Here is a situation that I found myself in, and I'm sure other smart-home users can relate to it. A couple of weeks ago, while on a business trip in China, my smart doorbell (such as ring) rang. I picked up my smartphone to see who was at the door. To my surprise, it was my daughter standing outside our house. Although it was daytime in my location, she was standing in the dark, back at home.

The defining advance of the next decade, if you listen to the prophets of Silicon Valley, will be the seismic and unavoidable ascent of artificial intelligence. It might be hard to take the thought seriously when a satnav sends you down a dead-end country road, or your phone's autocorrect feature turns a carefully-constructed text message to gibberish, but the milestones reached in the last year alone have been exceptional. DeepMind, the British AI company owned by Google, has defeated the world champion at Go, the ancient game that requires a finely-tuned sense of intuition to master. Driverless cars now seem like an inevitability rather than a curiosity. Error rates on image recognition technology have dropped from 25pc in 2011 to less than 4pc.

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At a theoretical level, the concept of artificial intelligence has fueled and sharpened the philosophical debates on the nature of the mind, intelligence, and the uniqueness of human beings. Insights from the field have proved invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language. Today, we're continuing our Q&A with Maggie Boden, Research Professor of Cognitive Science at the University of Sussex, and one of the best known figures in the field of Artificial Intelligence, answers four more questions about this developing area. ANNs are computer systems made of large number of interconnected units, each of which can compute only one (very simple) thing. They are (very broadly) inspired by the structure of brains.

Current deep learning technology is not enough for computers to understand language, a major figure in the field said today. The ability to learn the way people learn through observation and experience is what Facebook will use to teach chatbots and computers to carry on a conversation like a human, said Yann LeCun, the head of Facebook's artificial intelligence (AI) research lab. LeCun spoke about AI and steps being taken to make virtual assistant M stop relying on human training at the 2016 Wired Business Conference, as Wired reported. People have been a part of decisions made by Facebook's M since the bot debuted last year, before the launch of the company's bot platform. Facebook has begun research on ways to make machines understand language more independently.

In an earlier post I explored the value of using scalable machine learning to extract value from huge amounts of data. In this post, I will dive down into the technical side of things, particularly the challenges and benefits that come with making algorithms scalable on large clusters of computers. Machine learning algorithms are written to run on single-node systems, or on specialized supercomputer hardware, which I'll refer to as HPC boxes. They grew up in a world where they didn't have to scale across multiple nodes. It's relatively easy to get high performance when running algorithms on a single computer.

DeepMind, Google's artificial intelligence (AI) division, certainly thinks there's a risk. They've teamed up with Oxford University to develop a "red button" that would interrupt an AI machine's actions. Their paper "explores a way to make sure a learning agent will not learn to prevent (or seek!) being interrupted by the environment or a human operator."

The defining advance of the next decade, if you listen to the prophets of Silicon Valley, will be the seismic and unavoidable ascent of artificial intelligence. It might be hard to take the thought seriously when a satnav sends you down a dead-end country road, or your phone's autocorrect feature turns a carefully-constructed text message to gibberish, but the milestones reached in the past year alone have been exceptional. DeepMind, the British AI company owned by Google, has defeated the world champion at Go, the ancient game that requires a finely tuned sense of intuition to master. Driverless cars now seem like an inevitability rather than a curiosity. Error rates on image recognition technology have dropped from 25pc in 2011 to less than 4pc.

Artificial intelligence has shown what it can do when facing off against humans in ancient board games, with Deep Blue and Alpha Go already proving their worth on the world stage. While computers playing chess is nothing new, an online version of the ancient game lifts the veil of AI to let players see what the AI is thinking. You make your move and then see the computer come to life, calculating thousands of possible counter moves. Thinking Machine 6 is an AI-based concept art piece created by Martin Wattenberg. Rather than making players into chess champions, it shows the AI thinking process.

This week we posted a new Tech Note in which Jesse Engel discusses a new technique for speeding up the training of deep recurrent neural networks. This is Part II of a multi-part series detailing some of the techniques we've used here at Baidu's Silicon Valley AI Lab (SVAIL) to accelerate the training of recurrent neural networks. While Part I focused on the role that minibatch and memory layout play on recurrent GEMM performance, we shift our focus here to tricks we can use to optimize the algorithms themselves. There are two main takeaways in this blog post. First, differentiable graphs are a simple and useful tool for visually calculating complicated derivatives.