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Man and Machine Collaborating on the Factory Floor: A Nightmare or a Match Made in Heaven?
Whenever mankind works together with machinery, new methods are needed to cater to human unpredictability—and to ensure that robots can anticipate it.
To make things even more complicated, we must throw this extra consideration into the mix: in an industrial setting, the foremost requirement is transparency, in order to make sure production targets can be met. But deep learning is actually the opposite of this; you train the system to recognize patterns, but you lose control of how that system comes to its conclusions. Hence, an extra requirement of complex reasoning is that it must be sufficiently transparent (or explainable) for people to accept it, meaning that in the future, we will be talking about explainable AI.
Lifelong Learning: Also for Robots
Furthermore, we will be confronted with the issue of how machines can continually improve their reactions and ways of anticipating actions. This means that new reward systems based on implicit and explicit feedback signals must be developed. You can bet that, in the future, the concept of lifelong learning will no longer only apply to man, but to machines as well.
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Pieter Simoens is a professor at Ghent University, in Belgium, and is affiliated with imec. He received an M.Sc. degree in electronic engineering in 2005 and a Ph.D. degree in 2011 from Ghent University. Pieter is an assistant professor affiliated with Ghent's Department of Information Technology, as well as with iMinds, and is teaching courses on mobile application development and software engineering. He is the author or co-author of more than 70 papers published in international journals, or in the proceedings of international conferences, and has been involved in several national and European research projects, including FP6 MUSE, FP7 MobiThin and H2020 FUSION.
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