A great deal of the focus of artificial intelligence (AI) is on automating tasks so a person doesn’t have to do them. At its simplest level, AI enables users to program systems to perform tasks. For example, a coffee pot can be programmed to automatically start early in the morning so coffee is ready when the alarm goes off. The AI is performing limited thinking along the lines of, “Is it 5:45a.m. yet?”
A fresh perspective could change your view of everything. IBM Watson is AI built to help us see things from all sides. By deciphering the nuance in natural language, Watson uncovers a range of perspectives to help us make more informed decisions.
Ask the security systems integrators we polled in our recent survey, and they will tell you that storage is the backbone for capturing, retaining and analyzing data. More than ever, they’re relying heavily on hard disk drives (HDDs) and flash because capacity and endurance matter. The industry is moving beyond traditional security-focused surveillance and is adopting new use cases that could be referred to as smart video – cameras, storage, processing and deep learning algorithms combined to deliver solutions that make people’s experiences better.
Self-driving cars rely on AI to anticipate traffic patterns and safely maneuver in a complex environment. In this DRIVE Labs episode, we demonstrate how our PredictionNet deep neural network can predict future paths of other road users using live perception and map data.
AI startup Trefos is helping foresters see the wood for the trees.
Using custom lidar and camera-mounted drones, the Philadelphia-based company collects data for high-resolution, 3D forest maps. These metrics allow government agencies and the forestry industry to estimate the volume of timber and biomass in an area of forest, as well as the amount of carbon stored in the trees.