Google ratcheted up its rivalry with Apple and Amazon by unveiling new smartphones, an internet-connected speaker that channels a digital assistant, and other gadgets the company hopes to make indispensable. The devices announced Tuesday are part of Google's bold move to design and sell its own hardware, instead of just supplying Android and other software for other companies to make products. Google's previous attempts at hardware have had limited distribution and included such high-profile flops as its internet-connected Glass headgear. This time around, Google is betting that it can design software and hardware to work seamlessly with each other. That's an art Apple mastered over the past 15 years as it turned out finely crafted iPods, iPhones, iPads and Macs.

Neural attention has been applied successfully to a variety of different applications including natural language processing, vision, and memory. An attractive aspect of these neural models is their ability to extract relevant features from data, with minimal feature engineering.Brian Cheung is a PhD Student at UC Berkeley working with Professor Bruno Olshausen, as well as an Intern at Google Brain. By drawing inspiration from the fields of neuroscience and machine learning, he hopes to create systems which can solve complex vision tasks using attention and memory. At the Deep Learning Summit in Singapore, Brian will share expertise on the fovea as an emergent property of visual attention, ways we can extend this ability to learning interpretable structural features of the attention window itself, and finding conditions where these emergent properties are amplified or eliminated providing clues to their function. I asked him a few questions ahead of the summit to learn more.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

In fact, when I look at where computing is headed, it's clear to me that we are evolving from a mobile-first to an AI-first world. Devices like Amazon's Echo and Google Home are "always-on" intelligent assistants designed to sit quietly and await our bidding. As the devices recede into the background it's the AI-powered services that will come to the fore.

Tags: AI, Deep Learning, New York City, NY, RE.WORK, Singapore Explore the latest machine learning research, technology and applications, with 2 RE.WORK events: the Deep Learning Summit in Singapore (20-21 Oct) and the Machine Intelligence Summit in New York City (Nov 2-3).

Modern technology is fundamentally reshaping our electoral process. It's time to make it work for us. Public and open debate is a central achievement of democracy. It is a forum for people to articulate their viewpoints, exhibiting candid passion and offering sound facts and logic. It is also a powerful instrument to share informed knowledge with a wide audience of people who possess less subject matter expertise. In the philosophy of Aristotle, public debate is the task of a class of virtuous men, for they, in his mind, constitute ideal rulers and are responsible for sharing their wisdom with common households.

Lately, Artificial Intelligence and Machine Learning is a hot topic in the tech industry. Perhaps more than our daily lives Artificial Intelligence (AI) is impacting the business world more. There was about 300 million in venture capital invested in AI startups in 2014, a 300% increase than a year before (Bloomberg). AI is everywhere, from gaming stations to maintaining complex information at work. Computer Engineers and Scientists are working hard to impart intelligent behaviour in the machines making them think and respond to real-time situations.

Chatterbox Labs are looking for Data Scientists at junior, mid and senior levels to join our growing, UK-based team to help research, develop and extend our existing cognitive technologies. You should hold a PhD in the field of Machine Learning (or a related/similar field) and have evidence of applied research as well as theoretical. You will join our technical team who focus on the research and development of our multi-lingual Cognitive Engine for short form and long form text classification. You will work alongside our Chief Technology Officer and other Data Scientists who are also working on Cognitive Computing, Image Processing & Deep Learning methods. The specifics of each job will vary depending upon experience levels – we recognize that those exploring junior positions may have minimal experience outside of academia/research institutions.

The technology industry is plagued with buzzword bingo in support of the fashion driven nature of the technology beast. Often confusing and occasionally downright ridiculous, we're never going to prevent smart ass marketers, ably supported by their anal-yst surrogates from making stuff up. The least some of us can do is make clear what is under discussion without mindlessly parroting what others say or conflating one concept with another. The latest in this stream of marketing laden garbage is AI or Artificial Intelligence, smeared with ML or Machine Learning and DL or Deep Learning. Add a soupçon of'robotics' just to amp the volume to something people can'get' and you have the potential for an exotic mix that both captivates the sentient mind but can also plant fear.

Well this tutorial has been a long time coming. Neural Networks (NNs) are something that i'm interested in and also a technique that gets mentioned a lot in movies and by pseudo-geeks when referring to AI in general. They are made out to be these really intense and complicated systems when in fact they are nothing more than a simple input output machine (well at least for the standard Feed Forward Neural Networks (FFNN)). As with any field the more you delve into it the more technical it gets and NNs are the same, the more research you do into them the more complicated architectures, training techniques, activation functions become. For now this is just a simple primer into NNs.