The next BriefingsDirect Voice of artificial intelligence (AI) Innovation discussion explores the latest strategies and use cases that simplify the use of analytics to solve more tough problems.
Access to advanced algorithms, more cloud options, high-performance compute (HPC)
resources, and an unprecedented data asset collection have all come together to
make AI more attainable -- and more powerful -- than ever.
Major trends in AI and advanced analytics are now coalescing into top competitive differentiators for most businesses. Stay with us as we examine how AI is indispensable for digital transformation through deep-dive interviews on prominent AI use cases and their escalating benefits.
Major trends in AI and advanced analytics are now coalescing into top competitive differentiators for most businesses. Stay with us as we examine how AI is indispensable for digital transformation through deep-dive interviews on prominent AI use cases and their escalating benefits.
Listen
to the podcast. Find it on iTunes. Read a full transcript or download a copy.
To learn more about analytic
infrastructure approaches that support real-life solutions, we’re joined by two experts,
Andy Longworth, Senior
Solution Architect in the AI and Data Practice at Hewlett Packard
Enterprise (HPE) Pointnext Services, and Iveta
Lohovska, Data Scientist in the Pointnext Global Practice for AI and Data
at HPE. The discussion is moderated by Dana Gardner, Principal
Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: Andy,
what are the top drivers for making AI more prominent in business use cases?
Longworth: We
have three main things driving AI at the moment for businesses. First of all,
we know about the
data explosion. These AI algorithms require huge amounts of data. So we’re
generating that, especially in the industrial setting with machine data.
 |
| Longworth |
Also, the relative price of
computing is coming down, giving the capability to process all of that data at accelerating
speeds as well. You know, the graphics
processing units (GPUs) and tensor processing
units (TPUs) are becoming more available, enabling us to get through that
vast volume of data.
And thirdly, the algorithms. If
we look to organizations like Facebook, Google, and academic institutions, they’re
making algorithms available as open
source. So organizations don’t have to go and employ somebody to build an
algorithm from the ground up. They can begin to use these pre-trained, pre-created
models to give them a kick-start in AI and quickly understand whether there’s
value in it for them or not.
Gardner: And
how do those come together to impact what’s referred to as digital
transformation? Why are these actually business benefits?
Longworth: They allow
organizations to become what we call data driven. They can use the massive data
that they’ve previously generated but never tapped into to improve business decisions,
impacting the way they drive the business through AI. It’s transforming the way
they work.
AI data boost to business
Across several types of industry, data is now driving the decisions. Industrial organizations, for example, improve the way they manufacture. Without the processing of that data, these things wouldn’t be possible.
Gardner: Iveta,
how do the trends Andy has described make AI different now from a data science
perspective? What’s different now than, say, two or three years ago?
Lohovska: Most
of the previous AI algorithms were 30, 40, and even 50 years old in terms of the
linear algebra and their mathematical foundations. The higher levels of computing
power enable newer computations and larger amounts of data to train those
algorithms.
 |
| Lohovska |
Those two components are
fundamentally changing the picture, along with the improved taxonomies and the
way people now think of AI as differentiated between classical statistics and deep learning algorithms.
Now, not just technical people can interact with these technologies and
analytic models. Semi-technical people can with a simple drag-and-drop
interaction, based
on the new products in the market, adopt and fail fast -- or succeed faster
-- in the AI space. The models are also getting better and better in their
performance based on the amount of data they get trained on and their digital
footprint.
Gardner: Andy,
it sounds like AI has evolved to the point where it is mimicking human-like
skills. How is that different and how does such machine learning (ML) and deep
learning change
the very nature of work?
Let simple tasks go to machines
Longworth: It
allows organizations and people to move some of the jobs that were previously
very tedious for people so they can be done by machines and repurposes the
people’s skills into more complex jobs. For example, in computer vision and
applying that in quality control. If you’re creating the same product again and
again and paying somebody to look at that product to say whether there’s a
defect on it, it’s probably not the best use of their skills. And, they become
fatigued.
