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Google Stadia

18.07.2019

By releasing the Google Stadia project, the corporation plans to create a kind of Netflix for video games. In the app, games will be streamed to the user's device at the highest resolution of 4K, Ultra HD, High Dynamic Range (HDR), and 60 frames per second. In general, you no longer need an expensive high-end computer or game console. In the future, Google Stadia promises 8K resolution and over 120+ FPS.

Because games don't have to be downloaded to your own device, there's no need to worry about memory, installation, updates, or patches.

Google Stadia will also be integrated into the YouTube platform, making game streaming easier. In addition, additional features such as Crowd Play will be available, allowing you to directly connect to live streamers.

In November 2019, Google will launch Google Stadia in 14 countries. At the time of release, the service will offer reasonable prices and an excellent selection of games. The good news is that many Google Stadia features will be free to use at the Stadia Base tier, with some games purchased separately.

To get more features, you'll need the paid version of Stadia Pro, which will cost $9,99 per month. In this case, games from the Stadia database will be available on a subscription basis. Plus, with Stadia Pro, you get up to 4K resolution and 5.1 Surround Sound.

Google Stadia: release, price and available games Among the interesting releases: limited edition Stadia Founder's Edition for $129, already available for pre-order. It includes the Stadia Controller, Chromecast Ultra, three months of Stadia Pro access for you, and three more months for you to gift. Plus, with the purchase of the Founder's Edition, you'll be able to choose your exclusive gamertag before anyone else.

Google Stadia is designed to work on almost any device without sacrificing quality. The most important thing is a stable internet connection. At 29 Mbps, you can already play games at 1080p Full HD and 60 FPS. 4K resolution becomes possible already from 30 Mbps. Of course, you can also use a service with a slow connection. In this case, the resolution is reduced to, for example, 720p.

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Deep Learning Technology for Time Series Analysis 20.03.2016

Fujitsu Laboratories Ltd. announced the development of a deep learning technology capable of analyzing time series of data with high accuracy. In promising applications for the Internet of Things, time series of data can vary significantly, so identifying patterns in their change turns out to be a very difficult task for a person.

“Machine learning is a central technology of artificial intelligence. In recent years, all attention in this area has been focused on deep learning technology as a way to automatically extract characteristic values ​​necessary for interpreting and evaluating phenomena,” the company noted. “Huge amounts of time series of data are collected from devices, especially in the era of the Internet of things. By applying deep learning to this data and classifying it with a high degree of accuracy, further analysis can be carried out with the prospect of creating new products and solutions and opening up new lines of business."

Hailed as a breakthrough in artificial intelligence, deep learning technology delivers highly accurate image and speech recognition, but is still only applicable to limited types of data, Fujitsu explained. In particular, until now it has been difficult to accurately classify automatically the volatile time series of data coming from devices connected to the Internet of things.

The company has developed a deep learning technology based on chaos theory and topology to automatically and accurately classify volatile time series data. This technology makes it possible to accurately process even complex temporal data with a large amplitude of changes.

The latest technology uses the following procedures for training and classification: graphical representation of time series data using chaos theory; quantitative description of diagrams using topology; training and classification using convolutional neural networks.

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