Whitened Neural Network (WNN) is a recent advanced deep architecture, which improves convergence and generalization of canonical neural networks by whitening their internal hidden representation. Unlike WNN that reduced runtime by performing whitening every thousand iterations, which degenerates convergence due to the ill conditioning, we present generalized WNN (GWNN), which has three appealing properties. First, GWNN is able to learn compact representation to reduce computations. Second, it enables whitening transformation to be performed in a short period, preserving good conditioning. Third, we propose a data-independent estimation of the covariance matrix to further improve computational efficiency.

Sci-fi level Artificial Intelligence (AI) like HAL 9000 was promised since 1960s, but PCs and robots were dumb until recently. Now, tech giants and startups are announcing the AI revolution: self-driving cars, robo doctors, robo investors, etc. PwC just said that AI will contribute $15.7 trillion to the world economy by 2030. "AI" it's the 2017 buzzword, like "dot com" was in 1999, and everyone claims to be into AI. Don't be confused by the AI hype. Is this a bubble or for real?

Over the past decade, machine learning techniques have made substantial advances in many domains. In health care, global interest in the potential of machine learning has increased; for example, a deep learning algorithm has shown high accuracy in detecting diabetic retinopathy.1 There have been suggestions that machine learning will drive changes in health care within a few years, specifically in medical disciplines that require more accurate prognostic models (eg, oncology) and those based on pattern recognition (eg, radiology and pathology). However, comparative studies on the effectiveness of machine learning–based decision support systems (ML-DSS) in medicine are lacking, especially regarding the effects on health outcomes. Moreover, the introduction of new technologies in health care has not always been straightforward or without unintended and adverse effects.2 In this Viewpoint we consider the potential unintended consequences that may result from the application of ML-DSS in clinical practice.

Deep learning is a branch of machine learning where artificial neural networks -- algorithms inspired by the way neurons work in the brain -- find patterns in raw data by combining multiple layers of artificial neurons. As the layers increase, so does the neural network's ability to learn increasingly abstract concepts. For example, neural networks can learn how to recognize human faces. The first layer of neurons takes pixels from example images, the next layers learn the concept of how pixels form an edge, then that layer passes that knowledge to other layers, combining that knowledge of edges to learn the concept of a face. This process of layering knowledge continues until BAM! -- the neural network algorithms recognize specific features, and ultimately specific faces.

But artificial intelligence now'thinks' it has the answer. Using deep learning techniques, data scientists from Warwick Business School trained a computer system on 200,000 images from the website Scenic-or-Not, where members of the public vote on how beautiful a British scene is. These include Loch Scavaig on the Isle of Skye... and Newbury Road roundabout. The project was linked to earlier studies by the same team from Warwick's Data Science Lab that showed a direct correlation between residing in a scenic location and good health. If the AI could recognise beauty like a human, city planning for wellbeing could potentially be automated.

Want to get started with data science and artificial intelligence, but lack the skilled personnel to do it? You could be a candidate for machine learning software company H2O.ai's The Mountain View, California company today announced the beginning of beta testing for Driverless AI. The new product combines H2O.ai's automated machine learning and deep learning products, AutoML and AutoDL respectively, which provide automatic training and tuning of models on GPU-accelerated hardware. It's all about reducing complexity and making the most of what data science skill sets users already have, the company says.

The best models around are based on generative adversarial networks. Clune says that GAN is composed of two neural networks playing a game of cops and robbers--or cops and forgers, rather. These neural networks are commonly referred to as "deep neural networks"--they take data and combine them through a series of many transformations. For instance, GAN is often given images of tumors and then asked to predict whether they are cancerous. The high number of transformations is what makes a neural network "deep."

Intel on Thursday launched its Movidius Neural Compute Stick, which will enable users to easily develop artificial intelligence and deep learning applications, and even use the technology in research. It consumes just 1 watt of power and provides 100 gigaflops of performance. It runs on a Caffe framework and connects to a PC via USB 3.0. It requires a 64-bit PC running Ubuntu 16.04, with 1GB RAM and 4GB onboard memory to function. More importantly, it can easily function offline, which means that it will make it easier to develop AI prototypes.

All of a sudden every one is talking about them – irrespective of whether they understand the differences or not! Whether you have been actively following data science or not – you would have heard these terms. If you have often wondered to yourself what is the difference between machine learning and deep learning, read on to find out a detailed comparison in simple layman language. I have explained each of these term in detail. Then I have gone ahead to compare both of them and explained where we can use them. Let us start with the basics – What is Machine Learning and What is Deep Learning.

Today it was made public that Google has acquired Halli Labs, a very young (its first public appearance was on May 22 of this year) startup based out of Bengaluru, India, that was focused on building deep learning and machine learning systems to address what it describes as "old problems." The company says it will be joining Google's Next Billion Users team "to help get more technology and information into more people's hands around the world." Halli announced the news itself earlier today in a short post on Medium, and Caesar Sengupta, a product management VP at Google, also confirmed the acquisition with a Tweet. Welcome @Pankaj and the team at @halli_labs to Google. Google has now also confirmed the acquisition with a short statement it provided to TechCrunch.