NEWS IN BRIEF: AI/ML FRESH UPDATES

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Russian AI Cyber-Attack Threat

Pat McFadden warns at Nato conference that Russia aims to target UK's electricity grid using AI. London to launch Laboratory for AI Security Research to counter emerging threats.

Elon Musk's Twitter Investment: A Power Move

Jeff Jarvis, former TV critic and online media leader, warns of internet regulation affecting freedoms in his new book, The Web We Weave. He emphasizes the dangers of moral panic and the need to reclaim the web from tech bros to prevent stifling regulations.

Rise of AI in Online Crime

Police chief warns of criminals using AI to target victims in new ways. Urges law enforcement to adapt quickly to combat evolving threats.

Enhancing Outlier Detection with Feature Subsets

Identifying relevant subspaces in outlier detection is crucial for effective analysis of tabular data. Challenges include defining meaningful outliers and dealing with the curse of dimensionality in data with many features.

Decoding Driver Behavior with New Vehicle Tech

MIT AVT Consortium leads research on how drivers interact with emerging vehicle tech, aiming to shape future transportation through data-driven insights on consumer behaviors and system performance. Recent J.D. Power study shows modest increase in public readiness for AVs, but trust in AI remains crucial for broader adoption, highlighting the need for reliable and intuitive systems.

Mastering Data Governance for ML at Scale

Amazon DataZone enables organizations to establish data governance at scale, promoting self-service analytics and innovative ML projects. Financial institutions can leverage Amazon DataZone for effective marketing campaigns, ensuring secure access to customer datasets.

Charting a Path to U.S. Science Success

White House science advisor Arati Prabhakar discusses U.S. leadership in science and technology, focusing on cancer prevention, climate change, and AI. Prabhakar emphasizes the importance of sustaining global research leadership and accelerating technology movement into the market.

MIT breakthrough: Training more reliable AI agents

MIT researchers have developed a more efficient algorithm for training AI systems to make better decisions in complex tasks with variability, such as traffic control. The new method improves performance by strategically selecting tasks, making it 5-50 times more efficient than standard approaches, ultimately enhancing the AI agent's performance.