Scientists at IBM Research – Almaden have demonstrated that the TrueNorth brain-inspired computer chip, with its 1 million neurons and 256 million synapses, can efficiently implement inference with deep networks that approach state-of-the-art classification accuracy on several vision and speech datasets. This will open up the possibilities of embedding intelligence in the entire computing stack from the Internet of Things, to smartphones, to robotics, to cars, to cloud computing, and even supercomputing. The novel architecture of the TrueNorth processor can classify image data at between 1,200 and 2,600 frames per second while using a mere 25 to 275 mW, which is effectively greater than 6,000 fps per Watt. Like that kung fu master in the movies who simultaneously fights assaults from many opponents, this processor can detect patterns in real time from 50-100 cameras at once – each with 32 32 color pixels and streaming information at the standard TV rate of 24 fps – while running on a smartphone battery for days without recharging. The breakthrough was published this week in the peer-reviewed Proceedings of the National Academy of Sciences (PNAS).

Artificial intelligence is slowly, but surely, showing potential in improving modern healthcare. In the UK, researchers recently used four AI algorithms that beat doctors in predicting heart attacks. Moreover, Google's DeepMind is fighting blindness with machine learning. Lately, medical science is seeing potential in the ability of AI systems to find meaning in datasets that are too complicated for us to process. This potential is perfectly applicable in modern healthcare practices.

What did we learn Ability to predict BTC ZAR price based on USD seems possible..? Ability to quickly dump and store data for later analysis hugely valuable Spark not so great for time-series data (?) Future plans incl.

"As we move into 2017, Baidu's strategic evolution from a mobile-first to an AI-first company continues to gain momentum." There was a time when Baidu, Inc. (BIDU) was primarily a Chinese language Internet search provider, often dubbed as the'Google of China.' In recent years, Baidu embarked on a new journey that makes it a search, artificial intelligence (AI), and autonomous driving company that is working towards innovative, next-generation products and revenue streams. Baidu is leading the AI revolution in the mainland with huge investments, collaborations and acquisitions. AI technologies encompass deep learning, image recognition, computer vision, robotics, collaborative systems, machine learning and natural learning process, among other things.

I think that if you read Deep Learning chapters 1–5, you should be able to learn everything you need for that project except the Python programming. It's a little bit hard for me to know how feasible that is because it's hard to put myself in the position of someone coming in with no technical knowledge. It will obviously take a lot of patience and effort to absorb that much material rapidly starting from zero, but we tried to put enough detail in the book that you could get that far.

In March 2016, DeepMind's AlphaGo beat Lee Sedol, who at the time was the best human Go player in the world. It represented one of those defining technological moments like IBM's Deep Blue beating chess champion Garry Kasparov, or even IBM Watson beating the world's greatest Jeopardy! Yet these victories, as mind-blowing as they seemed to be, were more about training algorithms and using brute-force computational strength than any real intelligence. Former MIT robotics professor Rodney Brooks, who was one of the founders of iRobot and later Rethink Robotics, reminded us at the TechCrunch Robotics Session at MIT last week that training an algorithm to play a difficult strategy game isn't intelligence, at least as we think about it with humans. He explained that as strong as AlphaGo was at its given task, it actually couldn't do anything else but play Go on a standard 19 x 19 board.

I found www.fast.ai in April 2017 and was a bit blown away. An AI course focused on actually getting things done? I was just finishing Yaser Abu-Mostafa's CS1156x'Learning from Data' on edX, and while a great theoretical course, it did cut down a lot of my enthusiasm for Machine Learning. I guess learning to code in Python while writing Linear Regression models by hand has that effect. What really got me about Jeremy Howard's'Practical Deep Learning I' (which I'll call FAI01/FADL1) was that, over and over again, he'd explain a thing, you'd go do it, and all of a sudden you're catapulted to the forefront of applied ML.

The next generation of Microsoft's augmented reality headset, HoloLens 2, will feature a custom-made artificial intelligence chip, the company announced Monday. The new chip will be capable of analyzing speech and visual data directly without having to send the information to a cloud server for analysis. That change is meant to speed up performance for the Hololens 2 and make it work better as a mobile device. The new A.I. "coprocessor," a silicon chip designed but not manufactured by Microsoft, is meant to move the HoloLens further in terms of accurate recognition of different visuals and sounds being collected by the device. Typically, the HoloLens and other devices (like Siri on the iPhone) have to use cloud-based servers equipped with deep learning networks to make sense of the data the user provides.

Google's DeepMind has revealed a radical new research project designed to give AI's an imagination. The breakthrough means that systems will be able to think about their actions, and undertake'deliberate reasoning.' The radical system uses an internal'imagination encoder' that helps the AI decide what are and what aren't useful predictions about its environment. The breakthrough means that systems will be able to think about their actions, and undertake'deliberate reasoning.' The agents use an'imagination encoder'- a neural network which learns to extract any information useful for the agent's future decisions, but ignore that which is not relevant.

Google's London-based AI outfit DeepMind has created two different types of AI that can use their'imagination' to plan ahead and perform tasks with a higher success rate than AIs without imagination. Sorry if I made you click because you wanted AIs predicted flying cars. I promise this is cool too. In a post on their site, DeepMind researchers give a short review of "a new family of approaches for imagination-based planning." The so-called Imagination-Augmented Agents, or I2As, use an internal'imagination encoder' that helps the AI decide what are and what aren't useful predictions about its environment.