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.

In recent weeks we've seen incredible action in the intelligent assistant market. Google announcing the Google Assistant and associated devices to take on Amazon's Alexa, Microsoft at Ignite 2016 touting a new and improved Cortana, Salesforce launching Einstein, and Viv -- a start-up by the developers of Siri -- bought by Samsung. These AI-driven enhancements are becoming ubiquitous -- from customer service to marketing, from the home to the car, and from the factory to the community. They all have one thing in common -- they use predictions to deliver results which help you. Predictions are the result of predictive analysis, which, like data science, is red hot in the minds of executives and CMO's.

All right, don't panic, but computers have created their own secret language and are probably talking about us right now. Well, that's kind of an oversimplification, and the last part is just plain untrue. But there is a fascinating and existentially challenging development that Google's AI researchers recently happened across. You may remember that back in September, Google announced that its Neural Machine Translation system had gone live. It uses deep learning to produce better, more natural translations between languages. Following on this success, GNMT's creators were curious about something.

Machine learning isn't confined to science fiction movie plots anymore; it's fueled the proliferation of technologies that touch our everyday lives, including voice recognition with Siri or Alexa, Facebook auto-tagging photos and recommendations from Amazon and Spotify. And many enterprises are eager to leverage machine learning algorithms to increase the efficiency of their network. In fact, some are already using it to enhance their threat detection and optimize wide area networks. As with any technology, machine learning could wreak havoc on a network if improperly implemented. Before embracing this technology, enterprises should be aware of the ways machine learning can fall flat to avoid setting back their operations and turning the c-suite away from implementing this technology.

Usually aimed at the layman; your kids can understand them. A few ones are listed in the picture below. To view and access all the results, click on the Infographics link. Usually aimed at the layman; your kids can understand them. A few ones are listed in the picture below.

The world of today runs on oil – heating and cooling our homes, and powering almost all forms of transportation. Try and drive a car today without oil-based products, and you won't get far. Without oil, a car engine overheats, pistons and rings fuse to the cylinder walls, the engine block cracks and, of course, there is no gas. You could say oil is the key technology that allowed the automotive world we know today. But that's all about to change.

Let's punch through the noise around machine learning. The tech ecosystem is well acquainted with buzzwords. From "Web 2.0" to "cloud computing" to "mobile first" to "on-demand," it seems as though each passing year heralds the advent and popularization of new catchphrases to which fledgling companies attach themselves. But while the trends these phrases represent are real, and category-defining companies will inevitably give weight to newly coined buzzwords, so too will derivative startups seek to take advantage of concepts that remain ill-defined by experts and little-understood by everyone else. "It's clear that 9 of 10 investors have very little idea what AI is so if you're a founder raising money, you should sprinkle some AI into your pitch deck. Use of'artificial intelligence,' 'AI,' 'chatbot,' or'bot' are winners right now and might get you a little valuation bump or get the process to move quicker. If you want to drive home that you're all about that AI, use terms like machine learning, neural networks, image recognition, deep learning, and NLP. Then sit back and watch the funding roll in."

Artificial intelligence is the yin and blockchains are the yang of digital business. Artificial intelligence is the yin and blockchains are the yang of digital business. While AI helps us assess, understand, recognize and decide, blockchains can help us verify, execute and record. While the machine learning methods that are a part of AI help us find opportunity and improve decision making, smart contracts and blockchains can automate verification of the transactional parts of the process. AI and blockchains in that sense are complementary and synergistic.

What if a computer could predict who's a criminal and who isn't just by scanning their face? It sounds like creepy science fiction from a film like Minority Report, but a pair of researchers in China have been busy testing that exact theory out. Xiaolin Wu and Xi Zhang from China's Shanghai Jiao Tong University published a study called " Automated Inference on Criminality using Face Images ." They fed the faces of 1,856 people (half of which were convicted violent criminals) into a computer and set about analysing them. The photos were all of Chinese men aged between 18 and 55, with no beards, scars or other distinguishing features.

Artificial Intelligence (AI) is rapidly creeping into our everyday lives; it is no longer something we associate with solely appearing in sci-fi films. Not only is AI transforming online services, such as Apple Siri voice recognition or Facebook chatbot API for Messenger, but it is also transforming how organisations are using and understanding data to provide customers with personalised experiences. Some companies within the banking, automotive, insurance and telecom industries are already using AI to provide customers with real-life contextual experiences based on data. Swedish retail bank Swedbank has integrated the technology tool Nina, an intelligent virtual assistant, into its customer service strategy. Nina delivers automated customer service via the brand's website in a conversational manner enabling self-service capabilities to Swedbank customers.