Elon Musk invested millions in an effort to make sure that artificial intelligence is used for good instead of evil, but for much of the general public AI still seems like science fiction -- something far out in the distant future. However, if you talk to people who work closely with this kind of technology, which has been called deep neural networks, deep learning, smart machines, or machine intelligence, you'll find out that it has advanced significantly in the past few years, and even bigger progress is coming very soon. There are several signposts that indicate this progress, including big enterprises running their own experiments with AI systems, as well as a sudden wave of tech giants taking certain technologies open source. "The vast preponderance [of projects in enterprises] is still experimentation," Gartner Fellow and vice president Tom Austin told InformationWeek in an interview. He estimates that about half of large enterprises are experimenting with "smart computing" projects.

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid). Deep learning is often thought of as a set of algorithms that'mimics the brain'. A more accurate description would be an algorithm that'learns in layers'.

According to The Verge, Facebook is using artificial intelligence to produce detailed maps illustrating population density and the access to internet across the globe. This should help Facebook bring internet to parts of the world that are without access. Facebook has analysed 20 countries and 21.6 million square kilometres amounting to 350TB of data. Facebook is also reported to be creating deep learning AI which aims to find out what matters to Facebook users. Facebook is definitely not new to the AI game.

On Wednesday afternoon in the South Korean capital, Seoul, Lee Se-dol, the 33-year-old master of the ancient Asian board game Go, will sit down to defend humanity. On the other side of the table will be his opponent: Alphago, a programme built by Google subsidiary DeepMind which became, in October, the first machine to beat a professional human Go player, the European champion Fan Hui. That match proved that Alphago could hold its own against the best; this one will demonstrate whether "the best" have to relinquish that title entirely. Related: Google throws down the gauntlet. But can anyone beat its computer at Go? Lee, who is regularly ranked among the top three players alive, has been a Go professional for 21 years; Alphago won its first such match less than 21 weeks ago.

At the dawn of the cloud computing revolution, the winners were determined in their ability to rule with an "iron" fist (the hardware) but over the next ten years, we will see that having an iron-fist basis for rule is far easier than ruling with nuanced, multi-layered intelligence–and it takes a special kind of leader to do that. It takes a leader with boots on the ground for data collection, one who can deploy innumerable ears around every corner, every-watching eyes that are tuned to the whole of citizenry, listening without discretion, silently but busily meshing all of that information into a consciousness of sorts–a collective knowledge base that can be combed, smoothed, and rolled out to appease different divisions in that land. While indeed, this is a bit of dramatization for effect, this is exactly the kind of game of thrones that is happening just now among the giants of cloud. The iron throne is less a symbol than it used to be, the new seat of power is softer, changes position with the times, but always offers a 360 degree view. In short, winter is coming for some of the most seasoned of the old guard.

Just like nuclear physics, machine learning, AI, and data science can be used either for the better of for the worse. You can make either useful energy or terrible bombs using nuclear fission. The same applies to machine learning, and in my example below, it gets even worse than Hiroshima or Nagasaki. Here I am discussing a potential use of machine learning in military operations. The scenario below is entirely hypothetical.

Google's artificial intelligence (AI) ethics board, established when Google acquired London AI startup DeepMind in 2014, remains one of the biggest mysteries in tech, with both Google and DeepMind refusing to reveal who sits on it. Google set up the board at DeepMind's request after the cofounders of the 400 million research-intensive AI lab said they would only agree to the acquisition if Google promised to look into the ethics of the technology it was buying into. Business Insider asked Google once again who is on its AI ethics board and what they do but it declined to comment. A number of AI experts told Business Insider that it's important to have an open debate about the ethics of AI given the potential impact it's going to have on all of our lives. Artificial intelligence is the field of building computer systems that understand and learn from observations without the need to be explicitly programmed, as defined by Nathan Benaich, an AI investor at venture capital firm Playfair Capital.

At SVAIL (Silicon Valley AI Lab), our mission is to create AI technology that lets us have a significant impact on hundreds of millions of people. We believe that a good way to do this is to improve the accuracy of speech recognition by scaling up deep learning algorithms on larger datasets than what has been done in the past. These algorithms are very compute intensive, so much so that the memory capacity and computational throughput of our systems limits the amount of data and the size of the neural network that we can train. So a big challenge is figuring out how to run deep learning algorithms more efficiently. Doing so would allow us to train bigger models on bigger datasets, which so far has translated into better speech recognition accuracy.

Shortly after walking through the front doors of Baidu in Beijing last November, I was surprised to notice that my face had transformed into that of a cheerful- looking little dog. As I chatted with one of Baidu's AI researchers, the version of me shown on his smartphone had sprouted a very realistic-looking wet snout, fluffy ears, and a big pink tongue. The trick was performed on an app called Face You, released by Baidu last Halloween, which lets you add all sorts of spooky effects or animal characteristics to a digital image of your face. Face You makes use of an AI technique called deep learning to automatically identify key points on a person's face, so that software can then position and stretch a virtual mask with amazing accuracy. Deep learning is driving a lot more than just goofy apps at Baidu, though.

Major portions of this series of posts are excerpts from my new book Augmented: Life in the Smart Lane. I also asked my Facebook followers if there were any questions they'd like answered about AI here, and I've tried to incorporate answers to those questions into this series of posts also. Deep learning is a term we're increasingly using to describe how we teach Artificial Intelligence (AI) to absorb new information and apply it in their interactions with the real world. In an interview with the Guardian newspaper in May 2015, Professor Geoff Hinton, an expert in artificial neural networks, said Google is "on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation." Google is currently working to encode thoughts as vectors described by a sequence of numbers.