Google to Open Dopamine Source Code

The tool for neural network training is based on TensorFlow, a library for machine learning
30 August 2018   2218

Google Brain Team published the source code of the Dopamine framework, which allows the implementation of training with reinforcement for neural networks. The repository contains 15 Python files with documentation. The tool is based on TensorFlow, a library for machine learning.

The framework is based on the Arcade Learning Environment platform, which evaluates the performance of AI using video games. Developers also got access to sets of source data for training and tests on 60 games supported by the platform. This approach makes it possible to standardize the process of working with neural networks and to obtain reproducible results.

Dopamine supports 4 learning models: deep Q-learning, C51, Implicit Quantile Network and a simplified version of Rainbow.


Dopamine

Simultaneously with the placement of the source code, Google launched a website with tools to visualize the process of interacting with AI via Dopamine. The site supports work with multiple agents simultaneously, provides access to statistics, training models and planning through TensorBoard.

Pablo Samuel Castro and Marc G. Bellemare, Google Brain Team researchers expressed the hope that the flexibility and ease of use of the tool developed by their group will inspire developers to try out new ideas.

This is not Google's first step towards increasing the availability of tools for neural networks. In 2017, the company announced the launch of the project Google.ai, a project to democratize the achievements in the field of machine learning.

Get more info at GitHub.

BNC to Monitor BTC Community's Mood

The system called Twitter Sentiment analyzes over 34M BTC-related Twitter posts each week, using AI to track the mood of the community
22 January 2020   479

Blockchain-based New Zealand-based research firm Brave New Coin (BNC) has unveiled a new system for measuring the mood of the Bitcoin community based on Twitter messages.

According to BNC, the new Twitter Sentiment rating system analyzes over 34 million BTC-related Twitter posts each week. The company uses artificial intelligence (AI) algorithms that look for records containing the words bitcoin, $ BTC and BTC and others.

BNC notes that user sentiment continues to be a “significant” factor in the price and dynamics of digital assets, and a new technique has been developed to track these sentiments. According to the BNC, it took 18 months to launch the Bitcoin Twitter Sentiment. The data obtained is divided into seven categories - Opinion, Technical Information, Inside the Network, Advertising, Bots, Macros and Hacking.

For the week ending January 17, the most common entries were in the Opinion category - their number was 30.42% of all data received. In second place was the category Technical Information, and in third inside the network (includes information on mining and hashrate).

BNC spokeswoman Pierre Ansaldi said that during the first quarter of this year, the company will also launch community sentiment analysis tools for other crypto assets.