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

Although up until now, augmented reality has had an inauspicious debut — think Google Glass — it’s poised to transform how we interact with computers in the next two decades. AR now has technical limitations including a narrow field of view, less-than-ideal resolution and latency issues. Furthermore, the only way to interact with AR is via bulky glasses or helmets. But many experts believe that we are in the midst of a speedy evolution to the point where AR will enable us to project a virtual screen on every surface. Continue reading Augmented Reality and Artificial Intelligence Shaping the Future

Startup Clarifai has developed artificial intelligence technology based on deep learning that can identify what is in a video. This ability could be significant for search engines, which currently have to rely on textual clues around a video to guess what might be in it. Clarifai’s AI has the ability to identify objects, in addition to letting users know exactly when those objects will appear in the video. This technology could be used to help advertisers and other companies analyze their videos. Continue reading Clarifai’s Artificial Intelligence Can Recognize Video Content

By analyzing the acoustic properties of songs on Spotify, intern and PhD student Sander Dieleman hopes to advance the streaming service’s recommendation algorithms to aid users in discovering new and lesser known music. Rather than basing recommendations on the choices people with similar tastes make, they would be based on songs the user listens to. This method, which requires deep learning, would then mix more obscure but user relevant songs into the recommendations. Continue reading Spotify Intern Creates System to Improve Recommendations

Facebook is looking to expand its Graph Search algorithms to account for the use of slang and expressions, and translate them into searchable key words. The company adjusted its search algorithms in January, and the changes mostly worked. The new algorithms signal the beginning of new ways to search user information using natural and nuanced language, and provide more personalized ads. Other tech companies are looking into similar technologies. Continue reading Facebook Turns to Deep Learning to Grow Search Possibilities