If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Among other things, for a smartphone to be bang on trend these days it needs, a tall 18:9 display with minimum bezels (with a notch thrown in for good measure), a superb camera system and Artificial Intelligence and/or Machine Learning. Artificial Intelligence and Machine Learning are buzzwords being adopted and applied throughout our smartphones makeup, from the System on a Chip, all the way through to the operating system. So, is it just marketing hype, science fiction or is there fact being the fiction? Read on, and we--ll provide a straightforward, and where possible jargon-free overview. Artificial Intelligence is best characterized as the ability for a machine to exhibit practices including learning, behavior, and communication with no discernible difference from ourselves.
As with all new technology, emotions run wild and our excitement gets the better of us. The hype is everywhere -- projections of an AI-ruled planet akin to something out of a science fiction movie and computers that can almost read minds. In the midst of all these, entrepreneurs would like to know exactly what AI can do for their business. Now that the initial excitement is over, and the dust has settled a bit, it is easier for us to have more level-headed discussions about Artificial Intelligence and Machine Learning. This article attempts to explain what's real and what is still wishful thinking as far as these two new technologies are concerned.
This is a text version of this video: packagemain #5: Face Detection in Go using OpenCV and MachineBox. I found a very nice developer-friendly project MachineBox, which provides some machine learning tools inside Docker Container, including face detection, natural language understanding and few more. And it has SDK in Go, so we will build a program which will detect my face. We will also use OpenCV to capture video from Web camera, it also has Go bindings. MachineBox can be installed very easily by running Docker container.
In recent years, artificial intelligence, machine learning, and augmented reality have taken mobile app development by storm. When is it reasonable to build a machine learning app? With Apple and Google both encouraging developers to use these technologies -- and making it easier to do so -- businesses can vastly benefit by increasing user satisfaction and engagement by utilizing AI and ML. Are you wondering if you can implement AI for your business? There are numerous uses for AI in web and mobile applications.
After a lot of scandal and a great deal of confusion, Facebook has finally made clear what its privacy settings will look like in the wake of Europe's forthcoming GDPR (the General Data Protection Regulation). In a news release, the company said that everyone, no matter where they live, will be asked to review information on the way Facebook uses their data. The options will roll out in Europe first, ahead of GDPR implementation on May 25. On the face of it, the options seem comprehensive enough. Facebook will ask you to make choices about adverts, sensitive information and face recognition technology, and claims that it's developed better tools to access, delete and download information.
Developments in Artificial Intelligence (A.I.) are happening faster today than ever before. However, the nature of progress in A.I. is such that massive technological breakthroughs might go unnoticed while smaller improvements get a lot of media attention. Take the case of face recognition technology. The ability of A.I. to recognize faces might seem like a very big deal, but isn't that groundbreaking when you consider the nature of applied A.I. On the other hand, suppose an A.I. is asked to choose between a genre of music, such as R&B or rock. While it may seem like a simple choice, the mathematical algorithm that must be solved before the A.I makes a decision could take hours and days.
Thanks to advanced technologies, we now have natural language robotics and facial recognition that can measure and match unique characteristics for the purposes of authentication and identification. With personalized assistance machines, we are more able to detect, prevent, and deal with various threats on society. In the digital world, facial biometrics have the potential to be integrated anywhere you can find a camera. This is what we can expect to have next on our mobile phones. Biometric devices with today's data storage capabilities and fantastic speed of processing give us great clarity in seconds.
The episode highlights the risks large corporations run when they tie their brands so closely to social messaging. In 2015, then-CEO Howard Schultz shrugged of the "Race Together" fiasco as well-intention mistake and pressed on with his public efforts to engage in the debate over race in America. His successor, Kevin Johnson, is now scrambling to keep the Philadelphia incident from shattering the message Schultz was going for: Starbucks is a corporation that stands for something beyond profit.
US army researchers have developed a convolutional neural network and a range of algorithms to recognise faces in the dark. "This technology enables matching between thermal face images and existing biometric face databases or watch lists that only contain visible face imagery," explained Benjamin Riggan on Monday, co-author of the study and an electronics engineer at the US army laboratory. "The technology provides a way for humans to visually compare visible and thermal facial imagery through thermal-to-visible face synthesis." The thermal images are processed and passed to a convolutional neural network to extract facial features using landmarks that mark the corners of the eyes, nose and lips to determine its overall shape. The system, dubbed "multi-region synthesis" is trained with a loss function so that the error between the thermal images and the visible ones is minimized, creating an accurate portrayal of what someone's face looks like despite only glimpsing it in the dark.
Thermal cameras like FLIR, or Forward Looking Infrared, sensors are actively deployed on aerial and ground vehicles, in watch towers and at check points for surveillance purposes. More recently, thermal cameras are becoming available for use as body-worn cameras. The ability to perform automatic face recognition at nighttime using such thermal cameras is beneficial for informing a Soldier that an individual is someone of interest, like someone who may be on a watch list. The motivations for this technology -- developed by Drs. Benjamin S. Riggan, Nathaniel J. Short and Shuowen "Sean" Hu, from the U.S. Army Research Laboratory -- are to enhance both automatic and human-matching capabilities.