In turn, each layer of abstraction lets a larger group of developers build more proficient programs in less time. AI is at the level of the assembly language currently. Toolkits like TensorFlow are phenomenally helpful for data scientists previously used to working at the equivalent of the machine code level, but there are only about 19,000 data scientists worldwide. Bonsai's AI Engine works at a higher level of abstraction so millions of developers, and the companies that employ them, can more efficiently build AI into applications and systems. Imagine when the developers at GE, the U.S. Department of Education or the Red Cross are able to program intelligent applications as quickly and collaboratively as they might program a database. Scaled with AI technology, the unique expertise and data locked within these organizations stand to create monumental change.

We're taking a break today from the election and corporate scandals to do something that most leaders–in fact, most people–generally, don't do often enough: Thinking great big thoughts about how technology will change our lives. Many CEOs tell me their greatest fear is being blindsided by a competitor they never even thought of as a competitor, threatening to make the CEO's business irrelevant by using technology and a business model the CEO hadn't imagined. It's a main challenge now -- simply imagining what might be possible as technology advances ever faster. That's why I urge you to read Roger Parloff's new cover story on deep learning, how it's changing our lives, and how, as he says, it "will soon transform corporate America," and business globally for that matter. This is the technology powering the hugely improved speech recognition in so many products, including the Google Home device introduced yesterday as the company's answer to Amazon's Echo.

"Sandia National Laboratories researchers are drawing inspiration from neurons in the brain, such as these green fluorescent protein-labeled neurons in a mouse neocortex, with the aim of developing neuro-inspired computing systems to reboot computing. "Summary: Researchers explore neural computing to extend Moore's Law. Sandia explores neural computing to extend Moore's Law. Computation is stuck in a rut. The integrated circuits that powered the past 50 years of technological revolution are reaching their physical limits.

Major technology firms have joined forces in a partnership on artificial intelligence, aiming to cooperate on "best practices" on using the technology "to benefit people and society." Microsoft, Amazon, Google, Facebook, IBM, and Google-owned British AI firm DeepMind on Wednesday announced a non-profit organization called "Partnership on AI" focused on helping the public understand the technology and practices in the field. The move comes amid concerns that new artificial intelligence efforts could spin out of control and end up being detrimental to society. The companies "will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology," according to a statement. Academics, non-profit groups, and specialists in policy and ethics will be invited to join the board of the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI).

To infer multilayer deep representations of high-dimensional discrete and nonnegative real vectors, we propose an augmentable gamma belief network (GBN) that factorizes each of its hidden layers into the product of a sparse connection weight matrix and the nonnegative real hidden units of the next layer. The GBN's hidden layers are jointly trained with an upward-downward Gibbs sampler that solves each layer with the same subroutine. The gamma-negative binomial process combined with a layer-wise training strategy allows inferring the width of each layer given a fixed budget on the width of the first layer. Example results illustrate interesting relationships between the width of the first layer and the inferred network structure, and demonstrate that the GBN can add more layers to improve its performance in both unsupervisedly extracting features and predicting heldout data. For exploratory data analysis, we extract trees and subnetworks from the learned deep network to visualize how the very specific factors discovered at the first hidden layer and the increasingly more general factors discovered at deeper hidden layers are related to each other, and we generate synthetic data by propagating random variables through the deep network from the top hidden layer back to the bottom data layer.

Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.

Machine learning, which involves programs that get more accurate with experience, is fundamentally different from any kind of computing that's come before. "There's always been a simple division of labor: machines do number crunching, and humans make decisions," says Pradeep Dubey, an Intel Fellow at the company's Intel Labs division. Machine-learning programs--and in particular the high-profile deep-learning subset that can teach themselves--are different. These programs have the potential to discover new drug compounds or identify consumer trends without human intervention. For Dubey and others at Intel, it was clear that they needed to find a way to make machine-learning programs work well on Intel's architecture.

Artificial Intelligence (AI) was born at a workshop organized by John McCarthy in 1956 at Dartmouth. Ever since, AI has been a branch of computer science that studies the properties of intelligence and aims to synthesize it. Over the past 60 years, the field has seen at least 4 peaks and troughs (called "winters") of interest and sponsorship. During much of this time, researchers in the field have been stymied not only by the lack of funding, but by the lack of data, hardware and algorithms. In the last 5 years, however, all this has changed.

Google Translate has become a quick-and-dirty translation solution for millions of people worldwide since it debuted a decade ago. But Google's engineers have been quietly tweaking their machine translation service's algorithms behind the scenes. They recently delivered a huge Google Translate upgrade that harnesses the popular artificial intelligence technique known as deep learning. Machine translation services such as Google Translate have mostly used a "phrase-based" approach of breaking down sentences into words and phrases to be independently translated. But several years ago, Google began experimenting with a deep-learning technique, called neural machine translation, that can translate entire sentences without breaking them down into smaller components.

Nick Bostrom: London's DeepMind is winning the global race to develop human-level artificial intelligence (businessinsider.com) This is the best tl;dr I could make, original reduced by 75%. Nick Bostrom, one of the leading voices on artificial intelligence, has singled out London research lab DeepMind as the company closest to developing a system that can mimic human-level artificial intelligence - a target widely shared by those at the forefront of the AI industry. When asked who was leading the global AI race, Bostrom immediately responded with DeepMind. "Right now, I think here in London we have the DeepMind group who are, I think, the biggest [group] specifically focused on solving general intelligence," Bostrom told Business Insider at a breakfast meeting aboard the Sunbourn Yacht Hotel in East London on Wednesday.