>> You're not going to want to miss this episode
of the AI Show where we look at
amazing AI to make
beautiful presentations with the PowerPoint Designer.
Make sure you tune in.
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>> Hello and welcome to this episode of the AI Show where
we're going to look at the PowerPoint Designer.
I've got two special guests with us.
Derek, Priyanka why don't you introduce yourselves?
>> Hi. My name is Priyanka,
and I work on
the PowerPoint Intelligence Team and I'm really stoked to be here.
>> Fantastic. Derek.
>> Derek Johnson. I also work on the PowerPoint team and
our goal is to use AI to help people make better presentations.
>> Okay. So I am terrible at design.
Tell me about the PowerPoint Designer; the goals.
Maybe you can show me what it does because I
see the thing pop up but I never go
exploring because usually I'm doing
my PowerPoint presentations the
moment before I have to present them.
You know how it goes. So tell us a little about the project?
>> Yes. You're not alone with that.
We see a lot of people scramble at
the last second to pull presentations together.
The problem with that is you're not
going to be very effective with
your presentation if you don't really
focus on how to get that message across.
So our goal is to use AI,
and we use AI all across the company,
to figure out how to dial in that message in your slide.
So I will definitely show you a bunch of examples and dig in.
But at the end of the day, we want to
unlock the power of the product through AI.
>> Fantastic. So my guess is that with PowerPoint,
there's a lot of different things you can do.
Can you give me some examples of what you can do with it now?
Because I'm having a hard time
visualizing is I'm like a programmer type.
>> Yes. So I mean, you can do anything with PowerPoint and that's
part of the problem that if we start
talking about some of the AI that we have,
we're going to help you recognize people in
photos and make sure we
crop them to make sure that they're centered.
So why do I dig in here and show you some examples.
>> All right. Let's go to the screen.
>> So really timely,
great photo of black hole that was just released.
Let's make a project about this.
So we're going to do what a lot of
people around the world are doing.
We've got our favorite photo here of the black hole.
We're just going to drop that photo in.
This is just what you normally do.
But we'll see here it automatically pops
up and it's going to give you all of these suggestions.
So just with a single click here,
we've now found this suggestion
that's taken my photo of the black hole,
figured out how to crop it to make it the center of the action,
and we've actually even used AI here to figure out
what the right color palette is to make this slide really shine.
>> So I've always looked at this on the side
and I'm like those are really nice actually,
but I never really thought about the engineering behind it.
I mean, I feel like there's a lot and we'll get to the end [inaudible].
But is there any other features that go along?
This is pretty cool. You put
a picture and then you don't have to do anymore work.
>> Yes. Let me show you one more before we have
Priyanka talk about how we make it look so effortless.
So this is a slide that we see all the time,
a bunch of bullets just on a slide.
Kind of boring after you get to your 30th,
40th slide of this, the audience is pretty well asleep.
>> I just fell asleep right now it goes for both.
Oh, see now I am awake.
>> Here we go with a single click.
What we've done is, we've made this not only more colorful,
but we've also made it easier the kind of person remembers.
So we used a bunch of AI here to both separate these things
out as well as even find icons that match that content.
So now we've got icons that actually match
exactly what you're talking about and
it's shown to make it easier for the audience to actually remember
what you're talking about which at the end of
the day is the goal of PowerPoint.
>> That's pretty amazing because the picture bit
was so subtle that I'd
even realized that it actually added those in but
it looked at the text and added the actual pictures.
>> Right.
>> Okay. So let's get into the engineering bit of it
because I'm an AI person and as I'm looking at this,
it feels like it's a supervised problem,
It's an unsupervised problem,
It's a reinforcement learning problem.
There's all sorts of AI.
Can you describe the kinds of models are being built?
>> Absolutely. So one of the things that we
wanted to do was to make the experience seem
like magic to the customer but backing all of this is
not just one but multiple sets of
models that are solving various problems.
So if we go back to the image problem that Derek just showed.
Right? Can you switch back?
>> There it is. Let's spring it out.
