Artificial Intelligence applies algorithms to leverage deep learning and other techniques to solve actual problems. The sub-field of Vision Intelligence impacts the physical security industry directly when you consider that surveillance cameras are the ultimate end-point device, the "all-seeing eyes" of the Internet. Could "intelligence" be applied to video to create "human context" to eliminate the false alarms and tailgating pain points that have long plagued the physical security industry? As a security leader, Philip Jang, Sr. Manager Physical Systems & Technology at VMWare, has been exploring how AI technologies and digital transformation processes impact the physical security profession for several years. Jang has global enterprise experience in the planning, execution, delivery and automation of advanced technologies such as AI, Robotics, Drones, advanced analytics and IoT.

From a business perspective, there may not be a more promising area in tech at the moment than health care, partially because of its significance -- Americans reportedly spent $3.65 trillion on health care in 2018 -- but also because of the incredible advances and opportunities arising from new developments like machine learning and genetic engineering. Artificial intelligence has multitudinous implications for medicine, and the demand is proof: According to a report from CB Insights (via Becker's Hospital Review), health care AI companies raised $846 million from venture capital firms in the second quarter of 2019, an increase of about 13% year-over-year. One sub-segment that fascinates me personally and professionally is "vision intelligence," which we're pursuing in my newest company. This is the ability to diagnose medical scans even more accurately and faster than the human eye. These programs generally use a method called deep neural networks to train computers to recognize images -- a technique many say is inspired by the biological processes of the brain.

A new computer vision (CV) software library has been launched for the development of "vision-enabled" applications targeting the mobile, home, PC, and automotive markets. Built by CEVA Inc, the new library is optimized for the firm's own CEVA-MM3101 imaging and vision platform. This is positioned as a tool for application developers to add vision capabilities to System-on-Chip (SoC) systems incorporating the CEVA-MM3101. NOTE: So-called "vision enabled" applications can be found in areas such as wireless (or wired) sensor networks, mobile computing scanning apps, PCs, smart TVs, natural user interface (NUI) devices, and advanced driver assistance systems (ADAS). CEVA-CV is based on OpenCV, a standard library of programming functions for computer vision processing.