We spoke to Paul Davies, UK CMO at Microsoft and one of our Vision 100 on what it takes to be a visionary. Davies said it takes a lot of curiosity and that visionaries need to be curious about the world around them and what makes people tick. He said that they should also have courage and the bravery to try different things. He said he admires the astronaut Tim Peake for "inspiring a young generation and telling them they really can do anything with their lives." When asked what he feels the biggest change will be for the industry in the next ten years, he said it would be artificial intelligence, with "data being pulled together in different ways to create new experiences for consumers in ways that will be invisible."

In 2015, the Chinese e-commerce market generated an estimated 562 billion in sales, with shopping named as the fastest-growing online activity among Chinese consumers. Though the rise of e-commerce is hardly a surprise at this point, the global reach and consistent growth in this sector make it one of the most significant global trends. What makes this all the more notable is that the rapid transition from the brick-and-mortar shops of old is still heavily limited by technology – specifically, the limitations of online product searches, which, especially compared to a conversation with a real world sales associate, return far too many irrelevant and unspecific results to be of reliably convenient use. Accordingly, what is left is a global phenomenon that has succeeded in profoundly disrupting the traditional shopping experience, but has yet to reach its full potential. The bright side to this story is that the key to unlocking the next wave of e-commerce disruption has arrived, this time with the advent of artificial intelligence (AI).

Using the power of analogy, a new structure-mapping engine gives computers the ability to reason like humans and even solve moral dilemmas. Northwestern University's Ken Forbus is closing the gap between humans and machines. Using cognitive science theories, Forbus and his collaborators have developed a model that could give computers the ability to reason more like humans and even make moral decisions. Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. "In terms of thinking like humans, analogies are where it's at," said Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science in Northwestern's McCormick School of Engineering.

Its frustrating when people claim algorithms are unbiased because while that may be true in some sense it ignores important problems that may arise in real world contexts where they are trained and deployed by fallible humans on imperfect data. For the most part I believe algorithms are unbiased. The main places these regulations are targeted, insurance companies, have unbiased ground truth on claims and accident rates. It's silly to ban machine learning across many industries and applications, instead of banning it in the specific places it is causing problems (which is what, exactly?) There are actually principled ways of addressing bias in data. These methods are totally broken.

During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me. One important detail is missing in this scenario – continuous flow of transactional data is not needed for model retraining.

Artificial intelligence is usually associated with futuristic sci-fi movies and the rise of the machines against humanity. What many people don't realise is that it's commonplace today, and something you've probably not noticed has become a critical part of your life. The intelligence Facebook uses to make friends suggestions or how Google photos recognises faces and places are both examples of AI, and machine learning, in action. AI is also being used in organisations worldwide to augment and assist human employees to do their jobs better and smarter. In South Africa, Stellenbosch-based CLEVVA has developed an AI platform that enables companies to rapidly and easily deploy Virtual Advisors across every aspect of their businesses.

Chatbots have been around for a while. Yet, now that Facebook will allow businesses to deliver automated customer support and interactive experiences through bots, they will be everywhere soon. Brands might feel that they should replace their human employees with chatbots now, but they should think hard before shaking up the status quo. Ideally, humans and chatbots will work together to do the tasks they are best equipped to handle in order for both parties to be most effective. While bots are being touted by some as the next big thing for handling customer service needs, chatbots are not sufficiently equipped to serve as the complete answer to a brand's growing requirements.

The media interest surrounding deep learning has grown exponentially in the last few years. But what does it actually mean, and how will it change business and society? Deep learning is a subset of machine learning that refers to mapping artificial neural networks to recreate some of the same processes that the human brain performs, and using algorithms with speech, images and text, to recognise, identify and understand patterns in the data. Although this sounds simple, it involves complex processes and functions - but once trained, the application of deep learning algorithms could be world changing. For instance, a machine that learns like a human, but can rapidly process thousands of images and recognise patterns, is already showing promise for applying deep learning to medical imaging.

Monday 18th July 2016, Whitley: Sir Ben Ainslie has hailed Land Rover's artificial intelligence (AI) as a'game changer' ahead of the British America's Cup team's home event in Portsmouth (22-24 July). Land Rover, Title and Exclusive Innovation Partner to Land Rover BAR, is applying its big data processing power and machine learning expertise to help co-engineer the fastest boat in America's Cup history and bring the world's oldest sporting trophy to the UK for the first time. Land Rover engineers, embedded into the team for over a year, are using artificial intelligence to explore and find patterns in sailing performance data to help'make the boat go faster'. When testing, the sailing team receives over 16 GB of uncompressed data per day from sensors on the boat - the equivalent of filling an iPhone's memory. The ability to process and make sense of this volume of data is unprecedented in sailing.

Major landmarks around the world are Lighting It Up Blue on April 1 and 2 to raise awareness about Autism Spectrum Disorders (ASD) for World Autism Awareness Day at Forte Sangallo on April 02, 2016 in Nettuno, Italy. In recent months, artificial intelligence (AI) has been making its presence known in different fields of sciences. In fact, AI is deemed as a valuable asset in precision medicine. But now, a team of researchers is exploring the possibilities if machine learning could play a vital part in autism screening, diagnostics and intervention. Before delving deeper into the latest research on the importance of artificial intelligence in autism screening and diagnostics, let's first define the two most relevant subjects on the study - autism and machine learning. According to Autism Speaks, autism refers to the "general term used for group of complex disorders of brain development," which are marked by social interaction, verbal and nonverbal communication difficulties, as well as repetitive behaviors.