I fell in love with Machine Learning during my Master degree in Telecommunications Engineering and Information Technology. Since then I could never live without it and I see the world with different eyes. I have always been very fascinated by math and statistics, by how sometimes a very simple equation will describe extremely complex phenomena, how we can squeeze nature into a formula; at the same time my mind has always been captured by those phenomena, often very simple and part of our daily life reasoning and acting, that can't be represented by any mathematical form, no matter how convoluted. The idea of seeing the world through numbers has always exercised a certain spell on me. Then I discovered Machine Learning.

It isn't all doom and gloom, artificial intelligence will also bring about new and exciting opportunities. As former government technology adviser, Rohan Silva said: "The technology revolution is not just destroying jobs - it's creating huge numbers of new ones too, especially in areas like IT and the creative industries. And the good news is that the new jobs being created pay an average of 10,000 a year more than the jobs being lost to automation." So amid the fear and worry about artificial intelligence, it's important to remember that robots are built by humans – and humans are needed to program them, repair them, 'teach' them and keep developing the technology. So there's hope for us humans, yet.

The radio spectrum is a mess: It's congested, expensive, and there's no room for expansion. But DARPA has a plan to change that, by building a system where radio waves can work together using artificial intelligence, rathe than fighting for space. DARPA launched its latest Grand Challenge last week, and it plans to encourage researchers around the world to develop "smart systems that collaboratively, rather than competitively, adapt in real time to today's fast-changing, congested spectrum environment... to maximize the flow of radio frequency." That sounds exciting, because making radio frequency flow more easily means--theoretically, at least--faster data rates, fewer dropped signals, and cheaper connections. How does DARPA plan to do it?

Artificial intelligence is coming into the mainstream and there is no stopping it. Most anybody in the tech sphere will tell you that. The reason for that, of course, is the efficiency and new possibilities that this new field of technology offers. As the concept of cloud computing makes its way into the mainstream with networks finally building out to be able to accommodate it, artificial intelligence is being developed and used alongside it to streamline cloud processes such as maintenance, sorting and indexing algorithms. With all of the new development going on in the field of artificial intelligence, things like DeepMind's AlphaGo are moving from the realm of science fiction into reality.

In addition to work authored by humans, it also considers the literary output of artificial intelligence software. And the results of the latter are--surprisingly and scarily--not that bad. Researchers from Japan's Future University Hakodate submitted a short story called "The Day a Computer Writes a Novel," or "Konpyuta ga shosetsu wo kaku hi," and it comes across as something a human might have written (though not perhaps a human called Jonathan Franzen): I writhed with joy, which I experienced for the first time, and kept writing with excitement. The day a computer wrote a novel. The computer, placing priority on the pursuit of its own joy, stopped working for humans. The prize was created in the memory of Hoshi Shinichi, a science fiction writer whose novels include The Whimsical Robot.

Microsoft has released its open source Computational Network Toolkit (CNTK) on GitHub. Now the tools which are used by Microsoft researchers to accelerate approach and work in artificial intelligence, will be available for broad group developers through an open-source license. Xuedong Huang, the chief speech scientist from Microsoft, mentioned that it could be useful to everyone right from deep learning startups to well established companies that are into processing of lot of data in real time. Microsoft described CNTK as "a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph." It has been claimed as an alternative for some of the established deep learning frameworks, toolkits and libraries like Theano, Torch, TensorFlow and many more.

Struggling to think of a synonym? A new crossword-solving system could be the end of your lexicological lamentations. The web-based system, developed by researchers from the UK, US and Canada, makes use of artificial neural networks, which are based on the brain's own learning systems. The freely-available software was found to be better at solving standard crossword clues than commercial products. It can cope with single words (e.g.

In 1992, as personal computers were beginning to reshape everyday life, Sony co-founder Masaru Ibuka wrote that computing could never overshadow human achievement, because of one missing quality: creativity. "A computer isn't creative on its own because it is programmed to behave in a predictable way," he wrote in Fortune. "Creativity comes from looking for the unexpected and stepping outside your own experience. Computers simply cannot do that." But new projects are challenging the question of computer creativity--like Beyond the Fence, the world's first computer-generated musical, which opens at the Arts Theater in London today.

Vision algorithms have achieved impressive performances in visual recognition. Nevertheless, an image is worth a thousand words, and not all these words refer to visible properties such as objects and scenes. In this talk we will explore the subjective side of visual data, investigating how machine learning can detect intangible properties of images and videos, such as beauty, creativity, and more curious characteristics. We will see the impact of such detectors in the context of web and social media. And we will analyze the precious contribution of computer vision in understanding how people and cultures perceive visual properties, underlining the importance of feature interpretability for this task.

Is AI a threat or an opportunity? It is both, to those focused on the routine it is a threat, to those focused on innovating/bespoke it is an opportunity, as pointed out by John O. McGinnis & Russell G. Pearce[1]. The advice therefore should be to understand these new technologies and explore what opportunities they create. This should not be limited to iterative improvements to the current process but more importantly to identifying opportunities for transformational change. This last year has seen Artificial Intelligence (AI) emerging as a very hot topic within the mainstream media (see Elon Musk, Stephen Hawkins, Bill Gates, etc.)[2]. Many of the articles have invoked the spectre of a new generation of'Robots/AI' raising up to take skilled jobs, previously the preserve of the professions (see Susskind book)[3].