Neural Network to Recognize Depression

In subsequent testing, artificial intelligence managed to recognize depression in 77% of cases
06 September 2018   1549

Researchers from the Computational Science and Artificial Intelligence Laboratory (CSAIL) of the Massachusetts Institute of Technology (MIT) have developed a neural network that allows to determine the level of depression of a patient. Artificial intelligence is able to establish an oppressed psychological state, without relying on context and without asking specific questions. To obtain the test result, it is enough to record a patient interview in video or audio format.

By training the neural network, CSAIL scientists used 142 interview records from the Distress Analysis Interview Corpus, a compilation intended for the diagnosis of mental illness. Artificial intelligence analyzed speech of patients, revealing sound and text patterns. Patterns have become, including word-markers, such as "sad", "low", combined with long pauses and a monotonous voice. The oppressed condition of each patient was assessed on a scale from 0 to 27. Depression was considered to be a level of 15 and above.

In subsequent testing, artificial intelligence managed to recognize depression in 77% of cases. According to the developers, this result is one of the best among all available.

The new technology is considered as a tool that allows to simplify the work of the therapist and specify certain markers, which should be noted in the diagnosis.

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.