The terms Machine Learning (ML) and Artificial Intelligence (AI) are often used interchangeably, sometimes confused. But are they one and the same thing? If not what is the difference? In this article, however, we shall discuss what is machine learning, and why does it matter.
Artificial Intelligence is a feature that allows computers to perform human-like tasks. AI enables machines to learn from past experiences, process new data and natural language processing. From these technologies, machines can recognize patterns and perform specific human-like duties without much human intervention.
For example, Google self-driving cars use AI based on the data generated from its mounted laser and radars to know its surrounding and predict the actions of the other drivers on the road.
Machine learning is a branch of AI that allows builds on the idea that machines can learn from big data, recognize patterns and make human-like decisions based on the algorithms generated.
While machine learning started in the 50s, the last few decades have witnessed major developments and automation of processes through ML. A 2017 research on machine learning revealed that the Machine learning market had reached $1.4 billion and is likely to be worth $8.8 billion in 2022.
Why does machine learning matter?
With the growing varieties and volumes of data, enhanced computational processors and affordable unlimited cloud storage, machine learning is becoming more popular than ever.
The net effect of all these is that you could produce a machine learning model to quickly analyze more complex and big data to deliver faster results. The enhanced capacity also ensures efficiency in large-scale industries.
What are the examples of machine learning?
Entertainment & Media: machine learning enables Netflix to predict what viewers would love to watch based on big data analytics generated from the feedback and ratings online.
Similarly, Urbs Media and UK’s Press Association (PA) have partnered in a project dubbed Reporters and Data Robots (RADAR) to write 30,000 stories a month. With information obtained from government agencies and local authorities, the machine will use Natural Language Generation technology to write the stories. These robots will fill up the gap in coverage that humans could not.
Finances: With over 110 million AmEx cardholders and some $1 Trillion in transactions, American Express uses machine learning to detect and fraud. By studying patterns and algorithms, the financial giant is capable of saving millions of dollars that would otherwise be lost to fraudsters.
Healthcare: Another example of machine learning is in the analysis of CT-scans to diagnose ailments. China, for example, uses Infervision to diagnose lung cancer more effectively. Lung cancer is the leading cause of death in China due to air pollution and about 600,000 people die annually from the disease.
Infervision augments the work of the few radiologists in China to detect cancerous cells using deep learning and algorithms. This saves time and improves the accuracy of diagnoses.
Manufacturing: Automobile manufacturers are increasingly using machine learning to automate cars and repair services. Volvo uses big data to detect engine failures, safety monitoring and when car parts need service.
Likewise, BMW uses the same ML in all its processes right from design, engineering, sales and after-sales services. BMW is also leading in the invention of level 5 driverless cars.
Conclusion
In our incisive analysis of what is machine learning and how does it matter, you realize that machine learning has spread its tendrils virtually to every nook and cranny of life essentials. These are health, finance, manufacturing, food & beverages, entertainment, et cetera. Machine learning is the future of automation and efficiency to augment mortal human capabilities.
