What’s the Difference Between Machine Learning and AI?

What’s the Difference Between Machine Learning and AI?

In the past few years, machine learning developed into a catch-all buzzword for all things artificial intelligence related. Conflation of the two technologies can cause a great deal of confusion for many. Moreover,  hyperbolic news in response to machine learning development isn’t doing anyone any favors.

Artificial intelligence is all around us, and has powerful influence over the media we read or watch, what we buy and even who to connect to on social media. Today, machine learning is a state-of-the-art application of artificial intelligence that has profound effects on our lives. If you feel confused about the differences between machine learning vs. artificial intelligence, our primer is here to help.

What is Artificial Intelligence?

Basically, artificial intelligences are machines that exhibit humanlike intelligence. AI accomplishes tasks just like (or sometimes better than) humans.  Whether you’re battling enemies in a video game or auto-organizing your photo collection by object or face, you probably encounter artificial intelligence accomplishing a variety of tasks throughout your everyday life. We detailed common uses of AI in our breakdown of how big data makes an everyday impact.

There are two kinds of AI: applied and general. First one handles specific tasks. A computer chess opponent is just one example of a this kind of AI. General AI are jack-of-all-trades intelligences, capable of performing a variety of different tasks. IBM Watson, who can defeat opponents in Jeopardy by day and develop cutting-edge, new recipes by night (among other tasks) is a general AI.

There are many subsets of artificial intelligence, and machine learning is just one of them. Artificial intelligence, then, is a broad umbrella term for different types of ways machines can show humanlike intelligence. Some forms of AI besides machine learning include:

  • Natural language processing
  • Robotics
  • Symbolic logic (like rules engines)

So, what is the machine learning that everyone’s talking about? Let’s delve into machine learning’s specific traits.

The Difference Between Machine Learning and AI

The difference between machine learning and AI is how it learns and area of uses. Typically, an AI is programmed to behave a certain way and fulfill a task. Machine learning, meanwhile, is a unique subfield of artificial intelligence in which algorithms learn to fulfill tasks. This can occur through leveraging big data, from which the algorithm may discover patterns and new, more efficient ways to accomplish a task. This may or may not occur with the assistance of a human.

If you spoke to a chatbot that uses natural language processing, it uses ML under the hood as well. Conversational analytics is also a must for all chatbots. Machine learning applications like NLP lets machines understand the meaning and sentiment behind the words you speak or type. Also, drawing conclusions about the content of your message and how to adequately respond. In sophisticated situations, they might answer without a pre-written response.

Machine Learning Applications and their Types

Just like AI, there are two big categories of machine learning that the layperson will encounter. These are structured machine learning and unsupervised machine learning. First kind of machine learning involves training the AI to fulfill a certain task, like teaching a human. This means a team of people may intervene to improve it based on how well it can perform a task using data. For example, you might give it a set of images to test whether it can identify a certain object. After the results, you can tweak the AI to help it make the right decision.

Second type of machine learning includes machine learning applications dealing with pattern recognition. This is the type of machine learning used to find product recommendations, search results or movie recommendations for you each day. For example, an AI might look at your purchase history, then compare it with those of users who made similar purchases. It then determines what other products you’d likely be interested in, making a recommendation the next time you log in to shop. Another example is email spam filtering, which determines likely spam by comparing an email’s contents with messages that were identified as spam in the past. This is the essence of machine learning: parsing large amounts of data to form a conclusion or prediction from it.

So there you have it. What is artificial intelligence? It’s the technology that shows humanlike intelligence. Machine learning, meanwhile, is the specific subset of AI that learns to fulfill a task and improve upon it by interpreting big data. This may happen with the help of a human or not. 

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