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.

The omnipotent mythology surrounding artificial intelligence and machine learning, particularly as it relates to investing and trading, is the real bubble that needs to be burst. Such are messages from two of the quantitative trading world's brightest minds. Speaking at the Bloomberg Markets Most Influential Summit Wednesday, David Siegel, founder of the 37 billion Two Sigma Investments, warned the audience that artificial intelligence lacks common sense. Seven days ago Ewan Kirk, founder of the 4.5 billion Cantab Capital Partners and an original managed futures CTA pioneer, penned an influential piece in Institutional Investor titled "Beneath the Sizzle of Artificial Intelligence." His advice to investors was clear: buy the steak, not the sizzle.

The excitement around artificial intelligence (AI) has created a dynamic where perception and reality are at odds: everyone assumes that everyone else is already using it, yet relatively few people have personal experience with it, and it's almost certain that no one is using it very well. This is AI's third cycle in a long history of hype – the first conference on AI took place 60 years ago this year – but what is better described as "machine learning" is still very young when it comes to how organisations implement it. While we all encounter machine learning whenever we use autocorrect, Siri, Spotify and Google, the vast majority of businesses are yet to grasp its promise, particularly when it comes to practically adding value in supporting internal decision making. Over the last few months I've been asking a wide range of leaders of large and small companies how and why they are using machine learning within their organisations. By exposing the areas of confusion, concerns and different approaches business leaders are taking, these conversations highlight five interesting lessons.

Artificial Intelligence (AI) has always been the subject of dreams and visions about the distant future of humankind. Even though we are nowhere near a conscious robotic system, nowadays, AI systems are ubiquitous and showing tremendous successes in various fields of our everyday life. We are using these on a daily basis, often without even noticing. Whether it is the Virtual Personal Assistants on our mobile phones (such as Siri, Google Now, and Cortana), self-driving cars, the ranking of the web pages given your search query, or the classical textbook examples such as spam filtering and recommendation systems of online media providers and marketplaces like Amazon. Various fields of AI have made a major leap forward in the recent years. As most AI systems are too complex to be defined manually, we have to resort to automatically learning rules and patterns from data using sophisticated Machine Learning (ML) techniques.