Defining AI & ML

In my previous blog post, I explained the definition and various types of Machine Learning. In summary, ML is defined as a computer accessing data and learning for itself. The goal is to curate enough data for the computer to learn automatically, so human intervention is unneeded or limited.

While expanding my knowledge in Machine Learning, Data Science, and Artificial Intelligence, I’ve noticed that ML and AI have many overlapping characteristics. But, they also have a few polarizing differences. This post will highlight the differences between Machine Learning and Artificial Intelligence. Before going into the differences between the two, first, you should know what Artificial Intelligence is to ensure we’re all on the same accord. Artificial Intelligence can be defined as an algorithm that makes a computer-based system mimic human intelligence in various ways. Using an algorithm to create this AI removes the need for the computer to be pre-programmed as the algorithm can work within its intelligence. AI does have components of ML algorithms embedded within them, like reinforcement learning, for example.

Some everyday examples of AI include Siri, pricing algorithms for Uber/Lyft, airplane autopilot, email filters, or even mobile check depositing.

The Differences between ML & AI

One of the most apparent differences between ML and AI is that artificial intelligence empowers a machine to simulate or replicate human actions and behaviors. Machine Learning is just a subset of AI-focused on enabling the machine to instinctively learn from previously collected data through algorithms, preventing the need for constant programming.

With these two technologies being different, they also differ in goals. Artificial Intelligence is designed to make a computer or a human-like system controlled by a computer solve complex problems. Machine Learning is designed to create a machine that learns from data to give an intended output. Simply put, AI is made to perform human-like tasks while ML is using data to teach a machine what task to perform to produce accurate results. Machine Learning, having limited scope, is essentially a pie slice of the whole pie of Artificial Intelligence which has a wide range of scope.

When engineers create a Machine Learning solution, they are using an algorithm to guide the machine to complete a specific task. On the other hand, Artificial Intelligence is developed to complete various tasks ranging in complexity. AI is targeted to increase the likelihood of success, while ML is targeted to increase accuracy, with less of the success emphasis.

As mentioned in the previous blog post, the three main divisions of ML are supervised, unsupervised, and reinforcement learning. Artificial Intelligence can be divided into weak, general, and strong AI.

Key Take-Aways

If you walk away with anything, you should understand:

• AI simulates human intelligence to solve complex tasks; ML learns from previously accumulated data to make a prediction
• AI makes decisions; ML uses data to teach the system new things
• AI acquires and applies knowledge; ML leads you to said knowledge or insight
• Machine Learning is a subset of Artificial Intelligence