The mission of Google's DeepMind Technologies startup is to "solve intelligence." Now, researchers there have developed an artificial intelligence system that can mimic some of the brain's memory skills and even program like a human. The researchers developed a kind of neural network that can use external memory, allowing it to learn and perform tasks based on stored data. Neural networks are interconnected computational "neurons." While conventional neural networks have lacked readable and writeable memory, they have been used in machine learning and pattern-recognition applications such as computer vision and speech recognition.

Boston-based indico data solutions, which specializes in deep learning and artificial intelligence, announced it will be partnering with Manulife - known to us in the States as John Hancock - and its Lab of Forward Thinking (LOFT). With indico's platform, LOFT will build an AI-enabled solution that will help guide investors. The tool will use artificial intelligence and deep learning to analyze unstructured financial data - in text and image form - from sources like news articles and analyst reports. From this analysis, it will then make recommendations for portfolio managers to make more informed investment decisions - and hopefully, in less time than they do now. "Indico will help us accelerate our use of Deep Learning to improve the decision-making capabilities of our analysts, portfolio managers and researchers," Greg Framke, chief information officer at Manulife, said in a statement.

Sum-Product Networks (SPNs) are recently introduced deep tractable probabilistic models by which several kinds of inference queries can be answered exactly and in a tractable time. Up to now, they have been largely used as black box density estimators, assessed only by comparing their likelihood scores only. In this paper we explore and exploit the inner representations learned by SPNs. We do this with a threefold aim: first we want to get a better understanding of the inner workings of SPNs; secondly, we seek additional ways to evaluate one SPN model and compare it against other probabilistic models, providing diagnostic tools to practitioners; lastly, we want to empirically evaluate how good and meaningful the extracted representations are, as in a classic Representation Learning framework. In order to do so we revise their interpretation as deep neural networks and we propose to exploit several visualization techniques on their node activations and network outputs under different types of inference queries. To investigate these models as feature extractors, we plug some SPNs, learned in a greedy unsupervised fashion on image datasets, in supervised classification learning tasks. We extract several embedding types from node activations by filtering nodes by their type, by their associated feature abstraction level and by their scope. In a thorough empirical comparison we prove them to be competitive against those generated from popular feature extractors as Restricted Boltzmann Machines. Finally, we investigate embeddings generated from random probabilistic marginal queries as means to compare other tractable probabilistic models on a common ground, extending our experiments to Mixtures of Trees.

Reinforcement learning framework integrated with deeplearning4j. This is a tech preview and distributed as is.

Technology companies are hurriedly snapping up startups in the field of artificial intelligence, and Intel Corp. INTC 0.48 % is the latest to join a buying spree fueled by one of the hottest trends in the tech sector. The chip maker on Tuesday announced plans to pay an undisclosed amount for Nervana Systems, a 48-employee company working on semiconductors, software and services to exploit a popular AI technique called deep learning. Intel's move follows a deal disclosed Friday by Apple Inc. AAPL -0.59 % to purchase Turi Inc., a Seattle-based specialist in the field. The two acquisitions add to a string of 31 purchases since 2011 of AI startups by large companies, according to venture-capital research firm CB Insights. Factoring in smaller acquirers, PricewaterhouseCoopers LLP counts 29 related acquisitions so far this year, suggesting the total deal count for 2016 will top the 37 deals announced last year.

To celebrate summer 2016, we're putting on a special offer. This offer starts on 22 August and will run through to the end of 2 September, and is even applicable on our already heavily discounted Early Bird, Startup and Academic passes! Upcoming event topics include: Deep Learning, Healthcare, Chatbots & Virtual Assistants, FinTech, Machine Intelligence, Renewable Energy and Autonomous Vehicles. View all of our upcoming summits for 2016 & 2017 here & take advantage of this offer!

Machine learning is one of the most important computing developments of our time. Advanced machine learning techniques are powering an explosion in artificial intelligence. These are just a glimpse of what's possible. But it takes a massive amount of computing performance to train the sophisticated deep neural networks that power these new applications. Training can take days to weeks on even the fastest supercomputers.

Insurance giant John Hancock and a small Boston startup are teaming up in the hopes of using artificial intelligence to guide the company's investment decisions. John Hancock -- and its Canadian parent company Manulife -- have started working with indico data solutions, a startup that makes AI software designed to create easily digestible trends, themes and other important information out of the mountains of data that financial analysts have to keep track of. "These guys are all drowning in data," said Vishal Daga, chief customer officer for indico. "The idea is to build an application using these deep learning techniques that can help analysts whittle down that stack of reading material in a useful manner." Indico's software can analyze word choices from stock analyst reports and automatically detect whether the language is positive or negative.

In a recent media release, Sir Peng Tee Kaw, head of Moorfields' ophthalmology research centre, stated: "Our research with DeepMind has the potential to revolutionize the way professionals carry out eye tests and could lead to earlier detection and treatment of common eye diseases such as age-related macular degeneration."

When Gary Kasparov lost to chess computer Deep Blue in 1997, IBM marked a milestone in the history of artificial intelligence. On Wednesday, in a research paper released in Nature, Google earned its own position in the history books, with the announcement that its subsidiary DeepMind has built a system capable of beating the best human players in the world at the east Asian board game Go. Go, a game that involves placing black or white tiles on a 19x19 board and trying to remove your opponents', is far more difficult for a computer to master than a game such as chess. DeepMind's software, AlphaGo, successfully beat the three-time European Go champion Fan Hui 5–0 in a series of games at the company's headquarters in King's Cross last October. Dr Tanguy Chouard, a senior editor at Nature who attended the matches as part of the review process, described the victory as "really chilling to watch".