Facebook to Use AI to Find & Understand Memes

Facebook is going to use machine learning system called Rosetta to deliver a more personalized news feed, as well as tracking spam, offensive or banned content
13 September 2018   1508

Facebook introduced Rosetta - machine learning system, which in real time extracts text from more than a billion publicized images and videos in social networks in different languages, and then recognizes their context.

Rosetta performs simultaneously two independent processes: detection of areas that can contain text, and word recognition using the Faster R-CNN convolutional neural network on the ResNet18 architecture.

The algorithm recognizes English, Arabic, Hindi, German, Spanish and other languages, including those that have horizontal right-to-left writing, diacritics and other specific characters.

In the future, the corporation will try to teach the system to recognize more languages, types of text and image templates.

Facebook is going to use Rosetta to deliver a more personalized news feed, as well as tracking spam, offensive or banned content. Now it is sorted by operators and it takes a long time.

In June, 2018, researchers from Stanford talked about a model of machine learning that could create memes in the style of "advising animals." The authors noted that on average, an "artificial" meme is difficult to distinguish from "real" in the context of the quality of the joke in it.

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   188

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.