>> For this episode of
the AI Show I thought we would take a look at the
Global AI Nights keynote I got to do with Eric Boyd,
Corporate Vice President of Azure AI.
We filmed that earlier, hope you like it.
[MUSIC] Thank you so
much for that wonderful introduction Amy and Hank.
I have the privilege of having with me here Eric Boyd,
Corporate Vice President of Azure AI. How are you doing my friend?
>> I'm doing great. Good to see you again sir.
>> Fantastic. Everything going well?
>> Always, always going well.
>> Fantastic. So here's a question that I
want to start with that I think is important.
What is interesting in
Azure AI for both data scientists
and developers? What do you think?
>> One of the things we've really focused on is making Azure AI
really easy to use regardless of where you are in your AI journey.
So if you're really beginning
as a developer in AI and starting out,
you can get started immediately with things like
cognitive services that let you just directly
pull really powerful algorithms into
your application without having to
go through all the steps of training and everything.
On the other hand, if you're becoming more
sophisticated and starting to train your own models,
you can use the AML Azure machine learning platform
to go ahead and train your own models
using things like automated machine learning
which will really take all the work out for you.
You can use our new drag and drop interface
which makes it really simple to create models.
Or for more experienced and sophisticated data scientists,
we have really powerful tools
integrating with Jupiter networks and hyper-parameter tuning.
We really bring it all in one package and really focus on making
it easy for you to use and get started and get going with it.
>> Fantastic. So here's the question.
Because we have you here and everyone's watching hopefully,
can you give us an interesting story maybe a
behind the scenes look at a product-
>> Sure.
>> -you've built? What makes it interesting?
>> I mean, one interesting story,
a lot of our products really come from the research team.
So we work closely with MSR to see what are
the newest and most interesting things they've developed.
One of our researchers in MSR, Niccolo Fusi,
he was working on predicting where we
should be doing CRISPR gene editing.
So he built a machine learning model to try and figure out
among the 18 billion different places
you can cut and splice these genes,
what are going to be some of the best places for it.
He really found himself getting frustrated
with the amount of time he was spending
doing all the hyperparameter tuning
and selecting through all these models.
So he wrote a system that would
recommend what was the best model for him to use.
As he was using that he realized boy,
this is really useful.
So he further developed it and showed it off at a hackathon.
We did SLT review with Satya and his team and showed off hey,
this is the capability that could exist.
Then my team worked very closely with Nicolo and his team in
MSR to really turn that
into the automated machine learning product.
So really just working with
a research team to see what
are the groundbreaking things that they're
doing often in a field that's completely unrelated
to what the tool that we actually end up developing.
We have several iterations
like that and so it's really great to have that partnership
with MSR and to really bring
these innovations to life for all of our customers.
>> For those that are just hearing about automated ML,
why don't you explain what it is so people can get
a sense for how powerful it actually is.
>> What's exactly what Nicolo was trying to do
is he needed to create lots and lots of models,
and it's fairly tedious to go and create these models.
There is this process of I have some data and I pick which of
my different algorithms do I want to try and then I have
a whole bunch of hyperparameters that I need to tune,
things like the learning rate.
By just automating that process it did two things.
One is for inexperienced machine learning expert like Nicolo,
it's just saved him a ton of time.
You can very quickly learn these are
the parameters that are likely going to
work. These are the algorithms.
This data actually is learnable.
You have the right signals in there,
and so he can become much more productive.
But the other thing that it did is because
it's recommending a machine learning algorithm it
really opens machine learning up to people
who don't have the background and expertise in it.
So we've taken automated machine learning and
we've built a user interface on top of it,
or now all you need to do is say I want to predict home prices,
here's my dataset of
all the home prices and this is the column I want to predict,
give me the best model that you can.
We often get some skepticism from people and like yeah,
but it's not any good, right.
It works really well.
We've taken it to a lot of internal teams.
We routinely we say match
the performance of their data scientists
because we don't want anyone to feel bad.
We even have entered it into some contests,
machine learning contests, and we're
typically we've gotten top five percent performances regularly.
So you can probably do better if
you're a data scientist hand tuning it yourself,
but for effectively zero effort to get
a top five percent model is really great.
>> I always suggest you should literally just
start with automated on a new problem greenfield,
just start with automated ML and then
at least you'll know where to start later.
