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) …
Apple started using deep learning for face detection in iOS 10. With the release of the Vision framework, developers can now use this technology and many other computer vision algorithms in their apps. We faced significant challenges in developing the framework so that we could preserve user privacy and run efficiently on-device. This article discusses these challenges and describes the face detection algorithm. Apple first released face detection in a public API in the Core Image framework through the CIDetector class.
Apple has published its latest machine learning journal entry with a new article detailing the challenges of implementing facial detection features while maintaining a high level of privacy. Apple started using deep learning for face detection in iOS 10. With the release of the Vision framework, developers can now use this technology and many other computer vision algorithms in their apps. We faced significant challenges in developing the framework so that we could preserve user privacy and run efficiently on-device. Apple's iCloud Photo Library is a cloud-based solution for photo and video storage.
Automatic photo captioning is a problem where a model must generate a human-readable textual description given a photograph. It is a challenging problem in artificial intelligence that requires both image understanding from the field of computer vision as well as language generation from the field of natural language processing. It is now possible to develop your own image caption models using deep learning and freely available datasets of photos and their descriptions. In this tutorial, you will discover how to prepare photos and textual descriptions ready for developing a deep learning automatic photo caption generation model. How to Prepare a Photo Caption Dataset for Training a Deep Learning Model Photo by beverlyislike, some rights reserved.
I hugged a bot and I liked it. As a tech columnist, I've tested all sorts of helpful robots: the kind that vacuum floors, deliver packages or even make martinis. But two arriving in homes now break new ground. They want to be our friends. "Hey, Geoffrey, it's you!" says Jibo, a robot with one giant blinking eye, when it recognizes my face.
A simple trick goes a long way in hiding your webcam: tape. You don't think about your car until you get a flat. You don't appreciate your phone until the screen cracks. Cyber-security is something you take for granted, until someone hacks your account, steals your bank info, and spreads compromising pictures of you all over the Internet. Most people know about virus protection.
The search function in Google Photos uses TensorFlow. Google made waves Monday when it made its new artificial intelligence system TensorFlow open source. Google has used TensorFlow for the past year for a variety of applications. For example, Google Photos is scary good at search because it uses TensorFlow to recognize places based on popular landmarks or characteristics, like the Yosemite National Park mountain range. Other Google products that use TensorFlow include Google search, Google's voice recognition app, and Google Translate.
We are living in a world of data overload. From behavioral analytics to customer preferences, businesses now have so much data at their fingertips that they're unable to process and consume all of it in a meaningful way. This is where the magic of machine learning comes in. When applied to massive internal company datasets, machine learning technology can derive important insights and provide actionable recommendations and predictions at superhuman scale. But as automation, machine learning, and artificial intelligence technologies continue to show up in our daily experiences, more and more users are asking questions.
Ahead of the live unveiling of the Tesla semi truck, Elon Musk and his company have been tweeting teaser photos of the newest vehicle to be added to the fleet. The semi has been in the works for quite some time and the general public will finally get a look at it Thursday evening. The debut of the truck is schedule for 8 p.m. PST Thursday and there will be a live stream for anyone looking to follow along who can't be there in person. But before Musk and Tesla bring out the massive all electric truck, the two have shared some silhouetted photos and video of the truck. In a tweet Wednesday Musk bragged about his newest accomplishment, saying, "It can transform into a robot, fight aliens and make one hell of a latte," along with a photo of the truck's silhouette.
When you upload photos to Facebook, have you noticed that the website already seems to know who's in them? It's remarkable, and you can give the credit to big data. Face recognition software, like fraud detection and ad matching algorithms, draws on deep libraries of content in order to deliver the correct results. And these data collections are hard at work across the web and in many of your favorite apps. It comes as no surprise that developers have been hard at work on face recognition software since it's an integral part of security programs.
Artificial Intelligence has been a hot word across all industries lately. Think all the fuss around self-driving cars, Google's updated Assistant and the general talks of how conversational interfaces are the future of tech. Around 54 percent of retailers already use or plan to add artificial intelligence technology to their toolkit, with 20 percent planning to introduce some AI within the next 12 months, according to the latest report from SLI Systems. The increased adoption of AI in retail can be specifically attributed to advances in the deep learning. Deep learning is a specific machine learning approach to building and training neural networks.