Intel makes it possible for partners to deploy AI and computer vision at the edge with regular CPUs. Recent trends suggest HPC and AI are converging, and they’re probably worth paying attention to. A new supercomputer has arrived on the HPC scene, and it’s tasked with oil and gas research. AIOps aims to use machine learning to help IT. And Intel Health and Life Sciences’s Chris Gough recently joined other tech leaders to discuss how AI’s influence goes way beyond tech companies.
Good news: Deploying AI and computer vision at the edge doesn’t require the fancy infrastructure you might be imagining. With the Intel OpenVINO toolkit, you can start using these technologies with regular CPUs. In a recent interview, GM of Intel IoT Steen Graham answered key questions about this tech and how Intel partners have used it.
They may not be the seven wonders of the world (it’s hard for anything to top the coolness of the pyramids, right?), but the seven trends driving the convergence of HPC and AI are worth a quick visit. They reveal these disciplines have a lot to learn from each other. So, at the very least, roll down the car window and take a picture.
We’d like to introduce you to “Bubba,” a cloud-based supercomputer for geophysics research and exploration. DownUnder GeoSolutions will house this Intel-based HPC system in its Skybox Houston data center and use it to obtain a picture of the earth’s subsurface for oil and gas research.
And while we’re at it, we’d also like to introduce you to a new term: AIOps. This term refers to the application of machine learning and data science to IT operations, and in practice it could help IT get to the root of problems, automate routine processes, and much more. Expect to see more AIOps as data centers grow in complexity.
In case you missed the memo, AI isn’t just for tech companies. Chris Gough, GM of Intel Health and Life Sciences, joined tech leaders from a variety of industries at AtlanticLIVE to discuss how AI is changing the way they do business. He and Montefiore Health System’s Parsa Mirhaji explain how they navigate challenges of applying machine learning to medicine, a very a human-focused discipline.