Readers of my previous article, which sought to debunk some of the myths around robotic process automation and artificial intelligence, were hopefully convinced to not be fearful of an impending jobs apocalypse. However I hope they were not led to believe that a move to RPA is all smooth sailing and happy faces, because the truth is there are numerous processes and rules to consider before your business takes the plunge. Not least of these are the potentially significant legal implications that could arise from inviting robots to take charge of the inner workings of a business. Even though it is a fledgling area for many organisations, we have already encountered numerous legal conundrums as clients work through the changes in the way they engage within their existing ecosystem. Some of these legal implications are specific to an industry vertical or local regulatory settings, but I hope it is helpful to lay some of them out so others can ponder what it could mean for them. I'm hoping this article will open eyes a little wider within organisations looking to proactively prepare and make provisions for their journey into RPA and AI.

You know that company called Facebook? Yeah, the one that has 1.6 billion people hooked on their website. Take all of the happy birthday posts, embarrassing pictures of you as a little kid, that one family relative that likes every single one of your statuses, and you have a whole lot of data to analyze. In terms of analyzing the images, Facebook has undoubtedly made great progress with deep CNNs. A little over a week ago, the team at Facebook AI Research (FAIR) published a blog post detailing the computer vision techniques that are behind some of their object segmentation algorithms.

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

IBM's Watson AI (artificial intelligence) has added yet another skill to its collection. This time it has created the first-ever sci-fi AI-made movie trailer for "Morgan," that was released in theatres on September 2 by 20th Century Fox. Morgan, staring Kate Mara and Paul Giamatti, is a sci-fi thriller about scientists who have developed a synthetic humanoid whose potential has grown dangerously beyond their control. To prepare the machine for the task at hand, the IBM Research system analysed hundreds of horror/thriller movie trailers. In order to get an idea of the dynamics of a trailer, the computer then performed a series of visual, sound and composition studies.

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