5 Questions with Cockrell’s Newest Machine Learning and Artificial Intelligence Expert, Atlas Wang

Another major area of emphasis for you is AutoML — or machine learning models that train other machine learning models. This sounds like the beginning of a movie about robots taking over the world. Is that being too paranoid?

Speaking for myself as someone who works in this frontier now, I feel the status quo of AutoML is nowhere near a SkyNet level of scariness. AutoML is still itself a machine learning algorithm that is designed by humans. And it is still in its infancy, with many practical challenges standing in the way of widespread adoption. 

But it is exciting to me for many reasons. State-of-the-art AI and ML systems consist of complex pipelines with tons of design choices to make and tune for optimal performance. They also often need to be co-designed with multiple goals and constraints. Optimizing all these variables becomes too complex and high-dimensional to be explored manually. I consider AutoML to be a powerful tool and a central hub in solving those AI/ML design challenges faster and better.

AutoML lets machine learning algorithms try a task millions or billions of times, rapidly going through the trial-and-error process that would take humans much longer to perform. Then it can find an effective route that others can follow to solve similar tasks, without having to repeat the trial-and-error process. 

Our team has been contributing to improving the model selection, or neural architecture search (NAS) and algorithm discovery — also known as learning to optimize, or L2O — parts from the full AutoML scope. Asking a machine to take over the trial-and-error process from humans could drastically accelerate our research cycle. This will be especially helpful for organizations that want to use machine learning but don’t know it inside out.