Artificial Intelligence is widely used to detect or analyze data and provide answers where it would be a challenge for the human mind to do so with the set of considered data. Indeed, with millions of records, each of which have to be calculated to then achieve a result, would be almost impossible for any human. Here, training the AI methods to use data for certain predictions are useful.
Interestingly, much like humans, machine can learn from and with data for such AI technologies. So, almost like learning yourself, the data and the expected output is processed through the technology to teach the technology for such achievements. Here, data is repeatedly fed into the AI system, so that it can learn and then start working on real time data for its use. However, the most expensive thing for such AI solutions, is to provide accurate data for it to learn. Such data has to be accurate and non-redundant. Whilst accuracy provides the actual input data and output results, any data redundancy would reduce the inherent learning capacity of the system, as it would induce duplication for the learning mechanism, often causing errors in its real time use.
Similar AI technologies have been applied to prediction of Covid-19, however, the challenges with data still remains the same. Low quality, low volume or redundant data that maybe introduced repeatedly, would cause a failure for accurate prediction, causing a risk to life and society. Data redundancy has several causes, including collecting from different and unverified sources, developed and computerized outputs and human data entry errors. Interestingly, the challenge still remaining the same, the way out of Covid-19 using AI, seems to become collection of such valuable data.
https://link.springer.com/article/10.1007/s10489-020-01867-1
