Modern machine learning depends on the theory that computers can learn things they are not specifically programmed to do through pattern recognition. One of the most recent and most widely known examples is IBM’s Watson program.
Basically, machine learning is data analysis method that employs artificial intelligence so it can learn from and adapt to different experiences.
Machine learning has been around for many decades, but old machine learning differs from the kind we’re using today. One of the new abilities of modern machine learning is the ability to repeatedly apply complex mathematical calculations automatically to big data.
Examples of Machine Learning
In addition to IBM’s Watson program, other notable examples include:
- Personalized recommendations from sites like Netflix and Amazon, which compare browsing habits of the masses to suggest movies and products based on recent search/purchase history.
- Self-driving cars, like the one Google is working on. This project represents the essence of modern machine learning.
- Most obvious: fraud detection that helps organizations pick up on fake user accounts, fraudulent purchases, spam, and more.
How will it change the economy jobs?
It’s no secret that the world is changing fast, and technology is more evolved than ever. This technology is opening up new doors to individuals, businesses, and countries around the world. By automating systems (including everything from shipping warehouses to medical diagnosis), the world is becoming more efficient than ever before.
While there are reasonable fears for the economy in terms of job loss, experts have spoken out on the issue to say that every big revolution has led to a shift in the economy. For example, when equipment began doing the work of dozens of farm hands, the economy experienced a shift. But, from this, new sectors developed that employed more people in new areas. Likewise, this technological revolution will also lead to new sectors being developed, such as: IT/computer maintenance,
When looking at the statistics, 140 years of data has proven that technology has created more jobs than it has destroyed. And, the jobs that technology has automated were primarily hard, strenuous, or dangerous positions.
Why is it important?
Machine Learning has many applications that can contribute to society as a whole, not only making things easier and opening up new opportunities, but also improving current processes. For instance, many health care organizations are testing machine learning as a mean to sift through massive amounts of data that no human would ever be able to review.
Similar efforts allow machine learning to potentially review patient records, automatically notifying doctors and nurses of minute patterns and nuances in the patient’s history that can lead to a more accurate (and earlier) diagnosis.
Other applications are useful as well, like allowing businesses to realize risks ahead of time and see growing potential that can increase their revenues and improve the customer experience.
As a whole, machine learning is a huge contributing factor when it comes to economic growth and technological advancement. While many projects are still in their infancy, many great breakthroughs are just around the corner.