NEIL SATRA: Hi.
My name is Neil Satra, and I'm a product manager
for Google Cloud AI.
DEENI FATIHA: Hi.
I'm Deeni Fatiha, and I'm a product manager at DeepMind.
NEIL SATRA: If you're attending this talk,
it's likely that you've already heard
about the transformative benefits
that machine learning can bring to
your traditional manufacturing or industrial organization.
But how do you realize those benefits
without running into some of the common pitfalls
that early stage AI practitioners tend to run into?
Well, we've been deploying a technology
we've been calling industrial adaptive
controls in the real world for the past few years.
And here we are today to share our learnings with you.
What is industrial adaptive controls?
It's a technology that can optimize your control
loop to help you realize benefits, such as minimizing
your carbon footprint, gaining cost efficiencies,
and maximizing your production throughput.
And how does that work?
Well, the first step is that you define your control loop
optimization problem in the template shown here.
You define your objective function,
which might be minimizing your energy
usage over the next few hours or reducing
the batch time required to produce one batch of chemicals.
Next, you make available actions to the AI agent
that it can take to realize that objective.
Typically, this takes the form of setting various set point
values on your pieces of equipment.
Next up, we have the constraints.
These are the safety and business rules
that the AI must abide by in order
to operate your plant safely.
And finally, you have the state.
This is all the sensor data that tells the AI how the plant is
currently performing.
Once you have that definition in place,
you can go through this loop.
So let's take an example where we're
operating the chillers used to cool a data center.
Every five minutes, we're collecting all the sensor data
from the chiller plant and sending it
up to the cloud-based AI.
In the cloud, we're running the data
through deep neural networks that
can predict the future energy efficiency
and the temperatures from all the various set
point combinations possible.
The AI will pick the right set point combination
that will minimize your energy usage while obeying as many
of your safety and business constraints as possible.
And finally, it will make those set point recommendations
available to your local building management system, which
can validate and verify them before choosing
to implement them locally.
So where have we been using this technology?
25 years ago, AI beat the world champions at chess.
And the next big milestone was beating the world champions
at Go.
A few years ago, DeepMind achieved this milestone.
Next, we applied this technology to a real business problem
that we had in our Google data centers,
which was the high energy cost used to cool them.
After seeing sustained and consistent savings,
we made this technology available to external partners
in a limited capacity to apply this in their data
centers and commercial buildings and replicate the success we
saw.
And recently, we've been starting
to explore applying this technology to the paint shop
in the automotive production line, which
is one of the most energy-intensive steps
in the production of a car.
So now that you've heard about where we've been experiencing
applying machine learning in the real world, off to Deeni
to share some of the best practices
we've learned along the way.
DEENI FATIHA: Thanks, Neil.
When thinking about using AI for optimization,
the first step is to ensure that the problem is
well-suited for AI.
The problem should fit a machine learning formulation.
A common formulation is one that Neil just described earlier,
with objectives, constraints, actions, and states,
of which there should be a very high number of possible states
and actions.
Additionally, there should be enough
headroom for optimization in the process for the AI
to be able to drive significant value.
And finally, as a general rule, the process
should be one where errors are cheap.
Next, think carefully about the objective of the optimization.
An industrial process, say, in a manufacturing plant
may have several inputs and outputs.
What would be most valuable to optimize,
the amount of an input, the yield of an output,
or perhaps overall energy used in the process?
Once you've figured that out, you then
want to consider the constraints within which that objective
should be pursued.
If the objective is to minimize, let's say,
the energy consumed during the industrial process,
the constraint may be to do so while maintaining
a certain speed of throughput or, perhaps,
a certain temperature.
During this exercise of determining objectives
and constraints, it's also valuable to identify
the real constraints versus the practical ones.
For example, in an industrial mixing process,
it may be tempting to identify temperature as the constraint,
whereas the role of temperature is really
to maintain a certain shear rate as the chemical mixes.
So in this case, the shear rate would be the real constraint,
and the temperature would be a practical one.
Accurately identifying these distinctions
could be beneficial for the AI's learning.
