>> Narrator: From around the globe, it's theCUBE!
Covering HPE Discover Virtual Experience,
brought to you by HPE.
>> Artificial Intelligence, Monica Livingston,
hey Monica, welcome to theCUBE!
>> Hi Lisa, thank you for having me.
>> So, AI is a big topic,
but let's just get an understanding,
Intel's approach to artificial intelligence?
>> Yeah, so at Intel, we look at AI
As a workload and a tool that
is becoming ubiquitous across all of our compute solutions.
We have customers that are using AI in the Cloud,
in the data center, at the Edge,
so our goal is to infuse as much performance
as we can for AI into our base platform
and then where acceleration is needed
we will have accelerator solutions
for those particular areas.
An example of where we are infusing AI performance
into our base platform is
the Intel Deep Learning Boost feature set
which is in our second generation
Intel Xeon Scalable Processors
and this feature alone provides up
to 30x performance improvement for Deep Learning Inference
on the CPU over the previous generation
and we are continuing infusing AI
into our base platform with the
third generation Intel Xeon Scalable Processors
which are launching later this month.
Intel will continue that leadership
by including support for bfloat16.
Bfloat16 is a new format that enables
Deep Learning training with similar accuracy
but essentially using less data
so it increases AI throughput.
Another example is memory, so both inference
and training require quite a bit of memory
and with Intel Octane for system memory,
customers are able to expand large pools
of memory closer to the CPU, and where
that's particularly relevant is in areas
where data sets are enlarged like imaging,
with lots of images and lots of high resolution images,
like medical diagnostic or seismic imaging,
we are able to perform some of these models
without tiling, and tiling is where,
if you are memory-constrained, you essentially
have to take that picture and chop
it up in little pieces and process each piece
and then stitch it back together at the end
whereas that loses a lot of context for the AI model,
so if you're able to process that entire picture,
then you are getting a much better result
and that is the benefit of having
that memory accessible to the compute.
So, when you are buying the latest and greatest HPE servers,
you will have built-in AI performance
with Intel Xeon Scalable and Octane for system memory.
>> A couple things that you said
that piqued my interests are 30x improvement
in performance, if you talk about that
with respect to the Deep Learning Booster,
30x is a huge factor and you also said
that your solution from a memory perspective
doesn't require tiling and I heard context.
Context is key to have context in the data,
to be able to understand and interpret
and make inferences, so, talk to me
about some of those big changes
that you're releasing, what were some
of the customer-compelling events
or maybe industry opportunities that drove Intel
to make such huge performance gains in second generation.
>> Right, so second generation, these are
the processors that are out now,
so these are features that our customers
are using today, third generation is coming out
this month but for second generation,
Deep Learning Boost, what's really important
is the software optimization and the fact
that we're able to use the hooks
that we've built into the hardware
but then use software to make sure
that we are optimizing performance on those platforms
and it's extremely relevant to talk about software
in the AI space because AI solutions
can get super expensive, you can easily pay 2 to 3x
what you should be paying if you don't
have optimized software because then what you do
is you're just throwing more and more compute,
more and more hardware at the problem,
but it's not optimized and so what's really impactful
is being able to run a vast number of AI applications
on your base platform, that essentially means
that you can run that in a mixed workload environment
together with your other applications
and you're not standing up separate infrastructure.
Now, of course, there will be some applications
that do need separate infrastructure
that do need alliances and accelerators
and for that, we will have a host of accelerators,
we have FPGAs today for real time low latency inference,
we have Movidius VPU for low-power vision applications
at the Edge, but by and large, if you're looking
at classical machine learning, if you're looking
at analytics, Deep Learning inference,
that can run on a base platform today
and I think that's what's important
in ensuring that more and more customers
are able to run AI at scale, it's not just
a matter of running a POC in a back lab,
you do that on the infrastructure
that you have available, not an issue,
but when you are looking to scale,
the cost is going to be significantly important
and that's why it's important for us
to make sure that we are building in
as much performance as is feasible
into the base platform and then offering software tools
to allow customers to see that performance.
>> Okay, so talking about the technology components,
performance, memory, what's needed to scale
on the technology side, I want to then kind
of look at the business side, because we know
a lot of customers in any industry
undertake AI projects and they run into pitfalls
where they're not able to even get off the ground,
so converse to the technology side,
what is it that you're seeing, what are
the pitfalls that customers can avoid
on the business side to get
these AI projects designed and launched?
>> Yeah, so on the business side,
I mean you really have to start
with a very solid business plan for why you're doing AI
and it's even less about just the AI piece,
but you have to have a very solid business plan
for your solution as a whole.
If you're doing AI just to do AI because
you saw that it's a top trend for 2020 so you must do AI,
that's likely going to not result in success.
