Lawrence Livermore National Laboratory (LLNL) today announced it will receive a first-of-a-kind brain-inspired supercomputing platform for deep learning developed by IBM Research. Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses and consume the energy equivalent of a hearing aid battery – a mere 2.5 watts of power. The brain-like, neural network design of the IBM Neuromorphic System is able to infer complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips. The new system will be used to explore new computing capabilities important to the National Nuclear Security Administration's (NNSA) missions in cybersecurity, stewardship of the nation's nuclear weapons stockpile and nonproliferation. NNSA's Advanced Simulation and Computing (ASC) program will evaluate machine-learning applications, deep-learning algorithms and architectures and conduct general computing feasibility studies.

You don't necessarily need to write it as a neural network. At least in Tensorflow and Theano you can just write your computations as described and then let the library compute the gradients and update the values of your parameters (in this case 3x3x512). You would have to set a 3x3 constant filter filled with 1/9 so it computes the mean. Then apply it to the input to produce a map where every position is the mean around a 3x3 area. Then find a way to compute your 9-bit value for each position (maybe generating 9 copies of your map) or produce your number somehow and then select your weights (from the 512), for instance, put all your weights in a 9x512 matrix and multiply by a 512-dimensional vector with a one in the position you want.

Max Welling is a research chair in Machine Learning at the University of Amsterdam and has secondary appointments as full professor at the University of California Irvine and as a senior fellow at the Canadian Institute for Advanced Research (CIFAR). He is co-founder of "Scyfer BV" a university spin-off in deep learning. In the past he held postdoctoral positions at Caltech ('98-'00), UCL ('00-'01) and the U. Toronto ('01-'03). Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015. He serves on the board of the NIPS foundation since 2015 and has been program chair and general chair of NIPS in 2013 and 2014, respectively.

Poker playing artificial intelligence has already "approached the performance" of human experts and can use "state-of-the-art methods" in its gameplay. Researchers from University College London - including a staff member from DeepMind's Go defeating team - have created a series of reinforcement algorithms that are able to play Texas Hold'em and a simplistic Leduc poker. The AI is able to learn the game without any prior knowledge of strategies and taught itself by playing fictitious matches on its own, according to the paper Deep Reinforcement Learning from Self-Play in Imperfect-Information Games. Research student Johannes Heinrich and lecturer and David Silver explain in the paper that the Neural Fictitious Self-Play method they created used deep reinforcement learning "to learn directly from their experience of interacting in the game". The method learnt from its mistakes and developed ways to win the games, while also utilising neural networks.

Last week Google's AlphaGo defeated legendary Go player Lee Sedol in four out of five games marking another milestone in the field of Artificial Intelligence. This is reminiscent of a 6 game match in 1997 where IBM's Deep Blue defeated Gary Kasparov. While machine beat man in both these games, there have been remarkable improvements between the computational capabilities of Deep Blue and AlphaGo. Deep Blue relied on brute computational force to evaluate positions; however AlphaGo relied on a a concept called "deep learning" that resembles human decision making. Beating humans at complex games is interesting news, these deep learning techniques also have the potential to enhance how retailers engage with their customers and improve their operational effectiveness.

It's been an emotional week in the realm of game AI as the world watched the historic five-game showdown between legendary Go world champion Lee Sedol and Google DeepMind's famed deep learning AI AlphaGo. All five games were held at the Four Seasons Hotel in Seoul, South Korea, and as events played out, millions around the world became increasingly captivated. Anticipation for the match began growing in January, when Google's UK-based AI group DeepMind, led by CEO Demis Hassabis, announced their computer algorithm AlphaGo defeated three-time European Go champion Fan Hui 5 games to 0--a victory some experts didn't expect a computer to achieve for a decade. At the end of a Google blog post announcing the win was the promise of a best-of-five face-off between AlphaGo and 18-time international Go champion Lee Sedol, a match equivalent to IBM's Deep Blue defeat of Garry Kasparov in chess in 1997. Notably, Go is inherently more complex than chess and AlphaGo, at least in part, trained itself to play the game.

Two quantitative analysts using artificial intelligence in an online data science competition showed they could diagnose heart disease about as accurately as doctors. Qi Liu and Tencia Lee, hedge fund analysts and self-described "quants," built the winning algorithm in the competition, which could find indicators of heart disease. The online data contest challenged participants to develop machine algorithms that could measure cardiac volumes from MRIs provided by the National Heart, Lung and Blood Institute. Mr. Liu and Ms. Lee didn't know each other before they won the competition, beating out more than 1,390 algorithms. They met each other in a forum on the Kaggle site, where the competition was hosted over a three-month period.

It doesn't look like a place to make groundbreaking discoveries that change the trajectory of society. But in these simulated, claustrophobic corridors, Demis Hassabis thinks he can lay the foundations for software that's smart enough to solve humanity's biggest problems. "Our goal's very big," says Hassabis, whose level-headed manner can mask the audacity of his ideas. He leads a team of roughly 200 computer scientists and neuroscientists at Google's DeepMind, the London-based group behind the AlphaGo software that defeated the world champion at Go in a five-game series earlier this month, setting a milestone in computing. It's supposed to be just an early checkpoint in an effort Hassabis describes as the Apollo program of artificial intelligence, aimed at "solving intelligence, and then using that to solve everything else."

In addition to heavy investments from mammoths like Google and Facebook, smaller companies are also hopping on board. East-coast based ADP, for example, is using deep learning systems to help scout out necessary information, run big data analysis, and present prepared report to its CEO, a process that is improved upon each time the machine performs its operations. Saffron, a division of Intel, is using deep learning to match broad patterns of customer behavior to specific individuals, claiming that the technology predicts correct next moves - such as how the person will contact a company - 88 percent of the time. Companies such as Rare Mile Technologies are also offering up customized machine learning algorithms to a range of industries for various uses, including insurance fraud detection.

Artificial Intelligence represents the next chapter of the Information Age, and Google, Microsoft, Amazon, IBM, and others are engaging in an arms race to control the platform that dictate tech's future writes the New York Times. "The relationship between big companies and deep machine intelligence is just starting." So, what counts as artificially intelligent anyway? The Verge explains the difference between machine learning, deep learning, and neural networks, how they work, and why the future of AI is likely to be more subtle than you think. The next wave in technology isn't about the technology, but rather the market that emerges from the technology.