Automated techniques could make it easier to develop AI

4 days ago 36

Machine-learning researchers make many decisions when designing new models. They decide how many layers to include in neural networks and what weights to give inputs at each node. The result of all this human decision-making is that complex models end up being “designed by intuition” rather than systematically, says Frank Hutter, head of the machine-learning lab at the University of Freiburg in Germany.

A growing field called automated machine learning, or autoML, aims to eliminate the guesswork. The idea is to have algorithms take over the decisions that researchers currently have to make when designing models. Ultimately, these techniques could make machine learning more accessible. 

Although automated machine learning has been around for almost a decade, researchers are still working to refine it. Last week, a new conference in Baltimore—which organizers described as the first international conference on the subject—showcased efforts to improve autoML’s accuracy and streamline its performance. 

There’s been a swell of interest in autoML’s potential to simplify machine learning. Companies like Amazon and Google already offer low-code machine-learning tools that take advantage of autoML techniques. If these techniques become more efficient, it could accelerate research and allow more people to use machine learning.

The idea is to get to a point where people can choose a question they want to ask, point an autoML tool at it, and receive the result they are looking for.

That vision is the “holy grail of computer science,” says Lars Kotthoff, a conference organizer and assistant professor of computer science at the University of Wyoming. “You specify the problem, and the computer figures out how to solve it—and that’s all you do.”

But first, researchers will have to figure out how to make these techniques more time and energy efficient.

What is autoML?

At first glance, the concept of autoML might seem redundant—after all, machine learning is already about automating the process of gaining insights from data. But because autoML algorithms operate at a level of abstraction above the underlying machine-learning models, relying only on the outputs of those models as guides, they can save time and computa...

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