Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

With the purchase of DeepMind by Google for a rumoured 400 million pounds a chain of events was set off that began a debate in the glare of the media: just how far away was superintelligence, the AI singularity? Elon Musk was an investor in DeepMind, and a reader of Nick Boström's book "Superintelligence" and he became convinced that artificial intelligence was a threat to humanity "We are summoning the demon" he said. To the researchers behind the most recent developments in AI, the idea that our faltering steps towards artificial perceptual systems were anywhere close to a demon seemed ridiculous (speech recognition, object recognition). But the public perception remained and yet others with little knowledge of the technologies underpinng the advances added their voices to the fray. At the post conference banquet for NIPS 2014, a few of us were talking about the potential effect of these discussions on our research.

It all started out as science fiction: machines that can talk, machines that can think, machines that can feel. Although that last bit may be impossible without sparking an entire world of debate regarding the existence of consciousness, scientists have certainly been making strides with the first two. Over the years, we have been hearing a lot about artificial intelligence, machine learning, and deep learning. But how do we differentiate between these three rather abstruse terms, and how are they related to one another? Artificial intelligence (AI) is the general field that covers everything that has anything to do with imbuing machines with "intelligence," with the goal of emulating a human being's unique reasoning faculties.

Computer mastery of the complex board game Go has long been seen as a landmark for artificial intelligence. A mystery player causing a stir in the world of the complex strategy game Go has been revealed as an updated version of AlphaGo, the artificial-intelligence (AI) program created by Google's London-based AI firm, DeepMind. Known only by the name'Master(P)', since late December the anonymous player has beaten the world's best at Go in a string of online games, including defeating current world number one, 19-year-old Ke Jie. South Korea trumpets $860-million AI fund after AlphaGo'shock' Go is regarded as the most complex board game ever invented, and is famously difficult for computers to crack. But last year, AlphaGo showcased the strength of AI software when it stunned the Go world, first by defeating a professional human player, Fan Hui, and then going on to beat one of the Go world's top players, Lee Sedol.

Deep learning is already being used in the automotive industry, healthcare, and many more.

Summary: As deep learning expands those capabilities are finding their way into the not-for-profit community in the service of conserving the earth's wildlife and forests. The for-profit world may be driving AI but it's a solution to many problems in the not-for-profit world as well. We were particularly impressed by the use of deep learning technologies to solve problems in the pursuit of preserving natural resources including many species of animals and fish, and also including forests. For the most part the data problems that nature conservancy organizations face fall into these categories. Going back 20 years this meant putting intrepid feet on the ground with binoculars and note pads.

It was/is a lot material to cover in 90 minutes, and conceptual understanding / developing intuition was the main point. Of course, there is great online material to make use of, and you'll see my preferences in the cited sources;-). This year, having covered the basics, I hope to be developing use cases and practical applications showing applicability of Deep Learning even in non-Google-size (resp: Facebook, Baidu, Apple...) environments.

This year has been rollercoaster crash for many with numerous tragedies and crises occurring all over the world, but it doesn't mean that everything was grim in 2016. Join IBTimes UK as we take a closer look at the many new developments across various fields of technological research, each with the potential to revolutionise human life for the better. This section is devoted to the computer science research into replicating the human mind and helping computers solve complex tasks. For developments concerning the machines themselves, see our articles on robotics. In 2016, computer scientists have begun concentrating more efforts on building deep learning neural networks which are large webs of artificially intelligent classical computers that are trained using computer algorithms to solve complex problems in a similar way to the human central nervous system, and where different layers examine different parts of the problem to combine to produce an answer.

Following years of mounting interest in a type of artificial intelligence (AI) called deep learning, the biggest public cloud infrastructure provider, Amazon Web Services (AWS), today announced its first Amazon AI services that make use of deep learning. Deep learning generally involves training artificial neural networks on lots of data, such as photos, and then getting them to make inferences about new data. One of AWS' top competitors, Google Cloud Platform, introduced the Cloud Machine Learning service that can do deep learning earlier this year. In China, the Alibaba public cloud has the DT PAI service available for AI workloads. There is the new Rekognition image recognition service -- presumably drawing on the talent and technology from deep learning startup Orbeus, whose team Amazon hired in the past year.

AI systems have had a huge impact on the content marketing space. Netflix uses AI to unite information from diverse datasets and provide users with personalized suggestions. Google is experimenting with machine learning to offer more relevant results to search queries. Twitter recently acquired its own deep-learning machine for expanded data on images.