Machine learning aims to give machines the ability to learn and adapt in a similar way that humans do. While teaching computers to teach themselves seems impossible and maybe even illogical, today’s top scientists and researchers are doing just that. Exploring the basics, current applications and potential future of this process provides a deeper understanding of the potential of computer learning.
The Basics
While traditional computing requires an input of both data and a program, machine learning allows the computer to design its own program based off of data-sets and solutions. This program can then be used to solve additional problems. This extremely complicated process can be explained simply as getting computers to program themselves. For example, imagine that you could explain to your calculator all of the numbers in existence and provide solutions to all known mathematical equations (1+1=2, 2-1=1). Through the process of machine learning, the calculator can infer the basics of mathematics (multiplication, subtraction) and design a program that can solve any similar math problem. The basic steps involved in the computer’s learning process include collecting data, analyzing the data, developing a workable program, testing the program and constantly working to improve the overall programs efficiency and accuracy.
Current Applications
Computer learning is being applied to various business sectors like the financial industry, healthcare, retail, transportation and government agencies. Industries that work with large amounts of data are the most interested in learning how to efficiently apply the technology to their business. Financial services use the method to identify insights in data, prevent fraud and predict which customers are likely to default from paying bills. The health care industry uses such methods to more accurately identify symptoms and diagnose diseases. Other uses for computer learning involve finding new energy sources, generating targeted advertisements based off individual past purchases and identifying transportation trends.
The Future
This interesting method is currently being applied to areas such as advertisement, web searches, healthcare, banking and retail, but the potential of such technology does not limit it to these areas. Recent technological advancements in areas like artificial intelligence, robotics and data mining have created an even greater interest in this area’s future applications. Originally, this method involved simple pattern recognition algorithms and very specific tasks, but modern-day artificial intelligence researchers are creating robots that can learn for themselves, independently adapt and develop their own personalities and biases. Specifically, deep learning, a method which combines computer learning power with almost incomprehensible amounts of data, has made more advanced tasks possible. One nearly unbelievable example of such technology is the recent rise of self-driving cars. Imagine the advanced algorithms and vast amount of data points necessary to recreate a human-like comprehension and application of driving laws and accident avoidance measures.
This incredible computer learning method has extreme potential when coupled with upcoming technology like data mining and AI. Understanding the basics, current applications and the future of this revolutionary method sheds light on why so many free thinkers of the past warned about robots of the future surpassing human intelligence and ultimately taking over the world. While such a dystopia is far from today’s world, machine learning continues to be a hot topic and buzz word for researchers, techies, scientists, politicians, businessman and pretty much anyone interested in the direction technology is headed.