If you look at the same thing
again and again, you start to miss features of that and miss the things that
have gone wrong. A computer doesn’t get that same fatigue. You can train
a model to perform that quality-control step and it won’t become tired over
time. It can keep going for longer than, for example, an eight-hour shift that
a typical person might work. So, you’re seeing these practical applications, which
then allows the workforce to concentrate on other things.
Gardner:
Iveta, it wasn’t that long ago that big data was captured and analyzed mostly for
the sake of compliance and business continuity. But data has become so much
more strategic. How are businesses changing the way they view their data?
Lohovska: They
are paying more attention to the quality of the data and the variety of the
data collection that they are focused on. From a data science perspective, even
if I want to say that the performance of models is extremely important, and that
my data science skills are a critical component to the AI space and ecosystem,
it’s ultimately about the quality of the data and the way it’s pipelined and
handled.
Organizations
will realize that being more selective and paying more attention to the
foundations of how they handle big data -- or small data -- will get
them to the data science part of the process.
This process of data manipulation, getting to the so-called last mile of the data science contribution, is extremely important. I believe it’s the critical step and foundation. Organizations will realize that being more selective and paying more attention to the foundations of how they handle big data -- or small data – will get them to the data science part of the process.
You can already see the
maturity as many customers, partners, and organizations pay more attention to the
fundamental layers of AI. Then they can get better performance at the last mile
of the process.
Gardner: Why
are the traditional IT approaches not enough? How do cloud models help?
Cloud control and compliance
Longworth: The cloud
brings opportunities for organizations insomuch as they can try before they buy.
So if you go back to the idea of processing all of that data, before an
organization spends real money on purchasing GPUs, they can try them in the
cloud to understand whether they work and deliver value. Then they can look at
the delivery model. Does it make sense with my use case to make a capital
investment, or do I go for a pay-per-use model using the cloud?
You also have the data
management piece, which is understanding where your data is. From that sense,
cloud doesn’t necessarily make life any less complicated. You still need to
know where the data resides, control that data, and put in the necessary
protections in line with the value of the data type. That becomes particularly
important with legislation like the General
Data Protection Regulation (GDPR) and the use of personally identifiable
information (PII).
If you don’t have your data
management under control and understand where all those copies of that data
are, then you can’t be compliant with GDPR, which says you may need to delete all
of that data.
So, you need to be aware of
what you’re putting in the cloud versus what you have on-premises and where the
data resides across your entire ecosystem.
Gardner:
Another element of the past IT approaches has to do with particulars vs.
standards. We talk about the difference between managing a cow and managing a
herd.
How do we attain a better IT
infrastructure model to attain digital business transformation and fully take
advantage of AI? How do we balance between a standardized approach, but also
something that’s appropriate for specific use cases? And why is the
architecture of today very much involved with that sort of a balance, Andy?
Longworth: The
first thing to understand is the specific use case and how quickly you need
insights. We can process, for example, data in near real-time or we can use
batch processing like we did in days of old. That use case defines the kind of
processing.
If, for example, you think about an autonomous vehicle, you can’t batch-process the sensor data coming from that car as it’s driving on the road. You need to be able to do that in near real-time -- and that comes at a cost. You not only need to manage the flow of data; you need the compute power to process all of that data in near real-time.
So, understand the criticality
of the data and how quickly you need to process it. Then we can build solutions
to process the data within that framework and within the right time that it
needs to be processed. Otherwise, you’re putting additional cost into a use
case that doesn’t necessarily need to be there.
When
we build those use cases we typically use cloud-like technologies. That
allows us portability of the use case, even if we're not necessarily
going to deploy it in the cloud. It allows us to move the use case as
close to the data as possible.
When we build those use cases we typically use cloud-like technologies -- be that containers or scalar technologies. That allows us portability of the use case, even if we’re not necessarily going to deploy it in the cloud. It allows us to move the use case as close to the data as possible.
