MACHINE LEARNING

What Is Machine Learning?

Machine learning is a subfield of artificial intelligence in which systems have the ability to “learn” through data, statistics and trial and error in order to optimize processes and innovate at quicker rates. Machine learning gives computers the ability to develop human-like learning capabilities, which allows them to solve some of the world’s toughest problems, ranging from cancer research to climate change.

Most computer programs rely on code to tell them what to execute or what information to retain (better known as explicit knowledge). This knowledge contains anything that is easily written or recorded, like textbooks, videos or manuals. With machine learning, computers gain tacit knowledge, or the knowledge we gain from personal experience and context. This type of knowledge is hard to transfer from one person to the next via written or verbal communication.

Facial recognition is a type of tacit knowledge. We recognize a person’s face, but it is hard for us to accurately describe how or why we recognize it. We rely on our personal knowledge banks to connect the dots and immediately recognize a person based on their face. Another example is riding a bike. It’s much easier to show someone how to ride a bike than it is to explain it.

computers no longer have to rely on billions of lines of code to carry out calculations. Machine learning gives computers the power of tacit knowledge that allows these machines to make connections, discover patterns and make predictions based on what it learned in the past. Machine learning’s use of tacit knowledge has made it a go-to technology for almost every industry from fintech to weather and government.

What Is Deep Learning?

Deep learning is a subfield within machine learning, and it’s gaining traction for its ability to extract features from data. Deep learning uses Artificial Neural Networks (ANNs) to extract higher-level features from raw data. ANNs, though much different from human brains, were inspired by the way humans biologically process information. The learning a computer does is considered “deep” because the networks use layering to learn from, and interpret, raw information.

How Does Machine Learning Work?

Machine learning compiles input data, which can be data gathered from training sessions or other sources, such as data set search engines, .gov websites and open data registries like that of Amazon Web Services. This data serves the same function that prior experiences do for humans, giving machine learning models historical information to work with when making future determinations.

Algorithms then analyze this data, searching for patterns and trends that allow them to make accurate predictions. In this way, machine learning can glean insights from the past to anticipate future happenings. Typically, the larger the data set that a team can feed to machine learning software, the more accurate the predictions.

The idea is that machine learning algorithms should be able to perform these tasks on their own, requiring minimal human intervention. This speeds up various processes as machine learning comes to automate many aspects of different industries.

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