The question of whether robots will replace human jobs has been hotly debated for decades, but the rapid advances in Artificial Intelligence – and the populist socio-political environment in which we find ourselves – have given the debate around automation and employment an added sense of urgency. A report by the Nomura Research Institute indicates that by 2035 nearly half of all jobs in Japan could be performed by robots, and recently we've started to see these predictions materialise. Insurance firm Fukoku Mutual Life made 34 employees redundant by replacing them with IBM's Watson Explorer AI system, which is capable of analysing and interpreting data to automatically calculate payouts to its policyholders. This AI system will be able to read tens of thousands of medical certificates and factor in the length of hospital stays, medical histories and any surgical procedures before calculating payouts, but in spite of being described by the firm as "cognitive technology that can think like a human," the sums will not actually be paid until they have been approved by an actual member of staff. And it is this aspect of human oversight that proves crucial to the conversation: Instead of treating Artificial Intelligence and Automation as a zero-sum game and pitching AI as an adversary to human employment, it is important to point out that technological advances generally correlate to increased prosperity in the long-term.

This is part of a series on my quest to learn as much as possible about AI. To know why I'm doing this, check out my first post. In grad school, I primarily studied Ancient Greek philosophy. My only published piece of academic research is on his Atlantis myth. But now I work at a tech company and I'm writing about artificial intelligence -- a subject I'm very interested in but know little about.

In the latest installment of Information Age's Innovation Spotlight Series, Adam Spearing discusses how AI is impacting the customer journey. He expressed that technologies like AI are integral to remain competitive because of an increasing demand from people to be "predictably served". An expectancy of anticipation from the consumer means they want to be told what they want, before they want it and AI acts as a tool to satisfy this desire. "Research we have done has shown that people in the consumer space and people in the business-to-business space expect to be anticipated on their needs on what it is they want and I don't think there is a lot of technologies currently out there that offer that capability," said Spearing. He continued by saying that the technology is "going to transform the way you deal with the whole interaction of how customers operate with you. The ability to look at a tweet, or a post, or an email and understand the sentiment behind that and change the way you prioritise your interactions [will be significant]."

Helen of Troy may have had a "face that launch'd a thousand ships", according to Christopher Marlowe, but these days her visage could launch a lot more besides. She could open her bank account with it, authorise online payments, pass through airport security, or raise alarm bells as a potential troublemaker when entering a city (Troy perhaps?). This is because facial recognition technology has evolved at breakneck speed, with consequences that could be benign or altogether more sinister, depending on your point of view. High-definition cameras combined with clever software capable of measuring the scores of "nodal points" on our faces - the distance between the eyes, the length and width of the nose, for example - are now being combined with machine learning that makes the most of ever-enlarging image databases. Applications of the tech are popping up all round the world. In China, for example, fried chicken franchise KFC recently unveiled its first "smart restaurant" that uses facial recognition to predict what meal customers are likely to want, based on their age, gender and the time of day, while payments giant Alipay is experimenting with "smile to pay" tech.

Activists protest against the gang-rape of a 16-year-old girl in Brazil last year – videos and images of the assault were posted to social media and circulated widely, revictimizing the woman. As Facebook defended itself in a German court against claims that it does too little to counter abusive content on its platform, one of its lawyers made the intriguing claim "There are billions of postings each day. You want us to employ a sort of wonder machine to detect each misuse. Such a machine doesn't exist." For a technology company with a heavy investment in deep learning and filtering technologies that has repeatedly run afoul of free speech advocates for its aggressive stance on content removal this is certainly a curious claim to make.

In a fourth-floor Tufts lab, a computer program was in the process of convincing a student that she was actually interacting with a human. It was spring 2015, and the student had come to the lab for a study involving a new way of teaching people to play the piano. The beginning of the session had been fairly unremarkable. The researchers--Beste Yuksel, E16, then a Ph.D. candidate in computer science, and Kurt Oleson, A15, a brain science major with a minor in music engineering--put a headband-like contraption on the student's head and sat her down at a piano keyboard. In front of the keyboard was a computer screen that displayed the soprano line of a Bach piano chorale that she was supposed to play.

For the past year, we've ranked nearly 14,500 Machine Learning articles to pick the Top 10 stories (0.069% chance) that can help you advance your career in 2017. This machine learning list includes topics such as: Deep Learning, A.I., Natural Language Processing, Face Recognition, Tensorflow, Reinforcement Learning, Neural Networks, AlphaGo, Self-Driving Car. This is an extremely competitive list and Mybridge has not been solicited to promote any publishers. Mybridge A.I. ranks articles based on the quality of content measured by our machine and a variety of human factors including engagement and popularity. Academic papers were not considered in this batch.

This tutorial series shows how you can create a chatbot that can be deployed on two messaging applications: Facebook and Slack. In this final tutorial, I explain how you can enhance the chatbot by using IBM Watson Services. The news chatbot in this series uses developerWorks content as an example, but you can modify the content source to meet your own needs. In a previous tutorial, I described the importance of artificial intelligence (AI) in your chatbots and explained how it's hard to build your own AI--it requires not only rock stars in data science, but also a massive amount of data to train models. A small company typically does not have these kinds of resources.

Although this may sound a like a story line from a futuristic drama, the reality is this future may be closer than you think. Call any support line or customer service centre today, and you hear a very simple phone bot. It's an awful experience burned by repetitive questions resulting in a less than optimal experience. For most, the first response is to hit 0 and hope to speak to an actual person. Advancements in natural language processing and generation are quickly propelling this kind of experience to a point where you soon won't even know you're speaking to a bot when you hit 0. Instead, the system will adapt to your preferences. The voice is smooth and comforting, a replica of the company spokesman Morgan Freeman.

Cloud mania continues to grow as businesses move more and more workloads to platforms such as Microsoft Azure and Amazon Web Services (AWS). But while public cloud hype is stealing all the headlines, private data centers are quietly plodding along and growing, as well. There is so much data growth today that businesses have to invest in both public clouds and private data centers, hence the high adoption rate of "hybrid" environments. The landscape for public cloud services is set--Azure and Amazon have won that battle--but private data centers are in a state of change. The legacy model of buying best-of-breed components and cobbling the technology together to build a private cloud is a long, complex process that just can't keep up with the needs of a digital organization.