As another example of the significant advances we have been following in artificial intelligence and deep learning, Chinese search giant Baidu has introduced Deep Voice 2, the second iteration of its compelling text-to-speech system. The company introduced Deep Voice just three months ago, with the ability to produce speech “in near real time” that was “nearly indistinguishable from an actual human voice,” according to The Verge. While the first system was limited to learning one voice at a time, “and required many hours of audio or more from which to build a sample,” the updated version “can learn the nuances of a person’s voice with just half an hour of audio, and a single system can learn to imitate hundreds of different speakers.” Continue reading Text-to-Speech System Quickly Mimics Hundreds of Accents

At Nvidia’s GPU Technology Conference, the company’s chief executive Jen-Hsun Haung introduced Project Holodeck, which aims to provide an experimental multi-user virtual environment with real-time photorealistic graphics and real-world physics. The new technology, which uses Epic’s Unreal Engine 4 and Nvidia’s GameWorks, VRWorks and DesignWorks, is targeted at design engineers and their collaborators. Nvidia’s Project Holodeck demo involved Koenigsegg Automotive, a Swedish company that makes exotic sports cars. Continue reading Nvidia’s Project Holodeck: Photoreal Graphics in Shared VR

The traditional bluescreen/greenscreen method of extracting foreground content from the background for film and video production may be on its way out. That’s due to research that Adobe is doing in collaboration with the Beckman Institute for Advanced Science and Technology, to develop a new system that relies on deep convolutional neural networks. A recent paper, “Deep Image Matting,” reports that the new method uses a dataset of 49,300 training images to teach the algorithm how to distinguish and eliminate backgrounds. Continue reading Adobe’s AI-Enabled System Could Replace Greenscreen Tech

FaceApp relies on neural networks to paste a smile on a person’s photo or change his gender or age. The iOS app doesn’t always work reliably; if the person’s face is large, has a beard or isn’t looking straight at the camera, for example, the results can be unconvincing. Switching genders can produce convincing results, but can only be accessed in “collage” mode, for a very small image. But FaceApp, similar to the Prisma app that uses artificial intelligence to make selfies look like famous paintings, proves that AI is making it easier to manipulate photographs. Continue reading FaceApp Uses Neural Networks to Alter Age, Gender in Photos

As artificial intelligence and machine learning become less expensive, their role is taking off in corporate America, and will soon extend from routine tasks to more complex, sophisticated decision-making. The neural network, for example, mimics the operations of the human brain, enabling AI to learn without extensive human intervention. Companies that are moving towards AI include AIG, which has shifted funds that would have gone to outsourced projects to AI, and aims to hire more programmers with AI skills. Continue reading Corporations Are Adopting AI, Startup Debuts AI-Based Video

As predicted, artificial intelligence has been one of the most repeated phrases of CES 2017. It seems every other vendor here is slapping the “AI” label on its technology. So much so that it inspired us to take a (short) step back and look at what AI is in relation to machine learning. The reality is: there are still very few applications that can be legitimately labeled as artificial intelligence. Self-driving cars, DeepMind’s AlphaGo, Hanson Robotics’ Sophia robot, and to a lesser extent Alexa, Siri and the Google Assistant, are all AI applications. Most of the rest, and certainly most of what we’ve seen here at CES, are robust, well productized machine learning applications (usually built on neural network architectures), often marketed as AI. Continue reading CES 2017: Distinguishing Between Machine Learning and AI

Artificial Intelligence is finally here. After nearly 50 years in the doldrums of research, the science of designing “thinking machines” has jumped from academic literature to the lab, and even from the lab to the store. This is largely because its precursor, machine learning, has been enjoying a dramatic revival, thanks in part to the commoditization of sensors and large-scale compute architectures, the explosion of available data (necessary to train advanced machine learning architectures such as recurrent neural networks), and the always burning necessity for tech companies to find something new. We expect AI to have a significant presence at next month’s CES in Las Vegas. Continue reading CES: From Learning to Thinking Machines – the AI Explosion

