We sit down with Alicia Klinefelter, a 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 NVIDIA, her focus has turned more towards high-performance hardware, including machine learning circuits and systems.
When talking about AI, Alicia says that as hardware engineer she can see it more from a practical side, rather who is more on the algorithms or kind of theoretic side.
“For me, I see it more as a revolution of finally having enough computing power”, she adds.
It’s from the mid-2000s to late-2000s that we see the implementation of these algorithms at a larger scale. So AI it’s all about having those resources now available, that weren’t there before so.
For Alicia “Machine learning is the power of machines to learn through iterative training, the same way that a child might learn.”
Interested to learn more?
Be sure to check out part two of our conversation with Alicia, in which she offers some important insights on AI.
DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!
Alexander Ferguson 0:00
Welcome back to UpTech Report series on AI. We sit down with Alicia Klinefelter, research scientist for Nvidia, and asked 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 Nvidia, her focus has turned more towards high performance hardware, including machine learning circuits and systems. We start off first by asking Alicia to define AI and machine learning in her terms.
Alicia Klinefelter 0:33
In terms of kind of the reset revolution, again, for me, as a hardware engineer, you know, 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. Because I mean, a lot of the algorithms and the, you know, the underlying mathematics of machine learning have been around for decades, since the 1950s. And really, what kind of revolutionize these things is, you know, finally, in the mid 2000s, to late 2000s, we’ve been having this, you know, this enormous progression of compute power that has enabled a lot of us to finally, kind of implement these algorithms at a larger scale. So to me, you know, the power of AI is really, in having those resources now available that weren’t there before.
Alexander Ferguson 1:29
So if you had to explain AI machine learning, in very simple terms, for example, to your mother, grandmother, how would you do it?
Alicia Klinefelter 1:38
You know, it’s definitely, it’s the power for machines to learn at the most general level, and, you know, it’s, it’s, it’s kind of, it’s the powers of, or 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 explained 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 as a block. And then eventually, you learn about colors, and you can identify this as 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, you know, tell the 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, you know, 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 the real power that we’re starting to see now. So
Alexander Ferguson 2:57
let’s take a look at the difference between AI and machine learning,
Alicia Klinefelter 3:01
I usually think of AI as a superset of everything. And then really kind of machine learning is the implementation of how to get to this greater concept of our artificial intelligence, that’s usually where I kind of differentiate the two were kind of artificial intelligence is just a super broad concept that can mean anything. And then machine learning, as I mentioned, is really that specific implementation of how you do it,
Alexander Ferguson 3:24
let’s take a look at some of the other commonly used forms. Can you explain the difference between machine learning deep learning computer vision and natural language processing?
Alicia Klinefelter 3:33
So again, I think in this case, I would consider machine learning to be the superset of, you know, all these different types of learning methods, which deep learning is kind of one of them. And, you know, deep learning kind of only have having been popularized, and maybe the last decade, I would say, probably around with Alex net, essentially, where you basically have these kind of multi layer 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 multi layer networks. And in order to have multi layer 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 topologies besides deep network or neural network topologies, you could 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 topologies 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 I would describe computer vision is really just the ability I think of it really is. 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 genericized it as being like image processing. But and I know that for a while that, you know, we have, you know, a group of computer vision experts at Nvidia. 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 it a deterministic algorithm that someone had developed mathematically, you know, if you wanted to detect whether there was a car and 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 kind of 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, I mean, it’s, it’s a more specific application of machine learning is how I would think about it. But it’s basically, you know, parsing language essentially, almost as if a human being would like I mean, it’s basically a specific model for processing language in machine learning.
Alexander Ferguson 6:02
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