NASA's Marshall Space Flight Center and computing giant IBM plan to use artificial intelligence (AI) tech to improve climate change research, according to an announcement IBM posted on Feb. 1. Under the new partnership, NASA and IBM will create AI foundation models to analyze petabytes of text and remote-sensing data to make it easier to build AI applications tailored to specific climate change questions and tasks. "We hope these models will make information and knowledge more accessible to everyone and encourage people to build applications that make it easier to use our datasets to make discoveries and decisions based on the latest science," said Rahul Ramachandran, a senior research scientist at NASA's Marshall Space Flight Center. Foundational AI models can ingest massive amounts of raw data and find their underlying structure without explicit instruction. NASA is currently sitting on 70 petabytes of earth science data – a number expected to quadruple this year and into 2024 with future mission launches.

Federal agencies and their Federal Systems Integrator (FSI) partners are considering how to tap into artificial intelligence (AI) to advance their missions. The National Security Commission on Artificial Intelligence is calling on Federal leaders to double research and development spending on AI, to $32 billion by Fiscal Year 2026. As with any new technology, there is uncertainty on the best way to move from pilot projects in the lab to fully implemented production solutions. MeriTalk recently sat down with Bob Venero, CEO of Future Tech Enterprise, Inc., to talk about how agencies and FSI's can overcome barriers to get AI programs up and running quickly, and contribute to mission success. MeriTalk: We know Federal agencies are dipping their toes into using emerging technology including AI and machine learning (ML) to support a wide range of projects.

As the Defense Advanced Research Projects Agency (DARPA) continues exploring emerging technologies for the Department of Defense, it's considering the implementation of artificial intelligence (AI) to tackle electric grid cybersecurity and get ahead of 5G deployment. "In the area of cyber operations, we have a program RADICS [Rapid Attack Detection, Isolation and Characterization Systems], which is designed to help recover critical portions of the power grid in the event of a full blackout caused by malware," Scherlis explained. The program partners with power companies, the National Guard, the Department of Homeland Security, and the Department of Energy to enable a "black start" recovery, a restart to the electric grid without external power, in the event of a cyberattack. Scherlis mentioned the program in context of AI, but the program is generally building new tech to accelerate recovery through improved situational awareness, network isolation, and the ability to adapt to changing cyber situations. "The idea of this program is how can we understand what is the state of affairs in the grid and incrementally restore service, purge the malware, and bring service back to the most critical assets and then stage that out beyond. They've done a number of field trials," Scherlis added.

The US federal government is investing heavily in research on quantum computing, and AI is helping to boost the development. The White House is pushing to add an additional billion dollars to fund AI research that would increase AI R&D funding research to nearly $2 billion and quantum computing research to about $860 million over the next two years, according to an account in TechCrunch on Feb. 7. This is in addition to the $625 million investment in National Quantum Information Science Research Centers announced by the Department of Energy's (DoE) Office of Science in January, following from the National quantum Initiative Act, according to an account in MeriTalk. "The purpose of these centers will be to push the current state-of-the-art science and technology toward realizing the full potential of quantum-based applications, from computing, to communication, to sensing," the announcement stated. The centers are expected to work across multiple technical areas of interest, including quantum communication, computing, devices, applications, and foundries.

Federal artificial intelligence (AI) technology leaders agreed today that a human-centered approach to data management and automation generates stakeholder buy-in and improves agencywide perceptions of AI projects. At CXO Tech Forum: AI and Robotics Process Automation (RPA) in Government on Dec. 5, government officials speaking on several panels emphasized the importance of human-centered design in the development of AI capabilities. Anil Tilbe, Director of Enterprise Measurement and Design at the Veterans Experience Office (VEO), said that human-centered design is "extremely important" in AI development. "Using human-centered design, you're prioritizing human intelligence," he said. At VEO, Tilbe and his team are using information gathered from veterans and Veterans Affairs employees to build the AI environment.

While many – if not most – Federal agencies are taking at least preliminary steps towards embracing advanced technologies like artificial intelligence (AI) and machine learning (ML), they are moving with a degree of justifiable caution when it comes to relying on those technologies as part of their cybersecurity defenses, government tech officials said today at an event presented by Fortinet. The measured pace of AI and ML for security purposes is tied to the crucial nature of the cybersecurity mission, panelists said. Frank Konieczny, Chief Technology Officer at the U.S. Air Force, said that ML tech in particular requires establishing baselines for access and security to be sure that adversaries are not already impacting relevant data. And, he said, resulting data outputs from ML applications can still be somewhat opaque, and need to be examined more closely by human operators. "You ask, 'how did it figure that one out,'" he said, adding that process results in "a lot of time … by a human looking at [the result] again, which we don't want to do."

