For years, Swami Sivasubramanian’s wife has wanted to get a look at the bears that come out of the woods on summer nights to plunder the trash cans at their suburban Seattle home. So over the Christmas break, Sivasubramanian, the head of Amazon’s AI division, began rigging up a system to let her do just that.
So far he has designed a computer model that can train itself to identify bears—and ignore raccoons, dogs, and late-night joggers. He did it using an Amazon cloud service called SageMaker, a machine-learning product designed for app developers who know nothing about machine learning. Next, he’ll install Amazon’s new DeepLens wireless video camera on his garage. The $250 device, which will go on sale to the public in June, contains deep-learning software to put the model’s intelligence into action and send an alert to his wife’s cell phone whenever it thinks it sees an ursine visitor.
Sivasubramanian’s bear detector is not exactly a killer app for artificial intelligence, but its existence is a sign that the capabilities of machine learning are becoming far more accessible. For the past three years, Amazon, Google, and Microsoft have been folding features such as face recognition in online photos and language translation for speech into their respective cloud services—AWS, Google Cloud, and Azure. Now they are in a headlong rush to build on these basic capabilities to create AI-based platforms can be used by almost any type of company, regardless of its size and technical sophistication.
“Machine learning is where the relational database was in the early 1990s: everyone knew it would be useful for essentially every company, but very few companies had the ability to take advantage of it,” says Sivasubramanian.
Amazon, Google, and Microsoft—and to a lesser extent companies like Apple, IBM, Oracle, Salesforce, and SAP—have the massive computing resources and armies of talent required to build this AI utility. And they also have the business imperative to get in on what may be the most lucrative technology mega-trend yet.
“Ultimately, the cloud is how most companies are going to make use of AI—and how technology suppliers are going to make money off of it,” says Nick McQuire, an analyst with CCS Insight.
Quantifying the potential financial rewards is difficult, but for the leading AI cloud providers they could be unprecedented. AI could double the size of the $260 billion cloud market in coming years, says Rajen Sheth, senior director of product management in Google’s Cloud AI unit. And because of the nature of machine learning—the more data the system gets, the better the decisions it will make—customers are more likely to get locked in to an initial vendor.
In other words, whoever gets out to the early lead will be very difficult to unseat. “The prize will be to become the operating system of the next era of tech,” says Arun Sundararajan, who studies how digital technologies affect the economy at NYU’s Stern School of Business. And Puneet Shivam, president of Avendus Capital US, an investment bank, says: “The leaders in the AI cloud will become the most powerful companies in history.”
It’s not just Amazon, Google, and Microsoft that are pursuing dominance. Chinese giants such as Alibaba and Baidu are becoming major forces, particularly in Asian markets. Leading enterprise software companies including Oracle, Salesforce, and SAP are embedding machine learning into their apps. And thousands of AI-related startups have ambitions to become tomorrow’s AI leaders.
Read the source article at MIT Technology Review.