Deep learning models owe their initial success to large servers with large amounts of memory and clusters of GPUs. The promises of deep learning gave rise to an entire industry of cloud computing services for deep neural networks. Consequently, very large neural networks running on virtually unlimited cloud resources became

Machine learning is commonly associated with big data, but that’s changing quickly. While IoT, edge computing and intelligent edge devices arguably make big data even bigger, not all data at the edge is useful. Therefore, the data needs to be analyzed at the edge to separate the signal from the

No-code, low-code (horizontal) machine learning platforms are useful at scaling data science in an enterprise. Still, as many organizations are now finding out, there are so many ways that data science can go wrong in solving new problems. Zillow experienced billions of dollars in losses buying houses using a flawed

Internet of Things (IoT) is a popular topic in the current 4.0 revolution. Nanotechnology is the same, I trust you have at least read or heard the two phrases above. What about the Internet of Nano Things, a combination of IoT and Nano is something to look forward to. Today,

IoT is extending networks further and further from conventional workstations and centralized data centers. That trend has, in turn, created the need for computing power closer to those endpoints. Edge computing devices, such as gateways, first addressed that need. There’s now another option: tiny machine learning, or tinyML, which embeds analytics

Machine learning provides powerful tools to researchers to identify and predict patterns and behaviors, as well as learn, optimize, and perform tasks. This ranges from applications like vision systems on autonomous vehicles or social robots to smart thermostats to wearable and mobile devices like smartwatches and apps that can monitor

As device sensors proliferate across every company’s value chain – from new product development through inspection, tracking, and delivery – tinyML is surfacing to provide actionable insights, transforming business as we know it. There are sound economic reasons for all this interest and activity. McKinsey researchers predict IoT will have

As more enterprises break into edge AI, practitioners of the fast-growing technology are trying to meet the challenges of optimizing machine learning for small devices. At the Edge AI conference this week, experts from Ford Motor Company, Panasonic AI Lab and XMOS explored ways optimizing AI models can enable TinyML —