For example, if we’re talking
about a computer vision use case on a production line, we don’t want to be
sending images to the cloud and have the high latency and processing of the
data. We need a very quick answer to control the production process. So you
would want to move the inference engine as close to the production line as
possible. And, if we use things like HPE Edgeline
computing and containers,
we can place those systems right there on the production line to get the answers
as quickly as we need.
So being able to move the use
case where it needs to reside is probably one of the biggest things that we
need to consider.
Gardner: Iveta,
why is the so-called explore, experiment,
and evolve approach using such a holistic ecosystem of support the right
way to go?
Scientific methods and solutions
Lohovska:
Because AI is not easy. If it were easy, then everyone would be doing it and we
would not be having this conversation. It’s not a simple statistical use case
or a program or business intelligence app where you already have the answer or even
an idea of the questions you are asking.
The whole process is in the data
science title. You have the word “science,” so there is a moment of
research and uncertainty. It’s about the way you explore the data, the way you
understand the use cases, starting from the fact that you have to define your
business case, and you have to define the scope.
My advice is to start small,
not exhaust your resources or the trust of the different stakeholders. Also
define the correct use case and the desired return on investment (ROI). HPE is even
working on the definitions and the business case when approaching an AI use
case, trying to understand the level of complexity and the required level of prediction
needed to achieve the use case’s success.
Such an exploration phase
is extremely important so that everyone is aligned and finds a right path to
minimize failure and get to the success of monetizing data and AI. Once you
have the fundamentals, once you have experimented with some use cases, and you
see them up and running in your production environment, then it is the moment
to scale them.
I think we are doing a great
job bringing all of those complicated environments together, with their data
complexity, model complexity, and networking and security regulations into one
environment that’s in production and can quickly bring value to many use cases.
This flow is extremely
important, of experimenting and not approaching things like you have a fixed
answer or fixed approach. It’s extremely important, and this is the way we at
HPE are approaching AI.
Gardner: It
sounds as if we are approaching some sort of a unified reference architecture
that’s inclusive of systems, cloud models, data management, and AI services. Is
that what’s going to be required? Andy, do we need a grand unifying theory of
AI and data management to make this happen?
Longworth: I don’t
think we do. Maybe one day we will get to that point, but what we are reaching now
is a clear understanding of what architectures work for which use cases and
business requirements. We are then able to apply them without having to
experiment every time we go into this because it’s a complement to what Iveta said.
When we start to look at these
use cases, when we engage with customers, what’s key is making sure there is
business value for the organization. We know AI can work, but the question is, does
it work in the customer’s business context?
If we can take out a good deal
of that experimentation and come in with a fairly good answer to the use case
in a specific industry, then we have a good jump start on that.
As time goes on and AI develops,
we will see more generic AI solutions that can be used for many different
things. But at the moment, it’s really still about point solutions.
Gardner: Let’s
find out where AI is making an impact. Let’s look first, Andy, at digital
prescriptive maintenance and quality control. You mentioned manufacturing a
little earlier. What’s the problem, context, and how are we getting better
business outcomes?
Monitor maintenance with AI
Longworth: The
problem is the way we do maintenance schedules today. If you look back in
history, we had reactive maintenance that was basically … something breaks and
then we fix it.
Now, most organizations are in
a preventative mode so a manufacturer gives a service window and says, “Okay,
you need to service this machinery every 1,000 hours of running.” And that
happens whether it’s needed or not.
Read the White Paper on Digital Prescriptive Maintenance and Quality Control
That data from machinery may sense
temperature, vibration, speed, and getting a condition-based monitoring view
and understanding in real time what’s happening with the machinery. You can
then also use past history to be able to predict what is going to happen in the
future with that machine.
We can get to a point where we
know in real time what’s happening with the machinery and have the capability
to predict the failures before they happen.
The prescriptive piece comes
in when we understand the business criticality or the business impact of an
asset. If you have a production line and you have two pieces of machinery on that
production line, both may have the identical probability of failure. But one is
on your critical manufacturing path, and the other is some production buffer.
