Patience With Testing

Eric Moller, chief technology officer AtomicX, a company that is using machine learning for chatbot technology, said his focus now is on applying machine learning techniques to conversations and text data to make better predictions. What does he look for from an organization as a machine learning engineer? “Access to lots of interesting data sets, smart coworkers and buy-in from the top,” Moller said. “Machine learning is still a fairly scientific pursuit. Applying it to a new domain often means, a lot of failure needs to be tolerated before you start seeing results.”

An organization also needs good, structured data for machine learning engineers to be successful. “A lot of companies are jumping on the machine learning bandwagon without any of the necessary prerequisites to make good of these new technologies,” Moller said. “Most important: you need to be collecting a lot of structured data and searching for deep patterns where classic statistical/regression techniques aren’t providing adequate results.”

Making the Impossible Possible

Moller said he’s thrilled to see his company apply machine learning to problems that were always “impossible” to tackle using traditional computing paradigms. “We’ve mastered image recognition; machines can do it better than humans now,” he said. “At AtomicX we’re applying machine learning to text and conversational systems. It’s a very difficult problem since there’s so much collective human experience embedded into our language.”

Moller’s also bullish on generative design, another aspect of machine learning he said has “incredible potential.” “We’re already building lighter, stronger and more efficient structures and designs by simply feeding in the parameters we want optimized,” he said.

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