Machines that learn in the wild: machine learning capabilities, limitations and implications

Innovation Disruptive technologies Employment forecasting Artificial Intelligence (AI) Automation Future of work Algorithms Job automation Autonomous vehicles Telehealth
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Like many other areas of Artificial Intelligence, the technical capabilities of machine learning approaches are regularly oversold and this hype overshadows the real advances. Machine learning algorithms have become an increasingly important part of our lives. They are integral to all sorts of applications from the speech recognition technology in Siri to Google’s search engine. Unfortunately machine learning systems are often more noticeable in our lives because of failures rather than successes. We come face-to-face with the limitations of auto-text recognition daily, while spam filtering algorithms quietly remove mass mail from our inboxes completely unnoticed. Improvements to machine learning algorithms are allowing us to do more sophisticated computational tasks. But it is often unclear exactly what these tools can do, their limitations and the implications of their use - especially in such a fast moving field.

This short report comes out of a workshop exploring the capabilities and limitations of machine learning algorithms. Rather than a complete resource looking at the specific capabilities of different algorithms, this report is an introduction to some of the current capabilities and limitations in the field. It includes some areas where machine learning approaches are employed effectively and the challenges when using the technology.

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