Artificial intelligence, or machine learning, can increase the speed and accuracy of modeling for interconnection studies for large-scale renewables projects, said Xing Wang, global leader for grid modernization for Amazon Web Services (AWS) Energy and Utilities, in a panel discussion convened by the trade group ACORE. One type of interconnection study uses a model to evaluate how a new solar generating system will affect power flow on the grid. The model predicts power flow "but it doesn't solve," Wang said, meaning it doesn't provide a solution. "You need to find out where the issues are, and that requires years of engineering experience. We have a limited number of people who know how to do that."

Microorganisms perform key functions in ecosystems and their diversity reflects the health of their environment. However, they are still largely under-exploited in current biomonitoring programs because they are difficult to identify. Researchers from the University of Geneva (UNIGE), Switzerland, have recently developed an approach combining two cutting edge technologies to fill this gap. They use genomic tools to sequence the DNA of microorganisms in samples, and then exploit this considerable amount of data with artificial intelligence. They build predictive models capable of establishing a diagnosis of the health of ecosystems on a large scale and identify species that perform important functions.

I did a double-take last week when Micron Technology, one of the world's largest memory chip makers, acquired artificial intelligence hardware and software startup Fwdnxt. The move could be very interesting. If it bears fruit, Fwdnxt could bring Micron into direct competition with partners such as Intel and Nvidia, as Micron believes that memory and AI computing are converging into the same architecture. But it's no accident that one of the people at Micron in charge of this project is Steve Pawlowski, a former Intel chip architect who holds dozens of patents. Pawlowski is now vice president of advanced computing solutions at Micron.

Micron has a habit of building interesting research prototypes that offer a vague hope of commercialization for the sheer purpose of learning how to make its own memory and storage subsystem approaches more tuned to next generation applications. We saw this a few years ago with the Automata processor, which was a neuromorphic inspired bit of hardware that focused on large-scale pattern recognition. That project has since folded internally and moved into a privately funded effort from a startup aiming to make it market ready, which is to say that it has all but disappeared from view since that was a couple of years ago. There is more here for anyone interested in the Automata architecture, but for those curious about why Micron wants to get into the accelerator business with one-off silicon projects like that or its newly announced deep learning accelerator (DLA) for inference, it's far less about commercial success than it is learning how to tune memory and storage systems for AI on custom accelerators. In fact, the market viability of such a chip would be a delightful bonus since the real value is getting a firsthand understanding of what deep learning applications need out of memory and storage subsystems.