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"Enable natural and contextual interaction with tools that augment users' experiences using the power of machine-based intelligence. Tap into an ever-growing collection of powerful artificial intelligence algorithms for vision, speech, language, and knowledge."

"Google Cloud Video Intelligence API makes videos searchable, and discoverable, by extracting metadata with an easy to use REST API. You can now search every moment of every video file in your catalog and find every occurrence as well as its significance. It quickly annotates videos stored in Google Cloud Storage, and helps you identify key nouns entities of your video, and when they occur within the video. Separate signal from noise, by retrieving relevant information at the video, shot or per frame. "

"Intelligent video processing produces stable video output, detects motion, creates intelligent thumbnails, and detects and tracks faces"

"rtificial intelligence already pervades 21st-century life, from Siri's directions to Netflix's suggestions of what you should watch next. But how much emotional intelligence is inside computers, cell phones, and video game consoles? In the past, the answer has been "none" - even the most complex deep learning machine is still a machine. That's changing, though, thanks in part to Nevermind, a video game that can sense players' emotions and adjust the experience to fit."

Video Indexer is a cloud service that enables you to extract the following insights from your videos using artificial intelligence technologies:

"TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well."

"This morning it became apparent that Apple had acquired Emotient, an artificial intelligence startup. This startup works with emotion KPIs, meaning they're in the business of watching your face and body to decide how you feel. With that information, Emotient aims to detect attention, engagement, and sentiment. Implications from their public presentation of business suggests that they've been aiming their business at advertisers - as well as other odd product creators on mobile platforms."

"Interactive narrative systems attempt to tell stories to players capable of changing the direction and/or outcome of the story. Despite the growing importance of multiplayer social experiences in games, little research has focused on multiplayer interactive narrative experiences. We performed a preliminary study to determine how human directors design and execute multiplayer interactive story experiences in online and real world environments. Based on our observations, we developed the Multiplayer Storytelling Engine that manages a story world at the individual and group levels. Our flexible story representation enables human authors to naturally model multiplayer narrative experiences. An intelligent execution algorithm detects when the author's story representation fails to account for player behaviors and automatically generates a branch to restore the story to the authors' original intent, thus balancing authorability against robust multiplayer execution."

We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.

We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender.