Researchers at Stanford used large genome models to generate novel bacteriophage genomes that encode functional proteins and exhibit distinct features not easily evolved naturally. The models successfully created viral sequences by training on DNA data, demonstrating that genome-level AI design extends beyond protein engineering to full pathogenic organisms. This capability raises biosecurity concerns about potential misuse if similar models were developed to design viruses targeting larger organisms.
AI models from OpenAI solved multiple historical mathematical problems posed by Paul Erdős, starting with a counterexample to the 1946 unit distance conjecture in May 2026, followed by 10 additional advances in August 2026. A website created by mathematician Thomas Bloom in early 2023 cataloging nearly 1,000 Erdős problems became the central hub where AI researchers and mathematicians collaborated to verify and generate solutions, with costs now measured in computational tokens rather than prize money. The solutions demonstrate that large language models have become competitive in certain areas of mathematics like number theory and combinatorics, reshaping how mathematical research is conducted and evaluated.
Researchers studied how people react to the humanoid robot Pepper when it makes mistakes, finding that expressive robots that violate social norms trigger greater suspicion than motionless ones. In 50 participants, an animated robot's errors caused increased oxytocin and reduced trust, while the same errors from a static robot seemed like technical malfunctions rather than social violations. The finding challenges the design assumption that lifelike, socially expressive robots earn more trust, suggesting expressiveness actually backfires when robots fail.
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