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The machine-learning smarts that help Google know what’s in a photo and let Amazon’s Alexa carry on a conversation are getting a real job. “ML” platforms from vendors like Amazon, Google, IBM, Microsoft, and others can automate business processes on a previously impossible scale and free up employees for more creative, thought-intensive work. They also require a lot more commitment and, sometimes, coaxing than parking an Amazon Echo on a kitchen table or tapping a button to have Google back up the photos on your phone.
But the payoff can also be correspondingly greater. “Every business process is just badly written software,” observed Markus Noga, head of machine learning at SAP. The advent of on-demand AI lets companies do something about that: “We can really turn this into software and make the company run itself.”
At this summer’s Google I/O conference, the scene of multiple announcements of AI-as-a-service initiatives, CEO Sundar Pichai set his sights slightly higher: “I’m confident AI will invent new molecules,” he said during the opening keynote.
Raising all boats
At a minimum, machine-learning platforms let companies perform some of the same tasks AI tackles in consumer settings, just in larger numbers and with money on the line. For example, the Cloud Machine Learning platform Google opened for business last year provides image-recognition services—not too different from what Google Photos does for your phone’s pictures—that allow Airbus to correct satellite imagery to distinguish between snow and clouds.
But machine-learning platforms can also take on tasks that individual users would rarely bother with but for which companies might pay a great deal. Consider the job of grading the visibility of sponsorship signs and banners in a sports event, something traditionally done by “students with stopwatches,” as SAP’s Noga put it.
SAP’s Leonardo Machine Learning Foundation can do that a bit faster: “We’re able to have a look at every frame, we’re able to have a look at every pixel of HD or 4K video, and we’re able to process it faster than real time.”
That led to some surprises in a breakdown of a series of skiing competitions. “It turned out that the company buying the most expensive slots around the starting gates wasn’t getting the best exposure,” Noga said. “It was the cheaper spots around the sidelines of the course [that were getting the best exposure].” Why? That’s where TV cameras would zoom in longer.
Customer service represents another obvious application of machine learning’s ability to parse human input—think Siri, but at scale and self-improving. Deloitte Consulting helped an unnamed financial-services firm deploy an AI-based system that handles some 27,000 customer queries an hour in over a dozen languages.
“It’s smarter than a chatbot in that it’s not just a set of rote responses, but rather a learning set that’s actually doing back-end analytics,” said Anthony Abbattista, lead at Deloitte’s analytics and cognitive business unit.
Abbattista emphasized that this starts with extensive, upfront input from warm-blooded data sources: “Having a starting model, started by the experts and captured, is always the first step in this.”
Keeping your options open
A strong case can be made for companies to outsource ML services to companies that specialize in them—but not just one company, analyst Jan Dawson of Jackdaw Research wrote in an e-mail.
“There’s definitely some risk in building big stuff based on a single AI or machine-learning platform,” he said. “But that’s still far preferable for most companies to trying to build their own AI or ML capabilities.”
Abbattista made the same point. “We’re not looking at things and saying ‘we need a five- or 10-year system,’” he said. “We can plug and play with these providers as they come and go.”
Box, for example, first signed up with Google to use its Google Cloud Machine Learning Engine to automate image recognition. More recently, though, it announced plans to bring Microsoft’s Azure Machine Learning Platform onboard for other, not yet specified AI services.
“Today, there are billions of files in Box, and a significant portion of those are image files,” wrote Chief Product Officer Jeetu Patel in an e-mail forwarded by a publicist. “So when we looked at what problem we could solve first with machine learning, it made natural sense to start with providing an image recognition service through our partnership with Google Cloud.”
Google’s image-recognition services are free in the current private beta but won’t be when they ship later this year; Box will reveal pricing closer to then.
Patel ticked off some early applications of Google’s image-recognition: “We’re seeing retail customers using image recognition in Box to optimize digital asset management of product photos, a major media company is using this technology to automatically tag massive amounts of inbound photos from freelance photographers around the globe, and a global real estate firm is leveraging optical character recognition in Box to digitize workflows for paper-based leases and agreements.”
The pitfalls of platforming
In some scenarios, it makes more sense to keep AI in-house. PayPal, for instance, opted to build the fraud-detection AI it rolled out in 2013.
“Our risk and our problems are very unique,” said Hui Wang, senior director of global risk and data sciences at the firm. “We are a closed-loop network. We have a relationship with both buyers and sellers.” And her part of the company must rule on transactions “within milliseconds,” she said.
A 2015 Deloitte white paper hedged its endorsement of services built on IBM’s Watson with a warning against handing over “individual user information, such as financial transaction information or identifiable patient records.”
So, for all the attention paid to AI as a service—Dawson observed that “companies are talking about it a lot more because they see it as a badge of honor or an overt signal of innovation”—its business applications can lag behind those in homes. “We need to get the same technology from 9 to 5 that we enjoy in[to] the consumer space from 5 to 9,” Abbattista said.
Amy Webb, a professor at New York University’s Stern School of Business and author of the book The Signals Are Talking, predicted that will change soon enough; it has to.
“AI is our generation’s electricity,” she wrote in an e-mail. “Once businesses plug in to the AI grid, they’ll be ready for increased productivity, better workflows, new inventions, and the ability to scale their operations.”
Rob Pegoraro is a veteran technology journalist who covers computers, consumer electronics, telecom services, the Internet, software, and other things that beep or blink through reporting, reviewing, and analysis. Formerly the Washington Post’s tech columnist, Rob now writes for a variety of online and print outlets. His page is robpegoraro.com.