The
prescriptive piece goes beyond the prediction to understand the
business context of that machinery and applying that to how you are
behaving, and then how you react when something happens with that
machine.
As a business, the way that you are going to deal with those two pieces of machinery is different. You will treat the one on the critical path differently than the one where you have a product buffer. And so the prescriptive piece goes beyond the prediction to understanding the business context of that machinery and applying that to how you are behaving, and then how you react when something happens with that machine.
That’s the idea of the solution
when we build digital prescriptive maintenance. The side benefit that we see is
the quality control piece. If you have a large piece of machinery that you can
test to it running perfectly during a production run, for example, then you can
say with some certainty what the quality of the outcoming product from that machine
will be.
Gardner: So we
have AI overlooking manufacturing and processing. It’s probably something that
would make you sleep a little bit better at night, knowing that you have such a
powerful tool constantly observing and reporting.
Let’s move on to our next use
case. Iveta, video analytics and surveillance. What’s the problem we need to
solve? Why is AI important to solving it?
Scrutinize surveillance with AI
Lohovska: For video
surveillance and video analytics in general, the overarching field is computer
vision. This is the most mature and currently the trendiest AI field, simply
because the amount of data is there, the diversity is there, and the algorithms
are getting better and better. It’s no longer state-of-the-art, where it’s
difficult to grasp, adopt, and bring into production. So, now the main goal is
moving into production and monetizing these types of data sources.
That makes it hardware-dependent
and also requires AI at the edge, where most of the algorithms and decisions
need to happen. If you want to detect fire, detect fraud or prevent certain
types of failure, such as quality failure or human failure -- time is extremely
important.
As HPE Pointnext Services,
we have been working on our own solution and reference architectures to
approach those problems because of the complexity of the environment, the
different cameras, and hardware handling the data acquisition process. Even at
the beginning it’s enormous and very diverse. There is no one-size-fits-all.
There is no one provider or one solution that can handle surveillance use cases
or broad analytical use cases at the manufacturing plant or oil and gas rig
where you are trying to detect fire or oil and gas spills from the different
environments. So being able to approach it holistically, to choose the right
solution for the right complement, and design the architecture is key.
Also, it’s essential to have
the right hardware and edge devices to acquire the data and handle the
telemetry. Let’s say when you are positioning cameras in an outside environment
and you have different temperatures, vibrations, and heat. This will reflect on
the quality of the acquired information going through the pipeline.
Some of the benefits in use
cases using computer vision and video surveillance include real time
information coming from manufacturing plants, knowing that all the safety and
security standards there are met, and that the people operating are following
the instructions and have the safeguards required for a specific manufacturing
plant is also extremely important.
When you have a quality
assurance use case, video analytics is one source of information to tackle the problem.
For example, improving the quality of your products or batches is just one
source in the computer vision field. Having the right architecture, being agile
and flexible, and finding the right solution for the problem and the right
models deployed at the right edge device -- or at the right camera -- is
something we are doing right now. We have several partners working to solve the
challenges of video analytics use cases.
Gardner: When
you have a high-scaling, high-speed AI to analyze video, it’s no longer a
gating factor that you need to have humans reviewing the processes. It allows
video to be used in so many more applications, even augmented reality, so that
you are using video on both ends of the equation, as it were. Are we seeing an
explosion of applications and use cases for video analytics and AI, Iveta?
Lohovska: Yes,
absolutely. The impact of algorithms in this space is enormous. Also, all the
open source datasets, such as ImageNet
and ResNet, allow a huge amount of data to
train any kind of algorithms on those open source datasets. You can adjust them
and pre-train them for your own use cases, whether it’s healthcare, manufacturing,
or video surveillance. It’s very enabling.
You can see the diversity of
the solutions people are developing and the different programs they are tackling
using computer vision capabilities, not only from the algorithms, but also from
the hardware side, because the cameras are getting more and more powerful.
