A whistleblower complaint alleges John Solly claimed to have stored highly sensitive Social Security data on a thumb drive. Solly and Leidos, his current employer, strongly deny the allegations. John Solly, a software engineer and former member of the so-called Department of Government Efficiency (DOGE), is the DOGE operative reportedly accused in a whistleblower complaint of telling colleagues that he stored sensitive Social Security Administration (SSA) data on a thumb drive and wanted to share the information with his new employer, multiple sources tell WIRED. Since October, according to a copy of his résumé, Solly has worked as the chief technology officer for the health IT division of a government contractor called Leidos, which has already received millions in SSA contracts and could receive up to $1.5 billion in contracts with SSA based on a five-year deal it signed in 2023. Solly's personal website and LinkedIn have been taken offline as of this week.

These models excel in zero-shot and few-shot learning, enabling them to learn new tasks without parameter updates.

Neural Information Processing Systems http://nips.cc/

The recent request goes against decades of precedent and puts noncitizens at further risk of immigration enforcement actions. Workers at the Social Security Administration have been told to share information about in-person appointments with agents of Immigration and Customs Enforcement, WIRED has learned. "If ICE comes in and asks if someone has an upcoming appointment, we will let them know the date and time," an employee with direct knowledge of the directive says. They spoke on the condition of anonymity for fear of retaliation. While the majority of appointments with SSA take place over the phone, some appointments still happen in person.

The Association for the Advancement of Artificial Intelligence's 2025 Fall Symposium Series was held November 6-8, 2025, at the Westin Arlington Gateway in Arlington, Virginia. There were six symposia in the program: AI for Social Good: Emerging Methods, Measures, Data, and Ethics; AI Trustworthiness and Risk Assessment for Challenged Contexts; Engineering Safety-Critical AI Systems; First AAAI Symposium on Quantum Information and Machine Learning: Bridging Quantum Computing and Artificial Intelligence; Safe, Ethical, Certified, Uncertainty-aware, Robust, and Explainable AI for Health; and Unifying Representations for Robot Application Development. This report contains summaries of the symposia, which were submitted by most, but not all, of the symposium organizers. AI has demonstrated transformative potential across sectors such as aging, combating information manipulation, disaster response, education, environmental sustainability, government, healthcare, social care, transportation, and urban planning. Yet, the systematic development of AI For Social Good remains fragmented across those many research communities, with limited convergence around effective methodologies, equitable impact measurement, or access to important data and long-term engagement with targeted populations. The main objective for this symposium was to convene across disciplines and engage researchers, practitioners, and policymakers, with a particular focus on finding methods, measures and data that could be used in multiple settings. There were roughly 30 participants.