In the past few years, the market for artificial intelligence (AI) and machine learning technologies has gained strong momentum. What's interesting, though, is that the much of the innovation in this space is driven by disruptors, not legacy vendors. These'upstarts' are companies born in the internet age or companies that have transitioned into the AI market, and are building out useful AI products that will likely broaden in their impact over time. Much like how Amazon Web Services (AWS) became the infrastructure provider of choice for many companies with the rise of the public cloud, many of these upstarts will see their products widen in application. While many of these companies might have originally created their product for a specific use case, it's possible that they will grow into platforms on which the company may build an additional revenue stream.

Deep learning is having a serious moment right now in the world of AI. Loosely based on the brain's computing architecture, artificial neural networks have vastly outperformed their predecessors in a variety of tasks that had previously stumped our silicon-minded comrades. But as these algorithms continuously forge new grounds in machine intelligence, we're coming to an uncomfortable realization: transistor-based computers have hard limits, and those limits are approaching rapidly. Now, thanks to a new system developed by Princeton engineers, we may have one way to smash the speed barrier of our current processors: neuromorphic computing running on photons, not electrons, with silicon chips that work at the speed of light. Published this week on Arxiv, the new photonic neural network is so blazingly efficient that when pitted against a conventional CPU in solving differential equations, it performed roughly 2,000 times faster.

Mr. Sunil Patel is a Ph D Scholar at Gujarat Technological University, Gujarat, India. He received his Masters degree in Computer Engineering from Sardar Patel University, Gujarat. His research interest is in Computer Vision, Big Data Analysis and Distributed Computing.

Artificial intelligence may be making small and steady advances in general-purpose situations like digital assistants. But it's the more subtle AI accessibility features that have a more substantial impact today, especially for users with disabilities. For instance, an upcoming feature for Office apps like Microsoft Word and PowerPoint will automatically suggest image and slide deck captions, called alt-text, using AI algorithms. That way, when those files are presented to blind users, computer tools designed to translate the information onscreen into audio have text descriptions to work with. Microsoft is accomplishing this feat with its Computer Vision Cognitive Service, which uses neural networks trained with deep learning techniques to better understand and describe the contents of images.

Starred articles are candidates for the picture of the week. A comprehensive list of all past resources is found here. We are in the process of automatically categorizing them using indexation and automated tagging algorithms. IBM makes quantum computing available in the cloud 2016 Big Data 100: 20 Coolest Platform And Tools Vendors The fight against antimicrobial resistance across Europe Cool video pie chart Inside Facebook's Biggest Artificial Intelligence Project Ever How to tell two radically different stories from the same dataset Data science, no coding required: DataRobot's automated platform Google launches new machine learning platform TechCrunch Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Scienc... Forbes Beyond the hype: the hard work behind analytics success MIT Sloan Deep learning will be huge -- and here's who will dominate it Years You Have Left to Live, Probably - Nice interactive chart by FlowingData Alooma gets $11.2 million Series A to solve data science pain points AI program wrote a short novel, and almost won a literary prize How facial recognition can expose your life to strangers Data science, no coding required: DataRobot's automated platform Deep learning will be huge -- and here's who will dominate it Alooma gets $11.2 million Series A to solve data science pain points

What Led From Neural Networks to Deep Learning? The introduction of'Deep' architecture that supports multiple hidden layers. This creates multiple levels of representation or learning a hierarchy of feature which was absent for early neural networks. Improvements and changes to support for a variety of architectures (DBN, RBM,CNN, and RNN) to suit different kinds of problems.

Neural networks, machine-learning systems, predictive analytics, speech recognition, natural-language understanding and other components of what's broadly defined as'artificial intelligence' (AI) are currently undergoing a boom: research is progressing apace, media attention is at an all-time high, and organisations are increasingly implementing AI solutions in pursuit of automation-driven efficiencies. The first thing to establish is what we're not talking about, which is human-level AI -- often termed'strong AI' or'artificial general intelligence' (AGI). A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C. Müller and Nick Bostrom reported a 50 percent chance that AGI would be developed between 2040 and 2050, rising to 90 percent by 2075; so-called'superintelligence' -- which Bostrom defines as "any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest" -- was expected some 30 years after the achievement of AGI (Fundamental Issues of Artificial Intelligence, Chapter 33). This stuff will happen, and it certainly needs careful consideration, but it's not happening right now. What is happening right now, at an increasing pace, is the application of AI algorithms to all manner of processes that can significantly affect peoples' lives -- at work, at home and as they travel around. Although hype around these technologies is approaching the'peak of expectation' (sensu Gartner), there's a potential fly in the AI ointment: the workings of many of these algorithms are not open to scrutiny -- either because they are the proprietary assets of an organisation or because they are opaque by their very nature.

Creating machines to think for themselves may feel like the plot of a hardcore sci-fi novel. But in reality, it's what engineers and software developers are doing today to push the boundaries of technology. This limited-time offer bundles up high-level instruction on how to actual construct self-learning machines for 91 percent off from TNW Deals. TNW Conference is back for its 12th year. In over 14 hours of instruction, these four courses take you deep inside the artificial intelligence tech that underlies advanced Google web searches and even Tesla's self-driving cars.

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An original quantum foundations concept of a deep learning computational Universe is introduced. The fundamental information of the Universe (or Triuniverse) is postulated to evolve about itself in a Red, Green and Blue (RGB) tricoloured stable self-mutuality in three information processing loops. The colour is a non-optical information label. The information processing loops form a feedback-reinforced deep learning macrocycle with trefoil knot topology. Fundamental information processing is driven by ψ -Epistemic Drive, the Natural appetite for information selected for advantageous knowledge.