Challenges in Machine Learning
In the era of Artificial Intelligence (AI) technology a machine, or 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 technology has proven highly successful in extracting patterns from images and sensing anomalies to detect fraud. Machine Learning Algorithms (MLAs) are especially useful, because they can be programed to analyze large amounts of data, and then find anomalies that can be an indication of data theft or a cyber attack.
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 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 cannot discern “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 Machine Learning. 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.
Overall, 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.
At accentedge we use Artificial Intelligence and Machine Learning to help our business clients analyze large amounts of data – to better understand their customer and target their sales. We also use this technology to detect anomalies that indicate data theft and cyber attacks. Reach out to us to learn how AI and ML can help give your business the edge to maintain market share and stay competitive.
