The news was revealed by two exchange operators who are planning to apply artificial intelligence tools to perform market surveillance in the coming months, and according to a Wall Street regulator, they are not far behind, Reuters reports. The intentions of the use of the software is to for instance search through chat-room messages to detect any "bragging or back slapping" around the time of the launch of a big trade. It will also be able to unravel complex issues more efficiently, such as incidents called "layering" where orders are rapidly sent to exchanges to then get cancelled in order to artificially move a stock price. Tom Gira, executive vice president for market regulation at the Financial Industry Regulatory Authority (FINRA) believes that AI might even have the ability to detect new types of cicanery. "The biggest concern we have is that there is some manipulative scheme that we are not even aware of," he told Reuters.

Two captive harbour porpoises called Freja and Sif have helped to reveal that porpoises --and probably all cetaceans -- consciously adjust their heart rate to suit the length of a planned dive. By doing this, the animals optimise the rate at which they consume oxygen beforehand to match the intended depth and length of their dive. "Until now, we knew that the heart rates of porpoises and cetaceans in general correlate with different dive factors, such as dive duration, depth and exercise," says Siri Elmegaard of Aarhus University in Denmark, who led the research. "Now we can conclude that harbour porpoises have cognitive control of their heart rate." The discovery might also provide another explanation for how exposure to loud noise from shipping, sonar or subsea exploration harms cetaceans and possibly triggers strandings.

A few months ago I demonstrated how to install the Keras deep learning library with a Theano backend. In today's blog post I provide detailed, step-by-step instructions to install Keras using a TensorFlow backend, originally developed by the researchers and engineers on the Google Brain Team. I'll also (optionally) demonstrate how you can integrate OpenCV into this setup for a full-fledged computer vision deep learning development environment. The first part of this blog post provides a short discussion of Keras backends and why we should (or should not) care which one we are using. From there I provide detailed instructions that you can use to install Keras with a TensorFlow backend for machine learning on your own system.

Artificial intelligence and its machine learning subset are all the rage these days. That was evident when I spoke this week at the AI World event, which was packed with vendors and users seeking to understand what the hell AI and machine learning are--and wanting to know how they could use this old but revitalized technology effectively. Amazon Web Services, Google, IBM, Microsoft, and the other major cloud providers all have machine learning services in their clouds now. But most enterprises have no clue on what the heck to do with machine learning systems, whether cloud or on-premises. It is critical to find the right uses for machine learning.

Pop culture is filled with images of crooked robots defying and defeating humans. Dystopian universes depicting raging wars between machines and mankind are legion: Asimov's writings, Hollywoodian blockbusters such as The Matrix, countless video games, etc. As evolved as we are, all of this -- thankfully -- remains fantasy. The funny thing however is that all those universes are offsprings of a human activity that is soon going to be covered by machines: Art. I believe that robots already are able to produce Art, and will be increasingly able to do so when powered by Artificial Intelligence.

Artificial intelligence, once relegated to the realm of science fiction, is now found in everything from translation services to virtual assistants to video games. And as companies race to develop self-driving cars and offer increasingly personalized online experiences, they're building on research that was largely pioneered by a group of Canadian researchers who are still attracting plenty of attention and investment dollars. Montreal, in particular, has developed a concentration of expertise in the area of AI, largely thanks to the efforts of Universite de Montreal professor Yoshua Bengio, head of the Montreal Institute for Learning Algorithms (MILA). "(AI) will affect pretty much every economic sector; right now is just the tip of the iceberg," Bengio told The Canadian Press. "One of the things we are going to see more of is how these technologies affect how we interact with computers."

"We now have a new computing model to power AI and solve what was once unsolvable, with superhuman speed and intelligence." "It's like the Ratatouille movie, but instead of'anyone can cook' it's'anyone can do machine learning.'" "Focus on your company's challenges and what it is that you want to achieve." - Beena Ammanath, General Electric VP Data and Analytics "The biggest constraint is people's ability to understand the potential of what they can do." "Think of AI like a rocketship, where data is the fuel and neural networks are the engine." "Startups, consulting orgs, big players all want and try to do this but it's complex like playing 3D chess, so we have to be an extreme pattern matcher to plan, anticipate, execute many moves ahead to win." - Steve Ardire, Advisor for Software Startups

Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies. Most obviously, the speech-recognition functions on our smartphones work much better than they used to. When we use a voice command to call our spouses, we reach them now. We aren't connected to Amtrak or an angry ex. In fact, we are increasingly interacting with our computers by just talking to them, whether it's Amazon's Alexa, Apple's Siri, Microsoft's Cortana, or the many voice-responsive features of Google.

Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy. Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat's haul includes endangered and protected fish.

Internet of @ThingsExpo, taking place June 6-8, 2017at Javits Center, New York City, is co-located with the 20th International @CloudExpo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades. Help plant your flag in the fast-expanding business opportunity that is the Internet of Things: submit your speaking proposal today!