Research

Researchers detect ‘cognitive surrender’ in answering word problems

By feeding centuries-old nursery rhymes and folklore recordings into their own model, linguists in Louisiana hope to help a community control its digital destiny.

NVIDIA’s latest AI servers can run on coolant warmer than a hot tub — and that counterintuitive choice is one of the biggest efficiency leaps in data center history.

171 emotions. Proven to cause cheating, blackmail, deception — and impossible to remove.

The sensory enchantment of everyday life can upend the bland monotony of our overly optimized, anodyne world.

People associate AI with chatbots, robots, science fiction and more.

More Americans are using chatbots, and some are adopting AI summaries and smart speakers. But views about AI and how fast it’s advancing tilt negative – even for younger adults.

Computational functionalism dominates current debates on AI consciousness. This is the hypothesis that subjective experience emerges entirely from abstract causal topology, regardless of the underlying physical substrate. We argue this view fundamentally mischaracterizes how physics relates to information. We call this mistake the Abstraction Fallacy. Tracing the causal origins of abstraction reveals that symbolic computation is not an intrinsic physical process. Instead, it is a mapmaker-dependent description. It requires an active, experiencing cognitive agent to alphabetize continuous physics into a finite set of meaningful states. Consequently, we do not need a complete, finalized theory of consciousness to assess AI sentience—a demand that simply pushes the question beyond near-term resolution and deepens the AI welfare trap. What we actually need is a rigorous ontology of computation. The framework proposed here explicitly separates simulation (behavioral mimicry driven by vehicle causality) from instantiation (intrinsic physical constitution driven by content causality). Establishing this ontological boundary shows why algorithmic symbol manipulation is structurally incapable of instantiating experience. Crucially, this argument does not rely on biological exclusivity. If an artificial system were ever conscious, it would be because of its specific physical constitution, never its syntactic architecture. Ultimately, this framework offers a physically grounded refutation of computational functionalism to resolve the current uncertainty surrounding AI consciousness.

New research on how we've reduced agentic misalignment

Teachers in higher-needs schools were less likely than those in wealthier schools to have received guidelines, echoing previous research.

K-12 teachers are using AI — but a majority lack formal guidance.

School districts are making consequential AI decisions largely on their own—purchasing tools, training teachers, setting policies for student use, and trying to determine what responsible and effective adoption looks like. Without clear federal guidance or a reliable roadmap, what are the most advanced districts learning, and what does it reveal about what the field needs

A.I. can be a crutch that hurts our ability to think creatively.

FRIDAY, May 15, 2026 (HealthDay News) — Three out of five U.S. teens have tried AI chatbots, turning to the programs for entertainment, advice, friendship – and

Universities now must worry that graduates are leaving AI-proficient rather than knowledgeable about their subjects of study.

🤖 The Great Equalizer?

Aldeman: Nationwide, states' high school graduation rates far exceed the percentage of their students who are proficient in math, by up to 50 points

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Anthropic's AI Fluency Index measures 11 observable behaviors across thousands of Claude.ai conversations to understand how people develop AI collaboration skills.

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Not only are AIs far more agreeable than humans when advising on interpersonal matters, but users also prefer the sycophantic models.

Inequalities in learning between high-achieving students and their lower-performing classmates have grown dramatically, a new study finds.

While AI tools can momentarily improve student performance, Stanford University researchers caution that those gains may not persist once the technology is removed — raising questions about whether the tools are supporting learning or substituting for it.

By Benjamin Leiva, Ana Trindade Ribeiro, Chris Agnew, and Susanna Loeb This is the second of a series of research briefs aimed at understanding how people use AI-powered educational tools. Since the launch of commercially available generative AI, the most common engagement platform is through chat.

Which AI apps are people actively using? What’s actually making money, beyond being popular? We analyzed the data.