The confluence of machine learning and gene editing has the potential to transform health care, agriculture, national security, and many other fields. How can policymakers minimize the risks and maximize the benefits?
The Department of the Air Force cannot confidently apply artificial intelligence and machine learning systems to human resource management without an analytic framework to evaluate and augment their safety. The authors developed such a framework.
To extract meaning from Department of the Air Force personnel records, the authors applied natural language processing and machine learning approaches to text from officer performance reports to create the Personnel Records Scoring System.
The Department of the Air Force is seeking artificial intelligence and machine learning systems for mission areas and support functions. The authors reviewed how private-sector organizations assess such projects and developed a selection framework.
The authors conducted a literature and policy review, then implemented and tested a machine learning (ML) system to demonstrate the feasibility of applying ML to existing Department of the Air Force human resource management processes.
Using machine learning methods with data collected from 24 prison facilities in the Pennsylvania Department of Corrections, we determine which sources of data best predict a coming COVID-19 outbreak in a prison facility.
This report summarizes results from a fiscal year 2020 project examining the applicability of artificial intelligence and machine learning as an enabler of decision support in defensive counterspace missions. The project included a demonstration tool.
The cover story explores the postwar reconstruction effort in Ukraine — likely the largest postwar rebuilding effort in modern history — and how previous postwar and post–natural disaster reform and reconstruction efforts can inform policymaking.
Distributional shift can significantly degrade the performance of artificial intelligence systems and limit their application. In the context of cybersecurity, distributional shift could be especially dangerous as the threat of cyberattacks grows.
This report presents an assessment of the use of statistical distributions for predicting aircraft parts failure and an evaluation of how artificial intelligence can be used to determine the content of U.S. Air Force readiness spares packages.
The authors evaluate occupational exposure to U.S. technology patents and specific artificial intelligence technologies, such as machine learning, natural language processing, speech recognition, planning control, and evolutionary computation.
Machine learning has great potential to enable military decisionmaking at the operational level of war but only when paired with human analysts who possess detailed understanding of the context behind a given problem.
Self-driving laboratories (SDLs) promise to reshape our very understanding of research. But, as with all groundbreaking innovations, SDLs bring their own set of intriguing questions and potential challenges.
This weekly recap focuses on the costs and benefits of a four-day school week, how artificial intelligence is bringing a new era of social media manipulation, the effects of placing police officers in schools, and more.
Using generative artificial intelligence technology, U.S. adversaries can manufacture fake social media accounts that seem real. These accounts can be used to advance narratives that serve the interests of those governments and pose a direct challenge to democracies. U.S. government, technology, and policy communities should act fast to counter this threat.
The consequences of ignoring the problem of adversarial attacks in algorithmic trading are potentially catastrophic. In a world increasingly reliant on machine learning models, the financial sector needs to shift from being reactive to proactive to ensure the security and integrity of our financial system.
This study aims to create Medicare-based algorithms for predicting functional impairment, crucial for mortality and care use. Using machine learning and PAC data, memory and activity limitations were assessed with moderate success. While promising for PAC, broader older adult application is uncertain.