Last decade has been the golden era of Artificial Intelligence and Machine Learning. Almost every industry strived to improve their business insights by hiring data scientists with the hope of extracting useful information from their data. However, most of the ML platforms could not satisfy the sophisticated business needs. The ground breaking innovation for the machine learning and data science field should be regarded as continuous learning and human feedback option.
During the COVID-19 era, almost everything has changed. Therefore, many of the current machine learning models have failed since they were trained on the data from the old world. This resulted in an immense time loss for businesses. The key factor behind this failure was due to the lack of continuity and human intervention. If these two concepts could be implemented for the prediction models, then the businesses’ benefit from AI can be significantly better.
Sudden Shocks to the System – The Rise of Continuous Learning
When the world changes with instant shocks, continuous learning adapts to these changes substantially better than the batch learning models. Batch models require you to tune the parameters and retrain your model, otherwise they will be almost useless without modification at some point. Continuously learning models do not need these kind of modifications and can learn from the new data very fast, resulting in more accurate predictions without extra effort.
Inevitable Need – Human in the loop
When the ML system lacks human domain expert touch, mathematical models cannot perform well in real life. Assume that you woke up to a freezing snowy day, cleared your car from snow. Then, created a route to work from your favorite navigation application. Instead of the main road, navigation offered you to go from the shortcut which is slippery and hilly. If the application offered a human feedback feature, you could have provided information to the system about snowy conditions on the roads around you so the route planner can take this into account avoiding the risk of getting stuck the snow in some side road.
Businesses are usually dynamic and unstable. For such an unstable environment, ML models have to be fast and accurate. Moreover, they should be easy to reconfigure by the user. To sum up, continuous learning and business expert feedbacks are vital for the future of ML platforms. It will be much easier and less costly to extract insights from the data with these two features.
Author: Berke Kavak (Data Scientist at Tazi.ai)