The Social Science of AI: Intel’s Elizabeth Anne Watkins

**Sam Ransbotham** (0:02)
We know that artificial intelligence tools are augmenting human performance, but how do people really feel about that? On today's episode, find out how one company develops AI tools with end users in mind.

**Elizabeth Anne Watkins** (0:15)
I'm Elizabeth Anne Watkins from Intel, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:21)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, Professor of Analytics at Boston College. I'm also the AI and Business Strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:40)
And I'm Shervin Khodabandeh, senior partner with BCG, and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

**Sam Ransbotham** (1:05)
Welcome. Today, we've got a great guest, Elizabeth Anne Watkins, a research scientist at Intel.
Elizabeth, thanks for joining us today. Let's get started.

**Elizabeth Anne Watkins** (1:14)
Thank you so much for having me today.

**Sam Ransbotham** (1:17)
As the world's largest semiconductor manufacturer, Intel is probably a company that most people already know. But maybe can you tell us about Intel Labs and in general, and maybe your role specifically?

**Elizabeth Anne Watkins** (1:29)
Just like you said, we're not always top of minds in the big discussions around AI and the AI industry and the AI field right now. But there is so much fascinating work happening inside Intel, and we have such a unique perspective on the field and a unique way of entering that field that I'm really excited to bring some of that to light today in our conversation.
I just joined Intel in August of last year, and already it's been a really incredible experience meeting so many different teams. I joined Intel as a research scientist in the Social Science of Artificial Intelligence and work under Alamah Nachman in Intel Labs. And the group is called Intelligent Systems Research.

**Shervin Khodabandeh** (2:10)
Elizabeth, you mentioned Intel Labs is doing some unique things with AI. Do you mind sharing with us some of the things you're working on?

**Elizabeth Anne Watkins** (2:19)
A project that I'm particularly excited about is called MARI, an acronym which stands for Multimodal Activity Recognition in Industrial Environments. So basically, I'm going to start with a metaphor. Imagine that your computer could watch you put together, say, a piece of furniture that you ordered on the internet.
When you got to a tough part of the manual or you're holding a screwdriver, you're holding a piece of plywood, and you can't get back to the manual, imagine that your computer could see what you were doing, knew what the manual was going to tell you to say, and then help you to connect to those two. Imagine that your computer could actually tell you, hey, I think you are about to screw shelf A into bracket B, or something of that nature. And I know that every time I have received that flat pack that they say has an armchair in it, it's a really tough time for me to get from A to B. And it's processes like these that our people are doing inside of our facilities where they're actually building and manufacturing the semiconductor chips. So the folks who work inside of our factories, our technicians, are doing very involved and very delicate work handling parts and tools for all kinds of manual operations happening on the factory floor.
And so all of the work that they're doing is just as complicated, sometimes even more complicated, than getting that flat pack into an armchair. There's a lot of tools involved. There are all kinds of different processes, different pieces of equipment, different sizes of equipment. And so we are building computer systems, a little bit like the one that I described that said, hey, did you mean to put screw A into bracket B? We're building systems to help the people inside of our factories do this kind of really careful and really complicated work.

**Sam Ransbotham** (4:03)
That's a fun analogy. I mean, I think we all find flat packs challenging perhaps, though I have to admit, I kind of enjoy them. But I'm sure it's much more complicated within Intel. And what I liked about that example is, I feel like so often we're talking about automation. So can we get machines to learn how to do something that humans do? So it's machine learning at its core. And then we talk about augmentation and, well, how can machines help humans make a decision?