You have to make sure that you're understanding
why you're doing AI, if you have a workload
that could be easily solved, or a problem
that could be easily solved with data analytics,
use data analytics, AI should be used
where appropriate, a way to provide true benefit
and I think if you can demonstrate that,
you're a long way in getting your project off the ground,
and then there's several other pitfalls
like data, do you have enough data,
is it close enough to your compute in order
to be accessible and feasible, do you have
the resources that are skilled in AI
that can get your solution off the ground,
do you have a plan for what to do
after you've deployed your solution
because these files need to be maintained
on a regular basis, so some sort
of maintenance program needs to be in place
and then infrastructure, cost can be prohibitive
a lot of times if you're not able to leverage
a good amount of your base infrastructure
and that's really where we spend
a lot of time with customers in trying
to understand what their model is trying
to do and can they use their base infrastructure,
can they reuse as much of what they have,
what is their current utilization,
do they maybe have cycles in off times
if their utilization is diurnal
and during the night they have early Utilization,
can you train your models at night
rather than putting up a whole new set
of infrastructure that likely will not
be approved by management, let's be honest.
>> And I imagine that that is all part of
the joint better marketing strategy
that HPE and Intel have together
to have such conversations like that with customers,
to help really build a robust business plan.
>> Yeah, so HPE's fantastic at consulting
with customers from beginning to end,
looking at solutions and they've got a whole suite
of storage solutions as well which
are crucial for AI and Intel works together
with HPE to create reference architectures for AI
and then we do joint training as well.
But yes, talking to your HPE rep
and leveraging your ecosystem I think
is incredibly important because the ecosystem
is so diverse and there are a lot of resources available
from ISVs to hardware providers
to consulting companies that are able to support with AI.
>> So Monica, the ecosystem is incredibly important,
but how do you work with customers, HPE and Intel together,
to help the customer, whether its in biotech
or manufacturing to build an ecosystem or partnership
that can help the customer really define
the business plan of what they want to do
to get that for us functional collaboration
and buy-in and support and launch a successful AI project.
>> Yeah it really does take a village,
but both Intel and HPE have an extensive partner network,
these are partners that we work with
to optimize their solution, in HPE's case,
they validate their solutions on HPE hardware
to ensure that it runs smoothly
and for our customers, we have the ability
to match-make with partners in the ecosystem
and generally, the way it works, is in specific segments,
we have a list of partners that we can draw from
and we introduce those to the customer,
the customer generally has a couple of meetings
with them to see which one is a better fit,
and then they go from there, but essentially,
it is just making sure that solutions are validated
and optimized and then giving our customers
a choice of which partners are the best fit for them.
>> Last question for you, Monica,
we are in the middle of COVID-19 and we see things
on the news every day about contact tracing, for example,
social distancing, and a lot of the things
that are talked about on the news are human contact tracers,
people being involved in manual processes,
what are some of the opportunities
that you see for AI to really help drive some
of these because time is of the essence,
yet, there's the ethics issue with AI, right?
>> Yes, yes, and the ethics issue is not something
that AI can solve on its own, unfortunately,
the ethics conversation is something we need
to have broader as a society and from a privacy perspective,
how are we going to be mindful and respectful
while also being able to use some of
the data to protect society especially
in a situation like this, so, contact tracing
is extremely important, this is something
that in areas that have a wide system
of cameras installed, that's something
that is doable from an algorithmic perspective
and there's several partners of ours
that are looking at that, and actually,
the technology itself, I don't think
is as insurmountable as the logistical aspect
and the privacy and the ethical aspect
and regulation around it, making sure
that it's not used for the wrong purposes,
but certainly with COVID, there is
a new aspect of AI use cases, and contact tracing
is obviously one of them, the others that
we are seeing is essentially, companies are adapting
a lot of their existing AI solutions
or solutions that use AI to accommodate
or to account for COVID, like, companies
that have observations done and so
if they were doing facial recognition
either in metro stations or stadiums
or banks, they now are adding features
to their systems to detect social distancing, for example,
or detect if somebody is wearing a mask.
The technology, again, itself is not that difficult,
but in the implementation and the use
and the governance around it, I think,
is a lot more complex, and then, I would be remiss
not to mention remote learning which is huge now,
I think all of our children are learning remote
at this point and being able to use AI
in curriculums and being able to really pinpoint
where a child is having a hard time understanding
a concept and then giving them more support
in that area is definitely something
that our partners are looking at
and it's something that (webcam scrambles)
with my children and the tools that they're using
and so instead of reading to their teacher
for their reading test, they're reading
to their computer and the computer's able
to pinpoint some very specific issues
that maybe a teacher would not see as easily
and then of course, the teacher has the ability
to go back with you and listen and make sure
that there weren't any issues with dialects
or anything like that, so it's really just
an interesting reinforcement of the teacher/student learning
with the added algorithmic impact as well.
>> Right, a lot of opportunity is going to come out
of COVID, some maybe more accelerated than others
because as you mentioned, it's very complex.
Monica, I wish we had more time,
this has been a really fascinating conversation
about what Intel and HPE are doing with respect to AI.
Glad to have you back 'cause this topic is just too big,
but we thank you so much for your time.
>> Thank you.
>> For my guest Monica Livingston,
I'm Lisa Martin, you're watching theCUBE's coverage
of HPE Discover 2020, thanks for watching.