Iris Technology’s new approach to AI allows developers and enterprises to train and deploy models faster — with far less data and computing power than the industry standard — while retaining control of their intellectual property
After three years in stealth mode, Grand Rapids-based Iris Technology today publicly demonstrated webAI, a fundamentally different approach to computer vision. The new modular platform allows teams to train computer vision models faster with less data, run those models with computing power found in most consumer-grade laptops, and deploy those models dramatically faster than industry standards. On the webAI platform, developers and enterprises can build models and prototype quickly at no cost before engaging in an enterprise license.
“Most AI platforms today are built around the assumption that Big Data is the answer to the world’s problems,” said David Stout, co-CEO of Iris Technology, the creator of webAI. “webAI throws that assumption out the window. Our fundamentally different approach envisions a world where practically any developer, regardless of their budget or past experience with artificial intelligence, can train, deploy and iterate an AI model quickly and cost-effectively.”
Iris opened early access to webAI.com for 200 users today at the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. webAI’s user-friendly interface allows all developers — not just AI/ML experts — to build accurate computer vision models, as demonstrated at the conference where new users were able to build and train their own models within minutes.
webAI was developed around Iris’ proprietary Deep Detection™architecture and requires just one-fifth of the data and one-third of the training time for deployment compared to today’s state-of-the-art networks. webAI’s no-code / full-code development environment gives enterprises a single tool to collaborate and iterate from sandbox to full deployment quickly and cost-effectively.
“Enterprises are investing billions of dollars in artificial intelligence expertise, computing infrastructure and data acquisition/curation to fuel traditional AI experiments that have about a 13% chance of ever being deployed,” said James Meeks, co-CEO of Iris. “webAI’s novel architecture and simple interface enable rapid prototyping and iteration. Because it is free to train and affordable to deploy models on webAI, everyone from entrepreneurial developers to major enterprises now have the opportunity to create and deploy AI workflows that actually solve real-world problems.”
webAI embodies a unique approach to intellectual property (IP) protection and data privacy. webAI customers do not need to cede ownership of their data in order to utilize the platform — they own their trained models, and their data is inaccessible by Iris.
Prior to its public launch, webAI’s technology has already been piloted in a wide variety of computer vision applications across industries and sectors, including healthcare, transportation, retail and logistics, archaeology, and more.
“The possibilities for artificial intelligence to deliver transformative results with a small amount of unstructured data are practically endless,” Stout said. “webAI eliminates the barriers to entry for smaller organizations and removes roadblocks that have kept even the most well-resourced companies from seeing success in their AI projects.”
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