The pervasiveness of and increasing authority vested in artificial intelligence and autonomous systems has created tremendous anxiety amongst the public. This has led to industry- and academic-based initiatives to address ethics in AI/AS through research and public engagement. The IEEE Global Initiative for Ethical Considerations in the Design of Artificial Intelligence and Autonomous Systems is one such initiative designed to support engineers and serve as a springboard for developing operational IEEE Standards in AI ethics. Last week the IEEE Global Initiative released version one of its working reference, entitled "Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems." I had the honor of working with experts in information privacy and contributing to the Personal Data and Individual Access Control Committee.

The "willing suspension of disbelief" is the idea that the audience (readers, viewers, content consumers) is willing to suspend judgment about the implausibility of the narrative for the quality of the audience's own enjoyment. We do it all the time. Two-dimensional video on our screens is smaller than life and flat and not in real time, but we ignore those facts and immerse ourselves in the stories as if they were real. We have also learned the "conventions" of each medium. While we watch a movie or a video, we don't yell to the characters on the screen "Duck!" or "Look out!" when something is about to happen to them.

It is hardly surprising Artificial Intelligence is the number one ICT trend for 2017. We already have been warned about the rise of A.I. by Elon Musk, Stephen Hawking, Bill Gates and others. Their message is that our society can potentially benefit from Artificial Intelligence, but researchers must not create something that cannot be controlled. Coming years we will see in which direction this is heading. Artificial Intelligence is not something new.

This post highlights the various value-additions that machine learning can provide to actuaries in their analytical work for insurance companies. As such, a key problem of swapping specific risk for systematic risk in general insurance ratemaking is highlighted along with key solutions and applications of machine learning algorithms to various insurance analytical problems. 'In pricing, are we swapping specific risk for systematic risk?'[1] The hypothesis is that in normal market conditions, premiums are kept at low levels to increase revenues and market share. The traditional approach requires precise figures (point estimates) and so leads to understatement of uncertainty.

Two former allies of New Jersey Governor Chris Christie plan to appeal their conviction for intentionally causing traffic gridlock in Fort Lee during morning rush hour for a week in September 2013. Over the past five years, mobile tech has allowed companies like Uber, Lyft, and Juno to disrupt traditional travel with a new ride-hail industry worth billions. According to recent figures from the Massechusetts Institute of Technology (MIT), a mere 3000 ride-pools could even handle the business of New York City's entire taxi fleet with hardly any delay--provided, of course, that riders are willing to share. A study by MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) revealed this week that ride-sharing platforms similar to UberPOOL and Lyft Line could handle the passenger traffic of NYC's 14,000 taxis with just a few thousand vehicles. In addition, the team says, such programs could help reduce congestion on city streets (if not its' sidewalks) by an impressive 300%.

Of all the "What to watch" lists gumming up your social media feeds, "Which tech trends are on the horizon" should be the one you pause long enough to read. It's important to recognize what technology is increasingly in popularity so you can allocate the time and funds to make these trends pay off for your business. Heading into the new year I tackled some of the top trends in Digital Transformation along with some key trends in Customer Experience. However, with 2017 CES upon us (and by no means just a consumer show any longer) and a new year off to a fast start, we must not ignore the tech trends that will shape the enterprise in the year ahead. Here are my takes on what to spend and save on for enterprise technology in 2017. From technology budgets to knowing how tech will change business, tech trends matter for businesses big and small.

So here is my post-Christmas/pre-New Year Bulletin with some predictions about what's going to happen in 2017 ... enjoy. "To truly realise transformative value of machine-learning and digital intelligence platforms throughout your organisation, you need to understand how it intersects with both your organisational chart and your legacy tech. How would true automation evolve the current roles on my team? Do we have the right skill set to build new strategies to take advantage of the multiplying effect of thousands of experiments and tests?" "In 2017 I foresee progressive marketers using artificial intelligence (AI) capabilities to translate data science into powerful campaigns. When AI combines with machine learning and automation, data collection is enhanced and deeper analysis enables marketers to create and execute campaigns that automatically optimise timings, content, and channels."

Cleaning and preparing data is a critical first step in any machine learning project. In this blog post, Dataquest student Daniel Osei's takes us through examining a dataset, selecting columns for features, exploring the data visually and then encoding the features for machine learning. This post is based on a Dataquest'Monthly Challenge', where our students are given a free-form task to complete. After first reading about Machine Learning on Quora in 2015, Daniel became excited at the prospect of an area that could combine his love of Mathematics and Programming. After reading this article on how to learn data science, Daniel started following the steps, eventually joining Dataquest to learn Data Science with us in in April 2016. We'd like to thank Daniel for his hard work, and generously letting us publish this post.

There has been no shortage of promises recently about how deeply the application of machine-driven artificial intelligence is expected to change our society in the next decade. Microsoft announced its ambition to conquer cancer using natural language processing to analyze research papers in close to real time. Google--now a self-proclaimed AI-first company--will make computers sound just like humans by applying machine learning to a vast corpus of human voice samples. Facebook is using AI to analyze satellite footage to locate all human life and fulfill its promise to bring the internet to the world's entire population. And the list goes on.

The age of artificial intelligence (AI) is upon us. In the past few years, vast improvements have been made in how well computers can recognise objects in images and understand human voices. Progress in these areas has been made due to increased computing power and the availability of large stores of data, which, when combined, have made AI systems dramatically more effective. These same forces are also being used in marketing. AI, or'cognitive', marketing systems use industrial computing power, big data, and machine learning to improve marketing performance.