>> Right. The black hole really cool.
>> Let me even show you four photos
just to really drive this point home.
>> I love this. How much time and how many clicks
does it take for a user to get to a slide
like this when you're dropping a bunch of photos like that?
>> I feel like you did that way too fast.
I just started thinking about how cool the faces of astronomy is
versus you actually just put
four pictures in there and then it was done.
>> Which is so great right?
Because we want the focus of our customers to be on
the content and not on how they'd laid
out or how would they tell the best stories.
So for example with pictures,
some of the work that we do is with facial recognition.
We know that there are faces.
We have a model that tells us where
the faces are located so we don't
crop the photos incorrectly and we actually center them.
We also detect salient regions in our images to
position them the best way possible in this slide layout.
So that's just an example of the image AI that we're doing.
There's another really interesting model
that looks at the image and does
a multi-label classifier that says this is a logo,
this is a screenshot,
this is a chart or a table and we can do
different kinds of treatments based on that.
>> So basically what I would be
doing as a human except for we have AI.
Because whenever we look at pictures as
humans we're automatically drawn to faces.
So we know that intentionally so we're able to use AI to do that.
There was one interesting bit though
that I saw that was interesting
is I'm a vision guy, I like to be visionary.
I'm good at that. But I noticed like you
said that you were able to pull icons out.
So I'm assuming there's some an LP going on. It sounds like that?
>> Yes, there is. That's actually
the next problem I was going to talk
about is being able to analyze your text.
We have a couple of different kinds of models.
Here's another interesting one.
>> Yes. I mean, you're right. They are within NLPs.
They're only looking at what you're doing.
In this case, let me just undo it because it happens so fast.
It's hard to even tell what's happening.
We've got a slide here, where people have put a list of
dates and they're trying to get a timeline across.
This isn't a very effective way of getting someone to remember it.
So automatically, Designer can use
the NLP tech we have and all of the layout tech that we
have to determine that this was actually a set of
dates and it can lay out in this really visual way.
This is how people understand looking at dates.
>> Of course. I mean, that's really cool.
>> Yes. So just to give you an example.
Two of our text-based NLP models like you'd call it out.
So one of them is this, which we use
the slide tech to actually determine
what kind of textual body is this from its structure.
Is it a timeline?
Is it the process?
So one of those models helps us do that and then we match it with
the right graphic or
SmartArt and then we offer those up as suggestions.
>> That's really cool.
>> The other one that we showed with icons.
This is my personal favorite.
I just love how people's eyes light up.
They're like, "How did it find that icon that matches my text? "
>> Let me show you something cool though because
one of the things with AI is people often ask us,
'Well what happens when you get it wrong?
AI is not going to be perfect and we
have to design for a world where AI
gets you most of the way there but
the end of the day the user needs to be in the control.
So if we find one that's not right,
all we have to do is to
select the one that we don't like, and say,
maybe this camera is not exactly what we had in mind,
and we'll be able to get
the next recommendations that the AI system had.
So maybe this is a better representation of photo.
>> That's cool.
>> Now it's cool not only because we've got the right icon,
but we've just provided training data back to our ML systems.
So now we know we got it wrong
before and we're getting all of this signal,
and we've got a 100s and millions of users.
So this is a way for us to really get
this AI to become smarter and smarter.
>> So let's get into the engineering because
now that I'm getting a sense of what's going on.
You must be training dozens and dozens
of different models at the same time.
That probably takes a lot of time.
>> Yes, absolutely.
I mean some of the ones that we've talked about,
this is just a subset of everything
that's powering Designer behind the scenes.
This one in particular takes the text and develops
a recommendation model to give
a ranked list by confidence of icons that are match.
Like Derek was saying,
we're constantly retraining all of our models
with user interruptions by them telling us what worked,
what didn't and having the models learn from it all.
>> Yes. I love that you're interacting with the AI's.
Here's some designer ideas.
You're in the space of like, "Look,
you're using our AI now,
which one of them? Do you like this.
