[MUSIC]
>> Look, I have some guests with me, Luis and David.
Y vamos a hacerlo todo en español.
¿ Qué les parece?
>> Now I can't do it in Spanish,
they'll get mad at me, and sorry,
I moved early, but I have two of my friends here,
Luis and David, to talk about something that I'm
super excited about called AI at Scale.
Let's start with an introduction really quickly,
we'll start with you, David, and then you Luis.
>> Yeah, David Carmona,
I ran the AI innovation team,
software leadership, and marketing
on our coolest technologies around
AI innovation, so a very cool job.
>> Luis.
>> Thank you.
I'm a Partner Technical Advisor in the office of
the CTO coordinating our AI at Scale is ready.
>> Fantastic. Let's start first,
what is AI at Scale? We'll start with you, Luis.
>> AI at Scale is a new exciting trend
where AI models are growing
between 10-100 times per year.
By doing this, our models are getting
a very rich understanding of language, vision,
speech, and combinations of all of
these different types of data as well
into something that we call multimodality.
We have been talking about some examples of
these models with during, for example,
for language understanding and OpenAI's GPT-3 model,
which we have been talking about
quite a bit lately for language generation.
This new trend has been enabled by
a few things that happened in the past couple of years.
First, the discovery of
a new model architecture that we call Transformer,
which basically allows us to
parallelize the training of these models much more
effectively than we could before, so we're really
just at this point constrained by compute.
Another thing is a new training approach
that we call self- supervised learning,
where models are really learning from just looking at
labeled data and solving a bunch of masking tasks,
and as they repeat
this hundreds of millions to billions of times,
they get really good at understanding
the underlying concepts of the data.
Another thing has been the data itself.
We have huge amounts of data that are being
generated by the Internet, and finally,
large amounts of computers are facilitated by
the Azure Cloud that allows to bring together hundreds,
thousands, and very soon tens of thousands of
GPUs to allow us
to bring all of this compute power together.
>> That's cool.
>> Ten thousand a day outside.
>> On the business side. What does this
mean for businesses, David?
>> One of the coolest things that we're
learning about these models is that they are
more generic so you can
train them once and then you can apply to
multiple tasks or multiple scenarios
or even multiple domains or industries.
You do that by customizing them all so you
take that pre-trained model and then you can
fine-tune the model or even in
an easier or more amazing ways that you can also
provide just a few examples where we call
like the Few-Shot Learning or even out of the box,
it can take new task and we
call that the Zero-Shot Learning.
At the end, this is
a game-changer because it allows you to reuse
these model systems starting from scratch
on them and apply them to your scenarios.
>> Awesome. How do we get
started with AI at Scale? We'll go to you, Luis.
>> A couple of options.
The fastest, easiest way is to go and access
the APIs themselves that
contain the capabilities of these models.
For example, in Azure Cognitive Services
or Azure Cognitive Search,
or Power Virtual Agent.
Using these APIs, you
can very quickly use the capabilities of
these very large models that we're training for
things like text analytics,
search, Q&A, bots, conversational experiences,
etc., and you can always customize this to your own data.
Besides this we have
Turing private preview that we're
running where you can get access
to the Turing models
themselves and you can customize this to
whatever task you want to use in
your organization and you can do this
through the Azure Machine Learning Service.
Bot options are building on the state-of-
the-art software platform
so all the optimizations that we
have built in the past and that we
have open-source things like
DeepSpeed and the UNIX compiler and run time,
as well as a world-class infrastructure that we
have where you can use anything
from AGPUs of the latest NVIDIA GPUs
that we have in the platform
all the way to 4,000 GPUs interconnected by
the fastest networks inside
of the machine and across the machines.
>> All right, David, how do we use
AI at Scale at Microsoft?
We have about two minutes, by the way.
>> Yeah. I think I can actually go
back there because it's
a good example of what I was mentioning before.
In the past we traditionally
developed our new models in Microsoft by every team,
so you were working the Office team,
you develop your own NLP models, for example.
You have to train it from scratch.
Now what we do is that we have
a centralized team creating
these Turing model and the common result
the teams are customizing
that model and there are many features that you are
already using probably today
that are using that approach.
In Word you have the text prediction, semantic search,
you have a lot in Bing of course,
in Outlook you have the
auto-reply, the meeting insights.
Many technologies are of course in
Azure. Azure Cognitive Services is using already
this approach and you
have Azure Search and many others.
If you think about it, it's like using AI as
a platform so these things are
leveraging that AI as a platform inside Microsoft,
and the good news, thanks to
the stack that Luis was mentioning is that,
that will be available also for
the rest of developers out there.
In the future, we'll see companies,
just like we see Office or
Dynamics doing that in Microsoft taking
these pre-trained models and applying
them to their scenarios. Super-excited.
>> Cool. We'll stick with you, David,
what's next and how do folks find out more?
We got about 50 seconds.
>> What's next. You want me to take that first.
>> Yeah.
>> I'm, of course, amazed with
everything that has to do with
the learning of these models,
multimodality like Luis was mentioning before,
but I think we also have to get
excited about all the innovation that is
happening on the responsible use of
these AI technologies in areas like bias mitigation,
transparency, privacy where there's a lot going on.
>> We'll stick with you, David.
Where can people go to find out more?
>> aka.ms/AIatScale
>> That's fantastic. You know,
it's always a pleasure hanging out with you, David
and Luis, talking about AI at Scale.
It's pretty amazing stuff. Make sure you check it out.
There's a link down there that just
went away, but make sure you go there.