Emerging anxieties pertaining to the rapid advancement and sophistication of artificial intelligence appear to be on a collision course with historic models of human exceptionality and individuality. Yet it is not just objective, technical sophistication in the development of AI that seems to cause this angst. It is also the linguistic treatment of machine "intelligence." But what is really at stake? Are we truly concerned that we will be surpassed in our capacities as human beings?

Massive open online courses (MOOCs) were supposed to bring a revolution in education. But they haven't lived up to the expectations. We have been putting educators in front of cameras and shooting video -- just as the first TV shows did with radio stars, microphone in hand. This is not to say the millions of hours of online content are not valuable; the limits lie in the ability of the underlying technology to customise the material to the individual and to coach. That is about to change, though, through the use of virtual reality, artificial intelligence and sensors.

The code has been copied to your clipboard. Machines equipped with artificial intelligence (AI) have performed better than human beings in a high-level test of reading comprehension. Two natural language processing tools received higher test scores than humans in recent exams. One of the tools is a product of the American software maker Microsoft. The other was created by the Chinese online seller Alibaba Group.

In 2016, training expenditure in the US exceeded $70.6 billion--yet experts say that 90% of new skills are lost within a year if they aren't reinforced. Doing the rough math, by not reinforcing training, organizations run the risk of wasting up to $63.54 billion a year. If you're in charge of employee development--whether in HR, a Business Unit, or Sales Enablement--employee training is a big investment that can sometimes feel like a high-stakes gamble, especially when it comes to soft skills training. You might wonder, "Am I choosing the right solution? Will they adopt the skills? Will my investment turn results? Can I confidently report a viable ROI?"

One simple way to describe Machine Learning is letting artificially intelligent machines pick up information by themselves. It's a bit like leaving a person alone with a set of Lego: give them the bricks and come back later to see what they come up with. Using machine learning techniques with AI opens up technology to a whole new host of possibilities. These have the potential to disrupt entire industries in ever changing and exciting ways. Let's take a look at some of the industries which are being radically transformed by machine learning technology right now The modern classroom is definitely receptive to new technology.

It's a given that artificial intelligence will change many things in our world in 2018. But with new developments arising at a rapid pace, how can business leaders keep up with the latest AI to improve their performance? Perhaps the best place for executives to start is gaining an understanding of deep learning. As one of the most exciting and powerful branches of AI, deep learning has led to important breakthroughs that expand the possibilities of applying AI to business problems. First, let me provide a quick intro to the technology.

In 2016, two VCU Brandcenter students, Xia Du and Yanci Wu, won a Cannes Future Lion award for their concept Amazon Emma, an artificial intelligence application for Amazon Echo to combat dementia in seniors. Using natural language processing and machine learning algorithms, Amazon Emma could have personalized conversations with seniors to stimulate their minds and reduce feelings of social isolation. The project won at Cannes because it used new technology to address users' needs -- something agencies and brands constantly strive to do. As agencies and brands continue looking toward AI to reach consumers in increasingly customized ways, there is a greater need for marketers who can create experiences like Amazon Emma. In response, ad schools across the U.S. are introducing new degree programs, boot camps and classes on how to prepare students to use AI, and some brands have started advising schools on their approach. Ad schools that have long had interactive design curricula, like General Assembly and VCU's Brandcenter business program are adding new courses or revamping old ones to include AI into the overall user experience.

With companies from Airbus and Amazon to Uber throttling up development of their own autonomous aerial vehicles, Thrun believes "in a few years time, this will be the hottest topic on the planet." As usual, Thrun intends to be on the cutting edge of this emerging technology. The 50-year-old PhD computer scientist and former Stanford University professor, co-founded Udacity in 2012 and says the online school's self-driving car program has attracted 50,000 applicants since 2016. He expects the new flying car curriculum, which opens in late February and begins taking applications on Tuesday, to draw at least 10,000. Udacity is offering two 12-week terms, at $1,200 each, including a course in Aerial Robotics and one in Intelligent Air Systems, that provide an online certification in a fraction of the time of a traditional degree course.

Self-driving car pioneer Sebastian Thrun has shifted his gaze to the skies, as his Silicon Valley online school Udacity launches what it calls the first'nanodegree' in flying car engineering. With companies from Airbus and Amazon to Uber throttling up development of their own autonomous aerial vehicles, Thrun believes'in a few years time, this will be the hottest topic on the planet.' As usual, Thrun intends to be on the cutting edge of this emerging technology. Self-driving car pioneer Sebastian Thrun has shifted his gaze to the skies, as his Silicon Valley online school Udacity launches what it calls the first'nanodegree' in flying car engineering You can now learn how to build a flying car in just four months thanks to a new $400 (£295) online course. Online education provider Udacity, also owned by Sebastian Thrun, has announced two new'nanodegrees' teaching users to make driverless or flying vehicles.

Training set bugs are flaws in the data that adversely affect machine learning. The training set is usually too large for man- ual inspection, but one may have the resources to verify a few trusted items. The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these items to identify bugs in the training set and thus im- proves learning. Specifically, our approach seeks the smallest set of changes to the training set labels such that the model learned from this corrected training set predicts labels of the trusted items correctly. We flag the items whose labels are changed as potential bugs, whose labels can be checked for veracity by human experts. To find the bugs in this way is a challenging combinatorial bilevel optimization problem, but it can be relaxed into a continuous optimization problem. Ex- periments on toy and real data demonstrate that our approach can identify training set bugs effectively and suggest appro- priate changes to the labels. Our algorithm is a step toward trustworthy machine learning.