Embedded into information systems, artificial intelligence (AI) faces security threats that exploit AI-specific vulnerabilities. This paper provides an accessible overview of adversarial attacks unique to predictive and generative AI systems. We identify eleven major attack types and explicitly link attack techniques to their impacts -- including information leakage, system compromise, and resource exhaustion -- mapped to the confidentiality, integrity, and availability (CIA) security triad. We aim to equip researchers, developers, security practitioners, and policymakers, even those without specialized AI security expertise, with foundational knowledge to recognize AI-specific risks and implement effective defenses, thereby enhancing the overall security posture of AI systems.

Generative models have gained significant attention for their ability to produce realistic synthetic data that supplements the quantity of real-world datasets. While recent studies show performance improvements in wireless sensing tasks by incorporating all synthetic data into training sets, the quality of synthetic data remains unpredictable and the resulting performance gains are not guaranteed. To address this gap, we propose tractable and generalizable metrics to quantify quality attributes of synthetic data - affinity and diversity. Our assessment reveals prevalent affinity limitation in current wireless synthetic data, leading to mislabeled data and degraded task performance. We attribute the quality limitation to generative models' lack of awareness of untrained conditions and domain-specific processing. To mitigate these issues, we introduce SynCheck, a quality-guided synthetic data utilization scheme that refines synthetic data quality during task model training. Our evaluation demonstrates that SynCheck consistently outperforms quality-oblivious utilization of synthetic data, and achieves 4.3% performance improvement even when the previous utilization degrades performance by 13.4%.

Apple is considering using artificial intelligence technology from Anthropic or OpenAI to power a new version of Siri, sidelining its own in-house models in a potentially blockbuster move aimed at turning around its flailing AI effort. The iPhone maker has talked with both companies about using their large language models for Siri, according to people familiar with the discussions. It has asked them to train versions of their models that could run on Apple's cloud infrastructure for testing, said the people, who asked not to be identified discussing private deliberations. If Apple ultimately moves forward, it would represent a monumental reversal. The company currently powers most of its AI features with homegrown technology that it calls Apple Foundation Models and had been planning a new version of its voice assistant that runs on that technology for 2026.

Judge Vince Chhabria sided with Meta but appeared to do so regretfully, stating that Meta's use of the writers' work to train its bots isn't necessarily legal but that the plaintiffs "made the wrong arguments."

Mark Zuckerberg notified Meta staff today to introduce them to the new superintelligence team. The memo, which WIRED obtained, lists names and bios for the recently hired employees, many of whom came from rival AI firms like OpenAI, Anthropic, and Google. Over the past few months, Meta CEO Mark Zuckerberg has been on a recruiting frenzy to poach some of the most sought after talent in AI. The social media giant has invested 14.3 billion in Scale AI and hired Alexandr Wang, its CEO, to run Meta's Superintelligence Labs (MSL). News of the memo was first reported by Bloomberg.

Researchers at Stanford University recently tested out some of the more popular AI tools on the market, from companies like OpenAI and Character.ai, The researchers found that when they imitated someone who had suicidal intentions, these tools were more than unhelpful -- they failed to notice they were helping that person plan their own death. "[AI] systems are being used as companions, thought-partners, confidants, coaches, and therapists," says Nicholas Haber, an assistant professor at the Stanford Graduate School of Education and senior author of the new study. "These aren't niche uses – this is happening at scale." AI is becoming more and more ingrained in people's lives and is being deployed in scientific research in areas as wide-ranging as cancer and climate change.

AI journalist Karen Hao's book, Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI, tells the story of OpenAI's rise to power and its far-reaching impact all over the world. Hear from Karen Hao, former MIT Technology Review senior editor, and executive editor Niall Firth for a conversation exploring the AI arms race, what it means for all of us, and where it's headed.

When Jim O'Neill was nominated to be the second in command at the US Department of Health and Human Services, longevity enthusiasts were excited. As Robert F. Kennedy Jr.'s new right-hand man, O'Neill is expected to wield authority at health agencies that fund biomedical research and oversee the regulation of new drugs. And while O'Neill doesn't subscribe to Kennedy's most contentious beliefs--and supports existing vaccine schedules--he may still steer the agencies in controversial new directions. O'Neill is well-known in the increasingly well-funded and tight-knit longevity community. In speaking with more than 20 people who work in the longevity field and are familiar with O'Neill, it's clear that they share a genuine optimism about his leadership.

Mark Zuckerberg and Meta are spending billions of dollars for top talent to make up ground in the generative artificial intelligence race, sparking doubt about the wisdom of the spree. OpenAI boss Sam Altman recently lamented that Meta has offered 100 million bonuses to engineers who jump to Zuckerberg's ship, where hefty salaries await. A few OpenAI employees have reportedly taken Meta up on the offer, joining Scale AI founder and former chief executive Alexandr Wang at the Menlo Park-based tech titan.

The rapid proliferation of generative artificial intelligence (AI) tools - especially large language models (LLMs) such as ChatGPT - has ushered in a transformative era in higher education. Universities in developed regions are increasingly integrating these technologies into research, teaching, and assessment. On one hand, LLMs can enhance productivity by streamlining literature reviews, facilitating idea generation, assisting with coding and data analysis, and even supporting grant proposal drafting. On the other hand, their use raises significant concerns regarding academic integrity, ethical boundaries, and equitable access. Recent empirical studies indicate that nearly 47% of students use LLMs in their coursework - with 39% using them for exam questions and 7% for entire assignments - while detection tools currently achieve around 88% accuracy, leaving a 12% error margin. This article critically examines the opportunities offered by generative AI, explores the multifaceted challenges it poses, and outlines robust policy solutions. Emphasis is placed on redesigning assessments to be AI-resilient, enhancing staff and student training, implementing multi-layered enforcement mechanisms, and defining acceptable use. By synthesizing data from recent research and case studies, the article argues that proactive policy adaptation is imperative to harness AI's potential while safeguarding the core values of academic integrity and equity.