This Week in Analytics & AI News

The role of cloud and AI in taking computer vision to the edge. Faster OLTP performance for enterprises. A new world record for SAP HANA with Intel Optane PMEM. AI improves quality control for contact lens manufacturing and helps map the human brain.

Intel and AWS Webinar Series: Computer Vision at the Edge

Next up in our webinar series exploring computer vision at the edge: A closer look at the role of the cloud and AI, plus a discussion of choosing the best devices. Join AI and cloud experts from Intel and AWS on August 13 to learn how to cross the chasm on your computer vision journey.

Webinar: Achieve Blazing OLTP Performance Using Intel Optane PMEM on Oracle Exadata

How does this sound? 2.5x faster OLTP read IOPS, 10x faster OLTP latency, and 8x faster log writes. Those are the results of Oracle and Intel’s collaboration on the Exadata X8M platform with Intel Optane persistent memory. Learn how your enterprise can benefit from faster OLTP in this August 6 webinar.

Scale up—and up—With SAP HANA and Persistent Memory

Big news: SAP HANA set a new benchmark world record using the HPE Superdome Flex server and a system with Intel Optane persistent memory. The benchmark shows that with this technology, you can scale up memory capacity. You can also reduce restart times for critical systems like those used for disaster recovery.

Automated AI Solution Sharpens QC in Contact Lens Manufacturing

AI isn’t only helping companies work better. It’s also helping contact lens wearers see better. LEDA Technologies has developed an AI contact lens inspection tool using technologies from ADLINK and Intel that improves quality control processes by detecting lens defects automatically with a 95 percent accuracy rate.

Accelerating Brain Mapping With AI and HPC

Mimicking the human brain is an impossible feat—but that doesn’t mean researchers aren’t trying. Using functional magnetic resonance imaging to study the brains of people carrying out various cognitive tasks, researchers can create computational models of how the brain works, then use those models to train artificial neural networks.