Big data and advanced analytics have been a major part of the burgeoning technological landscape in recent years. As such, the terms of artificial intelligence (AI) and machine learning have become buzzwords across numerous industries. They’re often used in the same sentence or even used interchangeably.
AI and machine learning are not the same things, though they are related. The ubiquity of these terms can cause a lot of confusion, especially for those outsides of the tech realm.
Here’s what they are and how they’re different:
AI came first since it’s the broad idea of advanced computer intelligence. This is the concept that machines can be “smart” and taught to perform iterative tasks that humans perform. AI machines are meant to execute algorithms in order to “think through” problems.
AI is not really the system itself; it’s the concept that a machine can have intelligence that mirrors the human version in many ways. It is the scientific movement behind making appliances intelligent. This gets confusing since we often refer to robots and smart devices as “AIs.”
There are two types of AI that further break it down—general and applied:
The concept of AI has actually been present for a long time. While the idea that humans could create human-like machines has been around for centuries, the contemporary AI concept first gained traction in the early twentieth century, with the rise of a robot fascination in books, movies, and science labs, and then the invention of the computer.
Today, AI is everywhere—for example, in popular voice-recognition applications such as Amazon’s Alexa or Apple’s Siri.
Machine learning is more specific than the term artificial intelligence. It refers to the process of giving a machine the ability to actually learn from complex data it’s given. This requires that data be collected and analyzed (called “test” data), and the machine can then work out how to make an appropriate decision on its own.
Machine learning is thus a subset of AI and is one way that AI is realized. Machine learning was pushed forward by the notion that it would be more efficient for a machine to be able to learn on its own, rather than human teaching the machine everything with set algorithms.
It started to become a realistic practice with the introduction of the Internet, which has meant that a ton of data can be stored; far too much for humans to process alone. Machine learning allows all of this big data to be analyzed accurately and quickly without a human hand guiding the process.
An example of how machine learning works are in medical diagnoses. Advanced tools can be used to analyze certain parameters in patient information that have certain outcomes, leading to accurate predictions of disease progression, for instance. This data then also further research for patient outcomes and medical practices in general.
Within complex wireless networks, such as in hospitals, data is constantly streaming from patient devices and doctors’ platforms. Machine learning continues to streamline and improve data analytics in these kinds of networks, giving medical professionals more power to make forecasts and receive actionable insights from a wealth of information.
For businesses in general, machine learning allows company or customer data to be tracked, analyzed, and implemented into a comprehensive dashboard that shows trends and provides predictions for the future.
Put simply, here are the key differences between AI and machine learning:
It’s easy to confuse AI with machine learning. But it’s important to remember that AI is the overarching concept and machine learning is just one method of AI in action. In either case, these advanced concepts, practices, and tools are likely to continue changing and streamlining business practices across industries, from tech to healthcare to automotive.
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