Hollywood hasn't done artificial intelligence any favors, according to Oliver Christie. Speaking at the recent 2018 Pacific Design & Manufacturing Show in Anaheim, Calif., Christie, a consultant specializing in artificial intelligence, said we're in danger of letting the hype and hyperbole around AI cloud our thinking about the technology and its true capabilities. "We think of AI in a war type setting," he told the audience. "We think of the technology as if we're in a sci-fi world, but we're not. And these views are impacting decisions made in the real world."

Business adoption of artificial intelligence is accelerating, fueled by an explosion of data, the rapid growth in cloud computing and the emergence of advanced algorithms. In a survey of IT decision-makers that my company, CCS Insight, conducted in July 2017, 58 percent of respondents said they are using, testing or researching the use of artificial intelligence (AI) in their organizations. Respondents also estimated that as much as 30 percent of their business applications would be enhanced with machine learning within the next 24 months -- a bullish view, considering the technology's well-documented problems with trust, cost and the lack of skills needed to train machine learning systems. Speech-based and image-based cognitive applications are emerging at an accelerating rate for use in specific markets, such as fraud detection in finance, low-level contract analysis in the legal sector and personalization in retail. AI is also beginning to appear in systems designed for corporate functions such as customer service, HR, sales and IT.

This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.

Most of the autonomous vehicles that you're likely to encounter in the near future are either Level 2 or Level 4 autonomous. Level 2, which you'll find in a Tesla on the highway, means that the car drives itself in specific situations but expects you to be paying attention the entire time. Level 4 y...

Though its time horizon can't be predicted, artificial intelligence (AI) promises to foundationally influence modern society, for better or worse. A sub-genre of AI -- machine learning -- has garnered particular attention from the pundits for its potential impact on the world's most important industries. Due to the resulting hype, massive amounts of talent and resources are entering this space. But what is machine learning and why should we care about it in the first place? The answer is that, in the broadest sense, machine learning models are an application of AI in which algorithms independently predict outcomes.

Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

Artificial intelligence (AI) is making inroads in just about every job that involves data processing, repetition, or predictive thinking. Instead of worrying about being replaced by AI, it's time to get ready for when AI will arrive in your workplace. Here are some of the most frequently asked questions about AI in the workplace -- and the answers you need to know. Will I be replaced by AI? According to the report titled "A Future That Works: Automation, Employment, and Productivity" by McKinsey Global Institute, less than five percent of all occupations consist of activities that can be completely automated.

Technicians analyze data following the trial of an autonomous self-driving vehicle in a pedestrianised zone, during a media event in Milton Keynes, north of London, on October 11, 2016. Suburbs have largely been dismissed by environmentalists and urban planners as bad for the planet, a form that ne...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Awww, a SpotMini using its face-arm to help a buddy...adorable! Perhaps Boston Dynamics could release some behind-the-scenes footage (or outtakes!) to show what's going on here?

A reliable modeling of uncertain evidence in Bayesian networks based on a set-valued quantification is proposed. Both soft and virtual evidences are considered. We show that evidence propagation in this setup can be reduced to standard updating in an augmented credal network, equivalent to a set of consistent Bayesian networks. A characterization of the computational complexity for this task is derived together with an efficient exact procedure for a subclass of instances. In the case of multiple uncertain evidences over the same variable, the proposed procedure can provide a set-valued version of the geometric approach to opinion pooling.