In its early days, the AI initiative known as Project Maven had its fair share of skeptics at the Pentagon. Today, many of them are true believers. The rise of AI warfare speaks to the biggest moral and practical question there is: Who--or what--gets to decide to take a human life? And who bears that cost? In 2018, more than 3,000 Google workers protested the company's involvement in "the business of war" after finding out the company was part of Project Maven, then a nascent Pentagon effort to use computer vision to rifle through copious video footage taken in America's overseas drone wars. They feared Project Maven's AI could one day be used for lethal targeting. In my yearslong effort to uncover the full story of Project Maven for my book,, I learned that is exactly what happened, and that the undertaking was just as controversial inside the Pentagon. Today, the tool known as Maven Smart System is being used in US operations against Iran . How the US military's top brass moved from skepticism about the use of AI in war to true believers has a lot to do with a Marine colonel named Drew Cukor. In early September 2024, during the cocktail hour at a private retreat for tech investors and defense leaders, Vice Admiral Frank "Trey" Whitworth found his way to Drew Cukor. Now Project Maven's founding leader and his skeptical successor were standing face-to-face. Three years earlier, Whitworth had been the Pentagon's top military official for intelligence, advising the chairman of the Joint Chiefs of Staff and running one of the most sensitive and potentially lethal parts of any military process: targeting.

Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the abundances of both simple and complex species. We present a new approach to understanding these chemical and physical interdependencies using machine learning. Specifically we explore the case of CO modeled under the conditions of a generic disk and build an explanatory regression model to study the dependence of CO spatial density on the gas density, gas temperature, cosmic ray ionization rate, X-ray ionization rate, and UV flux. Our findings indicate that combinations of parameters play a surprisingly powerful role in regulating CO compared to any singular physical parameter. Moreover, in general, we find the conditions in the disk are destructive toward CO. CO depletion is further enhanced in an increased cosmic ray environment and in disks with higher initial C/O ratios. These dependencies uncovered by our new approach are consistent with previous studies, which are more modeling intensive and computationally expensive. Our work thus shows that machine learning can be a powerful tool not only for creating efficient predictive models, but also for enabling a deeper understanding of complex chemical processes.

The classical non-greedy algorithm (NGA) and the recently proposed proximal alternating minimization method with extrapolation (PAMe) for $L_1$-norm PCA are revisited and their finite-step convergence are studied. It is first shown that NGA can be interpreted as a conditional subgradient or an alternating maximization method. By recognizing it as a conditional subgradient, we prove that the iterative points generated by the algorithm will be constant in finitely many steps under a certain full-rank assumption; such an assumption can be removed when the projection dimension is one. By treating the algorithm as an alternating maximization, we then prove that the objective value will be fixed after at most $\left\lceil\frac{F^{\max}}{\tau_0} \right\rceil$ steps, where the stopping point satisfies certain optimality conditions. Then, a slight modification of NGA with improved convergence properties is analyzed. It is shown that the iterative points generated by the modified algorithm will not change after at most $\left\lceil\frac{2F^{\max}}{\tau} \right\rceil$ steps; furthermore, the stopping point satisfies certain optimality conditions if the proximal parameter $\tau$ is small enough. For PAMe, it is proved that the sign variable will remain constant after finitely many steps and the algorithm can output a point satisfying certain optimality condition, if the parameters are small enough and a full rank assumption is satisfied. Moreover, if there is no proximal term on the projection matrix related subproblem, then the iterative points generated by this modified algorithm will not change after at most $\left\lceil \frac{4F^{\max}}{\tau(1-\gamma)} \right\rceil$ steps and the stopping point also satisfies certain optimality conditions, provided similar assumptions as those for PAMe. The full rank assumption can be removed when the projection dimension is one.

WASHINGTON – The National Geospatial-Intelligence Agency has selected a team of commercial and academic partners to build an artificial intelligence system with synthetic data, which will further help the agency determine how it builds machine learning algorithms moving forward. Orbital Insight was issued a Phase II Small Business Innovation Research contract by the NGA, the company announced. It will collaborate with Rendered.ai As the organization charged with analyzing satellite imagery for the intelligence community, NGA has put increased emphasis on using AI for its mission. The agency sees human-machine pairing as critical for its success, with machine learning algorithms taking over the rote task of processing the torrent of satellite data to find potential intelligence and freeing up human operators to do more high level analysis and tasks.