Do you like that? You know like this, which is really cool.
So I understand Azure,Machine
Learning just came out a couple of months ago.
You are in the process of moving over.
We won't tell anybody, just couple of of our friendship.
Because you work in the Office group,
tell us a little about what that experience has been like
and what were some of the improvements that you've seen?
Feel free to be as technical as you want.
>> Yes. Absolutely. So prior to using Azure ML,
a lot of the modeling training work was being done
locally or through using tools that were
internal and I think a lot of the data scientists had gotten
used to the tediousness that came with crowning large sets,
waiting for a really long hours,
going and begging for machines.
>> A bit expensive machines.
>> Yes. They are very expensive machines.
>> Then so my manager for example has some of
these really awesome GPUs that people would be waiting to be like,
"Can I please, please have these?".
>> Like old school. Like when I was at school, I had to
wait for my super-computing time.
>> Yes. You got to partner.
>> Three and four in the morning.
>> Yes.
>> Right. But as the number of model scaled then
the models got more complex with DNNs, multilayer DNNs.
We soon realized that this wasn't
going to be scalable and so timing was
great with AML showing up
and so our data scientists are now starting.
Now, they're able to put their data into the data lake,
distributed compute over GPU that their eyes literally lit up.
>> They were very excited. Yes.
>> When they saw that capability.
I think it just increases the throughput of
the data scientists tremendously and
makes their work-life balance a lot better.
>> So if you could say one feature that has stood out
for you-all when you've been using Azure Machine Learning servers,
what has it been for
your data scientists and for you when you use them?
>> So, right now, we've been using for two models.
One of the Textus SmartArt recommendation models
and another for one of our image classification models.
I think the biggest one has been to be able
to the distributed GPU, computing,
send it out there and then wait for
the results to come back while they can go
work on something else at the same time and it's
not monopolizing your machine entirely.
>> We're trying to do a lot of crazy things.
A lot of them fail, that's the AML world.
You try a lot of things out,
not all the models work,
so to be able to run five,10 models at a time is going to
help us as a business keep-up and that's really important to us.
>> So let's talk about how are people reacting to this number one?
What's the future? Obviously there's tons of stuff
that you're going to be thinking about doing in
the future. How are people reacting?
>> Yes. I mean so one of the things that we see
is people are paying a lot of
high-end designers to create
great presentations and we want to learn from those techniques.
We want to learn how to help people craft better storylines.
So we showed you a bunch of examples of creating great slides.
Some of the things we want to go out next is
how do you create a great presentation?
How do I help the storyline come together,
add motion, more content?
So you're going to see a lot more coming
through here and not that kind of regard.
>> That's cool because right now it looks like most of the AI is
in the slide but now we're looking at next slide [inaudible].
>> You got it.
>> Yes. See. I can pay the medium to small dollars [inaudible].
>> So how are people reacting? Are they liking it?
>> Yes. I think we should show
you some of the reactions that we get on Twitter.
We're really excited to see people
really resonate with what's happening.
Some of these are great examples where it talks about
people's perception of AI is not PowerPoint
to start and we're really excited to see people realize that
this is what AI can look like for an everyday user.
Images keep coming. It's really exciting every day to look at how
people are using it and this spans everything from consumers,
to students, to people in enterprises.
They just want to get their work done and go home.
>> That's amazing. So it's not all about the killer robots brand.
It's about the killer presentation.
>> Yes.
>> You like how I wrap around?
>> I do. I love it.
>> Thank you. Awesome. Thanks so much for
spending some time with us Derek, Priyanka.
You've been learning all about the PowerPoint Designer and
the dozens name millions. I don't know if it's millions.
>> It is. It's 4.1 million a day and it's almost 48 seconds.
>> [inaudible] Somebody people are using it.
>> That's how many slides? Every single day
that we're designing on behalf of people.
>> Like I always knock that and thanks
so much Derek. Thanks so much, Priyanka.
Thanks so much for watching. We'll see you next time.
This has been another episode of the AI Show. See you later.
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