Machine Learning is a subset of Artificial Intelligence (commonly called AI). It allows computers to imitate the way that humans learn, by the utilization of algorithms and data. Machine learning algorithms are taught to seek out patterns and associations. in large data sets. Decisions are made using this analysis, improving with use, and becoming more accurate with greater input of data. Machine Learning applications are in common use, and we use them in our everyday lives without ever knowing.
Machine learning stretches back to 1950 when Alan Turing published a paper asking, “Can machines think?” This was followed in 1957 with Frank Rosenblatt designing “The Perception Model” (the first neural network). By 1967 the “Nearest Neighbour Algorithm” had been written, resulting in computers being able to process basic pattern recognition.
Work continued the development of machine learning and by 1981, Gerald DeJonge had developed “Explanation Based Learning.” This concept allowed for computers to analyze data and creates a general rule to discard unimportant information.
Moving on to the 1990s and machine learning made the move from a “knowledge-driven approach” to an “approach driven by data.” It was in this era that scientists started to create programs that analyzed vast amounts of data and drew conclusions from the results. This developed into the modern framework for Machine Learning.
Modern Applications of Machine Learning
The most frequently used machine learning applications in 2021 are (alphabetical)
· Automating Employee Access Control
· Banking Domain
· Image Recognition
· Language Translation
· Marine Wildlife Preservation
· Predict Potential Heart Failure
· Product Recommendations
· Regulating Healthcare Efficiency and Medical Services
· Sentiment Analysis
· Social Media Features
As you can see, many of these applications we use regularly in our everyday lives without being aware that it is machine learning that is responsible.
How Does Google Search Bring the Right results when You Search?
This is an example of Machine Learning that we use daily. Let us use the example of a donkey. How does Google find images of a donkey when you do an image search for the term donkey?
1. Google will first pull many images that are labeled donkey.
2. The machine-learning algorithm uses this data and searches for pixel patterns, and color patterns that will predict if an image is a donkey.
3. Google computers make a random guess whether patterns are good for identifying a donkey.
4. If it makes a mistake, then the algorithm adjusts its guess so that the algorithm gets it correct.
5. Finally, these patterns can be utilized by a large computer system modeled after a human brain to correctly identify pictures of a donkey accurately. These results are displayed in Google image search.
Why is Machine Learning Important?
These days there is so much data being generated every day, and we have affordable data storage. Machine learning can utilize this data and assist companies to identify profitable opportunities and be far more competitive in the future. Organizations such as Healthcare, Government, Transportation, and Financial Services are just examples of areas that have been revolutionized by Machine learning, and this is only the beginning.
Image Source: https://unsplash.com/photos/Ype9sdOPdYc