Google's DeepMind has moved on from playing Go to more serious matters - attempting to solve some of the world's biggest health problems. Projects include a tie-up with London Moorfields eye hospital, which will see it using one million eye scans to train its artificial intelligence system to diagnose potential sight issues, and development of an app to help doctors spot kidney disease. Google's entry on to the healthcare scene has been welcomed by some, notably doctors who are desperate to apply some cutting-edge technology to antiquated NHS systems. But less so by privacy groups and some patients, who have been surprised and concerned that their data - in some cases not anonymised - can be shared with the tech giant. So what does Google want with our health data and should we be worried?

The media interest surrounding deep learning has grown exponentially in the last few years. But what does it actually mean, and how will it change business and society? Deep learning is a subset of machine learning that refers to mapping artificial neural networks to recreate some of the same processes that the human brain performs, and using algorithms with speech, images and text, to recognize, identify and understand patterns in the data. Although this sounds simple, it involves complex processes and functions – but once trained, the application of deep learning algorithms could be world changing. For instance, a machine that learns like a human, but can rapidly process thousands of images and recognize patterns, is already showing promise for applying deep learning to medical imaging.

But stepping back from it, I realized that Siri was everywhere in the roughly two-hour and fifteen-minute presentation. What we saw on Monday was next-level Siri. The software started as a pretty simple voice assistant, then graduated to digital assistant. It is now nothing less than an artificial intelligence -- one that can rain AI fairy dust on any number of services and third-party apps. Hello, SiriKit As predicted, Siri finally has an SDK -- sort of.

Deep Learning Methods Within Video An End Game Application – We'll explore the use cases of using deep learning to drive higher video views. The coming Valhalla of Deep Learning is being realized in visual object recognition and image classification within video. Mobile video has and continues to transform the way video is being distributed and consumed. Mobile video advertising is the fastest growing segment projected to account for 25 billion worth of ad spend by 2021. Deep Learning and artificial intelligence are also growing within the very same companies who are jockeying for your cognitive attention.

The purpose of this blog post is to demonstrate how to install the Keras library for deep learning. Let me start by saying that Keras is my favorite deep learning Python library. It's a minimalist, modular neural network library that can use either Theano or TensorFlow as a backend. Furthermore, the primary motivation behind Keras really resonates with me: you should be able to experiment super quickly -- going from idea to result, as fast as possible. Coming from a world that mixes both academia and entrepreneurship, the ability to iterate quickly is extremely valuable, especially in the deep learning world where it can take days to weeks to train just a single model.

It is a strong indicator of today's globalized world and rapidly growing access to Internet platforms, that we have users from over 188 countries and 500 cities globally using our Text Analysis and News APIs. Our users need to be able to understand and analyze what's being said out there, about them, their products, services, or their competitors, regardless of the locality and the language used. Social media content on platforms like Twitter, Facebook and Instagram can provide unrivalled insights into customer opinion and experience to brands and organizations. A look at online review platforms such as Yelp and TripAdvisor, as well as various news outlets and blogs, reveals similar patterns regarding the variety of language used. Therefore, no matter if you are a social media analyst, or a hotel owner trying to gauge customer satisfaction, or a hedge fund analyst trying to analyze a foreign market, you need to be able to understand textual content in a multitude of languages.

The media interest surrounding deep learning has grown exponentially in the last few years. But what does it actually mean, and how will it change business and society? Deep learning is a subset of machine learning that refers to mapping artificial neural networks to recreate some of the same processes that the human brain performs, and using algorithms with speech, images and text, to recognise, identify and understand patterns in the data. Although this sounds simple, it involves complex processes and functions - but once trained, the application of deep learning algorithms could be world changing. For instance, a machine that learns like a human, but can rapidly process thousands of images and recognise patterns, is already showing promise for applying deep learning to medical imaging.

I think I read about activation functions "squashing inputs into outputs" five or six times before I finally started getting the gist of them. This was no fault of the material I was reading, they were just this abstract concept that I just accepted and shoved my inputs into when I was building a neural network. However, I finally feel that I have a much clearer picture of why they are used and how well each one performs for certain tasks. I hope somehow I can squash this concept into your head by the end of this article so something useful can come out in the future! I'm sure you will catch on much faster than I did.

So the question I have is: what does the frontier of the space of optimal networks look like, what are the inherent limits of depth vs expressivity of these models, and are there dimensional scaling laws that can describe all this in an information theoretic way? This recent paper gives a great treatment on the expressivity of convolution networks by using a deep layered architecture that generalizes convolutional neural networks called sim-nets. As a simple first step I wanted to see what could be done to visualize the operations a deep neural net performs. So I constructed a standard network that takes vector inputs of size 2 and produces vector outputs of size 3 which we can think of as a mapping of the cartesian plane into RGB color space. Taking many copies of this net and randomly initializing them, (with normally distributed weights and biases) we can plot them in a grid and see the networks' outputs as a set of images.

The device could be a major stride in diagnosing malaria, which affects over 200 million people annually. Malaria is a parasitic infection most commonly spread by mosquitos. It can be detected by assessing a patient's blood sample via microscope. Usually, a trained professional must be present to diagnose malaria, specifically a microscopist who can identify the malaria parasites in blood samples. But in the poorest areas of the world, where malaria is so prevalent, these professionals are in short supply.