The insurance industry is one that is just beginning to tap into the potential of artificial intelligence. If you've been monitoring the ImageNet challenge over the years, you know that AI's image classification surpassed human accuracy about 18 months ago, indicating the technology is reaching a stable and mature state. Once a technology is customer-ready, it's important that it's also customer-centric -- in that it solves an inherent problem. Using AI technology to automate a visual task, such as inspecting damage to a car, is a nearly instant way to provide insurance customers with crucial information about the extent of the damage. Image classification AI within the app compares the customer's photos with thousands of other anonymized crash photos to generate a cost estimate for their repair.

FusionOps was ahead of its time. Founded in 2005, the company has only recently found its groove with the AI wave. Focused on the supply chain area, they are doing some very cool stuff with Cloud, Big Data, and AI. Sramana Mitra: Tell us about the company and yourself. At that time, we had a vision and we still have the same vision.

Artificial intelligence is a massive opportunity, but triggers some risks which cannot be sorted through over-regulations that might damage the market. One of the main topics of the World Economic Forum 2017 was artificial intelligence (AI). I found extremely interesting the interview to Ginni Rometty, the Chairwoman, President and CEO of IBM. Because of these 3 revolutions, there is a huge amount of information that cannot be dealt by humans, but we need systems that can deal with such data, reason around it and learn. This led to the rise of artificial intelligence.

The application of swarm intelligence (SI) in the optimization field has been gaining much popularity, and various SI algorithms have been proposed in last decade. However, with the increased number of SI algorithms, most research focuses on the implementation of a specific choice of SI algorithms, and there has been rare research analyzing the common features among SI algorithms coherently. More importantly, no general principles for the implementation and improvement of SI algorithms exist for solving various optimization problems. In this research, aiming to cover such a research gap, a unified framework towards SI is proposed inspired by the in-depth analysis of SI algorithms. The unified framework consists of the most frequently used operations and strategies derived from typical examples of SI algorithms.

The shadowy arm of the US Defense Department devoted to funding cutting-edge technology is building an interactive robotics system powerful enough to perform an incredibly difficult task: a trumpet solo. Defense Advanced Research Projects Agency (Darpa), the US military's technology research arm, has handed over its first cheque to Kelland Thomas, associate director of the University of Arizona School of Information (and a jazz musician in his own right) to fund musical machines. "The goal of our research is to build a computer system and then hook it up to robots that can play instruments, and can play with human musicians in ways that we recognize as improvisational and adaptive," said Thomas. Machine learning is a complex field, and one that a scientist at Darpa's Robotics Challenge in Pomona, California, earlier this year likened to "a three-day-old child". A three-day-old child's brain is incredibly powerful, but it doesn't yet know how to riff like Charlie Parker.

The Deep Learning market is growing at an annual rate of 65 percent annually and is expected to reach $1.8 billion by 2022 according to a report by Research and Markets. Deep Learning is a kind of machine learning, but it is more super charged than just machine learning. Deep learning software algorithms derive insight by processing massive amounts of data, like images, video, audio or text. With standard machine learning, an analyst or programmer would first create a model that explicitly tells the computer what to look for in the data, but with deep learning, instead the software sifts through the data and tries to identify patterns on its own. Businesses are beginning to apply deep learning techniques to areas like customer churn prediction, financial fraud detection and product recommendation.

It's not always easy to interpret what people are saying. If you're particularly bad with social cues, you might find the help you need in the future with a wearable. According to researchers from MIT's Computer Science and Artificial Intelligence Laboratory and Institute of Medical Engineering and Science, the wearable features an artificially intelligent system capable of predicting if a conversation is sad, happy, or neutral based on an individual's vitals and speech patterns. Tuka Alhanai and Mohammad Ghassemi detailed their research in a paper [PDF] they will be presenting at the Association for the Advancement of Artificial Intelligence's conference in San Francisco next week. According to Alhanai, it might not be long before people can have AI social coaches in their pocket, but they believe their wearable is the first experiment to collect both physical and speech data in a passive yet robust manner, even while subjects are engaged in natural interactions.

Artificial Intelligence: How far have we reached since the term was coined? Artificial intelligence holds the key to a new era of innovation, one where computers begin to work intelligently on our behalf rather than under our command. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider "smart". Artificial Intelligence has been around for a long time – the Greek myths contain stories of mechanical men designed to mimic our own behavior. It's an era where technology will become more intuitive, more conversational, and more intelligent, will enable businesses to better know and serve their customers in ways previously unimaginable, and ultimately help solve some of the planet's biggest challenges.

With the shift to a value-based reimbursement model, hospitals and clinicians are looking for ways to increase efficiencies and improve patient outcomes. Artificial intelligence can help to streamline diagnoses and treatments by culling through volumes of data and pinpointing specific disease types or other patient data. Patients who need to be seen are seen quicker because a doctor or nurse wasn't spending time looking through reams of reports, which in turn increases satisfaction all around. The goal of cognitive computing is to make knowledge workers more effective, not to replace them, says Hal Andrews, president of healthcare at software company Digital Reasoning. "Any workflow that requires humans to read or skim or scan vast amounts of data, technology can make that more efficient," Andrews tells Healthcare Dive.

An artificial intelligence firm founded in Waterloo is being acquired by tech giant Microsoft. Maluuba's 50-employee team, including co-founders and former University of Waterloo students Sam Pasupalak and Kaheer Suleman, will become part of Microsoft's Artificial Intelligence and Research organization. Terms of the agreement, which was announced Friday, are not being disclosed. Maluuba has offices in Waterloo and Montreal. In a post announcing the agreement on the official Microsoft blog, Harry Shum, executive vice-president of the Artificial Intelligence and Research Group, said the Maluuba team will be consolidated in Montreal this year.