Semi-supervised learning involves labeling some data and providing some rules and structure for the algorithm to use as a starting point for sorting and identifying data. Using a small amount of tagged data in this way can significantly improve an algorithm’s accuracy. A common application of semi-supervised learning is to classify content in scanned documents — both typed and handwritten. Generally, semi-supervised learning algorithms use features found in both structured and unstructured algorithms in order to achieve this objective.
While this topic garners a lot of public attention, many researchers are not concerned with the idea of AI surpassing human intelligence in the near future. Technological singularity is also referred to as strong AI or superintelligence. It’s unrealistic to think that a driverless car would never have an accident, but who is responsible and liable under those circumstances? Should we still develop autonomous vehicles, or do we limit this technology to semi-autonomous vehicles which help people drive safely? The jury is still out on this, but these are the types of ethical debates that are occurring as new, innovative AI technology develops.
