>> Hello and welcome.
I am Caglayan Arkan,
Global Lead for the Manufacturing
and Resources Industry at Microsoft.
Today, we're talking about one of
the most important advances in manufacturing
and resources: Artificial Intelligence.
AI is everywhere, in our networks,
in our data-centers, and our devices,
in every application, enabling
collaboration between people and machines.
At Microsoft, we have
an approach that is both ambitious and broad.
An approach that seeks to democratize AI,
making it accessible for all.
In this special two-part series,
we will delve deeper into what our
AI-enabled future looks like.
We will approach it from both sides.
In this first episode,
we will explore the powerful new opportunities
AI opens up in terms of technology innovation.
In part two of the series,
we will look at the important questions around ethics and
trust when it comes to ensuring that AI is designed,
used, and governed responsibly.
Welcome to Digital Empowerment.
With me here today is Indranil Sircar,
our CTO of Manufacturing and Resources,
who will share exciting examples of AI innovation.
Later, during our discussion,
we will hear from Ted Way,
Senior Program Manager for Azure Machine Learning.
Ted will give us an inside look at Microsoft AI advancements,
including a demo of Project Brainwave.
Indranil, let us start with you.
Thank you very much for being here today.
>> I'm very happy to be here, Caglayan.
>> Indranil, of all the technologies reshaping our industry,
I would say that AI has one of the biggest roles here
in terms of the disruption
and resulting opportunity for our businesses,
our industries, for everyone,
our world at large.
>> Yes absolutely.
>> So, can you tell us more about
Microsoft's vision for empowering transformation through AI?
>> Yeah. Every company is unique,
and there is no single path to driving AI transformation.
Our approach to Microsoft AI is to meet
you wherever you are in your transformation journey.
Now, it could be by infusing AI into your existing environment,
or using an AI-powered business application.
A major differentiator is that Microsoft AI and Data
Platform enables you to reason over
data no matter where it resides.
Whether data is on-premise, in the Cloud,
or in the Edge,
you don't need to move your data to reason over it.
Recently, we announced Azure enhancements with
new capabilities to run containerized applications on the Edge,
Industrial IoT workloads and training machine learning models.
Azure is engineered for future with capabilities like
Enterprise-grade service-level agreements, scale,
built-in privacy, compliance and security, that
the manufacturer needs to make AI real, for every application,
every business process, and every employee.
>> Indranil, across all industries,
AI offers us new ways to
boost employee productivity and creativity,
increase business agility, improve customer engagement,
and jumpstart new product innovation,
and that is just the beginning of what's possible.
Now, tell us more if you will about how AI is
empowering the digital leadership agenda across the industry.
>> Yes. I mean, we view this from three different perspectives.
Specifically, from an industrial IoT perspective,
manufacturers are using AI to
optimize processes such as predictive maintenance,
quality, smart systems, and so on.
As we start to discuss more about
intelligent manufacturing or factory of the future,
we are seeing AI-enabled processes like
CNC machine calibration to
automate tuning to significantly
accelerate manual processes with operator level precision.
Or collaborative robots, and
also within health and safety scenarios, among others.
Second, we continue to see AI driving
significant workforce transformation starting with
accelerating the need for skills enhancement.
One of the examples would be using
mixed reality by augmenting employees skills,
redefining training and skills transfer,
while increasing the efficiencies on the shop floor.
The third area would be sustainability.
This is critical to our industry as we consume
natural resources including energy and water.
We also need to think about waste management.
Many manufacturers today are already utilizing green energy and
initiatives like circular economy
to drive sustainable approach to manufacturing.
AI is also transforming energy subvertical.
For example, AI is enabling
autonomous inspection of grid assets in power and utilities,
and digital asset management product quality
and performance in oil and gas.
>> Now, we know that AI is very
very critical to sustainability.
Microsoft has invested big in
programs like AI for Earth and AI for Good.
These extend the power of AI towards solving some of
the world's biggest challenges to
create a more sustainable and accessible world.
>> Yes, exactly. I mean,
the potential here cannot be understated.
I mean, AI can play
an important role in monitoring the health of our planet.
It can help us unlock insights that
will help us reduce climate change,
conserve energy, and preserve natural resources,
like water through sustainable manufacturing practices.
AI is also what will help us feed our growing population.
When it comes to precision agriculture,
AI is enabling capabilities like precision farm input,
land cover mapping and assessment, and so much more.
Through sensors, drones, connectivity,
data analytics, and AI,
Microsoft is enabling precision agriculture through
programs like FarmBeats to improve
yield while reducing resource consumption.
Microsoft's AI for Good program basically uses AI
to help save lives through things like disaster recovery,
addressing the needs of children,
protecting displaced people, and promote human rights.
>> Indranil, this is simply mind-blowing.
It's just an amazing time.
The true impact of AI will be the greatest when
we all work together to really transform our world.
That is what we're talking about here, aren't we?
Our focus is to help manufacturers achieve
strategies that incorporate sustainability,
and create value for their businesses,
their customers, their communities.
Let's make it real. Can you give us a few examples
of what manufacturers are doing
today in some of these important areas?
>> Of course. When it comes to sustainability,
we are working with companies like
Ecolab to provide and protect clean water,
safe food, and abundant energy.
