>> Hello, and welcome to another episode
of This Week in Cognitive.
I'm your host Noelle LaCharite,
and today I'm going to be talking to you about something
that was announced just a couple of weeks ago.
We recently decided, actually Satya,
our Developer Conference this year,
announced how important it was for us to start looking at all of
these pre-built models and
really kind of increasing the ability for you to leverage them.
So, some of the complications we've run into,
is that we have companies that really want to use
these models but they're in very strange location.
So, for example, maybe someone's in space right
and in the space station they want to be able to run models.
If you're in the space station or outside of our stratosphere,
it might be pretty difficult for you to hit
a cloud service running in a data center,
in the US or in Europe.
So, how can we make it easier for those types
of solutions to get access to this technology?
It's all part of that story where we're trying to democratize AI,
we want to make AI available to every industry, every use case.
So, one of the things we've worked really hard at
in Cognitive Services and in the applied AI org,
which really is all about
pre-built machine learning models and making
them accessible to you in the form of services,
web services, SDKs and now Containers.
So, now, we leveraged Docker Containers
in order to deploy this technology,
so that you can use this where you need it.
Often, we refer to this as the Intelligent Edge, right.
Edge Computing is all about,
having, wherever your data is putting the compute,
or the processing as close
to that data as possible as opposed to having
to travel the network in order to do any of
that work and any of that processing.
So, recently, an announcement was
made that identified all the containers that we've released.
Now, we have lots and lots of pre-built models,
these are the ones that are available today,
we'll be consistently releasing them over time,
but today we have Text Analytics.
If you look at Text Analytics, inside Text Analytics,
you'll be able to do Key Phrase Extraction,
and Language Detection, and Sentiment Analysis.
I often, again, kind of going along with that space analogy,
you might imagine being able to do this against maybe logs, right?
Like the captain's log,
that's being done, or astronaut logs,
and being able to do things where
we were identifying a trajectory that maybe
is identifying some loneliness or
some depression and being able to correct that,
and have based on those patterns,
identified ways to help people in space.
I love space as most of you know,
so I just kind of we'll go with that for
a little bit like Face Detection.
Again oftentimes,with these logs that astronauts do
I don't know if you've watched,
but there's lots of different astronauts that post
videos and updates on Twitter,
and it's just super exciting to watch them from space,
but it's also great to have the opportunity to analyze that data,
and even analyze it in space and have AI
provide recommendations to them about what they're seeing.
So, you get Face Detection,
Verification, and Emotion Detection,
all things that are pretty interesting.
So, we've got the ability to analyze text,
now we've got the ability to analyze faces,
we also have the ability to just recognize text.
Now, this is important in the case that, again, let's say,
they're taking notes or
maybe taking pictures of documents or something,
and you're in a remote location like Iceland
or in a country that doesn't have consistent Internet,
wouldn't it be great for you to be able to
document and take pictures of things,
and be able to have that text pulled
from that information like those images.
So, being able to detect and extract
printed text from any of these images,
no matter where you are on the planet
using any device that you can deploy
this Container to really
expands what you're going to be able to do with this technology.
Then of course, we have Computer Vision the ability to just use
Computer Vision to do image and video analysis at the Edge,
as well as recently launched Language Understanding,
being able to create language,
use language models at the Edge on devices.
I often see this in places where we want to,
we don't have a consistent Internet connection
or maybe we're in a third world country,
and we want to be able to help process or provide
a spoken language interface to make it easier for people who don't
have technology to get
access to that technology or what it can provide.
These Containers enable that to happen.
So, there's so much good that can come from being able to do this,
lots of opportunity in your businesses to be able to do this,
but I do want to point you all put this link in the notes,
but a blog was written and it actually
has a list of all the Containers,
a little description, but most importantly, the links,
not only to how do you configure the Container,
what is a Container,
but then also the step-by-step,
I mean each one of these steps are different per model.
We're going to spend some time in future episodes
going through and building some of these models on the Edge,
and it would be really cool we'll see what kind of devices
maybe if you have some suggestions on the devices we might use,
like put it in the comments,
it would be really cool to use your ideas and do
some fun experiments here on the show.
But you'll be able to through this link go
and see all the different examples that we have,
go and build your own containers,
and be able to deploy them and see just how easy it is
today to deploy a Pre-built Machine Learning Model
to the Intelligent Edge.
Really exciting stuff.
Thanks so much for joining us this week,
and I'll see you next time.