Improved defense intelligence collection and analysis of emerging technologies will rely on forming new partnerships between government and industry, according to leadership at the Defense Intelligence Agency. Doug Wade, the head of the DIA's China Mission Group, spoke during a Tuesday media discussion about his agency's effort to bring together the best of industry to identify specific threats China poses and coordinate responses . "So China is investing heavily right in its AI and ML capabilities," Wade said. "China's ability to use things like AI to ensure that they have strong…surveillance coverage of citizens, whether they're China citizens or whether they export that technology to other regimes around the world, and then those regimes use it to exert totalitarian control." Wade said that this potential exportation of AI technology should be an area of concern as a threat to U.S. national security, as well as to ally nations in Europe.

In my previous NextGov column, I reviewed the new ChatGPT artificial intelligence, asking it to perform tasks as varied as programming in C to telling me a bedtime story. I even interviewed the AI about why some people are afraid of artificial intelligences and the importance of ethics as the science of AI moves forward. I found that the ChatGPT AI from OpenAI was extremely adept at fielding just about any kind of question I could throw at it. Even though it's not connected to the internet or any live data streams--so you can't ask it about current events after 2021--it generally provided much better and more detailed information than you would ever find in something like a Google search. The AI is currently free to use, so everyone should give it a try.

The Department of Transportation is soliciting applicants to integrate artificial intelligence and environmental analytics into highway …

Experts at the National Institute of Standards and Technology want public and private entities to take a socio-technical approach to implementing artificial intelligence technologies to help mitigate algorithmic biases and other risks to AI systems, as detailed in a new playbook. These recommendations to help organizations navigate the pervasive biases that often accompany AI technologies are slated to come out by the end of the week, Nextgov has learned. The playbook is meant to act as a companion guide to NIST's Risk Management Framework, the final version of which will be submitted to Congress in early 2023. Reva Schwartz, a research scientist and principal AI investigator at NIST, said that the guidelines act as a comprehensive, bespoke guide for public and private organizations to tailor to their internal structure, rather than function as a rigid checklist. "It's meant to help people navigate the framework, and implements practices internally that could be used," Schwartz told Nextgov.

Government agencies collect a lot of data, and have access to even more of it in their archives. The trick has always been trying to tap into that store of information to improve decision-making, which is a major focus in government these days. The President's Management Agenda, for example, emphasizes the importance of data-driven decision-making to improve federal services. The volume of data that most agencies are working with is such that humans can't easily tap into it for help with that decision-making. And even if they can perform searches into that data, the process is slow.

The Federal Trade Commission sent a report to Congress detailing limitations artificial intelligence has in regulating disinformation and harmful …

In my previous Nextgov column, I got to interview the general manager of a company that is using artificial intelligence to help the military plan out its maintenance schedules. That program uses AI in a really good way that plays to its strengths, namely its ability to consider thousands of data points, much more than a human ever could, to come up with an action plan for maintenance that maximizes both efficiency and safety. However, when it comes time to actually perform the maintenance, those tasks must be delegated back to a human. But what happens if those physical tasks are also extremely complicated? The CV-22 Osprey is a perfect example of a military aircraft that is both revolutionary and complicated to maintain.

I've received a lot of feedback regarding my last few columns about drones, simulations and new uses for artificial intelligence. My most recent column about creating an AI that could task the kind of swarm intelligence found in bee colonies to do some amazing things, like controlling weather satellites, in particular generated a lot of buzz. Much of the feedback I received was from federal agencies and IT companies working on futuristic AI programs. I'll probably be highlighting some of them in the near future. But there was also a note from a company called Hypergiant about how they were working with the Army's Robotic Combat Vehicle program not for some advanced combat project, but simply using AI to help with predictive maintenance tasks.

Air Force personnel are set to soon be equipped with machine learning-driven capabilities to better counter COVID-19-connected disinformation.Building on a prior falsehoods-fighting pursuit and as part of a larger solicitation launched in April, the branch recently awarded machine intelligence startup Primer a $1 million, phase II Small Business Innovation Research, or SBIR, contract that'll underpin the integration of new solutions.The work is set to be formally announced in November, Nextgov confirmed late last week. "In any crisis, it is hard to verify information quickly," Primer's CEO and Founder Sean Gourley explained. "In a global crisis like the pandemic, where certain countries are actively spreading disinformation, speed and scale are the biggest issues, which machine learning can help with."Gourley Disinformation refers to falsities spread with the deliberate intent to deceive and mislead.

The project lead says that future troops may be deployed with watches or chest straps that could predict when they will get sick and how long it would take to recover. When U.S. Service members get ill at the last minute, it could cause serious consequences in regards to executing critical duties. To get ahead of the issues, the Defense Threat Reduction Agency (DTRA), leading health technology company Royal Phillips and the Defense Innovation Unit (DIU), launched a project to develop a technology that could predict whether a service member is getting sick 48 hours in advance. The project was launched 18 months ago and announced its completion on Oct 22. "By coupling large-scale data, with our experience in AI and remote patient monitoring with DTRA's drive for innovation, we were able to develop a highly predictive early-warning algorithm based on non-invasively collected biomarkers," Joe Frassica, chief medical officer and head of research for Philips North America, said in the release. Using 165 distinct biomarkers across 41,000 cases, the Phillips team created the Rapid Analysis of Threat Exposure (RATE) algorithm which is the "first large-scale empirical exploration of prediction of pre-symptomatic infection in humans."