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Machine Learning and Its Applications

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Anyone who's even remotely familiar with the world of information technology might have come across words like machine learning and artificial intelligence. Artificial intelligence has long been a part of pop culture. If tech bigwigs are to be believed, artificial intelligence and machine learning are the future of our world and technology. But what is machine learning? And how are machine learning and artificial intelligence connected?


Learning machines - how computers got smart

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In future, machine learning could improve transport, security, healthcare and revolutionise industry. But despite its reach, this powerful technology remains mysterious to most. Our panel of speakers, chaired by Marcus du Sautoy, discussed what we mean by machine learning and discovered some of the exciting current and future uses of this technology. We had presentations from the Head of Microsoft Research Chris Bishop, robotics researcher Sabine Hauert and machine vision researcher Maja Pantic. Visitors were also be able to take part in an interactive exhibition where machine learning developers and researchers showcased examples of the technology in action.


What happens when we teach a computer how to learn?

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Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. Get caught up on a field that will change the way the computers around you behave ... sooner than you probably think. This talk was presented to a local audience at TEDxBrussels, an independent event.


Machine Learning: What it is and why it matters

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Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.


Ways to think about machine learning

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We're now four or five years into the current explosion of machine learning, and pretty much everyone has heard of it. It's not just that startups are forming every day or that the big tech platform companies are rebuilding themselves around it - everyone outside tech has read the Economist or BusinessWeek cover story, and many big companies have some projects underway. We know this is a Next Big Thing. Going a step further, we mostly understand what neural networks might be, in theory, and we get that this might be about patterns and data. Machine learning lets us find patterns or structures in data that are implicit and probabilistic (hence'inferred') rather than explicit, that previously only people and not computers could find.