Here is the tech news you need to know this Tuesday. The mother of Uber CEO Travis Kalanick has died in a boating accident. His father is in a "serious" condition. The pair married at their home in Brentwood, California. DeepMind now plans to apply its algorithms to more scientific matters. The "social change platform" is best known as a site for activists to post petitions to the public.

Deep learning and Machine learning are becoming more and more important in today's ERP (Enterprise Resource Planning). During the process of building the analytical model using Deep Learning or Machine Learning the data set is collected from various sources such as a file, database, sensors and much more. But, the collected data cannot be used directly for performing analysis process. Therefore, to solve this problem Data Preparation is done. Data Preparation is an important part of Data Science. It includes two concepts such as Data Cleaning and Feature Engineering.

PwC's report on the impact of automation on jobs suggests that almost a third of UK jobs are at risk of being replaced by robots following advancements in artificial intelligence (AI) and automation. The report warns that without careful consideration certain low-skilled jobs will be drastically exposed over the next 15 years. But what if people step back from the doom-and-gloom for a second? Businesses can't ignore the significant benefits that come from bringing AI into the workplace forever. It wasn't too long ago that AI largely belonged in the genre of science fiction.

It's another year, and another generation of ARM Cortex processors are being launched to power the newer generation of smartphones and mobile devices. The Cortex-A55 and Cortex-A75 high-efficiency application processors may not yet be available for devices at this time, but they promise an amazing future filled with smartphones that get smarter as you use them. To date, the best Cortex processors you can have are the Cortex-A53 and the Cortex-A73, and chipmakers have been using these in big.LITTLE architecture configurations. The next generation will be the Cortex-A55 and A75. The Cortex-A55 will be the processor for midrange chipsets, mixing competent performance with efficient power handling.

Google CEO Sundar Pichai was obviously excited when he spoke to developers about a blockbuster result from his machine-learning lab earlier this month. Researchers had figured out how to automate some of the work of crafting machine-learning software, something that could make it much easier to deploy the technology in new situations and industries. But the project had already gained a reputation among AI researchers for another reason: the way it illustrated the vast computing resources needed to compete at the cutting edge of machine learning. A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). They told MIT Technology Review that the project had tied up hundreds of the chips for two weeks solid--making the technique too resource-intensive to be more than a research project even at Google.

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

A Canadian startup called Lyrebird has announced that it has developed a platform capable of mimicking human voice with a fraction of the audio samples required by other platforms such as Google DeepMind and Adobe Project VoCo. The Lyrebird synthesis software requires only 60 seconds of sample audio to produce it's synthetic sample. VoCo needs about 20 minutes to do the same. The quality of the voice reproductions that the software can make are mixed. Some are better than others.

Published 29 May 2017 by Mr. C One of the trickiest problems we have struggled to resolve at Sidepodcast is how to produce timely news podcasts when Mrs Christine is unwell. I lack the talent to step in to replace her and we've bounced around all manner of alternate ideas from leaning on the community to record shows for us, to cutting up old recordings to splice together new sentences. It turns out this girl is hard to replace. Over the weekend Christine was out with a cold, so we trialled a new idea - using synthesised artificial intelligence from the geniuses at Amazon to speak aloud instead. Amazon describe their product as an "AI service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice". There are various regional dialects to choose from.

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.

Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number of applications. The effectiveness of DNN methods can be attributed to leveraging the abundance of supervised data to learn value functions, Q-functions, and policy function approximations without the need for feature engineering. Nevertheless, the deployment of DNN-based predictors with very deep architectures can pose an issue due to computational and other resource constraints at test-time in a number of applications. We propose a novel approach for reducing the average latency by learning a computationally efficient gating function that is capable of recognizing states in a sequential decision process for which policy prescriptions of a shallow network suffices and deeper layers of the DNN have little marginal utility. The overall system is adaptive in that it dynamically switches control actions based on state-estimates in order to reduce average latency without sacrificing terminal performance. We experiment with a number of alternative loss-functions to train gating functions and shallow policies and show that in a number of applications a speed-up of up to almost 5X can be obtained with little loss in performance.