Data Science Terms You Should Know: The Difference Between AI, ML, and DL
If you follow technology or analytics in some capacity, there is a good chance that you’ve heard the following acronyms at some point: AI, ML, DL. These terms are used quite frequently in the industry and are sometimes even used interchangeably.
So what do these acronyms actually mean?
Great question! They are artificial intelligence (AI), machine learning (ML), and deep learning (DL), all aspects of data science.
The diagram below illustrates how everything is related. The largest, blue circle represents artificial intelligence. The mid-size, pink circle represents machine learning, which is a subset of artificial intelligence. The small, white circles represent deep learning, which is a subset of both artificial intelligence and machine learning. All machine learning and deep learning methods are part of artificial intelligence, but not all artificial intelligence methods are machine learning or deep learning. The smaller the circle, the more niche the modeling subtype is.
Artificial intelligence is a computer science term that is quite all-encompassing. AI refers to blending mathematics with technology in order to mimic human decision-making. It includes all machine learning and deep learning methodologies but can be as simple as an “IF this happens THEN that” statement.
Machine learning is a subset of artificial intelligence that focuses on leveraging applied mathematical techniques and specific algorithms to create a prediction.
Machine learning can be as simple as linear regression, or as complex as a long short term memory network. Machine learning models are quite flexible, having the ability to adapt and “learn” over time as they are continually exposed to new data. As the model gets retrained with new data, the underlying formula that fits the data is automatically adjusted to incorporate recent trends.
Machine learning can be using a logistic regression model or decision tree to predict whether or not a customer will buy the product. It can also be using clustering to determine patterns in customer behavior to identify subgroups.
Deep learning is a subset of machine learning algorithms called neural networks. Neural networks are algorithms that mimic the human brain’s behavior in decision-making and try to find the most optimal path to a solution. Oftentimes, they do not give insight into which variables are most impactful to the predicted value. Deep learning often consists of using multiple neural networks to reach a final decision.
An example of deep learning is using computer vision to determine if a picture is a cat or a dog. It looks at unstructured data (photos), extracts features from patterns in the data, and then determines if the picture is of a cat or of a dog.
Machine learning and deep learning might seem interesting, but they may not always be the right solution to your problem. A simple, logic-based algorithm may be able to solve 85% of your underlying problem. It may be that a machine learning model can help give insight into your sales forecast, while still understanding the individual drivers. In certain cases, a deep learning model might be the right application. At the end of the day, it’s important to evaluate what problem you’re trying to solve and what technology and data are available to solve it to determine which methods are even plausible to leverage. The more complex the better!
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