Currently, we are working on
several projects in the non-visible human spectrum. This is enabled by the
further development of the hardware acquiring those images that we can’t see.
Gardner: If we
can view and analyze machines and processes, perhaps we can also listen and
talk to them. Tell us about speech and natural language processing (NLP), Iveta.
How is AI enabling those businesses and how they transform themselves?
Speech-to-text to protect
Lohovska: This
is another strong field for how AI is used and still improving. It’s not as mature
as computer vision, simply because the complexity of human language and speech,
and the way speech gets recorded and transferred. It’s a bit more complex, so
it’s not only a problem of technologies and people writing algorithms, but also
linguists being able to combine the grammar problems and write the right
equation to solve those grammar problems.
This example is industry- or vertical-independent. You can have finance, manufacturing, retail -- but all of them have some kind of customer support. This is the most common use case, being able to record and improve the quality of your services, based on the analysis you can apply. Similar to the video analytics use case, the problem here, too, is handling the complexity of different algorithms, different languages, and the varying quality of the recordings.
A reference architecture,
where you have the different components designed on exactly this holistic
approach, allows the user to explore, evolve, and experiment in this space. We
choose the right complement for the right problem and how to approach it.
And in this case, if we
combine the right data science tool with the right processing tool and the
right algorithms on top of it, then you can simply design the solution and
solve the specific problem.
Gardner: Our
next and last use case for AI is one people are probably very familiar with,
and that’s the autonomous
driving technology (ADT).
Andy, how are we developing
highly automated-driving infrastructures that leverage AI and help us get to
that potential nirvana of truly self-driving and autonomous vehicles?
Data processing drives vehicles
Longworth: There
are several problems around highly autonomous driving as we have seen. It’s taking
years to get to the point where we have fully autonomous cars and there are
clear advantages to it.
If you look at, for example,
what the World Health Organization (WHO) says, there are more than 1 million
deaths per year in road traffic accidents. One of the primary drivers for ADT is
that we can reduce the human error in cars on the road -- and reduce the number
of fatalities and accidents. But to get to that point we need to train these
immensely complex AI algorithms that take massive amounts of data from the car.
Just purely from the sensor
point of view, we have high-definition cameras giving 360-degree views around
the car. You have radar, GPS, audio, and vision systems. Some manufacturers use
light detection and ranging (LIDAR),
some not. But you have all of these sensors giving massive amounts of data. And
to develop those autonomous cars, you need to be able to process all of that
raw data.
When you have built that,
tested it, and done all the good things that you need to do, you need to next be
able to get those models and that strategy from the developers back into the
cars again. It’s like the other AI problems that we have been talking about,
but on steroids because of the sheer volume of data and because of the impact
of what happens if something should go wrong.
At HPE Pointnext Services, we
have developed a set of solutions that address several of the pain points in
the ADT development process. First is the ingest; how can we use HPE Edgeline
processing in the car to pre-process data and reduce the amount of data that
you have to send back to the data center. Also, you have to send back the most
important data after the eight-hour drive first, and then send the
run-of-the-mill, backup data later.
At
HPE Pointnext Services, we have developed a set of solutions that
address several of the pain points in the ADT development process.
The second piece is the data platform itself, building a massive data platform that is extensible to store all the data coming from the autonomous driving test fleet. That needs to also expand as the fleet grows as well as to support different use cases.
The data platform and the
development platform are not only massive in terms of the amount of data that
it needs to hold and process, but also in terms of the required tooling. We
have been developing reference architectures to enable automotive
manufacturers, along with the suppliers of those automotive systems, to build
their data platforms and provide all the processing that they need so their
data scientists can continuously develop autonomous driving strategies and be
able to test them in a highly automated way, while also giving access to the
data to the additional suppliers.
