Challenges in Machine Learning

Challenges in Machine Learning

In the era of Artificial Intelligence (AI) a machine (computer) performs a specific task with the help of a model. Machine Learning (ML) is the study of these kinds of models and algorithms.

Machine Learning Algorithms (MLAs) have proven successful in extracting patterns from images and sensing anomalies to detect fraud. While Machine Learning has solved many problems, there is still a large gap compared to the abilities of human learning.

One of the biggest challenges in using ML technology is providing sufficient data to train a model.  Large amounts of data are required for the model to work. Humans learn using an adaption mechanism and are able to discern relationships using a variety of information.  Machine Learning, however, does not have this ability, but instead relies on a large set of data to train the model.

For Machine Learning to work it is important to select the appropriate set of features from the data you are using as an input. The success of your algorithm depends on the input data – more applicable and appropriate input data better will produce better performance. If the data you use includes features with overlapping features to different classes, the performance of the Machine Learning Algorithm will decrease. In this case, different approaches are needed to select the features that are appropriate to the output. Unlike humans, Machine Learning is unable to detect context. Instead, Machine Learning models perform successfully when the input best matches the training data set.

The concept of continuous learning is also lagging in ML. Training occurs in a batch – where you train the model and test its performance. At some point the performance does not increase further in the training of the model. At this point “learning” stops. This is not the case for humans who have the ability to continuously learn.

Altogether we can say there is a large gap between human learning and Machine Learning. The challenge ahead is to narrow this gap – to find greater efficiencies and new applications for this promising technology.


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