The importance of defect detection in manufacturing cannot be overstated. Unsealed bottle caps, hairline cracks in pipes, painting errors, or any kind of broken, defective, or mislabeled product often has a negative impact on a manufacturer’s reputation and sales. Defects can also trigger costs related to finding, recalling, destroying, and replacing damaged goods – not to mention the ever-present risk of product liability lawsuits.
Computer vision engineers work in the domain of computer vision: the subfield of computer science and artificial intelligence that seeks to make computers “see” images and videos at a high level, in the same way that humans can. More specifically, those with computer vision engineering skills can uses the AI tools to make it their job to solve real-world problems.
Large-scale industrial operations manage infrastructure networks spanning hundreds of miles. An energy company, for example, needs to maintain vast networks of electrical wires, distribution poles, transmission towers, electrical substations, and other critical assets. Because these assets are often located in remote and dangerous locations, inspecting them for maintenance issues demands high-risk expeditions, skilled labor, and an enormous amount of time and financial resources.
Edge AI is the intersection of edge computing and artificial intelligence: an AI paradigm that performs as much computation as possible on “edge” devices that are physically located close to the source of the data. This is in contrast to traditional approaches that first upload the data to remote servers running in the cloud, where the computation is then performed.
What are the benefits of edge AI over “traditional” Internet of Things (IoT) and cloud computing methods? The advantages of edge AI include:
5G and edge computing are inextricably intertwined technologies: each one enables the other. Edge computing depends on fast speeds and low latency in order to transfer large quantities of data in near real time—exactly what 5G is good at providing. For its part, 5G needs applications such as edge computing in order to justify its rollout to wider coverage areas. 5G allows for more and more computing to be done at the edge where the users and devices are physically located, offering unprecedented connectivity and power. The rollout of new technology developments such as edge computing and the 5G wireless network standard has created waves of excitement and speculation across the entire industry.
Chooch AI has created many very short computer vision demo videos to make the kind of problems computer vision solves totally clear. At Chooch, we can create proof of concepts, just like these demos, very rapidly for our customers – in days, not weeks or months. While other AI companies may provide powerpoints, the computer vision platform at Chooch AI allows customers to partner with us to create true computer vision solutions.
Computer vision and artificial intelligence need a lot of data. The more volume and variety of data that you can show to your model during AI training, the more high-performance and robust the model will be when examining data in the real world that it hasn’t seen before. There’s just one issue: what if you only have a limited amount of data in the first place? That’s where data augmentation comes in.
Synthetic data can now radically accelerate the creation of AI models for computer vision. Watch this 5 minute vision to see how it works. Then, to learn more and to sign up for a demo of the Chooch AI 3D Synthetic Data Generation tool on the computer vision platform, please visit Synthetic Data Generation
Chooch AI, a leading computer vision AI platform, is named as an Edge AI Tech Innovator for 2020. The report is titled “Emerging Technologies: Tech Innovators in Edge AI” (October 2020) and is available directly from Gartner. The report identifies twelve emerging Edge AI providers.
To build accurate computer vision models, you need data—and lots of it. Now, you can generate images with synthetic data and augmented data on the Chooch AI platform, and then use these synthetic images to train and deploy computer vision models. What you’ll learn in this webinar is how to use different technologies with the same goal: deploying accurate computer vision even faster. Watch the video or read the transcript below.