Today's leading minds talk AI with host Byron Reese
About this Episode
Episode 63 of Voices in AI features host Byron Reese and Hillery Hunter discuss AI, deep learning, power efficiency, and understanding the complexity of what AI does with the data it is fed. Hillery Hunter is an IBM Fellow and holds an MS and a PhD in electrical engineering from the University of Illinois Urbana-Champaign.
Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.
Byron Reese: This is Voices in AI brought to you by GigaOm, I’m Byron Reese. Today, our guest is Hillery Hunter. She is an IBM Fellow, and she holds an MS and a PhD in electrical engineering from the University of Illinois Urbana-Champaign. Welcome to the show, Hillery.
Thank you it’s such a pleasure to be here today, looking forward to this discussion, Byron.
So, I always like to start off with my Rorschach test question, which is: what is artificial intelligence, and why is it artificial?
You know that’s a great question. My background is in hardware and in systems and in the actual compute substrate for AI. So one of the things I like to do is sort of demystify what AI is. There are certainly a lot of definitions out there, but I like to take people to the math that’s actually happening in the background. So when we talk about AI today, especially in the popular press and such and people talk about the things that AI is doing, be it understanding medical stands or labelling people’s pictures on a social media platform, or understanding speech or translating language, all those things that are considered core functions of AI today are actually deep learning, which means using many layered neural networks to solve a problem.
There’s also other parts of AI though, that are much less discussed in popular press, which include knowledge and reasoning and creativity and all these other aspects. And you know the reality is where we are today with AI, is we’re seeing a lot of productivity from the deep learning space and ultimately those are big math equations that are solved with lots of matrix math, and we’re basically creating a big equation that matches in its parameters to a set of data that it was fed.
So, would you say though that that it is actually intelligent, or that it is emulating intelligence, or would you say there’s no difference between those two things?
Yeah, so I’m really quite pragmatic as you just heard from me saying, “Okay, let’s go talk about what the math is that’s happening,” and right now where we’re at with AI is relatively narrow capabilities. AI is good at doing things like classification or answering yes and no kind of questions on data that it was fed and so in some sense, it’s mimicking intelligence in that it is taking in sort of human sensory data a computer can take in. What I mean by that is it can take in visual data or auditory data, people are even working on sensory data and things like that. But basically a computer can now take in things that we would consider sort of human process data, so visual things and auditory things, and make determinations as to what it thinks it is, but certainly far from something that’s actually thinking and reasoning and showing intelligence.
Well, staying squarely in the practical realm, that approach, which is basically, let’s look at the past and make guesses about the future, what is the limit of what that can do? I mean, for instance, is that approach going to master natural language for instance? Can you just feed a machine enough printed material and have it be able to converse? Like what are some things that model may not actually be able to do?
Yeah, you know it’s interesting because there’s a lot of debate. What are we doing today that’s different from analytics? We had the big data era, and we talked about doing analytics on the data. What’s new and what’s different and why are we calling it AI now? To refer to your question from that direction, one of the things that AI models do, be it anything from a deep learning model to something that’s more in the knowledge reasoning area, is that they’re much better interpolators, they’re much better able to predict on things that they’ve never seen before.
Classical rigid models that people programmed in computers, could answer “Oh, I’ve seen that thing before.” With deep learning and with more modern AI techniques, we are pushing forward into computers and models being able to guess on things that they haven’t exactly seen before. And so in that sense there’s a good amount of interpolation influx, whether or not and how AI pushes into forecasting on things well outside the bounds of what it’s never seen before and moving AI models to be effective at types of data that are very different from what they’ve seen before, is the type of advancement that people are really pushing for at this point.
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