After months of delays, the US House of Representatives voted on Friday to extend a controversial warrantless wiretap program for two years. Known as Section 702, the program authorizes the US government to collect the communications of foreigners overseas. But this collection also includes reams of communications from US citizens, which are stored for years and can later be warrantlessly accessed by the FBI, which has heavily abused the program. An amendment that would require investigators to obtain such a warrant failed to pass. A group of US lawmakers on Sunday unveiled a proposal that they hope will become the country's first nationwide privacy law.

Last month, a 120-page United States executive order laid out the Biden administration's plans to oversee companies that develop artificial intelligence technologies and directives for how the federal government should expand its adoption of AI. At its core, though, the document focused heavily on AI-related security issues--both finding and fixing vulnerabilities in AI products and developing defenses against potential cybersecurity attacks fueled by AI. As with any executive order, the rub is in how a sprawling and abstract document will be turned into concrete action. Today, the US Cybersecurity and Infrastructure Security Agency (CISA) will announce a "Roadmap for Artificial Intelligence" that lays out its plan for implementing the order. CISA divides its plans to tackle AI cybersecurity and critical infrastructure-related topics into five buckets.

Machine learning methods can be unreliable when deployed in domains that differ from the domains on which they were trained. There are a wide range of proposals for mitigating this problem by learning representations that are ``invariant'' in some sense.However, these methods generally contradict each other, and none of them consistently improve performance on real-world domain shift benchmarks. There are two main questions that must be addressed to understand when, if ever, we should use each method. First, how does each ad hoc notion of ``invariance'' relate to the structure of real-world problems? And, second, when does learning invariant representations actually yield robust models? To address these issues, we introduce a broad formal notion of what it means for a real-world domain shift to admit invariant structure. Then, we characterize the causal structures that are compatible with this notion of invariance.With this in hand, we find conditions under which method-specific invariance notions correspond to real-world invariant structure, and we clarify the relationship between invariant structure and robustness to domain shifts. For both questions, we find that the true underlying causal structure of the data plays a critical role.

Artificial intelligence provides enormous benefits for cyber threat detection, but the technology can't do the job alone. That was the primary message during a session at the Ai4 2022 Cybersecurity Summit featuring two government cybersecurity professionals -- Garfield Jones, associate chief of strategic technology for the Cybersecurity and Infrastructure Security Agency (CISA), and Peter Gallinari, data privacy officer for the state of Tennessee. The duo discussed the promise of AI threat detection and fielded questions about what they saw as the future of such technology, the potential challenges and how humans will fit into the picture. Jones made it clear early in the panel that every cybersecurity system implementing AI will still require human involvement. "My perspective on this is that AI definitely has a future in threat detection and response," Jones said.

The presence of artificial intelligence in the federal workforce is poised to expand, with officials emphasizing the human component behind automation and machine learning technologies. Officials including Gil Alterovitz, the Veterans' Affairs National Artificial Intelligence Institute director, and Martin Stanley, the branch chief of Strategic Technology at the Cybersecurity and Infrastructure Security Agency, spoke during a Thursday panel and discussed digitization within their respective agencies. Alterovitz said that VA leadership has opened up new data scientist positions to serve as subject matter experts across the government. "We've been working toward building pathways toward developing and assessing that AI knowledge," he said. "We're working with a number of other agencies and really the idea there is to build that pipeline of talent with AI knowledge both from outside government [and] inside the government so that the result of that would be an agile and responsive federal workforce equipped with the necessary competencies for AI." Alterovitz also discussed the ethical parameters the VA has in place for its usage of automated technology.

Any new positive coronavirus test among teachers or students could prompt a stay-at-home quarantine in Los Angeles County under a proposed plan.

The far-reaching SolarWinds hack has hit not only federal agencies such as the Department of the Treasury, but computer systems for local U.S. governments as well. The far-reaching SolarWinds hack has hit not only federal agencies such as the Department of the Treasury, but computer systems for local U.S. governments as well. A U.S. cybersecurity agency said Wednesday that the far-reaching attack into the IT management company SolarWinds discovered earlier this month has not only affected key federal agencies, but also computer systems used by state and local governments. The hackers attached malware to a software update for SolarWinds' Orion system, which is used by many federal agencies and thousands of companies worldwide to monitor their computer networks. The hack infected several computer systems within the U.S. government, including at the departments of Treasury, Commerce, and Energy.

There is a gender gap in artificial intelligence (AI). A study by the World Economic Forum and LinkedIn found that only 22% of AI professionals are women. Research by the AI Now Institute found that women make up only 15% of the AI research staff at Facebook and only 10% at Google. Although the gender gap in AI echoes those in cybersecurity and information technology in general, the repercussions of a lack of diversity in AI broaden because the details of the how the systems work are not fully known. As a result, identifying and correcting bias introduced by the decisions of the development teams or the data they select to train their algorithms is difficult.