Original article was published by Akash Pandey on Artificial Intelligence on Medium
How Amazon gets Benefits from AI/ML ?
IN EARLY 2014, Srikanth Thirumalai met with Amazon CEO Jeff Bezos. Thirumalai, a computer scientist who’d left IBM in 2005 to head Amazon’s recommendations team, had come to propose a sweeping new plan for incorporating the latest advances in artificial intelligence into his division.
He arrived armed with a “six-pager.” Bezos had long ago decreed that products and services proposed to him must be limited to that length, and include a speculative press release describing the finished product, service, or initiative. Now Bezos was leaning on his deputies to transform the company into an AI powerhouse. Amazon’s product recommendations had been infused with AI since the company’s very early days, as had areas as disparate as its shipping schedules and the robots zipping around its warehouses. But in recent years, there has been a revolution in the field; machine learning has become much more effective, especially in a supercharged form known as deep learning. It has led to dramatic gains in computer vision, speech, and natural language processing.
Thirumalai came to Bezos for his annual planning meeting with ideas on how to be more aggressive in machine learning. But he felt it might be too risky to wholly rebuild the existing system, fine-tuned over 20 years, with machine-learning techniques that worked best in the unrelated domains of image and voice recognition. “No one had really applied deep learning to the recommendations problem and blown us away with amazingly better results,” he says. “So it required a leap of faith on our part.” Thirumalai wasn’t quite ready — but Bezos wanted more. So Thirumalai shared his edgier option of using deep learning to revamp the way recommendations worked. It would require skills that his team didn’t possess, tools that hadn’t been created, and algorithms that no one had thought of yet. Bezos loved it (though it isn’t clear whether he greeted it with his trademark hyena-esque laugh), so Thirumalai rewrote his press release and went to work.
Thirumalai was only one of a procession of company leaders who trekked to Bezos a few years ago with six-pagers in hand. The ideas they proposed involved completely different products with different sets of customers. But each essentially envisioned a variation of Thirumalai’s approach: transforming part of Amazon with advanced machine learning. Some of them involved rethinking current projects, like the company’s robotics efforts and its huge data-center business, Amazon Web Services (AWS).
The results have had an impact far beyond the individual projects. Thirumalai says that at the time of his meeting, Amazon’s AI talent was segregated into isolated pockets. “We would talk, we would have conversations, but we wouldn’t share a lot of artifacts with each other because the lessons were not easily or directly transferable,” he says. They were AI islands in a vast engineering ocean. The push to overhaul the company with machine learning changed that.
While each of those six-pagers hewed to Amazon’s religion of “single-threaded” teams — meaning that only one group “owns” the technology it uses — people started to collaborate across projects. In-house scientists took on hard problems and shared their solutions with other groups. Across the company, AI islands became connected. As Amazon’s ambition for its AI projects grew, the complexity of its challenges became a magnet for top talent, especially those who wanted to see the immediate impact of their work. This compensated for Amazon’s aversion to conducting pure research; the company culture demanded that innovations come solely in the context of serving its customers.
“If you asked me seven or eight years ago how big a force Amazon was in AI, I would have said, ‘They aren’t,’” says Pedro Domingos, a top computer science professor at the University of Washington. “But they have really come on aggressively. Now they are becoming a force.”
Amazon Uses An AI Management Strategy Called The Flywheel
Amazon’s approach to AI is called a flywheel .In engineering terms, a flywheel is a deceptively simple tool designed to efficiently store rotational energy. It works by storing energy when a machine isn’t working at a constant level. Instead of wasting energy turning on and off, the flywheel keeps the energy constant and spreads it to other areas of the machine.
At Amazon, the flywheel approach keeps AI innovation humming along and encourages energy and knowledge to spread to other areas of the company. Amazon’s flywheel approach means that innovation around machine learning in one area of the company fuels the efforts of other teams. Those teams use the technology to drive their products, which impacts innovation throughout the entire organization. Essentially, what is created in one part of Amazon acts as a catalyst for AI and machine learning growth in other areas. Amazon is no stranger to AI. The company was one of the first to use the technology to drive its product recommendations. But as AI and machine learning grow, the flywheel approach has become a keystone to Amazon’s expanding business — a central stone at the summit of the company, connecting the organization together. This is particularly unique at a time when many companies silo their AI efforts and don’t integrate them into the overall company.
AI Is Not Located In One Particular Office At Amazon — It’s Everywhere
The Amazon Echo, which features AI bot Alexa, has been one of the company’s most popular forays into machine learning. Amazon faced an uphill battle at the beginning, especially as it was one of the first companies to try its hand at creating a voice-powered virtual assistant that could fit on a countertop. Once the technology started to come together, divisions across the company realized that Alexa could be beneficial for their products. Some of the first skills for Alexa were integrations with Amazon Music, Prime Video, and personalized product recommendations from an Amazon account.
AI also plays a huge role in Amazon’s recommendation engine, which generates 35% of the company’s revenue. Using data from individual customer preferences and purchases, browsing history and items that are related and regularly bought together, Amazon can create a personalized list of products that customers actually want to buy.