Research. "We think this work has the potential to transform how we think about neurological and psychiatric disorders, [so that we see them] as a combination of circuit deficits," says Halassa, the senior author of the study.

Artificial Intelligence (AI) is having a broad and deep impact on the way services are exported globally. Be it for good or bad, there is no getting away from the reality that AI is an agent of disruption. One of the perennial front-runners of Nearshore outsourcing, Costa Rica, appears to be adapting to the AI opportunity faster than most countries in the region. Local companies are intensifying their AI development operations and a number of AI technologies are gaining traction there – all of which will influence Costa Rica's positioning in the next-generation of services delivery. The Latin American nation of nearly five million has long been seen as a tech epicenter of Central America ever since Intel chose it to open the biggest microchip factory in the region in 1997, with an initial investment of US$800 million.

Prime Minister Narendra Modi has urged business leaders and technocrats to build a bridge between the artificial intelligence and human intentions. In fact artificial intelligence has penetrated several aspects of our life in the past few years . The govt on its part has also been vocal about its intention to mainstream AI applications and several ministries along with NITI Aayog have come up various recommendation yo enhance the use of AI. So what are various policies on this aspect and how can they be used to further implement AI in emergening areas.