From neural data to cognitive organization Over the past few decades, cognitive neuroscience has accumulated abundant high-dimensional data, including intracranial recordings in humans and animals, EEG and MEG recordings, and high-resolution fMRI. Through open science1, large-scale repositories and research initiatives, scientists have access to such data to an extent not seen before2,3,4,5,6,7. Yet, central questions

Summary: A new study utilized artificial intelligence and ultra-sensitive magnetic brain imaging to map neuro-aging. By tracking a cohort of multilingual individuals from the Basque region of Spain, the team discovered a striking, direct gradient between language count and neurological preservation. Speaking two languages shaved approximately 6 years off a participant’s biological brain age, while

Amazon is hiring a robotics scientist to help build its first autonomous materials discovery laboratory, a new research capability that will use physical AI, lab robotics and foundation models to test materials linked to sustainability. The Applied Scientist III — Robotics & Physical AI, Autonomous Lab role sits inside Amazon’s Worldwide Sustainability organization and its

Are we alone in the universe? Consider mysterious ”extraterrestrial” radio signals. Unexplained gases on other planets. Things mistaken for UFOs. In the search for alien life, humans have been fooled more than once by signals that turned out to turned out to be false alarms. Now, researchers at Michigan State University show that artificial intelligence

Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are rapidly transforming photothermal, photoacoustic, and diffusion-wave sciences and technologies. These data-driven approaches offer powerful tools to address long-standing challenges in inverse problems, image reconstruction, signal interpretation, and multi-scale modeling, where traditional physics-based methods often face limitations in computational efficiency and robustness.

Enterprise CRM programs no longer fail because teams lack software. They fail because sales, service, product, finance, and data teams still work from fragmented signals. Leaders spend on platforms, integrations, and reporting layers, yet sellers still lose time updating records, chasing context, and interpreting pipeline risk. That gap now defines the next phase of CRM

An uncomfortable truth for the higher education sector is that artificial intelligence is reshaping how work gets done. Graduates will need to enter AI-enabled workplaces with a clear sense of how to work alongside intelligent systems, and millions of experienced professionals are going to require reskilling. Yet our institutions are not preparing new students or

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