MANDIP WARAICH: Hello, everyone, and welcome.
My name is Mandip Waraich, and I'm
the head of product for industrial AI
in Google's cloud business.
I lead a portfolio of artificial intelligence products
that help solve some of the most complex and hard
industrial problems at global scale.
Today I'm going to talk about three things.
First, the current state of automation
and industrial efficiency and how artificial intelligence is
revealing an entirely new universe of possibilities.
Second, Google Cloud's approach to bringing these AI
technologies to the market.
And third, our deploy methodology that helps
achieve the impact of AI at a global enterprise scale.
Let me first begin by sharing how we are approaching
our strategy to help industrial enterprise customers transform
themselves.
So our strategy hinges on three pillars.
First, is to provide tools and technologies
to connect your physical assets and workforce to the cloud.
Second, is to bring both your existing analytics
tools and new analytics capabilities that
augment human decision making.
And third, to drive end to end automation
of key industrial processes by embedding our differentiated AI
technologies into the industrial workflows.
As part of this session I will focus only
on our AI capabilities.
We are applying AI to help some of the largest enterprise
companies transform their businesses.
We're helping the largest airlines better plan
their schedules, aircraft utilization,
and manage unforeseen operational disruptions.
We're helping logistic companies achieve better fleet
utilization, route planning, and maximize
the life of their assets.
We're helping automotive companies bring their vehicle
telemetry data to the cloud and prevent component failure
and recalls in real time.
We'll be discussing two manufacturing focused
solutions, adaptive controls and visual inspection,
in more detail in the session.
Here are some of the companies that
are trusting Google to transform their industrial operations
and businesses using AI and machine learning technologies.
Before I get into the details of AI technologies,
I want to take a brief talk about where
we are in the broader industrial automation evolution.
For decades, advances in automated controls and systems
have made industrial processes and supply chains highly
efficient, but every subsequent advance in efficiency
has demanded greater investment while yielding leaner returns
to a point of flattening of the curve.
The staggering complexity of the industrial environment
is now combined with a dynamic and uncertain environment
like the one we're finding ourselves right now, where
there's dramatic volatility in market forces, disrupted
supply chains and retail channels,
uncertainty of demand, and cost pressures to stay afloat.
The current industrial systems are turning out
too brittle and unadaptable, narrow in their search
for efficiency gains with years of systemized rules
and heuristics, layers of operational buffers,
and inefficiencies capped and accrued by human expertise.
These constraints make industrial systems
a great candidate for AI machine learning based solutions.
For machine learning based systems,
each layer of complexity is another dimension
to explore for hidden correlations,
allowing every aspect of an industrial environment
to be analyzed minutely, temporally, and [INAUDIBLE],,
individually, and holistically, exploring new action spaces
and uncovering unfound efficiency gains.
These systems can adapt to internal and external headwinds
and learn to anticipate disruptions, allowing
stable autonomous operation.
We'll talk through two such examples of autonomous
controls technology, adaptive controls and visual inspection
AI, both aimed at achieving autonomous process controls
in industrial facilities.
Let's first talk about Industrial Adaptive Controls
platform that automates and optimizes
closed loop industrial systems like near track systems
or scaled up control processing plants.
This platform builds on years of Google's research
in reinforcement learning, where an AI agent learns directly
from sensory experience and develops new knowledge
by exploring the action space and then optimizes space
based on the most efficient future predicted state.
With no handcrafted rules, a general purpose learning
framework, and general APIs to take in sensory input
and produce set point recommendations,
this platform is capable to solve
several industrial optimization problems.
It turns out that operating a data center
with concrete actions and measurable rewards
is a great fit for such a problem.
Modern data centers, especially the large ones at Google,
are complex pieces of equipment with billions
of ways of operating them.
For example, suppose we have a simplified system
with 10 pieces of equipment, each which has 10 different set
points.
That's already over 3 million different permutations.
Keep in mind that a real data center
has much more than 10 pieces of equipment.
They interact with each other in non-linear manners
while environmental conditions, such as IT load and weather,
are constantly changing.
Adaptive controls platform has been helping
reduce energy consumption in Google data centers
through direct autonomous control of [INAUDIBLE] systems.
So how does this work?
AI optimizes a reward function.
This could be energy consumption or any other function expressed
as a mathematical equation of sensors,
while adapting to the changing variables like building load,
and still keep meeting operational constraints
like building comfort.
For example, in the data center scenario
the AI evaluates millions of allowable set point
combinations, predicts future states
of energy consumption and operational constraints,
removes those that violate equipment or safety
constraints, chooses the set points
that maximizes the objective function,
returns the recommended set points
into a VMS platform to be implemented directly
into the equipment.
The generalizability of this platform
allows a number of industrial processes and facilities
optimization problems to be solved using this approach.
Imagine the impact that this could have on the global energy
use in airports, malls, corporate buildings,
waste reduction in chemical plants,
smelters, metal processing facilities,
all while achieving higher industrial output.
Another problem space where we are applying Google's AI
capabilities is industrial defect detection.
Google's computer vision capabilities
are consistently rated as the best in the industry.
We've made these vision AI capabilities
available through our general APIs
that use pre-trained machine learning models trained
on billions of images, and through AutoML platform, where
you can bring your own image data
sets to train custom vision models.
Now we're taking this one step further.
We are partnering with some of the largest manufacturers
of the world to bring high accuracy premium
models for defect detection, starting
with assembly inspection and cosmetic inspection,
in an end to end automated solution.
We've created a dedicated breakout session
to cover the technical breakthroughs
of this technology, delivered by one of my teammates Yinfei.
Please do check it out.
LG has applied our machine learning models
to achieve 99.9 accuracy for cosmetic defect detection
in their glass display panels.
Similarly GlobalFoundries have used our machine
learning models for silicon vapor defect detection,
achieving 95% accuracy across hundreds of machine learning
models deployed in their factories, which
makes for a good segue into our final topic of scaling AI
at a global industrial scale.
It's one thing running a pilot on a small subsystem
in a sandbox, and it's a completely different thing
transforming an entire industrial workflow
to run autonomously using AI.
Working across multiple AI products, use cases,
and deployment environments, we've
honed the approach of deploying AI at a large scale.
We've come to embrace that the real world environments are
highly complex, heterogeneous, unstructured, and fragmented.
This reality has made us design a deploy anywhere approach
for machine learning.
Our goal is to provide interoperatability
across all computing platforms, legacy systems and devices
from cloud, hybrid to edge, from microcontrollers, CPUs, GPUs,
TPUs, to clusters of servers, industrial equipment
like high speed cameras and drones,
and hardware supporting MQTT and OPC UA industrial standards.
In working with the largest enterprise customers,
we have seen the challenge of managing hundreds
of machine learning models in production environment.
Therefore we developed managed services
that allow any kind of machine learning workload,
be it a time series, an image, or a video machine learning
model, to be deployed on any kind of edge device.
These services allow you to version control,
monitor performance, and retrain models
across a large fleet of edge devices.
You can learn more about these deployment platforms
in their dedicated breakout sessions.
You can also connect with any of our machine learning
specialists or contact us through our website
to get started.
Finally, I feel excited that AI as a discipline
has reached a stage where it's having
a direct and measurable impact on scaling
our collective human output.
Our technologies are truly living up
to their promise of furthering our progress as a society
and creating a more promising future.
Thank you once again for joining,
and I hope you'll enjoy the other next sessions.
Have a great day.