The network which recently partnered with Silicon Valley-based startup accelerator Plug and Play, eyes big growth potential in this space and has already made investments in startups like vPhrase and ConfirmTkt. It is set to announce one more investment in this vertical in the next one month, reports ET. Commenting on the investment plans, Apoorv Ranjan Sharma, President, Venture Catalyst, said, "We believe AI is going to hugely impact all verticals of business and will be critical for those like ecommerce since the algorithms developed by such companies have the power to read data in a much faster and efficient way, impacting business decisions and saving both time and costs." Sharma has already invested in couple of AI startups for example ConfirmTkt which has developed an algorithm to predict whether a ticket booked is likely to be confirmed or not. "Currently all online ticket-booking platforms allow ticket bookings but there is no mechanism to predict whether it will be confirmed or not," Sharma adds further.

Elon Musk invested millions in an effort to make sure that artificial intelligence is used for good instead of evil, but for much of the general public AI still seems like science fiction -- something far out in the distant future. However, if you talk to people who work closely with this kind of technology, which has been called deep neural networks, deep learning, smart machines, or machine intelligence, you'll find out that it has advanced significantly in the past few years, and even bigger progress is coming very soon. There are several signposts that indicate this progress, including big enterprises running their own experiments with AI systems, as well as a sudden wave of tech giants taking certain technologies open source. "The vast preponderance [of projects in enterprises] is still experimentation," Gartner Fellow and vice president Tom Austin told InformationWeek in an interview. He estimates that about half of large enterprises are experimenting with "smart computing" projects.

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid). Deep learning is often thought of as a set of algorithms that'mimics the brain'. A more accurate description would be an algorithm that'learns in layers'.

According to The Verge, Facebook is using artificial intelligence to produce detailed maps illustrating population density and the access to internet across the globe. This should help Facebook bring internet to parts of the world that are without access. Facebook has analysed 20 countries and 21.6 million square kilometres amounting to 350TB of data. Facebook is also reported to be creating deep learning AI which aims to find out what matters to Facebook users. Facebook is definitely not new to the AI game.

I took the tensorflow implementation of the language model from Zaremba et al., 2014, and changed the loss function from what it was (crossentropy with 1-hot vector representing the correct word) to a loss made up of two terms, the first is the loss from before and the second is a crossentropy loss with a 1-hot vector representing the closest synonym to the target word. I tried playing around with the weighting of these two terms, but no matter what I did the results did not improve over the original model. Doesn't this new loss function basically tell the network "the next correct word is'dog', but if you say its'puppy' thats also OK"?

On Wednesday afternoon in the South Korean capital, Seoul, Lee Se-dol, the 33-year-old master of the ancient Asian board game Go, will sit down to defend humanity. On the other side of the table will be his opponent: Alphago, a programme built by Google subsidiary DeepMind which became, in October, the first machine to beat a professional human Go player, the European champion Fan Hui. That match proved that Alphago could hold its own against the best; this one will demonstrate whether "the best" have to relinquish that title entirely. Related: Google throws down the gauntlet. But can anyone beat its computer at Go? Lee, who is regularly ranked among the top three players alive, has been a Go professional for 21 years; Alphago won its first such match less than 21 weeks ago.

So why does it take so much computing power and time to teach AI? The problem is that modern neural networks like Google's DeepMind or IBM Watson must perform billions of tasks in in parallel. That requires numerous CPU memory calls, which quickly adds up over billions of cycles. The researchers debated using new storage tech like resistive RAM that can permanently store data with DRAM-like speeds. However, they eventually came up with the idea for a new type of chip called a resistive processing unit (RPU) that puts large amounts of resistive RAM directly onto a CPU.

DEDHAM, MA--(Marketwired - Jan 28, 2016) - Rage Frameworks, a provider of knowledge-based automation technology and services, today announced major additions to its pioneering RAGE AI platform, adding two new powerful frameworks to its suite. RAGE AI significantly extends the frontier of deep learning and machine intelligence technology from "natural language processing" to "natural language understanding." RAGE AI incorporates deep linguistic parsing and proprietary innovations to understand meaning in context, which makes its solutions completely transparent, auditable and flexible. The platform facilitates unsupervised to supervised learning and contains several innovations to support automated knowledge acquisition including pragmatic knowledge. RAGE AI is not a black box and does not rely on statistical patterns present in training data.

Technology has given us one-tap access to taxis, laundromats, all of history's collected information, and sex. Yet it can't give us a decent to-do list. There's an entire corner of the Internet dedicated to making people more productive. Hundreds of apps run the gamut from beautiful list-makers to an electronic nag that literally curses at you until you get your shit done. It's remarkably fulfilling to spend so much time organizing and planning instead of actually getting stuff done.

The successful launch of Europe's first ExoMars mission earlier this month set the stage for a much more ambitious second act: arover landing on the Red Planet. But the timing on that mission may not be so certain. On March 14, the European Space Agency (ESA) and its Russian partners launched the ExoMars 2016 mission, an orbiter and lander that serve as a precursor to a full-blown rover slated to launch as early as May 2018. But funding issues and technical delays could push that ambitious follow-up mission to 2020. Rolf de Groot, ESA's coordinator of robotic exploration, told Space.com that it's going to be "very challenging"to have the mission fully prepared for its 2018 launch window but that program managers will know soon whether they'll have to start seriously thinking about a 2020 launch instead.