Google uses the technique while building systems that can recognize faces, products, landmarks and other objects in photos. In some cases, these algorithms are more accurate than something that is designed solely by engineers.
The new service is part of a widespread effort to expand the power of modern A.I. to businesses that are largely unfamiliar with this rapidly evolving technology. Like Google, a New York start-up called Clarifai offers an online service that helps customers train computer vision algorithms.
At the same time, several other start-ups, like Boston’s DataRobot and Silicon Valley’s H2O.ai, offer services designed to help businesses analyze the way products, customers, markets and employees behaved in the past and predict how they will perform in the future.
“They aim to automate data science in general,” said Randy Olson, a data scientist at Life Epigenetics, a company in Portland, Ore.
Tech giants like Google, Amazon and Microsoft have hired a large portion of the people who specialize in the machine learning techniques that are rapidly accelerating the progress of A.I. — a community of only 10,000 researchers worldwide, according to one estimate. That means most businesses don’t have the talent needed to explore the latest machine learning.
Credit Google Cloud AI
The question is whether these new services will work as advertised and how rapidly they will evolve in the years to come.
Google, Amazon, Microsoft and others already offer cloud-computing services that let businesses add existing machine learning algorithms to their own products. A company can take a Microsoft computer vision algorithm, for example, and slip it into a new smartphone app.
But with its new service, Google goes a step further, providing an automated way for businesses to build new algorithms. Businesses can upload their own images, provide a list of objects pictured in these images and train their own computer vision systems, tackling tasks that aren’t necessarily handled by existing technology, according to Google.
Initially, Google will open this service only to a small group of businesses. A Google product manager, Rajen Sheth, said the company would work with these customers to determine the price.
Risto Miikkulainen, a professor of computer science at the University of Texas at Austin who has long explored the kind of technology that underpins Google’s new service, agreed that it had the potential to help other businesses build their own A.I.
“It is really powerful technology,” said Mr. Miikkulainen, who is also vice president of research at Sentient Technologies.
But sometimes, there is no substitute for good old human labor. With Google’s new service, humans must label the data before the system can learn from it. Google can provide the human labelers, as do companies like CrowdFlower.
And even when an online service successfully automates a task, it’s not necessarily worth using.
James Bradley and his London company, NMT Vision, once used Clarifai to train and operate algorithms to identify websites that are selling products that infringe on copyrights. But he and his company now handle this on their own, mainly because the cost is lower.
Services like DataRobot and H20.ai may bill themselves as automated data scientists, but here, too, automation has its limits. “These services are only as good as their parts,” said Patrick Dougherty, a data scientist with the Seattle-based firm Slalom Consulting. “And humans still supply some of the parts.”
Google says that once images are labeled, its new service operates without human involvement. In a matter of minutes, it can retrain an existing algorithm using the customer’s images. Given more time, it can build a model from scratch, specifically for the problem at hand.
If you are a zoologist who wants an algorithm that identifies jaguars and giraffes, said Fei-Fei Li, chief scientist inside the Google cloud group, all you have to do is supply the right images. “You upload jaguars and giraffes,” she said. “And you are done.”
All that remains is determining how well it works.