In 2025, in accordance with Moore's Law, we'll see an acceleration in the rate of change as we move closer to a world of true abundance. Here are eight areas where we'll see extraordinary transformation in the next decade: In 2025, $1,000 should buy you a computer able to calculate at 10 16 cycles per second (10,000 trillion cycles per second), the equivalent processing speed of the human brain. The Internet of Everything describes the networked connections between devices, people, processes and data. By 2025, the IoE will exceed 100 billion connected devices, each with a dozen or more sensors collecting data. This will lead to a trillion-sensor economy driving a data revolution beyond our imagination.

Global Big Data Conference's vendor agnostic Global Artificial Intelligence(AI) Conference is held on January 19th, January 20th, & January 21st 2017 on all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc..). It will be the largest vendor agnostic conference in AI space. The Conference allows practitioners to discuss AI through effective use of various techniques. Large amount of data created by various mobile platforms, social media interactions, e-commerce transactions, and IoT provide an opportunity for businesses to effectively tailor their services by effective use of AI. Proper use of Artificial Intelligence can be a major competitive advantage for any business considering vast amount of data being generated.

The key to this new tool is that N2D2 doesn't just compare different hardware on the basis of recognition accuracy. It can compare hardware in terms of "processing time, hardware cost, and energy consumption." This is critical, said Duranton, because different applications for deep learning will likely require different parameters in various hardware implementations. The N2D2 offers benchmarking on a variety of commercial off-the-shelf hardware -- including multi/many-core CPUs, GPUs and FPGA. Barriers to edge computing As a research organization, CEA has been studying how best to bring deep neural networks to edge computing.

Harriet Green tells us she is very excited about the prospect of the cognitive era. And so she should be. The former Thomas Cook CEO, switched holidays for robots, when she joined IBM in 2015 to head up its Watson, Internet of Things, commerce and education department. "IoT is just an amazing force of the digitisation movement – connecting things to people," she tells Essential Retail. "It's really all about Watson's ability to take vast amounts of structured and unstructured data and process that data, whether its smell, sound, video or text."

IMAGE: This is the Architecture of the Adversarial Autoencoder (AAE). The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials. Since 2010 deep learning systems demonstrated unprecedented results in image, voice and text recognition, in many cases surpassing human accuracy and enabling autonomous driving, automated creation of pleasant art and even composition of pleasant music.

We learned that the emerging ecosystem of containers, microservices, cloud scalability, devops, application monitoring, and streaming analytics is not a fad. It's the future, already powering Silicon Valley's and Seattle's most advanced tech companies. Throw in machine learning and IoT and you have a comprehensive framework for the next phase of enterprise IT, with continuous improvement as its founding principle. At the same time, we became more aware of the widening gulf between this new world and most existing enterprise IT operations. That's why the hoary phrase "digital transformation" refuses to die -- the leap from legacy to modernity requires profound, multiphase, across-the-board change.

More cities are adding smart city features so that Internet of Things (IoT) sensors and other connected technologies can improve the lives of citizens and visitors. As everyone knows, technology moves fast and finding out what's in store next is crucial to stay in the game. Diabetics have been waiting for years for better technology to manage their condition. Some got tired of waiting and hacked together an open source hardware and software solution. The concept of a smart city has been around for more than a decade, but it was only recently that the phrase "smart city" became part of the modern lexicon.

Humanity has a major food problem. The world's population is expected to increase significantly over the next three decades, but our capacity for food production will struggle to keep pace. Although global fertility rates are actually falling, a general increase in life expectancy will mean a steady increase in headcount during our lifetimes. One 2015 UN DESA report claims the world's population will hit 9.7 billion by 2050 – an increase of some 2.3 billion over today. Of course, a general rise in life expectancy reflects a higher standard of living for more of the world, which is cause for celebration.

Two cucumber farmers in Japan have received an unusual boost to their business after their son adapted Google's powerful artificial intelligence software to carry out the arduous task of sorting vegetables. Makoto Koike returned to his parent's cucumber farm in 2015 after working as a computer systems designer in the automobile industry. While helping out on the farm, Koike realized that one of the most time-consuming processes could be overcome through automation. Taking inspiration from Google's powerful artificial intelligence computer program AlphaGo, Koike set about designing a sorting system using a $35 Raspberry Pi 3 computer and Google's open source deep-learning platform TensorFlow. "Each cucumber has different color, shape, quality and freshness," Koike said.

The world's largest hedge fund is building a piece of software to automate the day-to-day management of the firm, including hiring, firing and other strategic decision-making. Bridgewater Associates has a team of software engineers working on the project at the request of billionaire founder Ray Dalio, who wants to ensure the company can run according to his vision even when he's not there, the Wall Street Journal reported. "The role of many remaining humans at the firm wouldn't be to make individual choices but to design the criteria by which the system makes decisions, intervening when something isn't working," wrote the Journal, which spoke to five former and current employees. The firm, which manages $160bn, created the team of programmers specializing in analytics and artificial intelligence, dubbed the Systematized Intelligence Lab, in early 2015. The unit is headed up by David Ferrucci, who previously led IBM's development of Watson, the supercomputer that beat humans at Jeopardy! in 2011.