Jana Eggers is the CEO at Nara Logics, and a mathematician by training. Her unique perspective has her urging all CEOs to learn more about AI,
“I really encourage CEOs to get to understand AI. They can understand it to a level enough that it will be beneficial to their business. It's not something that I think they should just say, “Oh, that's my tech and that's my smart guys.” It's everyone. Don't put it off in a corner."
But, how do you actually do that? How do you stop talking about AI and put it into practice at your organization? On AI at Work, Jana shared her Four Tips to Stop Reading About AI and Start Doing AI.
1. Understand that AI is not magic; it is just maths – and it needs you.
“AI is not magic. There's nothing magic there. You don't have to understand the theory behind it to get what it's doing and to start understanding and thinking about the answers that it's given.”
2. Get serious about your digital transformation
"By that, I mean understand your data, what data you have, the quality of it. The quality doesn't mean that stops you from AI. Actually, AI is pretty good at handling strange anomalies, and it's better for you to not clean your data. With our customers, I'm always telling them, “Guys, we want the raw data.” Because you go in and clean it, then when something unclean comes in, we don't know what to do with it. Really understand what digital transformation means. It really means, “I've got all this digital data. How do I use it across my organization?”
3. Stop with the excuses: “We don’t have enough data” or “We don’t have the expertise.”
“Jeff Dean from Google was asked at a conference about a year ago, do mere mortal companies have enough data for AI? He said, absolutely, yes, without any doubt. One thing I want to encourage people on is that you have more data than you think. This is not about acquiring data. Also, you don't need the PhD's. Google, Facebook-- those folks, they absolutely need the PhD's. I'm not saying they're bad. I mean, listen, any PhD's that want to come join us, we love it. We're there for you. We want to have people like that, but you don't have to have them. Most companies aren't going to need them. You can build a neural net in seven to ten lines of code, and you don't even need to do that. You just need to understand what your organization is trying to accomplish.”
4. Build a learning organization
“There's a book called The Fifth Discipline, and the fifth discipline is being a learning organization. In tech we call it failing fast. You have lots of people say,“It's not about failing. It's about learning”, right? Which is true. That's really what this is about. One of the places he starts is, these are systems. What I like about that is, if you try and operate there's something called the beer game, which is really fun. It's about being a beer distributor. You can't just know one piece of the section.
You really have to understand, “Well, wait a second. My end outlet is doing a promotion. Well, that's going to impact my distribution. If I don't know that they're doing a promotion, I might think that they just figured out more marketing, or opened up to a new market. And so then I amp my production up. Well, then I have too much. And then, by the way, I've screwed up my supplier”.
It's one of the things that starts off in the book, so that people start understanding, “Oh wait, different organizations read signals wrong from the other organization”. Well, that's what you have in yours. When you're talking about something like the business, unit and engineering, and data science, and a UX organization, they all have to communicate together. I think The Fifth Discipline is a great book to really understand what a learning org is. I think it's actually going to be AI's hat trick to make us all learning orgs.”
"Where do I start?" Jana calls this the Chicken, Egg, and Bacon Problem.
“People say, where do I start? Is it the data? I start with the data? Or, do I start with an algorithm that I think is going to solve my problem? That's the chicken and egg problem. The data is the egg, the chicken is the algorithm, the bacon is the results, which I call the holy trinity of AI. Anytime you change any of them, you have to consider changing the others. My big recommendation is start with your own eggs. Start with your data. A lot of people go and try and buy data, and the problem is, they don't know what's been done with that data. They don't know how it's collected. They don't know how it's been transformed. They really need to understand the data that they're going to be making decisions on, so I do think you start with the egg in that situation. The algorithms are pretty easy to change out. I mean, you talked about some of the ops problems.
We, as an industry, haven't figured out how to do model management. There are some theories out there, but none of us has figured it out and what to do with that. But choosing an algorithm, you can switch the algorithms in and out, see what they do. When you ask what an executive can do, one thing is get their team started with their own data. And that's a problem, because by the way, there's lots of silos in organization. So you need to encourage your team. I talk about being free range eggs, right? You've got to have your data be free range. Don't worry as much about the algorithm, but make sure that the team's trying many of them, and thinking about, “Hey, what's the difference in how it is?”