- IBM refers to its Watson capabilities as a
cloud-based, cognitive computing platform.
And basically what they mean by that is,
their systems try to learn and make decisions
the way the human brain works.
They consume massive amounts of data
and then train their systems based on that data
to try to make decisions, and they're deploying this now
across a massive range of real-world applications.
Now, there are a couple of
artificial intelligence milestones that IBM had,
that basically captured people's attention,
made them start thinking about intelligent computers,
and also started to make businesses think about
commercializing artificial intelligence capabilities.
Way back in 1997, a predecessor to Watson,
IBM's DeepBlue computer beat the reigning
world chess champion, Gary Kasparov,
and that was done under tournament conditions.
And then back in 2011, Watson playing against
the two best human Jeopardy! players in the world,
won a $1 million Jeopardy! match.
And interestingly, they trained the Watson computer for that
using a variety of techniques from machine-learning,
which we'll look at in a subsequent part
of this presentation, and also
reinforcement learning techniques,
which are techniques that are very similar to,
for example, how we as humans learn.
For instance, as a baby when we're first learning to walk,
and we kind of get up and then we're holding on to something
and then we let go and we fall down,
that is something that registers in our brain,
and then we get up the next time and maybe we hold on longer
and so we reinforce the fact that if we're holding on,
it helps us stand up, and eventually
we learn to start walking and we may find that
as we fall to one side there is something that we need to do
to re-balance ourselves, to go back the other way,
and as we make mistakes, we learn from those mistakes
and we keep reinforcing our learning process,
and that technique was also used
as part of training IBM Watson.
Now, they also used a variety of
language-analysis techniques,
hundreds of different language-analysis techniques,
and they loaded up 200 million pages of content,
including all of Wikipedia at that time
for the purpose of training Watson
to compete in the context of Jeopardy!.
Now, one of the reasons we chose to feature IBM Watson
in this set of videos and our corresponding books
is that they have this no-credit-card-required policy
so that you can sign up for an account and access
their free Lite tier services,
which makes it really convenient and easy
for both students and professionals to experiment
with these incredibly interesting capabilities.
As you'll see over the next many videos,
Watson provides a lot of interesting things,
intriguing capabilities, such as being able to perform
natural language translation, one of the features
that we'll be using later in this lesson,
converting speech into text, and similarly
converting text into speech.
They have web services that can understand natural language,
web services that can help you implement chatbots,
so for instance, if you're responsible for customer service,
the first line of interaction nowadays through a website
is often a chatbot that helps route the person to the
correct customer service department.
There are capabilities for looking at text
and analyzing it for tone, and other capabilities
that allow you to look at both static images
and full-blown video, and recognize things
like objects within those images and video,
colors, people, et cetera.
And that's just a tiny subset of the capabilities available
through Watson and then the greater IBM Cloud as well.
Now, in this lesson we're going to take a look
at setting up an IBM Cloud account.
I will demonstrate a few of the Watson demos
that are provided by IBM directly.
In particular, we will be taking advantage of
three of the Watson services later on in this lesson.
We'll show you how to install the Watson developer cloud,
Python SDK, which is how we'll programmatically interact
with the Watson services, and as you'll see,
we're going to develop this traveler's companion
translation app, which is going to mash up several
Watson web services, specifically
their translation service, their speech-to-text service,
and their text-to-speech service,
and we're going to create an app that enables people
who only speak English and only speak Spanish
to communicate with each other directly,
despite that language barrier.
And as you'll see, it's relatively straightforward
to take advantage of these services
because of the powerful classes provided by
the Watson developer, Python SDK.