Dr. Taiwo Raphael Alabi is multilingual with an education spanning 3 continents at some of the best engineering schools in the world.He has spent his entire career in electronic and software engineering at Intel and DocuSign. With multiple highly cited publications in the field as well as 3 pending US patents, he currently drives narrow artificial intelligence initiatives at DocuSign. Some of his primary focuses in the field of AI are natural language processing, computer vision, recommender systems, and reinforcement learning. His interest also spans meta-learning and machine learning systems design. He enjoys traveling, hiking, obstacle racing, playing the guitar, and chasing after new challenges in his limited free time.

Episode transcription:

F: Thank you for joining us today, Taiwo.

T: Thank you for having me, Fred.

F: Awesome. First question. What was it that drove you to get involved in the machine learning field and for the sake of our listeners who may be unfamiliar with machine learning, you mind describing that as well?

T: Yeah, definitely. I will go to the last question for our, so what is machine learning? I like to define machine learning as the art of finding information. From data and it could be any kind of data. It could be massive data, small data, but just finding that information or that insight from that data and being able to use that insight to benefit an organization, a country or a course that to me is meshing.

F: And what drove you to machine learning?

T: Actually, that's a very interesting question for me. I think what drove you to Michelle learning. Taking a couple of years, years back now, which is probably like five years back. I used to work at Intel and while I was at Intel, I was one of the lead engineers that drove a concept then five years ago, which was kind of new, then internet of things. We started that project. And when I started a project, one of the pinpoints of that project was that. This was going to be the next revolution. And it was going to drive degeneration of massive amount of data on a scale that nobody else has ever seen. And that once this happened or happens, then there's going to be a need for ways to harness that data and the fact that it was going to be that explosion, that was what drove me to. Understand, even more about what IoT was going to curse. And because of that, I started looking at so many journal articles and lo and behold, I found FMLA that machine learning and artificial intelligence, narrow AI, strong AI. We're going to be the eventual solution to data explosion, and that curiosity drove me to multiple different universities. I eventually went back to school, Berkeley, and even now Stanford, just to try to understand ways whereby we can harness that data. And the curiosity obviously is what has driven me to become much more of a bit. Well, I'm an expert in this field, just because I want to understand how we can harness East sites from data major trends in artificial intelligence that you foresee dominating the industry within the next five years. That's actually a very interesting question. I think in the next five years, taking a look at where we're at today and you can probably already foresee what will be experiencing the next five years. So I expect that, since I started five years ago from IoT out, IoT is going to eventually explode within the next five years. And I expect that it's going to be a lot of information out there, information from edge computing, devices, information from even cars, et cetera. I think there's going to be, we'll probably be generating as much information, which is happening today as we've generated within the past, probably 10, 20 years probably generating those in two years. Within the next five years. And then we'll also see the scene. We're going to be getting slowly to the age of basically strong AI. So right now what we have is mainly narrow AI. So narrow AI in this sense is basically aspects of AI t