>> Hello, my name
is Noelle LaCharite, and welcome to this week in Cognitive,
where I have the pleasure of sharing with you some of
the new and awesome things going on
in Cognitive Services and Applied AI.
Always feel free to connect with me
on Linkedin or connect with me on Twitter.
I'll be happy to be
able to answer any questions you may have had
or just share with you some of the latest and
greatest that's going on in this space.
So, for those of you who are new,
thank you so much for joining
us this week and Cognitive is
all about getting just snackable content every week,
and being able to see some of the latest and
greatest samples that we've developed just for you.
So, the first thing though I
always like to do is make sure that you
are familiar with the architecture
that we are talking about.
Right? So, we have built
an entire platform that makes it easy
for you to consume machine learning models,
and in this specific episode we're actually
going to be talking about the pre-built models
that are available for you to use.
So, you can see here,
we have pre-built models that we call Cognitive Services.
There's actually a collection of these
we call Applied AI,
and what it means is that you can apply these models,
you didn't have to build them,
you didn't even have to have your own data.
It's Microsoft data, Microsoft models
but available for you to use as a web service.
But sometimes, that's not enough for sometimes
it doesn't completely answer your question.
So, the very next thing we offer is
the ability for you to customize those models.
So, I'll show you today exactly how you can do that with
one of our brand new generally available services.
Even sometimes at that point that
might not solve your problem completely.
AI, right? This is a research activity.
You have to do lots of experiments.
So, you may get to the point where you
want to experiment on your own,
code first, build your algorithm from scratch.
Obviously, if you've already have algorithm,
you don't want to do that but if you can
use one that's pre-built, you want to do that.
But sometimes, you need to build your own.
So in that case, we provide an entire stack
of tools and frameworks and most importantly,
compute that will allow you to power your own models.
So, here you can see we have
a huge selection of computing power available
to you no matter what your side
of our architecture you're on,
whether you're using our pre-built
models or whether you're
using models that you're building from scratch.
Of course, I don't want to go without
mentioning all the tools that are available for you.
We want to meet developers right where they are,
meet you where you're building these models,
whether you're building them in a language
through our SDKs like C#, Java,
Node or Python, or whether you're using some of the tools
like TensorFlow or Caffe or Cognitive Toolkit.
Right? So, we want to make sure that you have
all the tools you need to build what you want.
Now, a lot of people consider
Cognitive Services to be a superpower,
like a magic wand.
Right? So, they're business ready,
they run on Azure,
they're part of your subscriptions
that you have available to use,
and I want to just give you
a little taste of what that looks like.
First, the languages.
All of these languages are supported.
You have the ability,
we consider Java, Node, Python, C#.
These are our primary languages
right there, first-class citizen.
So if you're a developer in this space,
we want to make sure that you know you have
access to these models through these SDKs.
But there are so many things that you can do.
Today, I'm going to just focus on one,
but look at all the different models you have available.
You could do things in computer vision.
I recently created an object detection model that
can tell the difference between two cartoon characters.
Just really interesting technology that can help you
solve some fun things but
also can solve your business problems.
So, everything from speech to being able to analyze text,
to be able to create a bot very very
quickly that can answer all of the top of
mind questions for your customers or your employees.
These things are already at your fingertips.
But today, what I wanna do is show you very quickly how
you can leverage our newest member
of the family in Cognitive Services.
Our newly generally available Speech Service.
So, the first thing I'm gonna do is
actually show you the website.
So, you can go out to microsoft.com to our Doc site,
and you can actually see that speech has changed.
So, for those of you who've used speech in the past,
used to have to cobble together a bunch of
different APIs in order to leverage the Speech Service.
But today, instead, what you will
find is now they are unified.
It's a single subscription.
So, now rather than having to piece all these things
together and they normally go together anyway,
we've made it easier for you to consume.
So, I'm going to actually show you a very special part of
our Speech Service which are some
of our preview technologies,
but I just think they're really cool and interesting,
and I wanted to share with you what we've done with them.
So, I'm going to show you Custom Speech,
and this is the ability to do what I mentioned earlier.
Take an existing model that we've
provided and then adapt it for your use.
Now, many of you may be familiar with the JFK files.
You can go out to GitHub today,
rebuild this project all by yourself.
If you want to play around with it,
the benefit of this project is it allows you to take
structured and unstructured data in
a file directory, a blob storage,
and be able to make sense of it and create analytics and
relationships across that data to
make it easy for business users to consume,
which is a problem every business has.
Now, I'm not here to talk to you about that,
I will in a future episode, but not in this one.
In this one, I actually wanted to show you
a bot that I built on top of this.
Now, this is JFK's,
so you guys might remember.
It's like a controversy that happened way back when.
Well, I thought it would be important you know this was
based on a president getting assassinated.
What if that new president
wanted to ask someone questions?
So I went back, looked up
the head of the FBI at the time,
his name was J. Edgar Hoover,
and I said wouldn't it be cool if we built a Hoover bot.
So, I created this hoover bot
in order to allow me to leverage
that Cognitive Search capability in
JFK files but interface with a chat bot.
Not only one with text but
also with the ability to leverage speech.
So, what I'm gonna do here is
just start a conversation with this chat bot,
Mr. Hoover, and I'm simply going to say, "Welcome".
>> Nice to be here.
Thanks for having me.
How can I help you?
>> Now, the voice you just heard was Synthesized Speech.
It's a new service that you have the ability to use.
So, what I did is that we took a bunch of
audio from those JFK files.
We also took some audio that we were able to
find across the Internet and we
used about an hour of
audio in order to create the synthesized voice.
Now I was in luck because this is like 1960's audio,
so it's not awesome to begin with.
But it does give you the ability to start
thinking about what is your business going to sound like?
I created a fictitious version
of Mr. J. Edgar Hoover for the purpose of this bot.
But this is something that you're
going to want to think about as you're
building bots for your experience.
Not only what does it sound like in the form of text
but also what does it literally sound like.
Now, another thing that's really
interesting is I want to provide the ability
to do things that most
default language models just can't do.
So, here's an example.
What's the most important cryptonym
associated with GPFLOOR.
>> KAPOK. It is
the cable indicator for
the highest level of documents sensitivity.
KAPOK is above RYBAT,
which is above EYES ONLY.
>> So, the interesting thing about that
is that there's two things that happened here.
One is I'm using words that
are not in the default language model.
Meaning, if I spoke this to just a default chat bot,
it would say what is GPFLOOR?
I have no idea what that is.
Most importantly, it would not be able to execute
a query on the doc set,
and return this response.
So, I had to train the language model
on this custom word.
So, I trained it not only on GPFLOOR,
which definitely is custom,
but even cryptonym wasn't in the library either.
So, I took 268 of these words,
I trained it on the pronunciation data so that
the model knew what it was when I set it,
and then it was able to translate that
to a query and actually pull that information
from this blob storage full of
documents of structured and unstructured data.
This is incredibly difficult to
do if you had to do this from scratch,
if you had to build your own model to support this.
Now, it's available to you in a web service.
So, just to give you a quick wrap up
of what we did here and what you can do at home,
you can actually go out to GitHub.
There are three projects.
One is web chat,
which is this basic chat box you'll see here.
It's almost exactly the same minus I slept on a picture.
Then, we grabbed speech to text and text to speech,
and then we added the endpoints from
these services in to create this experience.
It literally took a few hours and most of
that was me just fine tuning the way I wanted it to work.
So, I hope you enjoyed today's episode.
It's really great to have
an opportunity to play with the latest technologies,
and I really look forward to
seeing you guys in the next one.