Artificial intelligence (AI) is becoming increasingly ubiquitous within the technology industry, with capabilities that are much more practical than most consumers may think. Smarter e-mail spam filters and autonomous vehicles are just two examples of how deep-learning systems and AI technologies enable machines to better interact with their surrounding environment and provide vast benefits to users. By using a series of layers within a neural network to analyze data, deep-learning systems continue to change the way computers see, hear, identify, and even respond to objects in the real world. While the combination of such skills has made it possible for machines to perform increasingly complicated, human-like functions, the future of deep learning is now being dictated by user-driven input methods. Neural-network algorithms are among the most interesting machine-learning techniques.

In my own experiments I've found ShakeWeight to really help generalization when using LambProp. You can train neural nets with trillions of layers on MNIST and not over fit. Plus you get your heart rate up and work the forearms.

If you think Google has a controversial reputation at this point in its business evolution, buckle up because things are really stepping up a gear -- as its machine learning DeepMind division pushes for access to publicly funded healthcare data.

She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing. AI, simply put, is an attempt to make computers as smart, or even smarter than human beings.

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This week, self-driving Tesla had a fatal crash. Other than that – a lot about robots, can AI create an art, cloning animals and more! Ray Kurzweil and people like him believe the Singularity is just behind the corner and promise the new perfect world. They are very optimistic about the future. But sometimes you should listen to the other side to better understand the problem or vision.

Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation.

Google DeepMind is training its artificial intelligence software to detect early signs of eye diseases. It's aimed at detecting two diseases -- age-related macular degeneration and diabetic retinopathy. The latter can lead to blindness if not detected early. DeepMind is analyzing about 1 million eye scans to develop an algorithm that can catch early warning signs of the conditions. "If you have an OCT scan done, a machine learning algorithm will be able to tell you if it's urgent versus something that's not so urgent. The company has caught flak from privacy critics in the past.

Google's artificial intelligence division, DeepMind, has teamed up with a hospital in England on a research project that could lead to detecting vision loss and other eye problems sooner, Popular Mechanics writes. The article says the project calls for giving DeepMind's computer more than a million anonymous eye scans to let it learn patterns and make predictions. DeepMind's idea is to train AI to detect early signs of age-related macular degeneration as well as vision loss related to diabetes.

Editor's note: Before continuing, you may first want to read part 1 of this in-depth overview here. NovuMind and Numenta were the only AI companies we visited during this Silicon Valley trip, but even though we were only there for a limited time, we were still able to gain some insights on the developmental trends of AI and deep learning. After Dr. Wu departed from Baidu, he founded the startup NovuMind to focus on deep learning and AI technologies. I accompanied TalkingData's CEO Leo Cui, Mr. Che Pingjue, and Beijing GZT Network Technology's Vice Chairman, Mr. Li Yangshong on their visit to NovuMind. Leo is an excellent go player (ranked at amateur 5-dan).