One of the toughest challenges with solving predictive maintenance, enabled by AI, is that there is usually no data available for training the system. Modern equipment may not break down for 5 years or more so there is insufficient data when the product is first launched on when and how it will break down. But AI systems using traditional methods such as Deep Learning can only operate when sufficiently large volumes of data are available for training the system and hence, they are incapable of addressing this problem.

One of the most impactful areas for businesses to deploy AI is in digital signage. AI-driven digital can deliver personalized content, respond instantly to changing conditions, and make every interaction more meaningful. A challenge that comes with deploying this kind of AI-powered digital signage is ensuring the content is flexible enough to adapt to different display sizes or settings, or to simplify the content so it can fit any display.

AI is moving from the cloud to the edge. In product after product, we see ever more intelligence being brought to the edge, and to the devices that reside there.

What do the manufacturing, robotics, transportation, video, city safety, and security industries all have in common? They are searching for more efficiencies in the process of real-time object detection and dynamic tracking. The answer to that search could be discriminative AI for many companies.

Server-like features being available at the edge of an enterprise’s network seems like a pipe dream, but if it were achievable, many industries would benefit greatly. In healthcare, manufacturing & robotics, retail, video, smart cities, public safety, and security, in particular, such feature availability in a compact form factor could be transformative in some cases and mission-critical for others.

Companies struggle to process enormous and diverse data efficiently, often leading to delays and higher cost due to complex needs and limited AI computing power. This is increasingly obvious and problematic at the Edge, where the form factors can limit performance, storage, and battery.

There are many industries seeking to deploy real-time, rugged edge AI computing power, particularly those that require low latency and precise results. Manufacturing, robotics, machine vision, Smart City, and Safety and Security applications require edge processing solutions, and companies are beginning to make investments to meet these needs.