Voices in AI – Episode 107: A Conversation with Nir Bar-Lev

[voices_in_ai_byline]

About this Episode

On Episode 107 of Voices in AI, Byron and Nir Bar-Lev discuss narrow and general AI and the means by which we build them out and train them.

Listen to this episode or read the full transcript at www.VoicesinAI.com

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm and I’m Byron Reese. Today I’m excited my guest is Nir Bar-Lev. He is the CEO and the co-founder of allegro.ai. He holds a degree in law and economics from the University of Haifa. He holds a Bachelor of Science in software engineering. He holds an MBA from Wharton and probably a whole lot more. Welcome to the show, Nir.

Nir Bar-Lev: Hi Byron, thank you so much, I’m honored to be on the show.

So I’d like to start off with just a signposting kind of question, which is about the nature of intelligence and when we talk about AI, do you think we’re really building something that is truly intelligent, or are we building something that can mimic intelligence, but it can never actually be smart?

So I think that you know when we talk about AI, there’s always a futuristic talk about you know ‘General Intelligence’ which is attempting to really mimic human intelligence. But apart from academia and maybe a handful of locations in the industry, when we talk about AI in general we’re actually talking about the ability to solve specific problems and really actually marry a couple things, right? We’re mimicking the ability to learn a specific problem and how to solve it. And we’re marrying it with actually some of the things that computers have already and have always done better than humans, — which is being able to manage and manipulate huge amounts of data really really quickly, and do calculations really really quickly.

And when you think of it like that, where do you think we are? Do you think we have key insights that are going to serve us forward? You know, that like we’re knowing fundamental truths or are we still like groping around in the dark, like even the techniques we do now may seem antiquated and outdated in a few years?

Yeah it’s a good question, and you know first I have to say that I feel less equipped to answer that than some of the professors in university. I’m coming at it from a very industry specific viewpoint and really practical viewpoint, and as you know from where I’m sitting, we are just at the beginning of a revolution around again being able to solve very specific problems with AI much much better. And that is going to open a huge opportunity for us.

At the same time we’re very, very, very far away from general intelligence. So I think that that’s not necessarily going to get us there. The practices that we’re using today in the industry and where the development is you know seems to some extent incremental in the sense that we’re using deep learning as really the forefront of AI, but there isn’t anything that is revolutionary in what’s going to happen in the next, I would say, five to ten years. And those are revolutionary things are going to come from academia. What we’re going to see is incremental developments in the science, but use revolutionary developments in the applicability of the science that already exists.

So your company is specifically trying to solve one problem relating to computer vision. So describe what you’re trying to do and why it’s so hard?

Absolutely. This goes to the heart of the applicability of what we’re doing. So let’s start with with maybe a context on AI and deep learning and why is it “intelligent.” So when traditional software engineers try to solve a problem, they are basically actually tasked with building out this workflow or this idea of ‘if this, then that’ for the software that they want to design where the software they need to see out in advance what are all the different situations that this software is going to incur and what to do there in order to reach a certain goal.

And then once they design that, the task is really to simply translate that into something that a machine or a computer can understand into codes. Whereas with AI and deep learning specifically, the idea is that we have an algorithm called a neural network and that’s a very, very simplistic way to try to mimic how the brain works. Literally it’s a network of neurons and nodes that, through a process of training work fermentation a.k.a. “learning” builds this flow all on its own, and obviously then by definition, it’s already translated into something I even understand.

Let me explain that a bit further. So let’s take an example from computer vision: if in traditional computer vision we wanted to identify, say a person or a face, what scientists and engineers were required to do is they were required to figure out what differentiates humans from anything else or what differentiates one face from another. Come up with those parameters and then turn them out and turn them into mathematical formulas or vectors that can identify them, [and] code map. So for example a human, and this is obviously very simplistic, has two legs and two hands and protruding things and maybe the texture is in this way or that, obviously the color and there is always something that looks different, maybe the head, maybe the hair, and they literally have to figure those things out to come up with some sort of mathematical identifier of the human.

What deep learning will do is it will look at an image for example, and it would try to figure out those things by looking at all the different possibilities to identify a human, all the different traits of that image, and come up with something similar. But what’s interesting is that we as humans have a notoriously difficult problem in really being able to describe what is different physically, in terms of something, one thing from the other. Whereas a computer actually isn’t [challenged by that]. They can look at an image and look at multiple other, you know mathematical identification, call it ID prints, that we may not even notice as humans. And in that way it actually can come up with a result that’s much better.

Listen to this episode or read the full transcript at www.VoicesinAI.com

[voices_in_ai_link_back]

Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.