|Realistic Examples of Machine Learning Applications|
Example 1- Curation of Internet’s Contents
* As we accumulate more and more information, how do we curate the content as to correctly distinguish truth from fiction? The task of curating the Internet’s contents is huge. We will need widespread collaboration some of it with automated tools based on AI and Machine Learning. Source: ACM printed edition of Communications for 2019-02.
Example 2- Cambridge on Brawls
* Researchers at the University of Cambridge in the U.K. working with colleagues at the Indian Institute of Science, Bangalore and India's National Institute of Technology, Warangal, have used deep learning to develop a drone surveillance system that automatically detects small groups of people fighting each other. Source:
Example 3- Detect Fake News
* Source: https://news.umich.edu/fake-news-detector-algorithm-works-better-than-a-human/. Researchers at the University of Michigan have developed an algorithm-based system that identifies linguistic cues in fake news stories. The researchers found the system successfully identified fake stories up to 76% of the time, surpassing the human success rate of 70%.
Example 4- Detect Fake Photo
* A new system developed by researchers at the University of California, Berkeley and Carnegie Mellon University, can identify inconsistencies in doctored images. The metadata of 400,000 Flickr photos was used to teach the system to distinguish imagery from two different sources. The researchers say digital imagery must be determined by the particular technologies or processes behind it, and the effects of those processes are generally consistent across the whole image. If the system can learn which devices produce which aspects of an image, it can ascertain whether an image has been altered by combining data from multiple devices.