Neurala | AI In Manufacturing

Why does traditional deep learning fail for most industrial applications?

The answer is data. AI powered vision inspection for manufacturing typically fails because for most industrial processes, sourcing images of good product is easy, but defective products are rare and unpredictable. Not having large numbers of defective product images present a challenge for traditional AI. In addition to data challenges, the lack of AI expertise and expensive hardware requirements place AI out of reach for many manufacturers. Brain Builder changes all of this.

Neurala’s at-the-edge learning is critical to a variety of applications, such as finding a lost child in a crowd. We looked at many companies to help us explore different applications of artificial intelligence for public safety, and Neurala had the neural network technology we were looking for.

Paul Steinberg, CTO

Neurala and the NVIDIA Jetson AI platform work together to develop innovative deep learning solutions for inferencing and learning at the edge. This enables a new class of intelligent machines.

Murali Gopalakrishna, Head of Intelligent Devices

NVIDIA

Neurala’s architecture is well-suited to run on the edge. It’s small and lightweight, and we can get the technology as close to the sensors as possible. There’s a feature set that is available now that’s real and tangible. Neurala has an architecture that can evolve as customer needs and requirements change

Charlie Elliot, Director of Product

Aeryon

Neurala's AI vision technology lets us process images faster, reduce our costs, and scale to meet our customer's needs in a way that simply wasn't possible before.

Tim Rowland, CEO

Badger Technologies