Facebook to Create a Dataset to Train Dialogue AI

The dataset includes 5 million characters and 700 million dialogues
19 September 2018   1364

Researchers from Facebook created a set of training data to improve the effectiveness of neural networks training, specializing in communicating with live users. It includes 5 million characters and 700 million dialogues.

Persona-based network architecture
Persona-based network architecture

In the basis of the software, developers have put a set of PERSONA-CHAT, developed jointly by Facebook experts and scientists from the Montreal Institute of Learning Algorithms. First of all, the increase in the volume of data is notable - the basic data-set contained only about a thousand personalities. But researchers are paying attention to a more important aspect. The PERSONA-CHAT content was created artificially, and the new set was formed on the basis of the Reddit user dialogues.

An interactive neural network, trained on a new set of data, leads more engaging dialogues than networks that did not have access to a collection of personalities. Moreover, the training of systems based on the characters is faster.

Choosing the right data set for learning artificial intelligence is one of the key tasks for developers. The accuracy and productivity of the software being created depends on it. In September 2018, Google in the test mode launched a special tool to find suitable collections.

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   472

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.