There’s been a bit of coverage recently
about our Natural Language Processing search beta, but all that’s being talked about
is the semantic search element. The
journey we’re embarking upon is much more
ambitious than that, so I want to take a few minutes to fill in the blanks.
NLP is an important ingredient to this product,
but it is not the product. The ‘product’
is a goal-oriented artificial intelligence specialized for solving travel
retail problems. We need natural
language only to provide a human-like interface into that intelligence.
Agency
We’re modeling our AI on the human-human
interactions that travel agents have with real people. But first let’s talk a little more about the
concept of agency. ‘Agency’ has a few
meanings, the most important one here being an actor able to interact with the
world. We develop agents ourselves every
day – subprocesses of the mind which are trained to take unsupervised control
of complex tasks for which we have developed some proficiency.
Driving a car is an example most of us can relate
to. When you first start, you have to
consciously direct all your actions.
Hands at 10 and 2, check the mirror, engage the clutch, watch the
speedometer. After a few years (hey –
I’m a slow learner) you develop what you probably call the ‘knack’ for it and
you can drive around listening to music or holding a basic conversation. Those things you had to think so carefully
about have receded back from your conscious focus, delegated to a specialized agent who frees up your
attention for other things. You can use
that comfortably in any ‘like’ scenario – ie you don’t need to develop a new
one when you exchange your Toyota for a Honda.
Neat mental tool and fundamental to learning.
AI
What we’re trying to do at Expedia is mimic this feat
of human intelligence with machine reasoning, to give the level of personalized
service and helpful, relevant support that a customer would receive from a
real, live agent.
That’s why what we’re doing here is so much more
than a semantic search service; it is more like a conversation which enables a
customer to start with their intent (a beach holiday, a romantic break, cheap
ski vacation etc) and, through an iterative exchange of ideas in
question-answer format, end up with the most suitable travel arrangements made.
This isn’t a straightforward journey. I was recently lucky enough to spend some
time with Dr. Steven Pinker discussing this at length, and we concluded
that we understand the “A” but we don’t understand the “I” so this kind of project is
always part research. You have to be
optimistic to be a computer scientist!
But search can only really get a little bit
better before we have to make the
leap to AI. To society this is the move
from easy simple access to information to the delegation of problems to
agents. Perhaps now is the right time to
touch on the bigger picture, what the future might look like:
The
future of search
You won't see this page anymore. The whole search
space will be superseded by a network of generalized intelligences and specialized
intelligences, and search engines like Google [as you know it today] will become
the back end for that network, no longer a user-facing experience.
Specialized intelligences will know how solve
specific problems – they'll have what we call domain expertise – such as
changing a washer in a tap, making a candle, or – ahem – planning a vacation.
They'll know how to organize loads of dissimilar data and services into
the logical relationships which allow us to achieve those tasks and only those
tasks. Generalized intelligences will be responsible for marshaling these
specialized intelligences, so that we don't even have to keep an index of the specialized
guys. So any time you want something you'll consult your generalized
intelligence which acts kind of like your e-majordomo; interpreting your
wishes, dividing them up into tractable problems, finding solvers and
delegating problems to them, then assembling the answer which carries the most
confidence and presenting it back to you in human language. Kind of like
how 'people' organizations work today – there are specialists who can undertake
specific tasks for you and generalists who can route you to the right
specialist (and sometimes have some supervisory function). It is a
pattern that we're used to.
Example; you're going to change your spark plugs
(because you have a classic car – we'll all be hydrogen fuel cells by then!)
so, assuming you're not an expert mechanic, you'll first look up the general
principles – disconnect HT cables, unscrew old plugs, set gap on new plugs,
screw in new plugs, reconnect HT cables. Then you look up the specific
details for your vehicle – Haynes manual kind of stuff – how to remove the
rocker cover for easier access, how to check the cam timing etc, and then you
get your tools and parts together. You need to go any find the right
plugs (obviously) but also need the correct size socket driver and gaskets and
grease etc. That's quite an assembly of information and collecting of
items etc from [potentially] lots of different databases and shops. Or
you could just pose the problem to your personal AI and head straight out to
the garage. Perhaps you’ll also be
receiving step-by-step instructions, via your HUD, overlaid in real time on the engine itself as you look at it.
This is long term view. It will happen piecemeal, with sites
gradually becoming more intelligent and starting to offer experiences which
allow you to pose your problem, rather than hunt out information and evaluate
it yourself. Imagine an Amazon where you
could just say “I have a leaky tap” and (perhaps after some Q&A to narrow
down the problem) you’d be shown a pipe wrench, a pack of 1/2 inch washers, and
some DIY guides showing how to apply those tools without flooding your kitchen. Right now it shows me a book called “Death
and Other Things” by Christopher Hall and a mains powered household gas
detector. Today the onus is on me to know
that I need a pipe wrench etc and go looking for those items individually, accumulating
as I go.
We kind of already do this. Back to our car analogy for a second, the
complex system that is the modern motor vehicle already contains a number of
these agents today. When I was young (oh
no – I have become my parents) my first car had manual everything. I had to change
gears, which meant developing a feel for torque and engine speed. I had to set the choke, which meant
developing an awareness of fuel/air mixture, and I had to switch the radiator
fan on and off, forcing me to have explicit knowledge of engine temperature.
In other words, driving a car used to require
many more proficiencies than it does today.
The mechanical complexities are managed for us by clever (well, just clever enough…) homunculi so all we
need to do is point it in the right direction and push one lever to go faster
and another to slow down.
Back
to travel
The most important question to ask about any advancement
in technology is how it will improve the human experience. A future in which specialized intelligences
take away more of our common problems is essentially connecting intent to
effect by fewer intermediary steps.
Appeals to the lazy in all of us.
From our adventures in machine learning
specifically, we expect to be able to benefit the traveler and the travel
business by:
- A better user experience; expert guidance through travel planning instead of imposing a significant research burden upon the traveler.
- More intuitive interface which can be entirely cohesive across dissimilar devices.
- Higher conversion rates on sites and apps.
- Faster support for travelers in-trip delivered at a lower cost.
To do this well any machine learning algorithm needs
a body of knowledge to train it. The
more comprehensive that body of knowledge, the more confidence you can have in the
answers an AI will produce. Expedia is
the world’s largest travel company and has been selling to and supporting
millions of travelers for over 15 years.
That experience is captured in petabytes of data generated by
instrumenting every aspect of the travel experience. This puts us in a really good position to do
something meaningful for the web travel universe with artificial intelligence,
as well as contribute to machine learning and machine reasoning disciplines.
That’s what our innovation labs programme is all about – benefiting our partners and contributing to our profession.