One example of our work together is the Water Risk Monetizer.
Based on Azure, this tool helps companies
understand and reduce their water usage.
On the factory side of it,
BAE systems along with our partner PTC is using HoloLens and
Mixed Reality to help first-line workers
assemble battery cells in less time,
and train new people 30 or 40 percent more efficiently.
Another example would be the use of
AI in manufacturing quality.
Jabil has been using machine learning and cognitive AI
in their manufacturing line
to improve quality and reduce waste.
Let me turn to Ted as he has been
closely associated with this project from engineering.
>> Thanks, Indranil. Hi, Caglayan.
>> Hello, Ted.
>>Jabil is one of the most advanced manufacturers in the world.
What they're doing with circuit boards that
they manufacture is that they're analyzing
the circuit boards and using AI to
predict whether there are manufacturing defects.
So, the circuit board is coming down the assembly line.
Today, they're taking pictures of it,
and a human has to look at those pictures,
and they only have a few seconds to make
a decision as to whether there's a manufacturing defect or not.
So, this is something that's really ripe for AI.
We worked with Jabil to build AI models that would analyze
the pictures and predict
whether there's a manufacturing defect or not.
The great thing about this is,
this really speaks to Microsoft's vision
of using AI to amplify human ingenuity, because now,
the people can then take the insights from the data,
and then be able to look at whether there's
a root cause for causing this manufacturing defect.
They can spend time on doing that higher value work
rather than just looking at these pictures,
because the AI model can look at these pictures.
Now, these AI models are also very big and complex.
ResNet-50 is one of
the models and it has eight billion calculations.
On a typical CPU,
if you want to just process a few images,
that's no problem at all.
But when you have an entire factory,
multiple assembly lines steps,
you're talking about a ton of data.
So, it really needs some form of acceleration of that AI,
and that's the work that we're doing with Project Brainwave.
By infusing Microsoft Research's Project Brainwave
into Azure machine learning,
we're able to help Jabil's data scientists accelerate that AI.
What you're seeing here is what the data scientists will use.
This is a Jupyter notebook.
They're writing code in Python.
They're using TensorFlow.
These are all familiar tools that data scientists use today.
They don't have to learn anything
new to be able to take advantage of
hardware acceleration of AI models on FPGAs.
So, as you can see here,
the steps are fairly simple to be able
to train and deploy that model.
Here's an example of a picture that's
coming off the AOI machine.
So, I'm going to send this picture to my model,
and then I'm going to predict whether
there's a manufacturing defect or not.
You can see here that the score says there's no defect,
and the result comes back in 11 milliseconds.
This means that the time that it takes
the picture to leave this machine,
go to the API,
run on the FPGA and come back is 11 milliseconds.
ResNet-50 with its eight billion calculations
only takes 1.8 milliseconds of that time.
So, let me jump over now to an app that we
built to showcase how fast this is running on the FPGAs.
You can see that I'm sending images to the API on the FPGA.
Median latency is only six milliseconds,
again, for that AI model.
Let me kick up the number of threads because
we're not sending images fast enough.
You can see that we're saturating that FPGA.
We're getting close to 500 images per second in this case to
be able to analyze the images and
determine whether there are manufacturing defects or not.
With these preliminary results,
we've seen that this model eliminates
about 75 percent of past components,
and it's correctly predicting 92 percent of the defects.
We're super excited about relationships like this where we can
bring in AI and to help them amplify their human ingenuity.
>> Ted, this is tremendous. How can we
take the learnings from
Jabil's work and apply to other manufacturers?
>> The great thing about the learnings is that
this pattern is repeatable anytime you have images.
So, you have images of auto components.
If you have images of plastic molding,
any type of products,
we're able to retrain our model to be able to
identify those defects on those products.
Not only are we able to do that with
pictures coming from existing cameras,
we're also partnering with other hardware partners.
So, for example, we have a partnership with Qualcomm,
in which we're building this vision AI development kit.
Qualcomm has built some chips that are able to accelerate
AI models just like we're doing with
Project Brainwave and FPGAs.
So now, this camera can then take in
pictures of scenes for workplace safety,
it's able to identify whether there are
things that are not supposed to be in a certain area.
We're doing a pilot with Shell in their gas stations.
One of the most dangerous things in a gas station are fires.
So, one of the risky behaviors that
lead to fires are people smoking.
So, we built an AI model with Shell that would take in images,
identify whether there's someone's smoking in a gas station,
and then be able to send an alert.
So, now you think about
these cameras that are able to identify risky behavior,
take in those images,
process it directly on the camera,
and then save the bandwidth by not
having to send this to the Internet.
All this camera is doing now is sending an alert,
and the gas station attendant can identify whether there are
risky behaviors happening in that gas station.
So again, whether it's workplace safety,
whether it's going to be products on the assembly line,
a lot of the things that are occurring in manufacturing,
we're able to use AI to help them out.
>> Ted, I'm blown away by how
our customers are using AI to innovate like never before.
Thank you so much,
and thank you so much, Indranil.
It'll be exciting to see even one year from now how
far AI will take us in terms of
breaking new ground in the industry.
Thank you all for joining us here today,
and stay tuned for part two in our AI series.