Digital lenders are pulling in all kinds of data, like purchases, SAT scores and public records. Digital lenders are pulling in all kinds of data, like purchases, SAT scores and public records. The terms of the next loan you get might depend less on your credit score and more on what a computer program thinks of your habits. Digital lending is expected to double in size over the next three years, reaching nearly 10 percent of all loans in the U.S. and Europe. There are now some 2,000 digital startups, many of which are using artificial intelligence to analyze the troves of data created every day.

The world is going to change more over the next five years than it has over the last fifteen. That, at least, is the opinion of Jeremy Waites, who describes himself as just a lowly storyteller, traveling around to telling tales about cool stuff. Officially that makes him an evangelist, and the'cool stuff' on which he evangelises is IBM's AI system, Watson. I met up with him at the recent Cloud Expo in London to discuss some of the wider issues about where AI fits in the business and wider world, what it can bring to those parties, and what impact it might have on the way work changes – or indeed continues to exist. Like a growing number of people in the AI business Waites is keen to stress that the'A' does not stand for'Artificial', but rather for'Augmented', on the basis that the former carries with it all those scary, sci-fi connotations.

With great power comes great responsibility--and artificial-intelligence technology is getting much more powerful. Companies in the vanguard of developing and deploying machine learning and AI are now starting to talk openly about ethical challenges raised by their increasingly smart creations. "We're here at an inflection point for AI," said Eric Horvitz, managing director of Microsoft Research, at MIT Technology Review's EmTech conference this week. "We have an ethical imperative to harness AI to protect and preserve over time." Horvitz spoke alongside researchers from IBM and Google pondering similar issues.

My other car drives itself

The research, which includes new analysis of CB Insights data on 450 insurtech deals over the last three years, appears in a new Accenture report titled "The Rise of InsurTech." The report was released today in conjunction with Accenture's Fintech Innovation Lab in London, which for the first time includes a dedicated insurtech stream comprising leading industry startups. According to the report, the combined number of deals across AI (including automation) and the IoT (including connected insurance) increased 79 percent in 2016. Even though the two technologies represented only one-quarter (24 percent) of the 216 insurtech deals globally last year, they accounted for 44 percent or US$711 million of total insurtech investment -- compared with just 10 percent of global insurtech investment in 2015. "We've seen a rapid acceleration of investment into and deal activity around intelligent automation and IoT start-ups over the last 12 months," said Roy Jubraj, a co-author of the report and Accenture's Digital & Innovation lead in the company's Financial Services practice in the U.K. and Ireland.

America's biggest tech companies are remaking the internet through artificial intelligence. And more than ever, these companies are looking north to Canada for the ideas that will advance AI itself. This morning, Google announced it's starting an AI lab in Toronto. At the same time, it's helping to fund a public-private partnership with the University of Toronto to develop and commercialize AI talent and ideas. In November, the company made a similar move in Montreal--a city that has also attracted Microsoft's attention.

We must recognize that our cyberdefense technologies are not working and will not work. Cases in point: Our most sensitive cyberoffense technologies have been hacked; power companies admit they would have great difficulty stopping a cyberattack and are being asked to be prepared to operate at much less than full capacity under a cyberattack; 70 percent of oil and gas companies have been attacked -- and the threat is growing. The cybersecurity industry is in chaos and needs to move toward new technologies -- cyberdefense technologies that are beginning to leverage analytics, machine learning and artificial intelligence (AI). Hackers are taking advantage of the same technologies, so the cyberdefense industry needs to jump on board. Let's quit playing catch-up and instead take a proactive approach to cybersecurity.

Need a teacher, airport guide, care taker, and customs control officer all in one? Standing at just under 3 feet tall with big cartoon eyes and flipper arms, Sanbot looks more helpless than helpful, but don't let that cute appearance fool you. Sanbot is packed with features to help "her" get the job done. Sanbot has 14 of them scattered throughout its three foot frame. Many of these are touch sensitive, allowing the robotic lady to respond with human-like emotion to a particular tap.

Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of whale sound detection against different background noise types (e.g., rain, wind). In comparison to classical FFT-based representation like spectrograms, we showed that the use of image-based pretrained CNN features brought higher performance to classify whale sounds and background noise.

An estimated 17.5 million people died from a cardiovascular disease in 2012, representing 31 percent of all global deaths. Most acute coronary events result from rupture of the protective fibrous cap overlying an atherosclerotic plaque. The task of early identification of plaque types that can potentially rupture is, therefore, of great importance. The state-of-the-art approach to imaging blood vessels is intravascular optical coherence tomography (IVOCT). However, currently, this is an offline approach where the images are first collected and then manually analyzed an image at a time to identify regions at risk of thrombosis. This process is extremely laborious, time consuming and prone to human error. We are building a system that, when complete, will provide interactive 3D visualization of a blood vessel as an IVOCT is in progress. The visualization will highlight different plaque types and enable quick identification of regions at risk for thrombosis. In this paper, we describe our approach, focusing on machine learning methods that are a key enabling technology. Our empirical results using real OCT data show that our approach can identify different plaque types efficiently with high accuracy across multiple patients.