"Thought vector" is a term popularized by Geoffrey Hinton, the prominent deep-learning researcher now at Google, which is using vectors based on natural language to improve its search results. A thought vector is like a word vector, which is typically a vector of 300-500 numbers that represent a word. A word vector represents a word's meaning as it relates to other words (its context) with a single column of numbers. That is, the word is embedded in a vector space using a shallow neural network like word2vec, which learns to generate the word's context through repeated guesses. A thought vector, therefore, is a vectorized thought, and the vector represents one thought's relations to others.

New lip-reading technology developed at the University of East Anglia could help in solving crimes and provide communication assistance for people with hearing and speech impairments. The visual speech recognition technology, created by Helen L. Bear, PhD, and Prof Richard Harvey of UEA's School of Computing Sciences, can be applied "any place where the audio isn't good enough to determine what people are saying," Bear said. Those include criminal investigations, entertainment, and especially where are there are high levels of noise, such as in cars or aircraft cockpits, she said. Bear said unique problems with determining speech arise when sound isn't available -- such as on video footage -- or if the audio is inadequate and there aren't clues to give the context of a conversation. The sounds '/p/,' '/b/,' and '/m/' all look similar on the lips, but now the machine lip-reading classification technology can differentiate between the sounds for a more accurate translation.

For better or worse, we've already taken big steps toward creating computers that think independently. You may be surprised to find out that along with academics and innovative startups, some of the world's biggest technology companies are on the forefront of the research and development that is driving the race to true artificial intelligence. Will computers ever pass the Turing Test, fooling a human into thinking they're conversing with another human over an extended period of time? That will be one of the key indicators that AI has reached the tipping point, heading into the uncharted waters of true, self-aware artificial intelligence. Most of us interact with computers that make decisions for us every day, at least in the form of recommendations.

Let's get started with lesson three of our Practicing Deep Learning Series. So far our focus has been on a very simple network comprised of a single neuron. Though we've discussed its parts, we have neglected to show it actually doing anything. The focus of part three is to start diving into some actual code to illustrate the simple network we've discussed. We will spend a fair amount of time on the single neuron network so that you can get familiar with Keras while gaining an understanding of the basics of a simple network. As soon as this is complete, we will be moving onto multilayer networks, which are much more powerful than the simple networks below.

There are so many deep learning libraries to choose from. Which are the good professional libraries that are worth learning and which are someones side project and should be avoided. It is hard to tell the difference. In this post you will discover the top deep learning libraries that you should consider learning and using in your own deep learning project. Popular Deep Learning Libraries Photo by Nikki, some rights reserved.

My name's Stelios and I work in Search Marketing on some of the biggest brands in Australia with Big Data Analytics requirements. Throughout 2015, I did a lot. I've very excited to start off 2016 with a blog post about some of the most exciting things I've ever seen throughout my short but eventful digital career: Big Data Analytics and Machine Learning algorithms. One of the biggest events in 2015 in my opinion was Google sharing to the public that for the past three months, search results received direct input from a machine learning, possibly deep learning algorithm. They further remarked it had returned more accurate search results than a Google Engineer, who up till 2015 could've told you what made a page rank in Google.

Stitch Fix values the input of both human experts and computer algorithms in our styling process. As we've pointed out before, this approach has a lot of benefits and so it's no surprise that more and more technologies (like Tesla's self-driving cars, Facebook's chat bot, and Wise.io's augmented customer service) are also marrying computer and human workforces. Interest has been rising in how to optimize this type of hybrid algorithm. At Stitch Fix we have realized that well-trained humans are just as important for this as well-trained machines. There are similarities and differences between training humans and computers.

If you think chatbots are hot right now -- with how they're being used in psychotherapy, turning into racist trolls, and presenting an existential threat to Apple -- just wait until they turn into full-fledged personal assistants. In five years time, digital personal assistants will even more important than your smartphone, says University of Washington computer scientist Pedro Domingos, author of "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World." "What you have right now on your smartphone is dozens of apps," Domingos tells Tech Insider, "with each app doing it's own thing." On any given Friday night, you use one app to find a restaurant, another to buy a movie ticket, another to figure out how to get to where you're going, and another to find a date to take out with you. "It's incredibly annoying," he says, since the apps "don't talk to each other and you have to learn all these different interfaces."

A blockchain intelligence vendor focused on combining the technology used to record and verify transactions with big data and artificial intelligence has attracted a pair of top technologist to serve in senior positions. Skry Inc., formerly Coinalytics, unveiled a name change this week along with the addition of new CTO and chief data scientist. The block chain analytics and intelligence firm based in Silicon Valley said Akash Singh, former CTO for data science at Chinese telecommunications giant Huawei (SHE: 002502) will serve as Skry's CTO. Singh also worked at IBM (NYSE: IBM), contributing to the development of its Watson cognitive computing platform. Also joining Skry is artificial intelligence researcher Masoud Nikravesh, former director of computational science and engineering at the University of California at Berkeley's Center for Information Technology Research in the Interest of Society.

The way we do, understand and organize work is about to change fundamentally. Technologies such as artificial intelligence and machine learning, robotics and 3D-printing are not only disrupting business models, but also revolutionizing the labor market. If 3D-printing enables anybody to produce whatever whenever - what does that mean for the future of manufacturing? If the gig economy grows further, will that eventually replace the 9-to-5 model? If smart machines learn to execute more and more cognitive tasks – does that steer us towards a post-work-society?