How AI is
helping Amazon become a trillion-dollar company PART II
GRAB AND GO
Given that the heart of the new fulfillment center system involves
using cameras and AI software to detect someone holding an item and placing it
on a shelf, you might think that the same technology is in use at Amazon
Go, Amazon’s automated grocery stores that allow customers to walk
in, grab what they want, and simply walk out the door, with everything being
charged to their account automatically.
Not so, says Porter. Although there is likely some consultation
going on between AI scientists across the company, Go’s hardware, which
includes color and depth cameras, as well as weight sensors and algorithms, was
independently developed. It reflects five years of work developing systems
capable of tracking people’s handling items in a wide variety of sizes, shapes,
and colors in complex environments like crowded grocery stores.
As of now, there are only four Amazon Go outlets–three in Seattle
and another in Chicago, with more on the way. But they are able to handle a
steady flow of customers who can scan their phone upon entry, shop as much or
as little as they want, pick thing off of shelves and put them back, and
accurately track what they end up leaving with, regardless of numerous
potential pitfalls along the way.
Dilip Kumar, the vice president of Amazon Go, says that the very
act of customers picking up an item presents a challenge to the system, since
it blocks the cameras’ view of an item. Go’s systems must be capable of tracking
what each customer in the store has picked up–possibly including multiple
identical items–regardless of how crowded the store is and even if two people
dressed identically are standing side by side and reaching across each other
for purchases. “You could be picking up an item here, [or] I could be picking
an item there. We still need to be able to associate my pick to me and your
pick to you,” Kumar says. “The challenge with all of this is not just being
able to build a sensor, but also dealing with varying lighting conditions. You
can look at color temperature. Things vary. What’s pink is not always pink
throughout the day.”
To deal with all of this, Kumar’s team designed algorithms that
analyze what the cameras are seeing and look for interactions people have with
products. In order to work, they have to be able to determine who took what at
“the moment of truth” as an item is removed from a shelf.
Kumar won’t say how accurate Go’s systems are, but it’s clear the
company wouldn’t make them available to the public if they were prone to high
error rates. Fo over a year, the original Seattle store–which is on the ground
floor of the headquarters building in which Amazon CEO Jeff Bezos works–was
accessible only to employees as the company fine-tuned the system.
Next up for the Amazon Go technology, Kumar says, is to boost its
algorithms so that they’re more powerful “per unit of compute” and to take
advantage of cheaper sensors. Combine those two factors and Go’s systems could
well be capable of more quickly identifying new items in stores without having
to train the algorithms to recognize them. That’s important, he points out,
when between 20% and 30% of items are new at any given time.
Asked if Amazon plans on porting the Go platform to its Whole
Foods empire, Wilke says that’s not likely. Rather, he sees Go as just one of
many ways–including Amazon Pantry, Amazon Fresh, Whole Foods, and others–of
getting groceries and other items to customers. Ultimately, Wilke says, machine
learning is an “advancing” technology, “which allows us to make some of these
experiences better.” He adds that “real estate is hard,” and that Amazon
doesn’t have long-standing expertise in it. But if a recent story by Bloomberg’s Spencer Soper is
correct–he reported that Amazon is
considering opening 3,000 Amazon Go stores over the next few years–the company isn’t fazed by the prospect of learning as
it goes.
ALEXA SKILLS FOR ALL
Odds are that when most people think of Amazon and AI, they think of the company’s
digital assistant, Alexa. To date, people have
bought millions of Alexa-powered Echo devices, and third-party developers have
built more than 45,000 skills–essentially voice-powered apps–that can do
everything from help with recipes to play family games to read the news.
Along with cranking out its
own Alexa gizmos at a furious rate,
Amazon has been working on helping third-party hardware manufacturers
integrate Alexa directly into their products. Known as Alexa Voice Service, the
initiative has spawned about 100 products so far from companies like Sonos,
Ecobee, Sony, Lenovo, and others. Rabuchin explains that Alexa Voice Service is
essentially a set of APIs in the cloud that enable hardware makers to utilize
Alexa. Amazon makes its front-end audio algorithms available to the third
parties, as well as guidance for building Alexa-powered devices.
