We've heard the buzz words 'machine learning' and 'artificial intelligence' numerous times in the past couple of years. But, what exactly do they mean? They are often used interchangeably—but, are they actually interchangeable, or is there a misunderstanding of the distinction?
These days, artificial intelligence (AI) is all around us. From Apple's Siri to Amazon's Alexa, we are constantly bombarded with ways to make our lives easier. What's interesting is that artificial intelligence has been around since the 1950s. However, AI's ability to work with larger data sets has just taken flight in the last few years.
To make the difference between the two terms a little clearer, AI is the broad concept of machines demonstrating intelligence, similar to the ways humans may respond, while machine learning is a subset of artificial intelligence, where computer systems learn algorithms to perform tasks by solely relying on patterns, rather than direct instructions.
So, what does that really mean and how is it actually implemented?
One example is AI being used in healthcare, where machine learning can be applied to track patterns of disease, to, ideally, collect more data on what diseases—such as Alzheimer's—look like. In this way, machines are able to learn over time what features in the brain scan to look for. Though, to truly emulate the mental capacities of the human brain, further study of pattern recognition in natural intelligence is required.
Another example of AI is being implemented in the manufacturing industry, specifically with cars. From using advanced machine learning algorithms to predict when vehicles need to be serviced, to improving driver and passenger experience, we are seeing a technological revolution in this industry. So much so, that BMW even boasts a fully autonomous vehicle—in other words, the car will be able to fully drive itself without any human assistance—by 2021!
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