According to a company spokesperson, Silgard applies her data/technical expertise on scientific research projects, including a recent study utilizing natural language processing to better track lung cancer patients. I lead a data science team at Fred Hutchinson Cancer Research Center. Our team's primary focus is building tools and services to help support data intensive research and Fred Hutch's mission of eliminating cancer and related disease. We wrangle data and develop machine learning and software applications to get the right information to our scientists. How did you get started in this field?

Robots have replaced thousands of routine jobs on Wall Street. That's the contention of Marcos Lopez de Prado, a Cornell University professor and the former head of machine learning at AQR Capital Management LLC, who testified in Washington on Friday about the impact of artificial intelligence on capital markets and jobs. The use of algorithms in electronic markets has automated the jobs of tens of thousands of execution traders worldwide, and it's also displaced people who model prices and risk or build investment portfolios, he said. "Financial machine learning creates a number of challenges for the 6.14 million people employed in the finance and insurance industry, many of whom will lose their jobs -- not necessarily because they are replaced by machines, but because they are not trained to work alongside algorithms," Lopez de Prado told the U.S. House Committee on Financial Services. During the almost two-hour hearing, lawmakers asked experts about racial and gender bias in AI, competition for highly skilled technology workers, and the challenges of regulating increasingly complex, data-driven financial markets.

IBM wants to keep its employees from quitting. And it's using artificial intelligence to do it. In a recent CNBC interview, CEO Ginni Rometty said that thanks to AI, the tech and consulting giant can now predict with 95% accuracy which employees are likely to leave in the next six months. The "proactive retention" tool -- which IBM uses internally but is also selling to clients -- analyzes thousands of pieces of data and then nudges managers toward which employees may be on their way out, telling them to "do something now so it never enters their mind," Rometty said. IBM's efforts to use AI to learn which employees might quit is one of the more high-profile recent examples of the way data science, "deep learning" and "predictive analytics" are increasingly infiltrating the traditionally low-tech human-resources department, arming personnel chiefs with more rigorous tools and hard data around the tricky art of managing people.

Three years ago, Google's self-driving car project abruptly shifted from designing a vehicle that would drive autonomously most of the time while occasionally requiring human oversight, to a slow-speed robot without a brake pedal, accelerator or steering wheel. In other words, human driving was no longer permitted. The company made the decision after giving self-driving cars to Google employees for their work commutes in San Francisco and recording what the passengers did while the autonomous system did the driving. In-car cameras recorded employees climbing into the back seat, climbing out of an open car window, and even smooching while the car was in motion, according to two former Google engineers. "We saw stuff that made us a little nervous," Chris Urmson, a roboticist who was then head of the project, said at the time.