Modern artificial intelligence systems rely on moving large amounts of data between memory and processors, a design that limits speed and increases energy use. The human brain works differently: it combines memory and computation within synapses, allowing fast, efficient learning and perception. Replicating this approach in hardware is a central goal of neuromorphic computing, especially for tasks like vision, where most real-world information is gathered and processed.

Artificial intelligence is getting expensive—and companies are starting to rethink their embrace of the disruptive technology.

Battery electrolytes aren't just one chemical, but a complex mixture of salts, solvents, and additives interacting and reacting with each other. Artificial intelligence has made great headway in helping select ideal materials to go into that chemical soup. But a team from the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) is using AI to generate the entire formulation, balancing the complicated tradeoffs and interactions that go into the electrolytes that make batteries possible.

What happens to language when a growing amount of text published in the press, online and on social media is written by machines? This question is not just important for the profession of journalism—it also has an impact on the richness of the language we all use to comprehend, describe and discuss reality itself.

Operating large language model (LLM) services like ChatGPT requires a server infrastructure on the scale of tens of thousands of units. However, constructing actual equipment every time a new AI semiconductor or system architecture needs to be verified incurs massive costs and time.