In episode six of season three we chat about the difference between frequentists and Bayesians, take a listener question about techniques for panel data, and have an interview with Katherine Heller of Duke.
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As the last in our series of blog posts on machine learning in research, we spoke to Dr Nathan Griffiths to find out more about machine learning in transport. Nathan is a Reader in the Department of Computer Science at the University of Warwick, whose research into the application of machine learning for autonomous vehicles (or “driverless cars”) has been supported by a Royal Society University Research Fellowship.
How can we create robots that can carry out important tasks in dangerous environments? Machine learning is supporting advances in the field of robotics. To find out more, we talked to Dr Rustam Stolkin, Royal Society Industry Fellow for Nuclear Robotics, Professor of Robotics at the University of Birmingham, and Director at A.R.M Robotics Ltd, about his work combining machine learning and robotics to create practical solutions to nuclear problems.
Imagine a future where self-driving cars, trains and buses are all seamlessly connected through an app, where traffic jams are a thing of the past and redundant car parks have been turned into green spaces. This could be the world we live in by 2030, says Cathis Elmsäter-Svärd, Chairwoman of Drive Sweden and a member of the Global Future Council on Mobility, in this interview.
Robotics undoubtedly has the potential to improve lives in the developing world. However, with limited budgets and expertise on the ground, putting this technology in place is no small task. Step forwards WeRobotics, a new Swiss/American NGO dedicated to meeting this goal through the creation of in-country ‘flying labs’. Co-founder Adam Klaptocz explains all.