A new tool in deep learning renders passwords less secure. Last week, the credit reporting agency Equifax announced that malicious hackers had leaked the personal information of 143 million people in their system. That's reason for concern, of course, but if a hacker wants to access your online data by simply guessing your password, you're probably toast in less than an hour. Now, there's more bad news: Scientists have harnessed the power of artificial intelligence (AI) to create a program that, combined with existing tools, figured more than a quarter of the passwords from a set of more than 43 million LinkedIn profiles. Yet the researchers say the technology may also be used to beat baddies at their own game.

I am not a real data scientist. I have never used a deep learning framework, like TensorFlow or Keras. I have never touched a GPU. I don't have a degree in computer science or statistics. My degree is in mechanical engineering, of all things.

Those most responsive to change will become the ones best equipped to survive, or so the well-worn maxim goes. Or, as Jeff Immelt, CEO of General Electric, has stated: 'if you woke up an industrial company today, you will wake up as a software and analytics company tomorrow.' History is littered with examples of companies and individuals who have failed to embrace change. In the age of rapid technological advancements, it is becoming imperative for all companies to recognise the impact new technology will have on their operations. In an interview with CNBC, Peter Diamiandis, MIT Space Engineer, said that 40% of companies will no longer exist in 10 years if they refuse to embrace new technologies.

The Open Neural Network Exchange (ONNX) is described as a standard that will allow developers to move their neural networks from one framework to another, provided both adhere to the ONNX standard. Companies must choose the framework they're going to use for their model before they start developing it, but the framework that offers the best options for testing and tweaking a neural network aren't necessarily the frameworks with the features you want when you bring a product to market. The press release states that Caffe2, PyTorch, and Microsoft's Cognitive Toolkit will all support the ONNX standard when it's released this month. Models trained with one framework will be able to move to another for inference.

AI startup DigitalBridge has created an app, which is designed to help people visualise how their home would look with a new lick of paint or some new furniture before making a decision they might later regret. The idea for the app came from David Levine and his wife, who wanted to redecorate the dining room in their Manchester (UK) home, but couldn't picture how any of the designs would look on their walls. The technology uses GPU-accelerated machine learning and computer vision to let people visualise how wallpaper, a coat of paint, new furniture and other home decor would look in their own rooms. "I figured I wasn't the only person who couldn't imagine what wallpaper would look like in a room," Levine said. In an independent survey conducted for DigitalBridge, about a third of shoppers said they'd delayed or cancelled decorating projects because they couldn't picture how items would look in their own homes.

Machine Learning and Deep Learning are very promising technologies. Every week comes with its new hyped successes. Yet, when it comes to applying machine learning and deep learning many people keep making the same mistakes. Here is one that is particularly troublesome: people often miss that you need to provide examples to learn from. They expect systems to learn from raw data without any supervision or feedback.

Predictive decisions are becoming a huge trend worldwide catering wide sectors of industries by predicting which decisions are more likely to give maximum results. The data mining, statistics, machine learning allows users to discover predictive intelligence by uncovering patterns and showing the relationship among the structured and unstructured data. This book will help you build solutions which will make automated decisions. In the end tune and build your own predictive analytics model with the help of TensorFlow. This book will be divided in three main sections. In the first section-Applied Mathematics, Statistics, and Foundations of Predictive Analytics; will cover Linear algebra needed to getting started with data science in a practical manner by using the most commonly used Python packages.

As someone who often finds himself explaining machine learning to non-experts, I offer the following list as a public service announcement. Machine learning means learning from data; AI is a buzzword: Machine learning lives up to the hype: there are an incredible number of problems that you can solve by providing the right training data to the right learning algorithms. Call it AI if that helps you sell it, but know that AI, at least as used outside of academia, is often a buzzword that can mean whatever people want it to mean. Machine learning is about data and algorithms, but mostly data: There's a lot of excitement about advances in machine learning algorithms, and particularly about deep learning . But data is the key ingredient that makes machine learning possible.

I'm excited about today's announcement from Microsoft that they have chosen Intel's Stratix 10 FPGA to power their new deep learning platform codenamed Project Brainwave. In this post I want to clarify why real-time AI is so critical, explain how Intel FPGAs accelerate the performance of AI applications and share how this project is a natural extension of a decades-long collaboration between Intel and Microsoft. From its Bing search engine to its Azure cloud, Microsoft processes streams of data on an enormous scale, and much of that processing has to happen instantaneously. This is the case with the company's new deep learning platform, code-named Project Brainwave, which is designed for real-time artificial intelligence (AI). Whether you're talking about search queries, video analysis, sensor streams, or any other type of data, real-time AI means that the system applies the AI algorithm as fast as it receives data, with ultra-low latency.

We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" of the inner body---challenging for methods based solely on Monte Carlo integration or diffusion theory. We approach the problem differently. Instead of simulating all light transport during rendering, we pre-learn the spatial and directional distribution of radiant flux from tens of cloud exemplars. To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source. The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. We make the key observation that progressively feeding the hierarchical descriptor into the network enhances the network's ability to learn faster and predict with high accuracy while using few coefficients. We also employ a block design with residual connections to further improve performance. A GPU implementation of our method synthesizes images of clouds that are nearly indistinguishable from the reference solution within seconds interactively. Our method thus represents a viable solution for applications such as cloud design and, thanks to its temporal stability, also for high-quality production of animated content.