AI in the real world: Tech leaders consider practical issues.

While market leaders and fast followers have not yet achieved mass personalization, the next rush is focused on investments in artificial intelligence (see Figure 1). Searching for a competitive advantage and fearful of disruption, board rooms and CXO's have rushed to artificial intelligence as the next big thing. The investment in pilots for AI's subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services. With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists. The seven success factors for AI foreshadow a world where limited players can deliver AI smart services.

Prisma transforms your photos into works of art using the styles of famous artists: Van Gogh, Picasso, Levitan, as well as world famous ornaments and patterns. A unique combination of neural networks and artificial intelligence helps you turn memorable moments into timeless art pieces.

A while back, I discussed Recurrent Neural Networks (RNNs), a type of artificial neural network in which some of the connections between neurons point "backwards". When a sequence of inputs is fed into such a network, the backward arrows feed information about earlier input values back into the system at later steps. One thing that I didn't describe in that post was how to train such a network. So in this post, I want to present one way of thinking about training an RNN, called unrolling. Recall that a neural network is defined by a directed graph, i.e. a graph in which each edge has an arrow pointing from one endpoint to the other.

DARPA wants to exploit the power of the internet of things to help the U.S. dominate battlefields. The Defense Advance Research Projects Agency will fund the development of sensors and artificial intelligence systems that could help break into, extract, and analyze information from enemy devices and communication systems. The components and systems will arm the U.S. with more data to analyze enemy moves and strategy. Information is king in wars, and DARPA wants to develop technology that can break into enemy systems. "They are talking about going into any situation and extracting information at any time, [with] artificial intelligence systems that can attack and hack any network," said Jim McGregor, principal analyst at Tirias Research.

Browse the business section of any bookshop and you'll find dozens of titles promising to share the secret to climbing the corporate ladder. But the day is not far off when such books will seem as quaint and outmoded as a housekeeping manual from the 1950s. One of the key workplace trends of the 21st century has been the collapse of the corporate ladder, whereby loyal employees climbed towards the higher echelons of management one promotion at a time. Cathy Benko, vice-chairman of Deloitte in San Francisco and co-author of The Corporate Lattice, says that the ladder model dates back to the industrial revolution, when successful businesses were built on economies of scale, standardisation and a strict hierarchy. "But we don't live in an industrial age, we live in a digital age. And if you look at all the shifts taking place, one [of the biggest] is the composition of the workforce, which is far more diverse in every way," she says.

IBM's Watson AI makes movie trailer for an upcoming film with only a little help from humans

The XGBoost library for gradient boosting uses is designed for efficient multi-core parallel processing. This allows it to efficiently use all of the CPU cores in your system when training. In this post you will discover the parallel processing capabilities of the XGBoost in Python. How to Best Tune Multithreading Support for XGBoost in Python Photo by Nicholas A. Tonelli, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python.

Blaise Agüera y Arcas, principal scientist at Google, works with deep neural networks for machine perception and distributed learning. In this captivating demo, he shows how neural nets trained to recognize images can be run in reverse, to generate them. The results: spectacular, hallucinatory collages (and poems!) that defy categorization. "Perception and creativity are very intimately connected," Agüera y Arcas says. "Any creature, any being that is able to do perceptual acts is also able to create."

As someone who watches technology trends closely as part of my business, I have been thinking about the future impact of all the technology innovations and automation we are currently experiencing and on the cusp of achieving. Many of the headlines I read about these trends -- and even some I write -- predict some pretty negative consequences right along with the monumental achievements and improvements. While improvements in machine learning, artificial intelligence, big data, and robot automation could mean huge advances in medicine, science, commerce and human understanding, it's also undeniable that there will be consequences as well. These technological advances represent a significant challenge to capitalism. Together, they are poised to potentially create jobless growth and the paradox of an exponentially growing number of products, manufactured more and more efficiently, but with rising unemployment and underemployment, falling real wages and stagnant living standards.