He is a German computer scientist and artist known for his work on machine learning, Artificial Intelligence (AI), artificial neural networks, digital physics, and low-complexity art. "We need to be super careful with artificial intelligence. It is potentially more dangerous than nukes." That was Elon Musk two years ago, on Twitter. What does it mean for a technology, when it faces serious doubts from a man who is passionate about creating a better world through innovation? Since its beginnings in the 1950s, artificial intelligence has been a favourite subject of science fiction. But now AI has entered the realm of fact: several studies predict that intelligent machines will have a big impact on how we work, how we move and even how wars are fought. Innovators and scientists around the world believe that now is the time to ensure that AI is beneficial above all for humans. And even if there are plausible reasons to be anxious about machines that could one day be more intelligent than we are, many scientists are ready to take up the challenge, as we explain in our feature on the following pages. Some people fret that artificial intelligence will end civilization as we know it. Others believe it can solve every problem.

Amazon Web Services appears to be ramping up its security chops. TechCrunch has learned that the e-commerce giant's cloud services group quietly acquired cyber security firm harvest.ai. The San Diego-based startup, co-founded by a team that includes two former NSA employees, uses machine learning and artificial intelligence to analyze user behavior around a company's key IP to try to identify and stop targeted attacks before valuable customer data can be swiped. We were alerted to the acquisition by a tipster, who said the purchase price for harvest.ai The tipster also said the team's 12 employees are relocating to Amazon's Seattle headquarters.

With the year being almost at an end, let me chime in to the gang of pundits who venture into prediction land and pronounce what we get out of our glass balls. So here are my 5 plus 2 bonus ones. Alexa paved the way, the Google Assistant is on its heels, Microsoft Cortana wants to get there, too – and Apple, amazingly, is a late starter in this environment. Amazon started with a pretty smart strategy by not overselling the capabilities of its underlying AI, as Apple did with Siri, which caused some grief for Apple and some laughs for many people around. More and more helpful Alexa skills are developed and implemented that improve its usefulness.

Artificial intelligence (AI) has spread its wings: from Tesla's self-navigating cars, to Google's DeepMind defeating the world champion at Go (a board game more complicated than chess), to IBM's Watson diagnosing diseases. Software is moving beyond executing black-and-white instructions to making complex and often subjective decisions. In 2017 society will begin to trust machines in much the same way that we trust people. AI introduces a kind of unpredictability not found in traditional software. In solving complex tasks, it is too difficult to build rules to cover every eventuality. Instead, AI systems learn from experience.

As each year wraps up, experts pull their crystal balls from their drawers and start peering into them for a glimpse of what's to come. At BigML, we have been following such clairvoyant claims carefully this past holiday season to compare and contrast them with our own take on what 2017 will bring... and, our predictions may come across as quite unorthodox to some experts out there. For the TL;DR crowd, our crystal ball is showing us a cloudy (no pun intended) 2017 Machine Learning market forecast with some sunshine peeking through for good measure. To put it more directly, enterprises need to look beyond the AI hype for practical ways to incorporate Machine Learning into their operations. This starts with careful decision making when choosing an internal platform for your organization that will help build on smaller, low hanging fruit type projects that leverage their proprietary datasets.

Amazon's "artificial intelligence" software or should we say smart assistant, Alexa, was the quiet star of CES 2017, appearing in a huge range of products, from fridges to smart lamps, to cars. Alexa has even made its debut in its first smartphone, the Huawei Mate 9 – Alexa, and this could be the start of a new trend in 2017 that sees smart assistants become important flagship differentiators. Of course, Google and Apple have been at this for a long time already, and Google Now is already baked in to every new Android smartphone release these days. So really the age of the smart assistant has been here for a while already. However, Google Now certainly isn't a unique selling point for manufacturers looking to stand out in the competitive high-end market, hence why custom assistants are starting to pop up.

For every up, there is a down. For every wrong, there is a right, and for every good thing, there is a bad to go with it. There are many benefits that autonomous weapons could bring and if you were ever in a position where you needed a weapon you'd be quite grateful to have one by your side. But, what if that weapon suddenly malfunctioned and aimed at you instead? This is the main worry for a lot of people when they think about autonomous weapons, and it seems that the majority of people in 89 different nations agree too.

I have written previously about Robotic Process Automation (RPA) and its potential impact on the financial services industry and more specifically on the finance and risk functions within the institutions. Most financial services firms have started exploring how to benefit from RPA – using software to mimic the actions a human would perform on a PC, then scaling up these actions as needed – and many have already automated a number of the repetitive, rules-based processes that are the typical starting points for RPA. Some firms that have implemented these tools have already seen dramatic reductions in average handling and/or cycle times. Across the industry, companies have been looking for ways to help address high variable costs and stagnant productivity growth against market challenges. The proliferation of new regulations – as well as firms' own initiatives to improve compliance and reduce risk – have driven up the demand for and market cost of finance and risk talent.

Iran introduced blocks on pornography so aggressive that they broke the internet around the world. The country's censorship laws on adult websites are infamously stringent, and require access pornographic websites to be cut off. But last week the state internet provider did so not only for those in Iran but for people across the world – as far away as Russia and Hong Kong. The strange ban was the result of the way that the internet provider cut off access to those sites. It did so using some of the basic mechanisms of the web – not just stopping people in Iran accessing the websites, but changing the directions that power the internet so that nobody could. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge – by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data. Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis.