Nvidia to Use AI to 'Clean Up' Photos

Researchers are confident that their method will help improve the quality of astronomical and MRI images
11 July 2018   1741

Researchers from NVIDIA, MIT and Aalto University showed how to reduce the noise level in photos using AI. The team trained its Noise2Noise system for 50,000 images from the ImageNet suite, using NVIDIA Tesla P100 graphics processors and the TensorFlow framework with cuDNN acceleration.

Usually, neural networks look for the difference between two kinds of photographs: noisy and "clean". The new method does not require the preparation of such pairs, the system only provides shots with different levels of interference for training. It determines how to improve the quality of the image, while not inferior to the old methods of correction.

Noise2Noise
Noise2Noise

"Noise" is most often found in MRI images, as well as in astronomical photos. Researchers are confident that their method will help improve the quality of such visualization.

Noise2Noise 2
Noise2Noise

The scientists presented their work at the International Conference on Machine Learning in Stockholm (ICML).

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   122

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.