The media industry’s interest in artificial intelligence goes much deeper than simply portraying its implications in movies such as “Her” or “Ex Machina.” Recommendations and push notifications are just two examples of how media uses AI. YouTube has evolved its use of machine learning algorithms to improve its content recommendations. In the early days, the site used “collaborative filtering” to feed videos to viewers. Now the company uses much more complex models based on deep learning powered by neural networks. Continue reading Media Companies Leverage Data-Driven AI to Evolve, Prosper

Yoshua Bengio, a leader in deep learning and professor at the University of Montreal, is opening Element AI, a startup incubator focused on this form of artificial intelligence. The incubator will help develop AI-centric companies coming from both Bengio’s university and nearby McGill University, part of Bengio’s stated goal of creating an “AI ecosystem” in this Canadian city. According to Bengio, Montreal is home to “the biggest concentration in the world” of researchers in the powerful field of deep learning. Continue reading Deep Learning Pioneer Yoshua Bengio Launches AI Incubator

In 2012, Microsoft chief executive Steve Ballmer and computer chip researcher Doug Burger believed they had found the future of computing: chips that could be programmed for specific tasks, dubbed field programmable gate arrays (FPGAs). Project Catapult, as it was called, was intended to shift the underlying technology of all Microsoft servers in that direction. FPGAs now form the basis of Bing. Soon, the specialized chips will be capable of artificial intelligence at a tremendous speed — 23 milliseconds versus four seconds. Continue reading Microsoft Speeds Up AI with New Programmable FPGA Chips

The cameras on Apple’s iPhone 7 and iPhone 7 Plus use machine-learning-enhanced image signal processing (ISP) to achieve looks created by professional Digital Single Lens Reflex (DSLR) cameras. The iPhone 7 Plus’ dual camera lenses opens up an even greater range of photography possibilities. The technology uses computer vision artificial intelligence that “learns” to recognize photos’ contents and create neural networks. A Chinese startup has introduced a device that beautifies the faces of those using phones to live-stream selfies. Continue reading Apple Uses Computer Vision to Give iPhone 7 DSLR Abilities

Microsoft, Google and Facebook are all pursuing chatbots, which will function as virtual assistants, answering questions, responding to requests, and anticipating needs. But building functioning chatbots, which are based on artificial intelligence, is harder than it sounds. To further progress, Google open-sourced one of its natural language tools. Although Facebook hasn’t yet open-sourced it, the company introduced DeepText, a natural language engine that it is just beginning to use with its own services. Continue reading Google, Facebook Develop Chatbots via Deep Neural Networks

Amazon is testing an as-of-yet unannounced new cloud service that will let businesses run a wider range of artificial intelligence software on its computers, say people close to the situation. This move puts Amazon, which launched Amazon Web Services in a limited offering in this area last year, in closer competition with Google, Microsoft and IBM, which have already launched various cloud services. The new service will help development of pattern recognition, speech transcription and other robust applications. Continue reading Amazon Creating New Cloud Services for Artificial Intelligence

Digital platforms Facebook, Twitter, Google, Microsoft and Periscope are implementing new ways to fight some of the worst misdeeds of the Internet: hate speech, pornography, graphic and gratuitous violence, threats and trolling. To do so, they are relying on a new range of solutions mainly but not entirely fueled by artificial intelligence. In recent months, all these Internet companies have been the target of lawsuits and harsh criticism for their inability to remove such content in a timely fashion. Continue reading Tech Firms Test AI Solutions to Combat Inappropriate Content

Google has just built its own chip as part of its efforts to speed up artificial intelligence developments. The company revealed that this is just the first of many chips it plans to develop and build. At the same time, an increasing number of businesses are migrating to the cloud, lessening the need for servers that rely on chips to function. That’s led some to believe that Google and other Internet titans that follow its lead will impact the future of the chip industry, particularly such stalwarts as Intel and Nvidia. Continue reading Google Develops its Own Chip to Speed Up Machine Learning