In machine learning, the ultimate goal is to train a machine or computer to learn and infer like a human, taking into account much more information and making better decisions in exponentially less time than humans are able to do.
At present, most unsupervised learning systems still require some human feedback and training after initial data analysis.
In contrast, unsupervised learning systems freely analyze patterns in unlabeled data, with no corresponding error or reward linked to a conclusion.
Despite significant progress, the underlying processes of unsupervised learning, what s really happening at the level of the artificial neurons, is still a mystery.
Risks include building models that fail or don t work in unexpected situations; however, the real gap lies in a lack of explanation by the system for its findings or results, human beings are still the ultimate interpreters.
It may be that an unsupervised learning system comes up with a set of conclusions that are nonsensical or seemingly indecipherable by human beings, so finding ways to synthesize analysis with meaning is a significant code that still needs to be cracked.