>> You'll know if it's learnable,
you'll know the types of things you can do
and often that's all you need to do.
We have a lot of customers that
they're using that and you can do it at scale
to build a hundreds of models that would
take you hours to put together by yourself.
>> That's cool. So we talked about automated ML,
that's something that's been in the product for a little bit.
What's new in Azure AI that people should really take a look at?
>> We had a lot of things that we just announced to build that are
really taking the product to the new level.
On the platform side really focusing on
the simplicity and on the operations the ML Op side of it.
But one of the new services I'm really
excited about is Personalizer.
So this is a service that we'll recommend
based on each individual user's unique attributes,
the things that you like to do and what you're interested in,
what content is going to be most relevant for you.
There are a lot of personalization systems out there,
this isn't a new idea,
but what's new about what we've done is this is
based on a reinforcement learning system.
So that really has made this much more powerful.
>> For those that are getting into machine learning,
what is reinforcement learning?
>> Sure. So the idea with reinforcement learning,
in a traditional machine learning model,
you take a massive dataset of hey,
here are all the interactions people had on my web site and
which users clicked on which thing and
you train the model to predict on that.
Reinforcement learning turns it on its head
and so it basically it will guess what's going to be
the content that's going to be best for
a user like you and it will
learn from your responses and reinforce the model as it goes.
The thing that's really powerful about that is,
we use this in MSN.
So there are new news articles coming out all the time.
So we can't train a new model based
on the historical performance of a new news article,
but reinforcement learning will
learn as it goes and so it will experiment,
it will try and then it will say oh,
this is going to be the content that works best.
So by using reinforcement learning in
that model you don't have to train it,
you set it up you let it go it performs
really really well and so that's
something we've been very excited about.
>> Is this being used anywhere?
>> Yeah. So I mean, I talked about we use it internally at MSN.
We also use it in Xbox and then their customers,
the Women's Tennis Association has recently
deployed this on their website and maybe I can-
>> Yeah. Let's take a look.
>> -show an example of that. So this
is a markup of the Women's Tennis Association site.
The idea is it looks like a MSN page where you have here
the main article and then a number of
other articles they could have selected from.
If we click to see how it works under the covers,
you can see what we did is in the requests,
we sent just some features
on what are the things that we know about
this user and since this is
a demo case there aren't a lot of features,
but this is where you would put the history,
the things that they've done in the past.
Then you send that in and then down at
the bottom of the page you get a response that says,
"All right, what's the thing that the
article that we should show?"
Then if I click into this particular article,
again, the thing that you see is we've
got this engagement reward up at the top.
So that's one of the key things with reinforcement learning,
is you need to tell the algorithm was this good or not.
Did the user do something that we liked or not.
So we give a reward and in this case
the reward is simply how far down
the page did they scroll and since I've
scroll further down the page we're saying,
"Oh, they did a better and better job."
So that then tells you oh they read this whole article,
this one's really relevant for
this type of user which goes back into
the reinforcement learning algorithm and
makes it better and better over time.
>> This is something that the user does not have to write,
is it just a service that they call?
>> It's a couple lines to make
the call out there and then everything
else this model will go and learn on its own.
We've had a lot of customers that
had been replacing their existing,
as I say personalization is not new and they're
seeing substantial improvements over the existing systems.
Really because reinforcement learning is
a really powerful tool to have underneath of it.
>> That's awesome. So where can people go to get started?
Because these are all cool stuff but sometimes it
feels like it's way over here over my head.
Where can I go to get started?
>> So that's a great question.
We've just put together a new getting
started with machine without Azure Machine Learning page.
On there you can see lots of
examples and interact with them directly and
notebooks and try things out and really experiment and learn with
all the tutorials and all the material that we've put together to
really help get you ramped up on machine learning.
>> Awesome, any final thoughts?
>> I mean, I think we're really
excited about the things that we've
developed and really glad that everyone has come.
I want to thank the organizers and all the participants.
I think this is going to be a great session
for you guys so really looking forward to it.
>> Fantastic. Well, thanks so much for spending some time with us.
Thanks so much for watching this portion of the keynote,
we're going to toss it back to
Amy and Hank who have some additional information.
[MUSIC] Hope you enjoyed
that as much as I did.
If you want to learn more about Global AI Nights go to
global.ainights.com to learn about an event near you.
Thanks for watching and we'll see you next time