Artificial Intelligence

Challenges in Machine Learning

Challenges in Machine Learning

This is the era of Artificial intelligence and Machine Learning. Without the use of explicit instruction, a machine (computer) performs a specific task with the help of a model. Studying these kinds of models and algorithms is called Machine Learning (ML).
Machine Learning algorithms have demonstrated well at extracting patterns from images, detecting fraud and many others like these. Though Machine Learning has solved many problems, still there is a large gap when we compare Machine Learning with human learning. The availability of sufficient training data is one of the biggest challenges facing ML.

You need enough example matching the case to the train a model. Another thing is that to train a model, you need a huge amount of data. As compare to ML human learns from a few examples but, this is not the case in ML where you need a large data set to train the model. The human learns from the adaption mechanism and learns about relationships between a variety of information.
The third thing is the selection of an appropriate set of features from the data you are using as an input. The performance of your algorithm depends on the input data, better and appropriate the input data better will be the performance. Features with overlapping distribution corresponding to different classes make it more difficult and the performance of the Machine Learning Algorithm (MLA) drops. There are different approaches through which those features are selected that are related to the output.
Human can detect the context but, it’s not the case in Machine learning. Machine Learning Models perform best when your case is best matching with the training data set. Performance is best when you use testing data from the training data set.
The concept of continuous learning is also lagging in ML. Training occurs in batch; you train the model and test its performance. A point will come after which the performance will not increase further on training the model. Learning stops at the point. This is not the case in a human who is a continuous learner.
All in all, we can say that there is a large gap between human learning and ML. We need to narrow down this gape which can solve many of these challenges.

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