Machine learning redesigns microscopic web sensors to be five times more flexible than nature-inspired versions, enabling detection of masses as small as trillionths of a gram.

AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, ...

How Isomorphic Labs Is Rewriting Drug Discovery. Google DeepMind spin-off Isomorphic Labs is building an AI drug design engine that it believes can“solve” all diseases. We spoke to its Chief Technolog ...

Koch, who studied vision, thought that by measuring people's brain responses as they looked at special optical illusions, scientists could figure out which parts of the brain are activated when ...

What if vaccine development didn’t have to take a decade? This piece looks at how AI is helping scientists ask better ...

The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions ...

The model’s release comes five years after DeepMind introduced its seminal AlphaFold neural network. The latter algorithm can ...

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

The global spatial biology market is projected to grow at a compound annual growth rate (CAGR) of approximately 15% over the ...

Modern neuroscience understands the brain as a set of specialized systems. Aspects of brain function such as attention, ...

Artificial intelligence allows tracing the evolution of genetic control elements in the developing mammalian cerebellum. An international research team led by biologists from Heidelberg University as ...

AI is ultimately a story about selfhood—and the answer will not be found in the machine, but in what mindful awareness allows us to recognize when we see ourselves reflected there.