---
title: Machine learning, explained  - The Hustle
description: This subcategory of AI gets name-checked all the time. What is it?
image: https://hs-sbup.thehustle.co/hubfs/The%20Hustle%20News/Assets/Images/669213695-httpsthdaily.s3.us-west-1.amazonaws.comfinal_size-machine-learning_20230720214439.webp
---

# Machine learning, explained

![Juliet Bennett Rylah](https://53.fs1.hubspotusercontent-na1.net/hubfs/53/assets/hubspot.com/web-team/WBZ/HubSpot%20Media/Blog%202021/Images/Nav/Optimized/TheHustle_Icon_Dark.svg) [Juliet Bennett Rylah](https://hs-sbup.thehustle.co/author/juliet-bennett-rylah) [Juliet Bennett Rylah Twitter profile ](https://twitter.com/JBRylah)

Published:  July 21, 2023

Updated:  October 11, 2023

Artificial intelligence (AI) and machine learning are also used interchangeably, but the two computer science terms are different.

![machine](https://hs-sbup.thehustle.co/hs-fs/hubfs/The%20Hustle%20News/Assets/Images/669213695-httpsthdaily.s3.us-west-1.amazonaws.comfinal_size-machine-learning_20230720214439.webp?width=595&height=400&name=669213695-httpsthdaily.s3.us-west-1.amazonaws.comfinal_size-machine-learning_20230720214439.webp)

AI is when a machine can perform a task that would have once required human intelligence. Machine learning is a subfield of AI.

- Got it? No? Let a computer scientist [explain it all to you](https://www.youtube.com/watch?v=5q87K1WaoFI) like an eight-year-old.

#### **The term…**

… was coined by computer scientist Arthur Samuel in the 1950s as “the [field of study](https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained) that gives computers the ability to learn without explicitly being programmed.”

This is done by feeding machines a bunch of data, which they use to identify patterns and make predictions or suggestions.

By feeding a computer several checkers games, Samuel taught it to play well enough to [defeat](https://history.computer.org/pioneers/samuel.html) a human champ.

Other examples include:

- Netflix and YouTube recommendations
- Email spam filters
- Predictive text

Accuracy increases over time, and the better the data, the better the outcome.

#### **A few methods include…**

… supervised machine learning, in which machines get labeled data (e.g., pictures of cats and dogs, labeled by humans, to teach a computer to identify dogs).

Unsupervised machine learning is when machines identify patterns from unlabeled data. For example, if given unlabeled customer data, a system could [cluster](https://www.simplilearn.com/tutorials/machine-learning-tutorial/supervised-and-unsupervised-learning#reallife_applications_of_unsupervised_learning) customers into segments based on their behavior.

Reinforcement trains models by telling them when they’ve made the correct choice. This is how you’d get a computer to excel at checkers, but it’s also how smart vacuums [navigate](https://www.linkedin.com/pulse/reinforcement-learning-coming-home-called-yours-bill-schmarzo/) your home.

#### **Are there any issues with machine learning?**

Machines can sort through massive amounts of data faster than we ever could, but:

- They can perform in unintended or unexpected ways, rendering them useless or harmful. See [this post](https://www.aiweirdness.com/dont-use-ai-detectors-for-anything-important/) from AI researcher Janelle Shane about how AI cheating detectors are often wrong.
- Because they’re trained by humans, they can also carry [human biases](https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai).

For more: Check out this cool [visual explainer](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/?ref=cloudhiker.net).

Topics: [Ai](https://hs-sbup.thehustle.co/tag/ai)

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