Once we've defined the problem, the next step
is to gather data to train the AI.
You'll want to ask the following questions
when accumulating data.
Firstly, is it plentiful and complete?
It's critical to get large amounts of historical data,
ideally spanning one to several years--
not just that, but the data must be high-resolution and contain
all the relevant states.
If you find some data is missing, which is common,
calculating estimated values based on the rest of the data
can work perfectly well.
If you're not already doing so, it's
vital that you start collecting data
now so it's readily available when you need it.
Secondly, if you have a good volume of data already,
you'll want to verify that it's accurate.
In the past, in order to check the accuracy of large volumes
of data, we've written unit tests
that sanity check the data against the domain
knowledge of human operators.
Finally, for optimization problems,
the more varied the data, the better.
In processes that are controlled manually,
it's not uncommon for operators to choose
set points that seem to work well
and to use those repeatedly.
But for the AI to find the best path to optimization,
it will need to see more varied data
to understand how the different actions can
lead to different outcomes.
When this type of high-variety data was not possible,
we conduct a guided exploration of the state space
to help the AI survey all the possibilities at its disposal.
How many of us know of an organizational change that
failed because the people were not sufficiently bought
into it?
This is why, as we get closer to deployment,
we place a heavy focus on creating user trust.
We do this by running the AI with human
in the loop for a few weeks so that the operators can
grow comfortable with how the AI is performing
and even give us feedback.
Additionally, we've built simulations
for industrial processes to mimic
how AI would impact them so that operators
can view those results.
This virtual approach is particularly
useful for processes where errors are more costly.
A critical component of deploying AI for optimization
is ensuring we are able to measure
its performance reliably.
It can be tempting to turn on the technology
and compare the performance of the system in a before
and after manner.
But there are many factors that affect the performance
of such systems.
So this approach can cause misleading results.
Imagine trying to optimize energy consumption
in an industrial facility.
The results can vary from day to day
based on anything from the weather to new processes
to faulty equipment.
To reduce the noise from these confounding factors,
the more robust way to measure the AI's performance
is by conducting A/B testing for a period of time,
where the system runs with and without the AI's help
on alternate days.
Once there's enough data from the A/B tests
to demonstrate the effectiveness of the technology,
the A/B tests can be stopped and the AI
can be turned on permanently.
It's not uncommon to hear claims about savings and optimizations
using opaque methods.
This is why we love the A/B testing method we've described.
It's intuitive, transparent, and leaves little room
for interpretation.
Finally, in implementing AI to optimize various processes,
we understand that each use case presents
a different optimization problem.
Off-the-shelf machine learning products
are unlikely to work unless they are customized
to your specific needs.
This is why we've spent a lot of time augmenting
our models with domain-specific knowledge
to ensure our solution is well-posed to achieve
the full extent of optimization possible.
With that, I'll hand it back to Neil
to discuss our AI's performance in different domains.
NEIL SATRA: Thanks.
So with all of these factors in place,
we were able to consistently get 30% to 40% energy savings
in our Google data centers.
But as we were going to external sites,
we acknowledged the variability between these sites
and the difficulty in always getting all the boxes ticked.
Hence, we set the success goal as crossing 20% savings
in each of the sites.
Similarly for you, you won't be able to get
all the factors in place.
But it's up to you to determine how many you do get in place
and, accordingly, what success looks like for you.
So to recap, AI is not a magic wand.
You got to choose the right problem for it
to solve and frame the problem in a way that fits
the AI solution you're using.
You might think you have a lot of training data.
But you got to verify that it is complete, accurate,
and representative.
This is not purely a technology problem.
You've got to build human trust for your operators to actually
adopt the technology.
And for widespread adoption, you're
going to need to use objective and fair methods to measure
the performance.
Finally, be prepared to invest the effort required
to take off-the-shelf products and inject them
with the domain knowledge built by their human operators.
With that, best of luck with your machine learning
and digital transformation journey.
And don't hesitate to reach out to your Cloud contacts
for guidance.
Thanks.
DEENI FATIHA: Goodbye.