Machine learning is a field of study that gives computers the ability to learn without being specifically programmed
Many people think the concept of machine learning is relatively new. But Arthur Samuel, an artificial intelligence (AI) pioneer, back in 1959, coined the term when he was working for IBM. Most experts say that machine learning is a subset of AI.
Machine learning allows a computer to do a set of tasks without being explicitly programmed to do so. Instead, these computers equipped with learning algorithms make decisions based on patterns and infer or reason what steps should be taken.
What is the difference? A boom gate or barrier that controls traffic entering a highway could be programmed to open and close at certain intervals during peak travel times to effectively reduce congestion. To determine the length of those intervals, someone would analyze traffic data over an extended period to come up with the right length of time to lift and lower the gate. The gate might also be programmed to stay in the raised position outside of the determined peak times.
This is an example of a machine being programmed to do specific tasks. Every day it does precisely what we have programmed it to do until someone adjusts the program. A boom gate with a learning algorithm would be something else entirely. What happens when you add machine learning to the mix?
The boom gate would itself analyze all the traffic data at a speed far greater than any human, and it would do this analysis continuously. It might learn that on Mondays, intervals are best set for every five minutes, whereas on Wednesdays, they need to be set for every ten minutes. It might also learn that rush hour is advanced two hours on the day before a big holiday. Eventually, it may even take weather into consideration and start operating the gate outside of regular rush hours because of an impending snowstorm.
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