Voices in AI – Episode 22: A Conversation with Rudina Seseri

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In this episode, Byron and Rudina talk about the AI talent pool, cyber security, the future of learning, and privacy.

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Byron Reese: This is Voices in AI brought to you by Gigaom. I’m Byron Reese. Today, our guest is Rudina Seseri. She is the founding and manager partner over at Glasswing Ventures. She’s also an entrepreneur in residence at Harvard Business School and she holds an MBA from that same institution. Welcome to the show, Rudina.

Rudina Seseri: Hello Byron. Thank you for having me.

You wrote a really good piece for Gigaom, as a matter of fact; it was your advice to startups—don’t say you’re doing AI just to have the buzzwords on the side, you better be able to say what you’re really doing.  

What is your operational definition of artificial intelligence, and can you expand on that theme? Because I think it’s really good advice.

Sure, happy to. AI—and I think of it as the wave of disruption—has become such a popular term, and I think there are definitional challenges in the market. From my perspective, and at the very highest level, AI is technology, largely computers and software, that possesses or has some level of intelligence that mirrors that of humans. It’s as basic as one would imagine it to be by the very name artificial intelligence.

Where I think we are in the AI maturity curve, if one wants to express it in such a form, is really the early days of AI and the impact it is having and will have going forward. It’s really, what I would call, “narrow AI” in that we’re not at a point where machines, in general, can operate at the same level of diversity and complexity as the human mind. But for narrow purposes, or in a narrow function—for a number of areas across enterprise and consumer businesses—AI can be really transformational, even narrow AI.

Expressed differently, we think of AI as anything—such as visual recognition, social cognition, speech recognition—underpinned with a level of machine learning, with a particular interest around deep learning. I hope that helps.

That’s wonderful. You’re an investor so you get pitches all the time and you’re bound to see ones where the term AI is used, and it’s really just in there to play “buzzword bingo” and all of that… Because, your definition that it’s, “doing things humans would normally do” kind of takes me back to my cat food bowl that fills itself up when it’s empty. It’s weighing and measuring it so that I don’t have to. I used to do it, and now a computer does it. Surely, if you saw that in a business case, like, “We have an AI cat food bowl,” that really isn’t AI, or is it? And then you’ve got things like the Nest, which is a learning system. It learns as you do it, and yours is eventually going to be different than mine—I think that is clearly in the AI camp. What would be a case of something that you would see in a business case and just roll your eyes?

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