The latest research from MeriTalk finds what while artificial technology (AI) development is viewed by many in government and industry as still in the blush of youth, the technology is poised to rapidly outgrow its present suit of clothes. The research project – conducted by MeriTalk and underwritten by Pure Storage – polled government and industry executives and IT decision makers at this summer's AI World Government event, who said among other top-line findings that hybrid cloud infrastructure is a key enabler for AI adoption, and that the military and intelligence agencies are expected to lead Federal government's push into AI tech adoption. We sat down last week with Nick Psaki, Federal CTO at Pure Storage, to get his thoughts on the research results, and his longer-term outlook on how AI adoption is likely to gain momentum in the coming years before becoming an ultimately ubiquitous technology layer that will leave an indelible mark on society. MeriTalk: In examining the AI study results, what are the most important findings for Federal government officials that are currently undertaking AI or robotic process automation (RPA), or are considering doing so? Psaki: Two things jump out – the current feelings about AI maturity, and then the time to breakthrough to mission-critical tasks. On the first one, if you add respondents who said AI development is between "a little" (42 percent) and "moderately" (48 percent) mature, combined that's a big number (90 percent).

The Joint Artificial Intelligence Center (JAIC) has been utilizing resources to work with private industry to understand best practices and techniques when working with different datasets to best implement AI. "Currently, I spent about three months talking to about 20 different small and big AI companies as a way to kind of fully understand how to best interact, how to change talent, as well as techniques," Chief of Operations at JAIC Col. Stoney Trent said Thursday, June 6 at the C4ISRNET annual conference. Col. Trent mentioned that commercial groups aren't entirely proficient at incentivizing for rigorous testing and that it could represent a business risk for them to do so. "What the Defense Department has to offer in the space is encouraging an incentive structure for better testing tools and methods that allows us to understand how a product is going to perform when they're under conditions of national consequence," Col. Trent said. Further, Col. Trent said that he believes adversarial interaction today is a much lower-scale priority than designing the AI. He explained that with AI, JAIC wants to take a step-by-step approach to building up AI functions, because without careful considerations in the testing, problems can be multiplied heavily.

Artificial intelligence (AI), following on the heels of its older sibling RPA (robotic process automation), is no longer waiting to be born, but remains more of a toddler on the Federal IT scene–still learning to walk before trying to run, but bulking up from an appetite for serious Federal government tech interest and investment. Factors that stand in the way of rapid growth in use of the technology may be fairly said to include inertia, budget, lack of understanding, scarcity of obvious projects, insufficient compute power (legacy data centers), and a dearth of large data sets necessary to leverage the technology. But a host of Federal IT policy aims and nascent efforts at agencies are providing plenty of push for the AI Age to kick into higher gear, and point to what may become before too long the largest factor in shying away from AI: a lack of imagination. From the military, to the intelligence community, to civilian agencies, the growth in stated demand for AI projects is impossible to ignore. Intelligence agency officials ticked off a partial list of AI projects and priorities they'd like to pursue, and identified important long-term benefits from getting into the game including drastically reducing the amount of time analysts spend on lower-level monitoring work, and creating a workforce culture that is more comfortable with taking chances on new technology.

Government and policy-makers shouldn't put up unnecessary barriers to deploying artificial intelligence (AI) over concern of any perceived risks associated with the technology. Instead, policymakers should encourage innovation while crafting targeted solutions for specific problems if they occur, according to a report by the Information Technology Innovation Foundation, a science and technology policy think tank. There are a vast and diverse array of uses for AI, from rapidly analyzing large amounts of data to detecting abnormalities and patterns in transactions to extracting insights from datasets such as the link between a gene and a disease. AI is a field of computer science devoted to creating computer systems that perform operations characteristic of human intelligence, such as learning and decision making. Policy debates around AI are dividing into two positions: those that want to enable innovation, and those who want to slow or stop it, according to the report "Ten Ways the Precautionary Principle Undermines Progress in Artificial Intelligence." The precautionary principle is the idea that if a technological innovation carries a risk of harming the public or the environment, then those proposing the technology should bear the burden of proving it will not.