AI to Cool Down Google's Servers

DeepMind's algorithm allowed to save energy - for 9 months the efficiency index increased from 12% to 30%
20 August 2018   1561

Google used DeepMind's AI technology to fully automate the cooling system in its data centers. The corporation began to use the algorithm in 2016, but then it just gave the engineers advice on reducing costs. In August 2018 the system began to work completely autonomously.

Researchers trained the algorithm using the "Training with reinforcement" method. Every five minutes, the AI ​​collects data from thousands of sensors inside the data center. The algorithm determines which configurations of the cooling system reduce the energy consumption in the  best way and independently includes them. Although its work is fully automated, the company's engineers can intervene at any time.

We wanted to achieve energy savings with less operator overhead. Automating the system enabled us to implement more granular actions at greater frequency, while making fewer mistakes.
 

Dan Fuenffinger
Data Centre Operator, Google

In total, the system monitors more than 120 different parameters of the data center operation, including air conditioning control, closing and opening of windows, fan speed and others.

After full automation, the DeepMind's algorithm allowed to save more energy - for 9 months the efficiency index increased from 12% to 30%.


Energy Consumption

According to Google data center vice president Joe Cava, the project will help the company save millions of dollars and reduce carbon dioxide emissions into the environment. In the long term, the system will help solve the problem of climate change, according to representatives of Google.

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   470

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.