At the crux of art and science is humanity. Humans create art and seek the truth through scientific discovery. Man created technology and is shaping its capabilities in his own image. What happens when the technology is capable of not only sensing and learning, but also creating and thinking? Throughout history, art has been humankind's interpretation of the world, translated through applied creativity to produce architecture, painting, drawing, dance, music, stained glass, illuminated manuscripts, tapestry, ceramics, lithography, wood carving, graphics, performance, theater, design, illustration, video, sculpture, photography and writing.

The Artificial Intelligence revolution is here. We are moving further into an age, where the imagination stirred from our childhood spent watching movies, is now becoming reality. Leading us into this age are the typical (and untypical) tech giants, who are fiercely competing for the next break through. Project Oxford is Microsoft's venture into the world of artificial intelligence and deep learning. It takes in several key areas, including image, facial, text and speech recognition, and hopes to implement the technology into its computer operating systems and smartphone software.

Imagine a day in the future when autonomous vehicles ply our roads. On the surface, these vehicles may bare a resemblance to cars of today, but underneath they will be bristling with high-resolution sensors and will rely on state-of-the-art deep learning algorithms to maneuver through the world. Sounds straightforward enough, but there is a problem. Barring an enormous breakthrough in communication systems, if we were to try to operate our autonomous cars through the current cloud computing infrastructure, the system would fail. The sheer volume and velocity of data generated would overload our networks and what's more, the latency and faultiness of remote computing would be dangerous.

The next blockbuster drug could be developed with help from machine-learning techniques that are rapidly spreading from AI research to pharmacology labs. Deep Genomics, a Canadian company that uses machine learning to trace potential genetic causes for disease, announced Tuesday that it's getting into drug development. It joins a growing list of AI companies betting that their techniques can help produce powerful new drugs by finding subtle signals in huge quantities of genomic data. Deep Genomics was founded by Brendan Frey, a professor at the University of Toronto who specializes in both machine learning and genomic medicine. His company uses deep learning, or very large neural networks, to analyze genomic data.

Genetic mutations are the cause of countless diseases and disorders, from cancer to autism to cystic fibrosis. Now, startup company Deep Genomics is applying decades of research into machine learning and genomic science to develop genetic medicines – accelerating treatments that address the root causes of these conditions. "If you have smoke billowing out of the tailpipe of your car, you don't just put a filter on the tailpipe – you have to look under the hood and address the original problem," says Brendan Frey, the co-founder and CEO of Deep Genomics, and a U of T engineering professor with cross-appointments in the department of computer science and the Centre for Cellular and Biomolecular Research. "That's what we're doing: applying our platform for the discovery-phase development of medicines that address genetic problems." Developing new drugs is expensive, slow and inefficient – when researchers identify a protein involved in a disease, pharmaceutical companies often use a'guess-and-test' approach to see whether any of the known drug molecules in their arsenal is a match to the protein's unique shape.

Researchers are using AI and other deep learning methods to create an algorithm that's capable of detecting DR with an accuracy rate of 94 percent When we talk about artificial intelligence here at RTInsights, we typically cover topics like recommendation engines, such as selling houses on Trulia, or implementing some kind of predictive analysis to make CRM or marketing more effective. But artificial intelligence can be used for actually improving the lives of ordinary people--according to a new study conducted by researchers from the Byers Eye Institute at Stanford University, AI could soon be used to fight vision loss due to a complication that stems from diabetes. Diabetic retinopathy (DR) affects the blood vessels at the back of the eye while a patient has diabetes, and it's known to cause preventable blindness. The researchers are using AI and other deep learning methods to create an algorithm that's capable of detecting DR with an accuracy rate of 94 percent. That number applies to all the disease stages--mild to severe--so it's not only beneficial to those who are dealing with severe vision issues due to DR.

When it comes to personal privacy and overall security, we often think of passwords, fingerprints, and even our own faces as being the keys that unlock our world, but what about your voice? If someone could perfectly mimic your voice, what kind of damage could they do? If they contacted people you know, could they lie their way into gaining private information about you? Unfortunately, we may soon live in a world where such a danger is real, thanks to extremely powerful -- and admittedly very cool -- deep learning technology that can mimic your voice using just 60 seconds worth of your speech. Canadian tech startup Lyrebird's new voice-copying tech was unveiled this week, and it sounds like something straight out of a futuristic thriller.

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A deep neural network's ability to teach itself is a strength, because the machine gets better with experience, and a weakness, because it's got no code that an engineer can tweak. That's why the creators of Google Deep Mind's AlphaGo couldn't explain how it played the game of Go. All they could do was watch their brainchild rise from beginner status to defeat one of the best players in the world. If a robocar makes a mistake, engineers must be able to look under the hood, find the flaw and fix it so that the car never makes the same mistake again. One way to do this is through simulations that first show the AI one feature and then show it another, thus discovering which things affect decision making.

Google caused a stir when it open sourced its TensorFlow software back in November 2015, and the technology is starting to make its way into the mainstream. The machine learning software library is the next generation of DistBelief, which was internally developed by the Google Brain team at the search giant for a multitude of tasks such as image search and improving its speech recognition algorithms. TensorFlow is a deep neural network, so it learns to perform a task through positive reinforcement and works through layers of data (nodes) to help it determine the correct outcome. By open sourcing the TensorFlow library of machine learning code, Google is facilitating the simpler construction, training and deployment of complex deep neural nets. TensorFlow doesn't exactly give every developer the ability to harness machine learning but it does provide both a Python and C/C API to link into a developer's program.