Authoring Creativity With AI: Researcher Patrick Hebron

**Shervin Khodabandeh** (0:02)
How does the use of generative AI in creative fields translate to opportunities for the future? Find out on today's episode.

**Patrick Hebron** (0:11)
I'm Patrick Hebron, author of Machine Learning for Designers, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:17)
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** (0:36)
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.
Hi, everyone. Today, Sam and I are excited to talk with Patrick Hebron. He's the author of Machine Learning for Designers and he's held roles at Nvidia Omniverse, Stability AI, and Adobe. Patrick, thanks for taking the time to talk with us.

**Patrick Hebron** (1:18)
Thanks for having me. It's great to be here.

**Sam Ransbotham** (1:20)
So, I have to say, right off the bat, I'm curious about why machine learning is different for designers. What does the four designers part of that mean?

**Patrick Hebron** (1:28)
When I wrote the book, this was not an intersection that was very sensible one to most people. So, I'd been working on that intersection since the time of my masters, and I sort of got into the idea that in design, there can be really challenging configuration problems and machines can help to play a role in figuring out how to sort through lots of different permutations and come to an arrangement that might be useful to people. So, that was what was happening in my own work at the time.
And then as the technology was starting to advance quite a bit, it seemed to me that there was going to be some really big differences in how we thought about the production of software as a result of AI. Conventional software is always correct about mundane things, like, say, two plus two.
And machine learning, of course, enables you to do much more complex things, like identify faces in photos or a million things. But it's not always right about those things. There's an inherent imprecision to that. And this fact alone carries a huge implication when you're designing software, thinking about how the user navigates through a process and particularly what happens when they hit a dead end or a misunderstanding.
O'Reilly approached me about writing that book, and I was really excited to tackle this subject and start to help designers to think about how this would transform their practice.

**Sam Ransbotham** (2:49)
That is a fundamentally different approach, because we're used to software being very deterministic. We're used to processes working the same way they work whenever you test them in the lab.
But then they work differently when you introduce noise and fuzziness into the whole thing. So how do people deal with the fact that what they test and what they work on isn't necessarily what happens when it goes into production?

**Patrick Hebron** (3:13)
Yeah, it's funny because I don't want to liken machine learning models too much to a human, but I guess one thing we do have in common with them is this kind of imprecision, right? We're capable of grand notions, but you can sort of never guarantee that what's in someone else's head is cohesive in exactly the same way that it is in yours, right?

**Sam Ransbotham** (3:33)
Yeah, I find most people are not cohesive with what I think.

**Patrick Hebron** (3:36)
Right, same.
So one thing is to remember that there is conventional software still around, right? And so having sort of a backup plan or reverting to conventional functionality when a more complex AI system fails is one mitigation. Of course, there's a challenge with that, which is that probably if what your software is doing required AI in the first place, then the fallback may be difficult because the conventional software is not up to the job.
But having the machine sort of present back to the user what it's understood I think is very important. So it doesn't just sort of go off and act on a misconception.
Another challenge is discoverability. We see this with, say, Alexa, right?
There's all these features, but they're hidden somewhere in there. And so how do you know what you are not able to do? This, I think, is in certain ways a regression from traditional software. Giant menu systems have been sort of the enemy of my career, I guess. But at the same time, they do have a certain upside, which is there's kind of an obvious path to learning what the software can do, right? So you go find some particular feature that you need at the moment. You find it in this menu system, and it's probably adjacent to some related features. And so this exposes you at least to seeing their names, and perhaps this will lead you to explore towards them. You don't necessarily have that with, say, an emergent feature set or ability to speak to your computer and ask for something.