Throughout modern history there has been a recurrent fear that jobs will be destroyed by technology. Everybody knows the story of the Luddites, bands of workers who smashed up machinery in the textile industry in the second decade of the 19th century. There has been wave after wave of technological advance since the first Industrial Revolution, and yet more people are working than ever before. Jobs have certainly been destroyed. Banks, for example, no longer employ clerks to log every transaction in ledgers with quill pens.

Qualcomm Technologies (QCOM) announced that it has acquired machine learning startup Scyfer for an undisclosed amount. Scyfer B.V. is a startup that is affiliated with University of Amsterdam and focuses on applying machine learning techniques to different fields. Through the acquisition, Qualcomm hopes to further incorporate AI technology into different devices, including cars, machines and robotics. Netherlands-based Scyfer was founded in 2013 to provide AI for companies in industries such as manufacturing, healthcare and finance. Management was headed by Co-founder and CTO Tijmen Blankevoort, who was previously co-founder of Cyno Intelligent System.

The emerging technologies on the Gartner Inc. Hype Cycle for Emerging Technologies, 2017 reveal three distinct megatrends that will enable businesses to survive and thrive in the digital economy over the next five to 10 years. Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms are the trends that will provide unrivaled intelligence, create profoundly new experiences and offer platforms that allow organizations to connect with new business ecosystems. The Hype Cycle for Emerging Technologies report is the longest-running annual Gartner Hype Cycle, providing a cross-industry perspective on the technologies and trends that business strategists, chief innovation officers, R&D leaders, entrepreneurs, global market developers and emerging-technology teams should consider in developing emerging-technology portfolios. The Emerging Technologies Hype Cycle is unique among most Gartner Hype Cycles because it garners insights from more than 2,000 technologies into a succinct set of compelling emerging technologies and trends. This Hype Cycle specifically focuses on the set of technologies that is showing promise in delivering a high degree of competitive advantage over the next five to 10 years (see Figure 1).

Earlier this year, I had the pleasure of addressing a large audience at a conference, presenting to a group of mixed professionals (including C-suite, Manager, Mid-level and executives, across a broad range of industry including IT, services govt, utilities, education, non-profit, hospital, health care, Financial services, insurance, automotive, Pharmaceuticals, aviation, aerospace, medical devices). The topic was, embracing and humanizing customer self-service channels; creating a personalized user experience in self-service channels. I have provided an overview of the presentation here for your convenience. At the conference I had the pleasure of meeting Mr. Rohit Mandana and we had a very interesting discussion about life outside of our professional engagements and then as these things do, our passion for our professions jumped back in and we were back and hot on the trail of bots, chatbots, AI and the role that they are playing in reshaping life as we know it. Sure there's all the latest stuff like Google Home and Uber Driverless (although now suspended I believe this tech will proceed) but we're beyond that now.

Are you looking to gain in-depth knowledge of machine learning and deep learning? Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios. The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques.

The overwhelming majority of IT executives in North America either have machine learning programs in place now or plan to have them in the near future. As in the related fields of artificial intelligence and data mining, machine learning is rapidly becoming as essential to 21st century business as email or Wi-Fi.The two leading implementations of machine learning today are: predictive analytics, which reduces uncertainty in decision-making; and recommender systems, which increase cart sizes by suggesting other items that match user preferences. A survey presented by 451 Research and Blazent found that more than two-thirds (67.3 percent) of execs either have or plan to have predictive analytics in place. Nearly the same number of execs (66.7 percent) currently use recommender systems or have implantation projects on the books. Once machine learning systems are in place, execs have found countless use cases for them across all enterprise.

The startup behind the Prisma style transfer app is shifting focus onto the b2b space, building tools for developers that draw on its expertise using neural networks and deep learning technology to power visual effects on mobile devices. It's launched a new website, Prismalabs.ai, detailing this new offering. Initially, say Prisma's co-founders, they'll be offering an SDK for developers wanting to add effects like style transfer and selfie lenses to their own apps -- likely launching an API mid next week. Then, in the "next month or so", they also plan to offer another service for developers wanting help to port their code to mobile. This was, after all, how the co-founders originally came up with the idea for the Prisma app -- having seen a style transfer effect working (slowly) on a desktop computer and realized how much potential it would have if it could be made to work in near real-time on mobile.

In this paper, we consider several compression techniques for the language modeling problem based on recurrent neural networks (RNNs). It is known that conventional RNNs, e.g, LSTM-based networks in language modeling, are characterized with either high space complexity or substantial inference time. This problem is especially crucial for mobile applications, in which the constant interaction with the remote server is inappropriate. By using the Penn Treebank (PTB) dataset we compare pruning, quantization, low-rank factorization, tensor train decomposition for LSTM networks in terms of model size and suitability for fast inference.

The reference books that line the shelves of developer Ultra Ultra's modest Copenhagen office offers insight into the aesthetic of its first game, Echo. Prometheus: The Art of the Film and Star Wars provide sci-fi reference points, while Metal Gear Solid, Blame! and Neon Genesis Evangelion--all three of which are represented in some form on the shelf--provide the inspiration for character design. Echo is made up of many familiar parts, but parts that are remixed in a way that makes them feel new. This is fundamental to Echo not just aesthetically, but also mechanically. Unsurprisingly, given Ultra Ultra's staff of ex-IO Interactive Hitman developers, Echo is a stealth game.

It could be argued there is only one proven Big Data application -- web search. Nothing so far has met the sheer size and complexity of indexing the web at the precision, recall, and freshness Google delivers. In its quest to structure the web well beyond text documents, around 2011 Google realized it had to fundamentally change the way it was indexing images. Google's DistBelief system -- the inception of the newly formed Google Brain team -- pushed the boundaries of how deep learning could be applied to massive problems by training on a highly distributed configuration of thousands of CPUs. The publication of this system marked a key milestone for Google and the tech industry at-large. By applying the deep learning techniques Geoff Hinton and Yann LeCun had been researching for over a decade, Google was finally able to create a production system that could scale to understand and structure information from images.