Defining AI and Machine Learning | Interview with Alicia Klinefelter (Part 1)


– [Narrator] Welcome back to
UpTech Report Series on AI. In this video, we sit down
with Alicia Klinefelter, research scientist for NVIDIA, and ask her to help define AI, and the different types of
AI, that we often hear about. Alicia is an expert in her field. She has a PhD in Electrical Engineering from the University of Virginia. Since joining INVIDA, her focus has turned more towards high-performance hardware, including machine learning
circuits and systems. We start out first by
asking Alicia to define AI and machine learning in her terms. – In terms of kind of
the recent revolution, again for me as a hardware engineer, I think of it a little bit differently than maybe someone who
is more on the algorithms or kind of theoretic side. For me, I see it more as a revolution of finally having enough compute power. Essentially, to do a lot of
these complex algorithms, that you know, have been limiting
this revolution for years, cause I mean a lot of the
algorithms in the, you know, the underlying mathematics
of machine learning have been around for
decades, since the 1950’s. Really, what kind of revolutionized
these things is finally, in the mid 2000’s to late
2000’s, we’ve been having this, you know, enormous
progression of compute power that has enabled a lot of us to finally kinda implement these
algorithms at a larger scale. So, to me, the power of AI is really in having those resources now available that weren’t there before so. – [Narrator] If you had to
explain AI and machine learning in very simple terms, for example to your mother or grandmother, how would you do it? – You know it’s definitely, it’s the power for machines to learn at the most general level. And, you know, it’s the power
of machines to learn through, kind of, iterative training, the same way that a child might learn. And I think, you know,
that’s usually the way I explain it to my mom is, you know, when you’re first learning as a child, you know, you’re given a
lot of concrete examples of what something is. You know, you’re given a series of blocks and eventually you learn, this is a block, and then eventually
you learn about colors, and you can identify this is a red block. And I think, you know, a lot
of AI is like that to me, where, you know, as long
as we have the data, the training data, to essentially
tell a machine, you know, this is how you can classify
different categories of things, then it can basically
learn through a series of pretty simple arithmetic choices, essentially how to identify
different or new things. And, you know, I think beyond
that, if my mom asks how, you know it might be a little bit more of a complicated answer. But, it’s really just the power for computers we traditionally
thought were only good at doing things we told them to do, to actually be more adaptive
and to actually learn on their own. And I think, you know, that’s real power that we’re
starting to see now, so. – [Narrator] Let’s take
a look at the difference between AI and machine learning. – I usually think of AI as
a super-set of everything, and then really machine
learning is the implementation of how to get to this greater concept of artificial intelligence. That’s usually where I kind
of differentiate the two. Where, kind of, artificial
intelligence is just this super broad concept that
can mean anything essentially and then machine learning,
yeah as I mentioned, is really that specific
implementation of how you do it. – [Narrator] Let’s take a look at some of the other commonly used forms. Can you explain the difference
between machine learning, deep learning, computer visioning, and natural language processing? – So again, I think in this case, I would consider machine
learning to be the super-set of, you know, all these different
type of learning methods, which deep learning is
kind of one of them. And, you know, deep learning
kind of only having been popularized in maybe the
last decade I would say, probably around with AlexNet essentially, where you basically have these kind of multilayered networks. And which again, I think is something that was enabled more
by the compute power, so you know, in order to
be deep in your learning, you need multilayer networks, and in order to have multilayer networks, you need to have a lot
of complex compute power. So I would say, you know, compared to the really simplistic models that used to define machine learning, which can encompass lots
of different, you know, other types of network
typologies besides deep network or neural network typologies. You can even just do simple,
you know, linear regression, and that can be considered
machine learning so, you know, there’s a lot of different
types of algorithms and network typologies that are under that machine learning umbrella. But, you know, something
like deep learning or neural networks is
something that’s very specific within machine learning. And as far as I understand, the way that I would
describe computer vision is really just the ability. I think of it really as
image processing essentially, is the ability for a
machine to look at an image, or a stream of images,
and basically detect or parse something about
that image, something unique. So, I kind of genericize it as being like image processing (laughs). But, and I know that
for a while that that, you know, we have, you know, a group of computer
vision experts at INVIDIA and I know for a long
time before we even got interested in AI, that, you know, they had relied on these
very traditional algorithms to do a lot of their image processing, what I guess I would almost call a deterministic algorithm that someone had developed mathematically. You know, if you wanted to detect whether there was a car in an image, you know, there was a very
deterministic way, to say, you know, go through all the pixels and then identify these particular things and you’ll figure out if there’s a car. And then kind of machine
learning came through and was able to break all
of their accuracy records for doing any type of image recognition or object detection. And natural language processing, you know, as far as I understand, it’s almost like a, I
mean it’s a more specific application of machine
learning is how I would think about it. But, it’s basically parsing
language essentially, almost as if a human being would. Now I’m like, it’s basically a specific model for processing
language in machine learning. – This was just a taste. Stay tuned as we share the
full, deep-dive interviews we had with each one
of our panel of experts and our upcoming episodes
focused on specific topics that will transform the way you think about artificial intelliegence. All this, on UpTech
Report’s news series on AI. (upbeat music)

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