Play2Live Implements Interactive Tasks for Streamers Based on Neural Networks
LIMASSOL, Cyprus, June 7, 2018 /PRNewswire/ —
With its help, viewers will be able to set tasks for streamers choosing different conditions. Play2Live users will vote with LUC tokens for different tasks and set their price for streamers. Task constructor demo is available here.
Play2Live (P2L) – the world’s first decentralized streaming platform for gamers and esports fans – implemented algorithms for real-time monitoring of video streaming, recognition of complex objects and video content based on neural network, allowing to set interactive tasks for streamers and viewers, and monitor if the task was accomplished. This will provide new level of interaction between platform users and establish economy for the LUC (Level Up Coin) token – the sole mean of payment within P2L.
By utilizing an advanced task manager, viewers will be able to set tasks for streamers, choosing different conditions. By voting on P2L with LUC tokens, users can set the price for performing a specific task for their favorite streamers, including: challenging the streamer to complete the game on the hardest difficulty level, or to use a specific weapon, equipment or skills within given period on a specific location (for example, while playing Fortnite eliminate 10 opponents in Flush Factory using Sniper Rifles), keep streaming for three hours straight or to start a stream on a different game, etc.
Any other user can support the tasks with LUC tokens or assign their own tasks. A streamer can perform all tasks and receive all tokens or perform some of them and receive less. In case of success streamer receives a reward equal to the price of the task, and in case of a failure all tokens are returned to the viewers. How to determine whether a task was accomplished by the streamer or not? The neural network monitors the stream and determines with the highest precision if the task was accomplished. In fact, the task itself is a smart contract with a deposit in LUC tokens.
Most actively involved users receive rewards in LUC. This allows for gamification of the entire platform; the more actively users participate in various activities, the more tokens they earn.
Alexey Burdyko, CEO and founder at Play2Live:
“Play2Live facilitates numerous ways of interaction between a streamer and a viewer including a bilateral system of tasks, enhanced content generation process and many others. Compared to the simplistic chat communication and donation options provided by the existing platforms, it is a real step forward in terms of interaction. We use Computer Vision algorithm to analyze streaming videos. These are neural networks, trained for recognition on their own datasets, including Time Series and OCR, and HUD of games. We performed global optimization of algorithms to fasten system operation time. This will allow launching the analysis of the stream even on the equipment without GPU. In future, this will also allow to perform analysis on tape drive equipment, rather than on Play2Live servers, and transmit along with the video stream meta information with results of the analysis. We are pretty sure that implementation of such functionality will help to change the pattern of watching this kind of content forever.”
Vladislav Arbatov, CTO at Play2Live, is responsible for training the neural network. Arbatov is the author and leader of AI developments in the fields of Computer Vision and Natural Language Processing, the creator of effective algorithms for automated visual recognition of human condition, the creator of the first AI-service Neuronaming and a number of dialog systems for large businesses.
Vladislav Arbatov, CTO at Play2Live:
“Interactive tasks will be developed for each popular game, and by the end of the year such functionality will be available for more than 300 games. The internal system of the neural network training will allow to add new types of events as quickly as possible. We also plan to work closely with the user community – we will ask fans what tasks would be the most interesting for the particular game.”
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