In fact Prasad, who
will outline his vision for Alexa’s future at WebSummit in Lisbon, Portugal,
later today, has already given the world a sneak preview of what this shift
might look like. In June at the re:Mars conference, he demoed a feature called Alexa Conversations, showing
how it might be used to help you plan a night out. Instead of manually
initiating a new request for every part of the evening, you would need only to
begin the conversation—for example, by asking to book movie tickets. Alexa
would then follow up to ask whether you also wanted to make a restaurant
reservation or call an Uber.
To
power this transition, Amazon needs both hardware and software. In September,
the tech giant launched a suite of “ on the go ” Alexa products,
including the Echo Buds (wireless earphones) and Echo Loop (a smart ring). All
these new products let Alexa listen to and log data about a dramatically larger
portion of your life, the better to offer assistance informed by your
whereabouts, your actions, and your preferences.
From a software
perspective, these abilities will require Alexa to use new methods for
processing and understanding all the disparate sources of information. In the
last five years, Prasad’s team has focused on building the assistant’s mastery
of AI fundamentals, like basic speech and video recognition, and expanding its
natural-language understanding. On top of this foundation, they have now begun
developing Alexa’s intelligent prediction and decision-making abilities
and—increasingly—its capacity for higher-level reasoning. The goal, in other
words, is for Alexa’s AI abilities to get far more sophisticated within a few
years.
A more intelligent Alexa
Here’s how Alexa’s
software updates will come together to execute the night-out planning scenario.
In order to follow up on a movie ticket request with prompts for dinner and an
Uber, a neural network learns—through billions of user interactions a week—to recognize
which skills are commonly used with one another. This is how intelligent
prediction comes into play. When enough users book a dinner after a movie,
Alexa will package the skills together and recommend them in conjunction.
But reasoning is
required to know what time to book the Uber. Taking into account your and the
theater’s location, the start time of your movie, and the expected traffic,
Alexa figures out when the car should pick you up to get you there on time.
Prasad imagines many
other scenarios that might require more complex reasoning. You could imagine a
skill, for example, that would allow you to ask your Echo Buds where the
tomatoes are while you’re standing in Whole Foods. The Buds will need to
register that you’re in the Whole Foods, access a map of its floor plan, and
then tell you the tomatoes are in aisle seven.
In
another scenario, you might ask Alexa through your communal home Echo to send
you a notification if your flight is delayed. When it’s time to do so, perhaps
you are already driving. Alexa needs to realize (by identifying your voice in
your initial request) that you, not a roommate or family member, need the
notification—and, based on the last Echo-enabled device you interacted with,
that you are now in your car. Therefore, the notification should go to your car
rather than your home.
This level of
prediction and reasoning will also need to account for video data as more and
more Alexa-compatible products include cameras. Let’s say you’re not home,
Prasad muses, and a Girl Scout knocks on your door selling cookies. The Alexa
on your Amazon Ring, a camera-equipped doorbell, should register (through video
and audio input) who is at your door and why, know that you are not home, send
you a note on a nearby Alexa device asking how many cookies you want, and order
them on your behalf.
To make this possible,
Prasad’s team is now testing a new software architecture for processing user
commands. It involves filtering audio and visual information through many more
layers. First Alexa needs to register which skill the user is trying to access
among the roughly 100,000 available. Next it will have to understand the
command in the context of who the user is, what device that person is using,
and where. Finally it will need to refine the response on the basis of the
user’s previously expressed preferences.
“This is what I believe
the next few years will be about: reasoning and making it more personal, with
more context,” says Prasad. “It’s like bringing everything together to make
these massive decisions.”
The
elephant in the room
From a technical perspective,
all this would be an incredible achievement. What Prasad is talking
about—combining various data sources and machine-learning methods to conduct
high-level reasoning—has been a goal of artificial-intelligence researchers for
decades.
From a consumer’s
perspective, however, these changes also have critical privacy implications.
Prasad’s vision effectively assumes Alexa will follow you everywhere, know a
fair bit about what you’re up to at any given moment, and be the primary
interface for how you coordinate your life. At a baseline, this requires
hoovering up enormous amounts of intimate details about your life. Some worry
that Amazon will ultimately go far beyond that baseline by using your data to
advertise and market to you. “This is ultimately about monetizing the daily
lives of individuals and groups of people,” says Jeffrey Chester, the executive
director of the Center for Digital Democracy, a consumer privacy advocacy
organization based in Washington, DC.
When pressed on this
point, Prasad emphasized that his team has made it easier for users to
periodically auto-delete their data and opt out of human review. Neither option
actually keeps the data from being used to train Alexa’s myriad
machine-learning models, though. In fact, Prasad alluded to ongoing research
that would switch Alexa’s training process to one where models can quickly be
updated anytime there is new user data, more or less guaranteeing that the
value from said data will be captured before it’s disposed of. In other words,
auto-deleting your data will mean only that it won’t still be around to train
future models once training algorithms have been updated; for current models,
your data would be used in roughly the same way. (In follow-up requests, an
Amazon spokesperson said the company did not sell data collected by Alexa to
third-party advertisers nor to target advertising, unless the user were
accessing a service through Alexa, such as Amazon.com.)
Jen King, the director
for privacy at Stanford Law School’s Center for Internet and Society, says
these types of data controls are far too superficial. “If you want to give
people meaningful control, then you have to be able to respect their decision
to completely opt out or give them more choices over how their data is being
used,” she says. “Giving somebody functional help in a location-specific way
could be done in an extremely privacy-preserving manner. I don’t think that
scenario has to be inherently problematic.”
In practice, King
envisions this to mean several things. First, at a bare minimum, Amazon should
have users opt in rather than opt out to letting their data be used. Second,
Amazon should be more transparent about what it’s being used for. Currently,
when you delete your data, it’s not clear what the company may have already
done with it. “Imagine that you have an AI surveillance camera in your home and
you forgot it was on and you were walking around the house naked,” she says.
“As a consumer it would be useful to know, when you delete those files, if the
system has already used them to train whatever algorithm it’s using.”
Finally, Amazon should
give users more flexibility about when and where it can use their data. Users
may be happy, for example, to give up their own data while wanting their kids’
to be off limits. “Tech companies tend to design these products with this idea
that it’s all or nothing,” she says. “I think that’s a really misguided way to
approach it. People may want some of the convenience of these things, but that
doesn’t mean they want them in every facet of their life.”
Prasad’s ultimate
vision is to make Alexa available and useful for everyone. Even in developing
countries, he imagines cheaper versions that people can access on their
smartphones. “To me we are on a journey of shifting the cognitive load on
routine tasks,” he says. “I want Alexa to be a productivity enhancer ... to be
truly ubiquitous so that it works for everyone.”
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