Amazon is also working with institutions to let them create
customizable skills for Echo devices placed in college dorms or hotel rooms. As
an example, Steve Rabuchin, VP for Alexa voice services and Alexa skills kit,
recalls staying in a Marriott hotel and being able to get Alexa to turn the
lights on and off, turn on the TV, change the channel, and ask where the gym
was located.
The next frontier for Alexa is letting consumers create their own
custom skills. In the past, that required some basic software development
knowledge. But Amazon wanted to democratize the Alexa skills creation
process, so it launched what it calls Blueprints–a template-based
Alexa skills creation tool that just about
anyone can figure out.
Blueprints let anyone teach
Alexa new tricks, no coding required.
Creating a skill with Blueprint is as easy as filling in a few
fields and hitting save. And while the skills generally won’t be as
sophisticated as ones built by professional developers, and can’t be made
publicly available, they do allow for custom skills nearly any Alexa user to
leverage AI for some highly personal purposes–such as giving instructions to a
housesitter or stepping through a workout regimen.
AMAZON AI EVERYWHERE
One of the primary drivers of Amazon’s rise to a
near-trillion-dollar company has been Amazon Web Services, its massive
cloud-based storage and server business. AWS has become a cloud standard for
companies and developers wanting access to the same kind of AI and machine learning
technology that powers Amazon offerings suxch as Alexa, Amazon Go, Amazon Prime
Video’s X-Ray feature, estimates for product delivery times on Amazon.com, and more. “Our mission in AWS,” says
Sivasubramanian, VP of Amazon machine learning, “is to put those machine
learning capabilities in the hands of every developer and data scientist.”
Sivasubramanian says that there’s excitement
about machine
learning’s potential in nearly every sector of the
economy. But while executives at countless companies see how it can help their
businesses, “it’s still in its infancy. [Those executives] look to us and say,
‘How can you actually help us take advantage of these machine learning
capabilities to transform our customer experience?’ ”
To date, Sivasubramanian says, there are tens of thousands of
customers using AWS-based machine learning services across sectors including retail,
real estate, fashion, entertainment, health care, and others. Those customers
have a variety of levels of AI competence. Some are what Sivasubramanian calls
experts–people with PhDs in machine learning–while others are simply app
developers. Amazon has tailored its AI and machine learning offerings to match
both sorts of customers’ needs.
Some of those users have deep experience and the ability to build
their own machine learning models; others just want to take advantage of models
that have been created for them. That’s why Amazon built SageMaker, an
end-to-end machine learning service meant to help developers build and train
machine learning models and run them either in the cloud or on devices such as
smartphones.
Sivasubramanian ticks off a wide variety of examples of corporate
customers using AWS’s AI and machine learning services. Among them include
Intuit which is using SageMaker to build fraud-detection models; Grammarly,
which predicts what a user is writing and what corrections are required; CSPAN,
which analyzes thousands of hours of video in order to recognize celebrities
and specific politicians, as well as to double the number of videos it has
indexed; DuoLingo, which is using Amazon’s Polly text-to-speech service to
generate individual language learning sessions; Liberty Mutual, which is using
Amazon’s conversational API as a service, Lex, to build a chatbot that enables
the insurance company to handle many users’ questions; and the NFL, which is
analyzing plays in order to predict what the next one will be.
He says that usage of AWS’s machine learning tools has grown 250%
over the last year, and that since last November, AWS added more than 100 new
features or services to its machine learning portfolio.
One of them is DeepLens. Designed so that developers can build and
fully train a machine learning model within 10 minutes of unboxing, the camera
system is already being used in many ways Amazon never imagined.
Of course, among those unorthodox applications is the project
Sivasubramanian built to satisfy his wife’s request. And what he learned was
that DeepLens was smarter than he even realized. “Initially, I had it notify
for any animal, including my dog,” he says. “But this is the fun of machine
learning: you constantly tune it to make sure you exclude things that are false
positives, to make sure it gets more and more accurate. It’s an ongoing project
so [my family] can have the best bear detector in the world.”
BY DANIEL TERDIMAN
https://www.fastcompany.com/90246028/how-ai-is-helping-amazon-become-a-trillion-dollar-company?utm_source=postup&utm_medium=email&utm_campaign=Fast%20Company%20Weekly&position=5&partner=newsletter&campaign_date=10052018
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