Artificial Intelligence, data science, and machine learning – all fall in the same domain. The catch is which among them serves the right purpose given the situation. Over the years, we have seen the immense applications of data science, AI and ML in varied fields. The results delivered talk volumes about how efficient they are and how better can they be deployed in the coming years.

AI, being a replica of human intelligence aids in making better decisions by understanding data in-depth, identifying the patterns and trends which otherwise would have been difficult for humans to do the same manually. The thing with AI is that you need lots and lots of data to be able to understand the data. If you do not have a huge amount of data to deal with, the AI model would deliver results only for a small amount of data. In such a case, the accuracy of the prediction or decision could be low. Simply put, the more the amount of data, the better is the model trained to be able to deliver results with improved efficiency and accuracy. But the problem isn't the availability of data for we know the amount of data generated on a day-to-day basis is humungous. The area of concern here is what to do when the model trained is deployed to work with new data? Will the model be successful in applying the knowledge gained to deal with new data sets? This is exactly where machine learning comes into play.

Why machine learning over artificial intelligence?

With machine learning, it is possible for the machine to learn from the huge amounts of data we give as inputs. The machine is in a position to apply the knowledge it has gained to new pieces of data that streams into the system. Additionally, one of the best features of ML is in the area of fraud detection. This is no less than a blessing for the financial service sector such as banks, insurance companies, NBFCs, etc. The day we get to see computers and machines in a state of handling almost all the real-world situations is not too far.

Today, talking about ML and its possibilities to augment human cognition is critical. People tend to get confused between data science, AI and ML. Each of these has their own applications and deploying one in place of the other wouldn't turn out to be fruitful. Technology expert Jordon argues how AI-related projects have failed in the past and how ML projects succeed by augmenting human cognition. "ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines to design algorithms that process data, make predictions and help make decisions," wrote Jordan in the Harvard Data Science Review. He insisted on how there cannot be a better way than ML to deal with large-scale data.

In a nutshell, the road to success is a lot easier when technology is not limited to data science and AI. With more importance laid on ML, it is very much possible for the companies to reach greater heights by finding patterns in large quantities of data.