WASHINGTON: The National Geospatial-Intelligence Agency (NGA) will announce plans in May to contract with commercial companies to for analyze satellite and other imagery data of military targets, says David Gauthier, head of NGA's new(ish) Commercial and Business Operations Group. While the first contracts will be small, the move is a big step toward the spy agency's goal of creating a "hybrid" pool of data that combines commercial imagery with low-resolution but high re-revisit rates with traditional high-resolution that is less timely Intelligence Community imagery provided by the National Reconnaissance Office (NRO) and others. "We do foresee in the future a hybrid architecture, where we definitely require both national systems for their capabilities, and commercial systems for their capabilities," he said. While Gauthier wouldn't provide a budget for the new effort, he told me earlier this week that the plan is to evaluate the capabilities of a number of commercial companies to meet NGA's needs. "I don't want to discuss numbers at this time, but we are still operating at small scale and plan on contracting with multiple vendors to compare and contrast their capabilities," he said.

Employee identities are the new security perimeter of any business. According to the Verizon Mobile Security Index 2018 Report, 89% of organizations are relying on just a single security strategy to keep their mobile networks safe. And with Gartner predicting worldwide security spending reaching $96B this year, up 8% from 2017, it's evident enterprises must adopt a more vigilant, focused strategy for protecting every threat surface and access point of their companies. IT security strategies based on trusted and untrusted domains are being rendered insufficient as hackers camouflage their attacks through compromised, privileged credentials. It's happening so often that eight in ten breaches are now the result of compromised employee identities.

Bottom Line: Zero Trust Security (ZTS) starts with Next-Gen Access (NGA). Capitalizing on machine learning technology to enable NGA is essential in achieving user adoption, scalability, and agility in securing applications, devices, endpoints, and infrastructure. Zero Trust Security provides digital businesses with the security strategy they need to keep growing by scaling across each new perimeter and endpoint created as a result of growth. ZTS in the context of Next-Gen Access is built on four main pillars: (1) verify the user, (2) validate their device, (3) limit access and privilege, and (4) learn and adapt. The fourth pillar heavily relies on machine learning to discover risky user behavior and apply for conditional access without impacting user experience by looking for contextual and behavior patterns in access data.

To help close the gap, one Pentagon unit is now offering $100,000 in prizes to develop algorithms that can interpret high-resolution satellite images. The contest is called the xView Detection Challenge, and starts next month. Entrants will use a trove of hand-annotated satellite images released by the Pentagon to train algorithms to identify details relevant to disaster relief or humanitarian missions. Objects of interest include damaged buildings, utility trucks, and fishing boats. The project is being run by DIUx, an organization started by former Defense Secretary Ashton Carter to make it easier for his department to work with technology companies, particularly startups.

What will happen to a person's artificial intelligence (AI) when they retire? When a prospective employee interviews for a job, will his or her AI be questioned alongside them? Will companies hire AI straight from a factory, or will the system undergo a sort of apprenticeship before being put to work? More importantly--and more realistic in the near-term--what will be the line at which machines are not reliable enough or morally appropriate to use and humans take over? These, along with many more immediate questions, are among the topics USGIF's Machine Learning & Artificial Intelligence Working Group seeks to generate discussion around.

To read the original article visit my blog: www.globalfuturist.org The rise of Artificial Intelligence (AI), and an increasingly connected society has already, according to the UK's MI5 made it "much harder for spies to hide in the shadows", but now, if Robert Cardillo has his way, so called robo-automation tools will perform 75 percent of the tasks currently done by the new front line of American intelligence spies – the analysts who collect, analyse, and interpret images beamed from drones, satellites, and other feeds around the globe. Cardillo, the director of the National Geospatial-Intelligence Agency, (NGA), announced his push toward "automation" and "AI" at a conference this week in San Antonio. The annual conference, hosted by the United States Geospatial Intelligence Foundation, brings together technologists, soldiers, and intelligence professionals to discuss national security threats, changes in technology, and data collection and processing. AI is on the rise, and last year former President Barack Obama's White House created a Defcon Scale for Cyberattacks, and released a white paper on its potential future impacts in the final months of the administration, and police forces around the world are increasingly using preliminary "pre-crime" technologies to predict when, where and by whom crimes will likely be committed.