Google agrees to a $700 million settlement over claims of monopolizing the Android app market, with automatic consumer refunds and changes to allow alternative app stores and billing systems.

Illumina plans to divest its cancer detection spinoff GRAIL by Q2 2024 following antitrust rulings and regulatory pressure, as detailed in its press release.

Airbnb is using AI to prevent disruptive New Year's Eve parties, with the technology screening high-risk bookings in various countries, including the US and UK, to ensure neighborhoods remain peaceful.

University of Massachusetts Amherst researchers have discovered a potential method to inhibit antibiotic-resistant bacteria by interfering with their invasion mechanisms, which could prevent the development of resistance.

The study by an MIT economist suggests colon cancer screenings reduce cancer risk by 0.5%, which is double previous estimates, underscoring the importance of actual screenings over invitations.

Researchers at MIT and the Chinese University of Hong Kong improved optical device production using a machine learning-based method, enhancing alignment with design specs.

MIT scientists created a new therapy using lipid nanoparticles to reduce neuroinflammation in Alzheimer's, showing potential in early tests on human cells and mice.

UCSD researchers have identified blood biomarkers signaling suicidal thoughts in individuals with depression, potentially impacting mental health treatment.

San Bernardino County won a NACo award for its innovative use of Starlink to bring broadband to regional parks, improving connectivity for visitors and park operations.

UMass Amherst study links household chemical exposure to lower pregnancy chances, with phthalates in items like makeup and vinyl flooring prolonging time to conceive.

An older adult died in a Raynham fire, with officials highlighting the absence of working smoke alarms at the scene. Investigations point to an accidental origin, emphasizing the need for fire safety.

MIT scientists developed a machine-learning model to rapidly predict chemical reaction transition states, a task previously difficult and time-consuming.

MIT researchers' study on deep neural networks shows a strong similarity to human auditory processing, which could improve hearing aids and other auditory devices.