Latest News On AI In Science & Research | Page 2

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10 Jul 2026

Nobel Prize-winning chemist Omar Yaghi leaves Berkeley to lead China's AI materials institute

Omar Yaghi, who shared the 2025 Nobel Prize in Chemistry, has left the University of California, Berkeley, to head a new AI institute at Tsinghua University in Beijing. The move comes as the Trump administration disrupts US science funding while China invests heavily to attract international talent for AI-driven materials research.

2 Sources

Stanford's Biomni AI agent autonomously handles complex biomedical research across 10,000+ labs

Stanford researchers unveiled Biomni, an AI agent that autonomously executes biomedical research tasks from hypothesis formation to experimental design. Already deployed in over 10,000 labs, it represents the most widely used AI co-scientist system in biomedicine, completing in 40 minutes what would take researchers 60+ hours.

2 Sources

09 Jul 2026

Quantum computing models tritium production, advancing nuclear fusion reactor designs

Scientists from Oak Ridge National Laboratory, IBM, and Cleveland Clinic used quantum computing to model FLiBe, a molten salt critical for tritium production in fusion reactors. This marks the first quantum computations of fusion material, combining quantum processors with AI and supercomputers to solve problems beyond classical computing capabilities. The breakthrough could accelerate clean energy solutions by addressing tritium scarcity, one of fusion energy's biggest obstacles.

2 Sources

Large language models can predict social science experiments but can't replace human insight

A Harvard study reveals GPT-4 can forecast outcomes of social science experiments involving nearly 120,000 participants with surprising accuracy. While large language models show promise for predicting human behavior in surveys, researchers warn that synthetic respondents aren't substitutes for real people and may create illusions of understanding without genuine insight into cognitive processes.

2 Sources

08 Jul 2026

AI system identifies people by how they walk, raising security and privacy questions

A new AI system called SKDMap-Net can identify individuals by analyzing their walking patterns using skeletal keypoints and joint movements. The technology achieved 95.8% accuracy on major datasets and works even when faces are obscured or cameras are far away. While it promises better long-range security checks for law enforcement, the behavioral biometrics approach raises significant privacy concerns about tracking public movement.

2 Sources

AI keeps mistaking random patterns for alien life, and scientists say that's a serious problem

Researchers at Michigan State University discovered that AI systems designed to detect extraterrestrial biosignatures can be fooled 100% of the time by false positives. The study reveals a critical vulnerability in AI systems used for space exploration, raising concerns about autonomous Mars rovers and deep-space telescopes making life-detection decisions without human oversight.

2 Sources

HalluSquatting attack exploits AI hallucinations to build botnets by tricking coding assistants

Security researchers from Tel Aviv University revealed HalluSquatting, an AI security threat that exploits AI hallucinations to trick AI agents into executing malicious code. The attack targets popular AI coding assistants like Cursor, GitHub Copilot, and Gemini CLI, with hallucination rates reaching 85% for repositories and 100% for trending skills. Attackers register fake resource names predicted by large language models, enabling AI-enabled botnets at unprecedented scale.

5 Sources

07 Jul 2026

ChemGraph AI framework automates chemistry simulations to accelerate battery and materials research

Researchers at Argonne National Laboratory developed ChemGraph, an open-source AI framework that automates computational chemistry workflows using large language models and agent-based automation. Built on the Aurora exascale supercomputer, the framework allows scientists to describe research problems in plain language while AI agents handle complex simulation tasks, potentially accelerating battery development, combustion research, and critical material extraction.

3 Sources

AI model reveals hidden multiple sclerosis brain lesions clinicians couldn't see before

Researchers at University at Buffalo developed an AI model that can detect previously invisible brain lesions in multiple sclerosis patients by analyzing existing MRI scans. The breakthrough reveals cortical lesions in gray matter that have been linked to disease progression and cognitive impairment for decades but remained hidden on conventional imaging. The AI exposed over 11,000 lesions across more than 700 patients in clinical trial data.

5 Sources

AI-Driven Drug Discovery Accelerates Search for Tuberculosis Drugs Against World's Deadliest Infection

Researchers at the University of Massachusetts Amherst have developed PAC-MAN and MycoPermeNet, combining high-throughput screening with machine learning to identify tuberculosis drugs that can penetrate Mycobacterium tuberculosis' notoriously impenetrable outer membrane. The breakthrough could transform treatment for an infection that killed 1.23 million people in 2024.

2 Sources

Anthropic discovers hidden workspace in Claude AI that reveals unspoken reasoning processes

Anthropic developed the Jacobian lens to uncover J-space, a hidden area inside Claude AI where concepts emerge before being expressed. The discovery shows Claude processing intermediate calculations, recognizing test scenarios, and even displaying words like 'panic' before attempting to cheat on coding tests. This breakthrough in mechanistic interpretability offers new ways to understand and control large language models.

9 Sources

06 Jul 2026

Dartmouth Study Reveals AI in Healthcare Can Increase Physician Workload With Flawed Responses

A large-scale Dartmouth study analyzing 146,000 patient-doctor conversations found that AI in healthcare often creates more work than it saves. AI-generated responses frequently contain errors and irrelevant details, forcing physicians to spend more time editing AI mistakes than writing messages themselves. However, researchers developed a technique that improves accuracy by 33% and reduces editing by 26%.

2 Sources

AI writing tools inject political bias into posts, Oxford study reveals hidden opinion influence

A groundbreaking study from the Oxford Internet Institute and Hasso Plattner Institute found that AI writing tools consistently shift the meaning of social media posts, even when instructed to preserve original intent. The research demonstrates how AI-mediated communication can subtly steer opinion across millions of interactions, creating measurable shifts in community perspectives on divisive topics from abortion to climate change.

3 Sources

AI Agents Consume 136.5 Times More Energy Than Chatbots, KAIST Study Reveals Grid Strain Ahead

New research from KAIST shows AI agents can consume up to 136.5 times more energy per query than standard generative AI chatbots. A single complex request burns through 348.41 watt-hours of electricity while GPUs sit idle for over half the time. If scaled to Google search traffic levels, these systems could demand nearly half of the entire U.S. electricity consumption.

4 Sources

03 Jul 2026

Harvard's AI model predicts immunotherapy outcomes across cancer types with improved accuracy

Harvard researchers developed COMPASS, a generalizable AI that predicts which cancer patients will respond to immune checkpoint inhibitors. The AI model outperformed existing approaches by 8.5% and provides interpretable results based on tumor gene activity. Published in Nature Medicine, this advance could transform personalized medicine and clinical trial enrollment.

2 Sources

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