DeepMind was a relatively unknown artificial intelligence (AI) startup in London up until 2014, when it was bought by Google for around 400 million. Today some of the smartest people in the world are queuing up to work at DeepMind, according to an article by Celemency Burton-Hill in The Guardian in February. Interestingly, the same article states that no one has ever left DeepMind, which has created a series of algorithms that can learn for themselves and beat the best humans at games like Go and "Space Invaders." Based in up-and-coming King's Cross, DeepMind now employs around 250 people. However, as Burton-Hill points out, getting a job there is far from easy.

DEDHAM, MA--(Marketwired - Mar 30, 2016) - Rage Frameworks, a provider of knowledge-based automation technology and services, today announced new deployments of its traceable "deep learning" technology known as Rage AI across several global financial services, consumer products and manufacturing firms. The challenges these organizations faced required the understanding and interpretation of complex documents and integration of other transaction data from enterprise resource planning (ERP) systems to identify significant cost efficiencies and compliance conformance. RAGE AI incorporates deep linguistic parsing and proprietary linguistics-based innovations to understand the real meaning of documents and interpret them as a human would, and can operate completely unsupervised or with assistance by human experts. With its traceable, deep learning technology, RAGE AI significantly extends the frontier of deep learning and machine intelligence from "natural language processing" to "natural language understanding." The platform reads and interprets documents within its context, and as a totally transparent solution, RAGE AI enables knowledge workers to move forward confidently knowing the reasoning behind the platform's insights is completely auditable.

Lawrence Livermore's new supercomputer system uses 16 IBM TrueNorth chips developed by IBM Research (credit: IBM Research) Lawrence Livermore National Laboratory (LLNL) has purchased IBM Research's supercomputing platform for deep-learning inference, based on 16 IBM TrueNorth neurosynaptic computer chips, to explore deep learning algorithms. IBM says the scalable platform processing power is the equivalent of 16 million artificial "neurons" and 4 billion "synapses." The brain-like neural-network design of the IBM Neuromorphic System can process complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips, says IBM. The technology represents a fundamental departure from computer design that has been prevalent for the past 70 years and could be incorporated in next-generation supercomputers able to perform at exascale speeds -- 50 times faster than today's most advanced petaflop (quadrillion floating point operations per second) systems. The TrueNorth processor chip was introduced in 2014 (see IBM launches functioning brain-inspired chip).

One of the scientists responsible for AlphaGo, the Google DeepMind software that trounced one of the world's best Go players recently, says the same approach can produce a surprisingly competent poker bot. Unlike board games such as Go or chess, poker is a game of "imperfect information," and for this reason it has proved even more resistant to computerization than Go. Gameplay in poker involves devising a strategy based on the cards you have in your hand and a guess as to what's in your opponents' hands. Poker players try to read the behavior of others at the table using a combination of statistics and more subtle behavioral cues. Because of this, building an effective poker bot using machine learning may be significant for real-world applications of AI.

AlphaGo handily beat 18-time world Go champion Lee Sedol 4-1, and in doing so taught us several interesting lessons about where AI research is today, and where it is headed. One fascinating thing about AlphaGo is the unusual way it was designed. The software combined deep learning--the hottest AI technique out there today--with a much older, and far less fashionable, approach. Deep learning involves using very large simulated neural networks, and usually it eschews logic or symbol manipulation of the kind pioneered by the likes of Marvin Minksy and John McCarthy. But AlphaGo combines deep learning with something called tree-search, a technique invented by one of Minksy's contemporaries and colleagues, Claude Shannon.

Deep Learning Workshop: Deep Learning (DL) is now driving another renaissance and surge of excitement in Neural Network research and applications. Startling results on real world large scale tasks have been reported especially since 2009 (although DL's origins date back to the 1960s). We are pleased to have speakers from major IT companies such as Microsoft, Google, Facebook, Baidu, as well as top academic pioneers in this area. Presentations will cover both academic and industrial perspectives. Participation in the Deep Learning workshop is limited, and priority will go to regular conference attendees, after which attendance will be on a first come, first served basis.

Our recent research article which was accepted for publication in the proceedings of World Congress of Computational Intelligence (WCCI 2016) presents our experiments with Neural Turing Machine (NTM), recently proposed by Google researchers. We published one of the first NTM open source implementation which was able to repeat experiments in the paper. Recently, we work on improvements that enable faster and more stable NTM learning. NTM proved that it is very powerful in learning and generalizing long sequences. It can outperform standard recurrent neural networks as well as popular gating recurrent nets (LSTM). We extended NTM to be able to efficiently predict sequential patterns.

Today some of the smartest people in the world are queuing up to work at DeepMind, according to an article by Celemency Burton-Hill in The Guardian in February. Interestingly, the same article states that no one has ever left DeepMind, which has created a series of algorithms that can learn for themselves and beat the best humans at games like Go and "Space Invaders." Based in up-and-coming King's Cross, DeepMind now employs around 250 people. However, as Burton-Hill points out, getting a job there is far from easy. Fortunately, a number of Quora Q&As offer an insight into "What does it take to work at Google DeepMind?" and "What is it like to work at Google DeepMind?"

Choong-am Dojang is far from a typical Korean school. Its best pupils will never study history or math, nor will they receive traditional high-school diplomas. The academy, which operates above a bowling alley on a narrow street in northwestern Seoul, teaches only one subject: the game of Go, known in Korean as baduk and in Chinese as wei qi. Each day, Choong-am's students arrive at nine in the morning, find places at desks in a fluorescent-lit room, and play, study, memorize, and review games--with breaks for cafeteria meals or an occasional soccer match--until nine at night. Choong-am, which is the product of a merger between four top Go academies, is currently the biggest of a handful of dojangs in South Korea. Many of the students enrolled in these schools have been training since they were four or five, perhaps playing informally at first but later growing obsessed with the game's beauty and the competitiveness and camaraderie that surround it.

Teaching a computer to play Go at a superhuman level is cool, but not especially useful for you or me. But what if a computer could read a few dozen pages of text, like the manual for a new microwave, and then answer questions about how it works? Reading and comprehending text is incredibly difficult for computers, but a Canadian company called Maluuba has made progress with an algorithm that can read text and answer questions about it with impressive accuracy. Most importantly, unlike other approaches, it works with just small amounts of text. It might eventually help computers "comprehend" documents.