How has A.I. overall been changed by Machine Learning? | Robbie Allen Interview (Part 1)

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Alexander Ferguson 0:00
Welcome to the UpTech Report series on artificial intelligence. I’m Alexander Ferguson. For our second series of deep dive interviews, I sat down with Robbie Allen, CEO of Infinia ml in Durham, North Carolina. Robbie has a degree in engineering and management from MIT, and is completing his PhD in computer science at UNC Chapel Hill. He owns six patents and has authored eight books. So he has a great deal of knowledge in the space. I start off by asking Robbie, how has the resurgence of machine learning changed AI overall,

Robbie Allen 0:35
what’s happened is that machine learning has been included as a part of some of the other aspects of artificial intelligence, like computer vision, like natural language processing. And so we’re seeing sort of a renaissance and many of those fields of artificial intelligence, and large part thanks to machine learning as an underlying technology driving them.

Alexander Ferguson 0:55
What’s the difference between machine learning, deep learning and reinforcement learning.

Robbie Allen 0:59
So machine learning, sort of a fundamental, you know, definition is the way I describe it, is automating something, and learning patterns with data. So essentially, you start off with the dataset. And it’s essentially learning the software is learning patterns in that data. So that when you give a new data in the future, it can make a prediction, or it can tell you, you know, how it’s similar to what it seen in the past. And so that’s really what machine learning is, deep learning is a specific type of machine learning that you can think of is just a more complicated version, it’s allows you to kind of really go at a very deep level, to use the term to find patterns in a more intricate way than, you know, kind of maybe what we’ve done in the past. And that’s primarily due to the computational resources that are now available, as well as the enhanced sets of data that are available. Now we can apply essentially more processing more capability at traditional machine learning algorithms, so that they can find patterns in more nuanced ways. So Reinforcement learning is a way to train an algorithm based on a reward system. So essentially, you can you identify a behavior that you think is good. And so what happens is, the algorithm will try out lots of different approaches. And if once it, you know, gets rewarded, it then kind of files that away, okay, that was that the path I took to do that was the right path. And so I’m going to keep track of that, I’m gonna try to do more of that. versus, you know, something that was not a success, you know, with natural language, that’s a special case that, you know, it really, there’s two main fields within machine learning natural language, and then vision, which is image processing. Those are the two main data inputs that you get. There’s also tabular numeric data that you can also work on. But oftentimes, you’ll hear about natural language processing or computer vision as the two main areas, vision has probably had the biggest advancements to date. And you may have heard the most about it, whether it’s facial recognition, or things of that nature. Just because it’s a more constrained problem space, right, you’re talking about an image. And really, you’re kind of trying to focus on identifying maybe things in the image, versus natural language tends to be a little more unbounded, you know, and again, there’s multiple languages, there’s all sorts of nuances to language that are not present with images. And I think it tends to be a harder place to kind of get the same level of accuracy. And then when you start talking about generating text, that’s, that’s a whole nother game. And my first company Automated Insights, that’s what they do is, is automatically generate text. And it’s decidedly not machine learning based at least the text generation piece, because of this probabilistic, you know, sort of attribute of machine learning, we can’t deliver results that are right 80% of the time, because people are so finely tuned to any issues in text, they can easily identify when there’s something out of whack, right? An image actually can have a little bit of error built in, and people be okay with it. But if your sentence comes out garbled one out of 10 times, you’re going to know it and you’re not going to, you know, appreciate that. And so, natural language is a little bit harder case in the machine learning space, just because people are very finely tuned to text. And if something’s off, you can spot it very instantly. And it doesn’t sound very good.

Alexander Ferguson 4:21
What is natural language processing? And how does it work?

Robbie Allen 4:24
Traditional natural language processing techniques are what are referred to more as brute force techniques. That is you’re trying to definitively describe all the different parts of speech, for example. And so you’ll have these very complicated rules based systems that you know, in heuristics that are kind of built in to try to automatically determine all the different parts of speech. But the problem with speech and language is that is changing all the time. You know, it’s a very tricky thing to really kind of define discreetly, and so with machine learning when it came to prominence, What that allows you to do is actually automatically learn the rules of language, versus someone actually having to, you know, define all those by hand. Now, you can just throw a lot of data at it, and have it tried to automatically detect the patterns and detect the rules of speech and the rules of language. Traditionally, you may try to build a natural language processing system offer traditional, you know, voice based capabilities. But guess what, everybody says things a little bit differently, I have my own accent from the southern United States. And you could probably hear that a little bit. And so traditional natural language processing and speech processing techniques would fall apart, because, you know, they have to be built off of a particular type of speech system, they weren’t able to really handle the nuance and the accents that were present. Now, with machine learning based systems, it can be built with the initial base model, but it can also learn over time, in fact, you know, Alexa, and some of these other systems have capabilities where you can actually talk to it and have it learn your specific ways of speaking. And that just wasn’t possible before.

Alexander Ferguson 6:02
How did you get interested in AI, and specifically machine learning,

Robbie Allen 6:06
it really starts back all the way to the mid 90s, when I started working in computer science, in that kind of continued on to my first job, which was at IBM’s, networking hardware division, and then followed by working at Cisco. And so I’ve always kind of, in my early part of my career involved in the sort of networking and hardware space. But even back then the core of what I did was automate things. And really the way you can think about what is artificial intelligence, really hope to promise is just an enhanced ability to automate. And so I’ve been, you know, fascinated with abilities to automate things from from, you know, many years back, and now with artificial intelligence. And, you know, I did some of my work at MIT, you know, looking at artificial intelligence in the mid 2000s, even then, it just still wasn’t at a point where you could do it at scale, and have the kind of impact that we’re seeing today. It’s only, you know, early 2011, and 12. You know, again, there was a confluence of things that happened that now makes machine learning very practical tool to use when you’re trying to solve certain business problems. And that’s just like, I’m a kid in a candy store, because now it opens up all these different things to automate that just weren’t possible before.

Alexander Ferguson 7:18
How do you go about solving these business problems with machine learning?

Robbie Allen 7:21
The initial part is really translating the business problem to a machine learning problem. Because it’s not always the case that somebody tells you. All right, we have this invoice processing system, and it’s highly manual and repetitive. And now we want to automate it. Well, okay, that’s good. That’s a good frame to start with. But you then have to translate that well. What does that entail in terms of a machine learning problem? Like what are the what are the ways that we’re going to apply machine learning to maybe break down that business problem into a set of a series of machine learning problems that we can then ultimately stitch together to deliver a solution? So I’d say the first step is really helping define the business problem in machine learning terms.

Alexander Ferguson 8:03
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