You were an academic before launching TAZI AI. Why did you make the leap to entrepreneur?

Zehra Cataltepe: After I graduated, I did a short postdoc, worked at a startup company, and then started working for Siemens Corporate Research. My work there was in creating AI-based predictive maintenance models to assist mechanical engineers. Our goal was to automatically predict faults and defects that could cost a company millions. The speed of AI-based predictions could significantly cut risk and maintenance expenses. However, I eventually switched to academia to do more independent research. The schedule also allowed me to be at home with my kids outside the work day. While in academia, I also consulted for industry research projects, wrote papers, and filed patents, but the time came when I had had enough of papers and projects. I wanted to see AI in industry again—as a product in real action.

What was the problem you were trying to solve with TAZI AI?

ZC: The usual AI methodology is that somebody gives you a dataset which shows the past behavior of their system, and you build an AI model based on that dataset. What happens in real life is that, after you’ve got your data, time passes and the world changes. You put your model into production and suddenly realize that it doesn’t work as well as it worked in training. Consider how the domain experts, like the mechanical engineers I mentioned earlier, operate. They don’t look at only the dataset. They have accumulated tens of years of experience on the specific problem you’re trying to solve, as well as similar problems. Their knowledge is a lot more valuable than the mere sum of what is in the dataset. The other issue is bias and other problems with models that only become apparent in longer-term deployment. Right now, only data scientists know how to correct these issues in AI. A business owner has to find the issue and explain it to a data scientist, who then has to fix it. That’s a difficult path.

My cofounder and husband, Tanju, came up with the idea of training AI while it is in production. Instead of waiting for it to fail and then retraining, we need the AI to be continuously learning and continuously updating itself, even when in production. This is how we humans operate. When we work at our jobs, we use a lot more than our school years, or our “training sets.” We should also establish a symbiotic relationship between AI and human domain experts. AI can learn from human expertise and humans can learn from AI’s ability to digest billions of data points immediately.

Think of AI as an employee in your company. You will do good work together if, and only if, that employee adapts to your needs. That is, only if you can understand how they’re operating, and they, in turn, can respond to your feedback. If one of these things doesn’t happen, you are not going to be able to work with that employee. So I see AI models as newbie apprentices in our organizations, which make us more capable and our jobs and lives easier.