Three early-career engineers who are making a big difference in three very different areas of technology are to receive the Royal Academy's prestigious Silver Medal at the Academy Awards Dinner at the Tower of London on Thursday 23 June 2016. The Silver Medal celebrates outstanding personal contributions to UK engineering, which has resulted in successful market exploitation. Professor Dame Ann Dowling OM DBE FREng FRS, President of the Royal Academy of Engineering, says: "Damian Gardiner, Demis Hassabis and Tong Sun have all demonstrated the power of use-inspired research in taking ideas they have developed in academia and applying them to solve real-world problems. They are working with colleagues all over the world and making an enormous impact early in their careers that is both enriching academic knowledge and generating real economic benefit for the UK." Dr Damian Gardiner is taking the world of product authentication by storm, with his Cambridge University start-up company ilumink Limited acquired by Johnson Matthey's Process Technologies Division in 2015. They were keen to adopt his unique method of printing'liquid crystal' material onto any surface using an ink-jet printer.
It's been an emotional week in the realm of game AI as the world watched the historic five-game showdown between legendary Go world champion Lee Sedol and Google DeepMind's famed deep learning AI AlphaGo. All five games were held at the Four Seasons Hotel in Seoul, South Korea, and as events played out, millions around the world became increasingly captivated. Anticipation for the match began growing in January, when Google's UK-based AI group DeepMind, led by CEO Demis Hassabis, announced their computer algorithm AlphaGo defeated three-time European Go champion Fan Hui 5 games to 0--a victory some experts didn't expect a computer to achieve for a decade. At the end of a Google blog post announcing the win was the promise of a best-of-five face-off between AlphaGo and 18-time international Go champion Lee Sedol, a match equivalent to IBM's Deep Blue defeat of Garry Kasparov in chess in 1997. Notably, Go is inherently more complex than chess and AlphaGo, at least in part, trained itself to play the game.
Google's AlphaGo software has defeated human Go grandmaster Lee Sedol 4-1 in a five-game series. Despite Lee coming back to win the fourth game (see page "Machine outsmarts man in battle of the decade"), for many the realisation of what was taking place was stark. "I didn't think AlphaGo would play the game in such a perfect manner," Lee admitted in shock. The showdown has drawn eyes from around the world – 30 million people watched it in China alone. Like Deep Blue checkmating chess grandmaster Garry Kasparov, or Watson answering questions on Jeopardy!, it represents a milestone in our relationship with machines.
Defining artificial intelligence isn't just difficult; it's impossible, not the least because we don't really understand human intelligence. Paradoxically, advances in AI will help more to define what human intelligence isn't than what artificial intelligence is. But whatever AI is, we've clearly made a lot of progress in the past few years, in areas ranging from computer vision to game playing. AI is making the transition from a research topic to the early stages of enterprise adoption. Companies such as Google and Facebook have placed huge bets on AI and are already using it in their products. But Google and Facebook are only the beginning: over the next decade, we'll see AI steadily creep into one product after another. We'll be communicating with bots, rather than scripted robo-dialers, and not realizing that they aren't human. We'll be relying on cars to plan routes and respond to road hazards. It's a good bet that in the next decades, some features of AI will be incorporated into every application that we touch and that we won't be able to do anything without touching an application. Given that our future will inevitably be tied up with AI, it's imperative that we ask: Where are we now? What is the state of AI? And where are we heading? Descriptions of AI span several axes: strength (how intelligent is it?), Each of these axes is a spectrum, and each point in this many-dimensional space represents a different way of understanding the goals and capabilities of an AI system.