At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. To close out 2017, we recently asked some of the leading experts in Big Data, Data Science, Artificial Intelligence, and Machine Learning for their opinion on the most important developments of 2017 and key trends they 2018. This post, the first in this series of such year-end wrap-ups, considers what happened in Machine Learning & Artificial Intelligence this year, and what may be on the horizon for 2018. "What were the main machine learning & artificial intelligence related developments in 2017, and what key trends do you see in 2018?"

We are creating an intelligence that is external to humans and housed in the virtual economy. This is bringing us into a new economic era--a distributive one--where different rules apply. A year ago in Oslo Airport I checked in to an SAS flight. One airline kiosk issued a boarding pass, another punched out a luggage tag, then a computer screen showed me how to attach it and another where I should set the luggage on a conveyor. I encountered no single human being. The incident wasn't important but it left me feeling oddly that I was out of human care, that something in our world had shifted. That shift of course has been going on for a long time. It's been driven by a succession of technologies--the Internet, the cloud, big data, robotics, machine learning, and now artificial intelligence--together powerful enough that economists agree we are in the midst of a digital economic revolution. But there is less agreement on how exactly the new technologies are changing the economy and whether the changes are deep. Robert Gordon of Northwestern University tells us the computer revolution "reached its climax in the dot-com era of the 1990s."

The Fourth Industrial Revolution is fundamentally changing the ways that people work and live in three main ways. First, it is untethering some types of work from a physical location, making it easier to remotely connect workers in one region or country to jobs in another – but also making it less clear which set of employment laws and taxes apply, creating greater global competition for workers, potentially weakening employment protections and draining public social protection coffers. Second, human labour is being displaced by automation, robotics and artificial intelligence. Opinions differ on the extent of what is possible: Frey and Osborne's (2013) study found that 47% of US employment is at high risk of being automated over the next two decades, while a 2016 study of 21 Organisation for Economic Co-operation and Development (OECD) countries, using a different methodology, concluded that only 9% of jobs are automatable. In general, lower-skilled workers are more likely to see their jobs disappear to automation, increasing their vulnerability and exacerbating societal inequality.

As a kid, I saw the 1968 version of Planet of the Apes. As a future primatologist, I was mesmerized. Years later I discovered an anecdote about its filming: At lunchtime, the people playing chimps and those playing gorillas ate in separate groups. It's been said, "There are two kinds of people in the world: those who divide the world into two kinds of people and those who don't." And it can be vastly consequential when people are divided into Us and Them, ingroup and outgroup, "the people" (i.e., our kind) and the Others. The core of Us/Them-ing is emotional and automatic. Humans universally make Us/Them dichotomies along lines of race, ethnicity, gender, language group, religion, age, socioeconomic status, and so on. We do so with remarkable speed and neurobiological efficiency; have complex taxonomies and classifications of ways in which we denigrate Thems; do so with a versatility that ranges from the minutest of microaggression to bloodbaths of savagery; and regularly decide what is inferior about Them based on pure emotion, followed by primitive rationalizations that we mistake for rationality. But crucially, there is room for optimism. Much of that is grounded in something definedly human, which is that we all carry multiple Us/Them divisions in our heads. A Them in one case can be an Us in another, and it can only take an instant for that identity to flip. Thus, there is hope that, with science's help, clannishness and xenophobia can lessen, perhaps even so much so that Hollywood-extra chimps and gorillas can break bread together.

Here's an in-depth look into some of the ways that artificial intelligence is creating jobs and transforming today's legal profession.

Elon Musk says a Tesla car will drive from LA to New York City autonomously by early 2018.Tesla The deadline is approaching for Tesla CEO Elon Musk to deliver on one of his loftiest predictions for the company. In October 2016, he said a Tesla car would drive itself from LA to New York City without any assistance from a human driver by the end of 2017. Musk made a slight revision to that statement in August, claiming the autonomous road trip would happen early in 2018 if he doesn't meet his original deadline. But the self-driving capabilities in Tesla's cars are still ahead of the competition. The Autopilot system included in its cars can help drivers navigate highways and parking lots, and the company says every vehicle produced in its factory has the hardware for complete, autonomous driving that could be activated when the necessary software and government regulations come into place.

We are incredibly proud to announce today that LawGeex has been selected as a Gartner Cool Vendor. Every year, Gartner selects tech companies with a product or service that is "interesting, new and innovative". More importantly, the global consultancy also flags to the business world major new disruptive sectors they need to know about. The distinction of being Gartner Cool Vendor has been achieved in the past by names such as Box, Dropbox, Evernote, Cloudera, and Instagram--then operating in disruptive spaces they created, but which we now take for granted as obvious leaders and benchmarks. This year for the first time ever Gartner recognized legal Artificial Intelligence (AI) companies making a "profound efficiency impact on the way legal services are delivered."

If there's any emerging technology that's gripped the public consciousness in recent years it's AI and machine learning (ML). Autonomous vehicles, shopping recommendations, Siri and Alexa, these are just a few of the day to day examples of the rapid evolution of ML applications. The fervour around AI and ML's development is only fuelling these advancements. As public interest grows we're already seeing more students attracted to ML and AI courses. Just look at the popularity of Professor Andrew Ng's Coursera course on machine learning or the record number of Stanford students who enrolled in the machine learning class this semester.

Assumption-Based Argumentation (ABA) has been shown to subsume various other non-monotonic reasoning formalisms, among them normal logic programming (LP). We re-examine the relationship between ABA and LP and show that normal LP also subsumes (flat) ABA. More precisely, we specify a procedure that given a (flat) ABA framework yields an associated logic program with almost the same syntax whose semantics coincide with those of the ABA framework. That is, the 3-valued stable (respectively well-founded, regular, 2-valued stable, and ideal) models of the associated logic program coincide with the complete (respectively grounded, preferred, stable, and ideal) assumption labellings and extensions of the ABA framework. Moreover, we show how our results on the translation from ABA to LP can be reapplied for a reverse translation from LP to ABA, and observe that some of the existing results in the literature are in fact special cases of our work. Overall, we show that (flat) ABA frameworks can be seen as normal logic programs with a slightly different syntax. This implies that methods developed for one of these formalisms can be equivalently applied to the other by simply modifying the syntax.

The most dramatic cybersecurity story of 2016 came to a quiet conclusion Friday in an Anchorage courtroom, as three young American computer savants pleaded guilty to masterminding an unprecedented botnet--powered by unsecured internet-of-things devices like security cameras and wireless routers--that unleashed sweeping attacks on key internet services around the globe last fall. What drove them wasn't anarchist politics or shadowy ties to a nation-state. It was a hard story to miss last year: In France last September, the telecom provider OVH was hit by a distributed denial-of-service (DDoS) attack a hundred times larger than most of its kind. Then, on a Friday afternoon in October 2016, the internet slowed or stopped for nearly the entire eastern United States, as the tech company Dyn, a key part of the internet's backbone, came under a crippling assault. As the 2016 US presidential election drew near, fears began to mount that the so-called Mirai botnet might be the work of a nation-state practicing for an attack that would cripple the country as voters went to the polls.