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What’s the Difference Between AI and Machine Learning?

June 17, 2017
As computer systems become smarter than humans, we should learn the difference between our new overlords.

Buzzwords can help get a lot of attention on the web. But while these SEO keywords might help people find what they are looking for, they may also add fluff and garbage to searches. With terms like 3D printing, and IIoT eliciting such a positive response, there’s no end in sight. Add artificial intelligence (AI), machine learning, neural networks, and deep learning into the mix, and it can be confusing to keep up with which is which. So, to begin:

What is AI?

AI: Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. AI has had some success in limited, or simplified, domains (Courtesy of

First, there are different types of artificial intelligence (AI): weak and strong.  Weak AI might behave as though a robot or manufacturing line is thinking on its own. However, it’s supervised programming, which means there is a programmed output, or action for given inputs.  

Strong AI is a system that might actually change an output based on given goals and input data. A program could do something it wasn’t programmed to if it notices a pattern and determines a more efficient way of accomplishing the goal it was given. 

For example, when an AI program was instructed to obtain the highest score it could in the video game Breakout, it was able to learn how to perform better and was able to outperform humans in just 2.5 hours. Researchers let the program run. To their surprise, the program developed a strategy that was not in the software. It would focus on one spot of bricks to poke a hole so the ball would get behind the wall. This minimizes the work, as the computer no longer has to move the bat while the score would increase. This also minimizes the chances of missing the ball and ending the game.

Keep in mind that the computer isn’t seeing the bat, ball, or rainbow stripped bricks. It “sees” a bunch of numbers. It knows what variables it controls, and how it is able to increase points based on how it controls the variables in relation to the other numbers.

"Under AI there are a lot of different technologies: Some of them exist and function, others are not yet mature, others are simply buzzwords,” says Matteo Dariol, a product developer for Bosch. “In my experience, in real-world manufacturing, I have not heard of anyone using AI for operations, it is more plausible that R&D centers are studying and testing certain algorithms. Some industrial components like PLC, drives, motors, already include certain neural networks that could fall under the wide umbrella of AI, typical applications are providing more energy efficiency or quicker reaction time."

AI has bled into a general term that could mean several things, including machine learning. Creating a lot of confusion is that some people associate AI with independent thinking. However, from the definition a machine vision application of picking up a part and setting it in a particular orientation. By definition this action is what a human would do, and requires some level of intelligence. It may not take much intelligence, but it does fit the AI definition.  

Neural Networks and Big Data

Neural Network: A computer system modeled after the human brain.

Big Data: Essentially a large set or sets of data that are needed for programs to accurately use AI features. As things become more complex—moving from AI to machine learning or machine learning to deep learning—the more data you have, the better these systems will be able to learn and function.

Machine learning is sometimes associated with a neural network. Similar to how the human brain operates, neural networks have many connections between nodes and layers of nodes. Training algorithms can use neural networks, so when input in the form of data is entered the system, it will figure out, learn, decide, etc. what the best course of action is. Using a massive amount of data (often called Big Data) the algorithm and network learn how to accomplish goals and improve upon the process. This type of extensive connectivity is referred to as deep learning.

Deep Learning

Deep Learning: Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is the study of artificial neural networks and related machine learning algorithms that contain more than one hidden layer. (Courtesy: Wikipedia)

“Deep learning is a special type of machine-learning algorithm—it is multiple layers of neural networks that mimic the connectivity of the brain, and these types of connectivity seem to work much better than pre-existing systems,” said Samarjit Das, a senior research scientist at Bosch. “We currently have to define parameters for machine learning based on our human experience. When we look at images of apples and oranges, we need to define features manually, so that machine-learning systems can identify the difference. Deep learning is the next level because it can create those distinctions on its own. By just showing sample images of apples and oranges to a deep-learning system, it will create its own rules realizing that color and geometry are the key features that distinguish which are which, and not have to teach it based off human knowledge.” 

Machine Learning

Machine Learning: A type of AI that can include but isn’t limited to neural networks and deep learning. Generally, it is the ability for a computer to output or do something that it wasn’t programmed to do.

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