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The collective benefit from using Machine Learning does not rest alone on analysing Big Data to extract information automatically Benefits from using ML create several opportunities that further translate to variety in application that can improve the way processes and tasks are accomplished. However, despite its numerous advantages, there are still risks and challenges. These are some of these limits:
Time in learning
It is impossible to make immediate accurate predictions with a ML system. As it learns through historical data. The bigger the data and the longer it is exposed to these data, the better it will perform. For example, using a system to play games and beat human opponents would require feeding the system with historical data and continuously exposing it to newly acquired data to make better predictions or decisions.
Error diagnosis and correction
One notable limitation of Machine Learning is its susceptibility to errors. The actual problem with this inevitable fact is that when they do make errors, diagnosing and correcting them can be difficult because it will require going through the underlying complexities of the algorithms and associated processes.
Limitations of predictions
Unlike humans, computers are not good storytellers. ML systems know more what they can tell humans. Thus, they cannot always provide rational reasons for a particular prediction or decision. Machine Learning is also prone to hidden and unintentional biases depending on the data provided to train them. Like any other computers in addition, ML systems are for answering questions and not for posing them. Human collaboration is still important to better evaluate the outputs of these systems.