Many of the images created by Graphcore, which are technically graphs, are based on Microsoft's ResNet – a neural network that won the ImageNet classification competition in 2015. Since then, other ResNets have been developed. This image shows the full training graph for Microsoft Research ResNet-34 architecture hosted on Graphcore's IPU from December 2016.

In 2012, Google researchers found a whimsical way to show off the power of the company's artificial intelligence technology: They trained computers to recognize cats in YouTube videos. The project took years to pull off and required 16,000 computer processors to analyze 10 million images. That type of AI, known as deep learning, now powers Amazon.com's While such software can seem magical, it still typically requires thousands of computers to spend months scanning millions of data points. Ben Vigoda, an MIT-trained computer scientist (and nephew of late actor Abe Vigoda), says he can cut out most of the grunt work and make AI projects doable for businesses without Google-level resources.

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GM CEO Mary stands next to a Chevrolet Bolt EV self-driving car at a news conference in Detroit on Dec. 15, 2016. With billions of dollars committed to research and testing of vehicles driven by artificial intelligence rather than humans, the last thing automakers and tech firms want is balkanized regulations that vary from state to state or out-of-date federal rules for this fast-developing technology. So General Motors, Toyota, Volvo and ride-hailing service Lyft had a unified message for members of the House of Representatives on Tuesday: Set a national framework for testing and deploying autonomous vehicles -- and do it soon. "One of the most significant challenges that we face today with respect to the testing of autonomous vehicle technology is the patchwork of policy initiatives at the state level," Gill Pratt, CEO of the Silicon Valley-based Toyota Research Institute, said in testimony to the House Subcommittee on Digital Commerce and Consumer Protection. "More and more states are developing legislation and regulations that are unfortunately creating impediments to the development of autonomous vehicle technology."

That hard-to-imagine prospect was eHarmony's reality when it first launched 16 years ago. Users created a dating profile, filled out a 450-item questionnaire, and reviewed matches without the ability to see any other users' pictures. Over the years, the site has added photos and made its clunky interface easier to navigate. That, however, hasn't stopped free mobile apps like Tinder and Bumble from stealing users away from the dating site stalwart. "People do end up on those sites looking for relationships, and we see that as our challenge," says eHarmony CEO Grant Langston.

Amidst the free t-shirts and apps to help you find parties on campus always lie a few hidden gems for those with the patience to hunt. ThirdEye, one of those gems forged out of PennApps, UPenn's hackathon, is being acquired today by TheBlindGuide for an undisclosed sum. Started by three current Penn students, ThirdEye brings object recognition to mobile to help the visually impaired. Originally created as an add-on for the now obsolete Google Glass, the ThirdEye of today exists as a mobile app. It uses Google's Cloud Vision API to identify objects and read their descriptions aurally.

It's called the Fourth Industrial Revolution. Curiously, rapid developments happening in fields previously thought to be disjointed are now amplifying each other. We are seeing this in the areas of artificial intelligence (AI), machine learning, robotics, nanotechnology, 3D printing, genetics and biotechnology. On a different tangent altogether, smart systems are able to address a diverse set of issues ranging from climate to supply chains. What must be underscored here is a sense of urgency. Perhaps less than five years is what we have to enable this transition.

Way back in 1943, Alan Turing thought that if we move around some 1's and 0's a machine could simulate any mathematical equation. When this theory became a reality, researchers began to see if a computer could actually begin to reason like a human brain. And that is when the race to Artificial Intelligence (AI) began. Scientists continued to make gains in the AI realm. Machines will be capable, within twenty years, of doing any work a man can do. Well, 1985 came and went.

For years, marketing was considered more art than science. But more recently, as marketing automation software has proliferated, marketers have had to blend the art of storytelling with the science of data. Then along comes artificial intelligence (AI) and machine learning, which promise to help marketers make sense of all that data. Some experts believe AI's impact on marketing will be hugely significant, that it could even change the nature of marketing entirely -- enabling brands to break through the noise and deliver a more personalized experience to customers. Not surprisingly, though, there are challenges ahead for organizations seeking to add AI to their marketing technology stack.

Artificial intelligence is coming to banking -- scratch that, it's already here, but customers may not have noticed. AI is already playing a role in consumers' lives, whether they know it or not. Talking to Siri, looking at recommendations from Amazon or Netflix, or chatting with Google Home about the temperature -- AI is all around us, and we're growing more comfortable with it all the time. That's good, says Arif Ahmed, senior vice president of payments innovation for U.S. Bank, because AI is set to help customers in important ways, and in the not-too-distant future. "Emerging artificial intelligence will improve the customer experience without compromising privacy," Ahmed told Bank Innovation.