The world of artificial intelligence has exploded in recent years. Computers armed with AI do everything from drive cars to pick movies you'll probably like. Some have warned we're putting too much trust in computers that appear to do wondrous things. But what exactly do people mean when they talk about artificial intelligence? It's hard to find a universally accepted definition of artificial intelligence.
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.
Of all the interesting obstacles slowing down the advancement of artificial intelligence, computer vision may be the most compelling. This is due to the multifaceted challenge of programming a machine with enough inductive reasoning to extrapolate information from observations and come up with plausible and accurate conclusions. Of course, this is the end goal of artificial intelligence research – endowing a computer with the power and ability to think, at least within reason. When it comes to translating flexible human thought processes into more structured machines, there are a handful of problems that slow down the computer's mastery. While we move around the world and throughout our daily routines, we see an uncountable number of images that our brain parses through and then separates into different classifications.
The computer listens to the soloist and classifies the performance according to style. The styles are "lyrical," "syncopated," "pointilistic," and "frantic." The absolute meanings of these terms is not important. What is important is that the player and computer agree so that the computer can understand what the player intends. The computer actually learns about style from examples (see Dannenberg, Thom, and Watson, A Machine Learning Approach to Musical Style Recognition").
A well-established company based in Vienna, originally spun out from two top universities has a mission to build cutting edge Virtual Reality, Augmented Reality and Visual Computing solutions. They provide world-class recognition solutions and are looking to expand their existing team rapidly. By applying to this role you understand that we may collect your personal data and store and process it on our systems.