For example, the sensor
suppliers need to see what’s happening to their sensors while they are on the
car. The platform that we have been putting together is really concerned with
having the flexibility for those different use cases, the scalability to be able
to support the data volumes of today, but also to grow -- to be able to have
the data volumes of the not-too-distant future.
The platform also supports the
speed and data locality, so being able to provide high-speed parallel file
systems, for example, to feed those ML development systems and help them train
the models that they have.
So all of this pulls together
the different components we have talked about with the different use cases, but
at a scale that is much larger than several of the other use cases, probably
put together.
Gardner: It
strikes me that the ADT problem, if solved, enables so many other major opportunities.
We are talking about micro-data centers that provide high-performance compute (HPC)
at the edge. We are talking about the right hybrid approach to the data
management problem -- what to move, what to keep local, how to then have a
lifecycle approach to. So, ADT is really a key use-case scenario.
Why is HPE uniquely positioned
to solve ADT that will then lead to so many enabling technologies for other
applications?
Longworth: Like
you said, the micro-data center -- every autonomous driving car essentially
becomes a data center on wheels. So being able to provide that compute at the
edge to enable the processing of all that sensor data.
If you look at the HPE portfolio
of products, there are very few organizations that have edge compute solutions and
the required processing power in such small packages. But it’s also about being
able to wrap it up in, not only the hardware, but the solution on top, the
support, and being able to provide a flexible delivery model.
Lots of organizations want to
have a cloud-like experience, not just from the way they consume the
technology, but also in the way they pay for the technology. So, by HPE providing
everything as-a-service allows being able to pay for it all, as you use it, for
your autonomous driving platform. Again, there are very few organizations in
the world that can offer that end-to-end value proposition.
Collaborate and corroborate
Gardner:
Iveta, why does it take a team-sport and solution-approach from the data
science perspective to tackle these major use cases?
They
can attack the complexity of those use cases from each side because it
requires not just data science and the hardware but a lot of
domain-specific expertise to solve those problems, too.
Lohovska: I agree with Andy. The way we approach those complex use cases and the fact that you can have them as a service -- and not only infrastructure-as-a-service (IaaS) or data-as-a-service (DaaS) -- but working on AI and modeling-as-a-service (MaaS). You can have a marketplace for models and being able to plug-and-play different technologies, experiment, and rapidly deploy them allows you to rapidly get value out of those technologies. That is something we are doing on a daily basis with amazing experts and people with the knowledge of the different layers. They can then attack the complexity of those use cases from each side, because it requires not just data science and the hardware, but a lot of domain-specific expertise to solve those problems. This is something we are looking at and we are doing in-house.
And I am extremely happy to
say that I have the pleasure to work with all of those amazing people and
experts within HPE.
Gardner: And
there is a great deal more information available on each of these use cases for
AI. There are white papers on the
HPE website in Pointnext Services.
What else can people do, Andy,
to get ready for these high-level AI use cases that lead to digital business
transformation? How should organizations be setting themselves up on a people,
process, and technology basis to become adept at AI as a core competency?
Longworth: It is
about people, technology, process, and all these things combined. You don’t go
and buy AI in a box. You need a structured approach. You need to understand
what the use cases are that give value to your organization and to be able to
quickly prototype those, quickly experiment with them, and prove the value to
your stakeholders.
Where a lot of organizations
get stuck is moving from that prototyping, proof of concept (POC), and proof of
value (POV) phase into full production. It is tough getting the processes and
pipelines that enable you to transition from that small POV phase into a full
production environment. If you can crack that nut, then the next use-cases that
you implement, and the next business problems that you want to solve with AI,
become infinitely easier. It is a hard step to go from POV through to the full
production because there are so many bits involved.
You have that whole value chain from grabbing hold of the data at the point of creation, processing that data, making sure you have the right people and process around that. And when you come out with an AI solution that gives some form of inference, it gives you some form of answer, you need to be able to act upon that answer.
You can have the best AI
solution in the world that will give you the best predictions, but if you don’t
build those predictions into your business processes, you may well have never
made them in the first place.
Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.
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