The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us? The new White House report on artificial intelligence takes an appropriately skeptical view of that dream. It says the next 20 years likely won't see machines "exhibit broadly-applicable intelligence comparable to or exceeding that of humans," though it does go on to say that in the coming years, "machines will reach and exceed human performance on more and more tasks."

Peter Drucker was prescient about most things, but the computer wasn't one of them. "The computer ... is a moron," the management guru asserted in a McKinsey Quarterly article in 1967, calling the devices that now power our economy and our daily lives "the dumbest tool we have ever had." Drucker was hardly alone in underestimating the unfathomable pace of change in digital technologies and artificial intelligence (AI). AI builds on the computational power of vast neural networks sifting through massive digital data sets or "big data" to achieve outcomes analogous, often superior, to those produced by human learning and decision-making. Careers as varied as advertising, financial services, medicine, journalism, agriculture, national defense, environmental sciences, and the creative arts are being transformed by AI.

It's Monday lunchtime and gym-goers at Virgin Active in Moorgate, London, are grabbing a bike for their group cycle class. But this isn't any ordinary spin class, where the teacher enthusiastically shouts instructions like "sprint" and "climb" and the backdrop is an uninspiring grey wall. This is the "Pack", a class divided into three teams that compete in a series of interactive challenges while each rider's bike data is tracked in real-time and projected on to a screen. "We created the Pack in response to the growing demand for cycle-based classes and technology that tracks workout progress," says Virgin Active group chief information officer Andy Caddy. "It creates a wholly differentiated group cycle offering."

It's no secret that technology is revolutionising consumer and brand interaction. In most cases, consumers are changing their behaviour faster than most retailers can adapt their marketing strategies. Marketers must engage savvy shoppers across a plethora of channels, the competition is intense, and customer satisfaction and retention have become top priorities for most brands. In our modern era, it is imperative to know and UNDERSTAND who our customers are, what they like/dislike, what will motivate them to buy or buy again, and why they leave. It is vital to have a forward-looking approach and to predict answers to questions such as: "What will my customer be interested in next week? In which city is my customer likely to shop? What is the most effective channel to connect with customers when they are ready to buy? Which products are my prospects waiting for?"

Machine Learning is making its presence felt on the worldwide stage as a major driver of digital business success. A good proof of that was our recently completed second edition of the Valencian Summer School in Machine Learning celebrated last September 2016 in Spain. Over 140 attendees representing 53 companies and 21 academic organizations from 19 countries travelled to Valencia for a crash course in Machine Learning and it was a great success! What are the next steps? Encouraged by the level of interest and motivated by our mission to democratize Machine Learning, we continue spreading Machine Learning concepts with this series of courses.

Adversarial instances and robust models Generative Adversarial Network http://arxiv.org/abs/1406.2661 - Train classifier net as oppose to another net creating possible adversarial instances as the training evolves. Apply genetic algorithms per N training iteration of net and create some adversarial instances. Apply fast gradient approach to image pixels to generate intruding images. Goodfellow states that DAE or CAE are not full solutions to this problem. Adversarial instances and robust models Generative Adversarial Network http://arxiv.org/abs/1406.2661 - Train classifier net as oppose to another net creating possible adversarial instances as the training evolves. Apply genetic algorithms per N training iteration of net and create some adversarial instances.

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Artificial intelligence (AI) is expected to be a revolution on how we interact with the world around us as part of a connected environment. As part of the series of guest posts named Thoughts Leaders' Corner, here is a very interesting article from Aric Dromi, Chief Futurologist at Volvo Car Group and cofounder of TEMPUS.MOTU. I hope you will enjoy it and if you are interested to the legal issues of the Internet of Things that are part of this digital revolution, check my blog post here! We are living in an experience based interaction simulation. A place that is governed by transparent intelligence technologies.

In 1997, a computer program codenamed Deep Blue, defeated Russian chess grandmaster, Garry Kasparov. In 2006, Deep Fritz dethroned the then World Champion Chess player, Vladamir Kramnik. In 2016, Google developed an artificially intelligent computing system named AlphaGo. It added another name on the "list of humans" defeated by a machine, the highly ranked South Korean Go player, Lee Sedol. Prior to the dethroning of Kasparov, Kramnik and Sedol, it was thought that artificial intelligence still had ways to go before it could outwit and outmatch gifted human players.

A year ago, Danny Lange took over as the head of machine learning at Uber. The ride-sharing company, which launched in 2009, is, essentially, a tech company: It operates entirely through an app. Lange manages a team in San Francisco, and Uber has a smaller team in Seattle. And machine learning has become the underlying foundation for every part of the company. "Machine learning and AI technologies can really solve some very fundamental business problems that are really hard to create hardwired solutions to," said Lange.