The Difference Between AI and Machine Learning: 20 Facts
The Difference Between AI and Machine Learning: 20 Facts
As is revealed in discussions on the same over at runrex.com, Machine Learning and Artificial Intelligence (AI) are among the hottest buzzwords these days. These two are also often used interchangeably to the point that many people wonder if there is a difference between them. This article will look to try and explain what they are about as well as highlighting the differences between them.
What is Machine Learning?
To understand the differences between the two, we should first take a look at their definitions according to guttulus.com. Machine Learning is defined as the study of computer algorithms that improve automatically through experience.
What is Artificial Intelligence?
Artificial Intelligence, on the other hand, has got as many definitions as it has implementations as discussed over at runrex.com. We consider AI as any application that achieves its goals by exhibiting intelligence that was built upon general-purpose tools, which the application combined in an order that it saw fit. This means that AI achieves its goals in a way that was not spelled out before like you would have with traditional computer programs.
One is a subset of the other
To differentiate between the two, the subject matter experts over at guttulus.com point out that one is a subset of the other. This is because Machine Learning is a subset of Artificial Intelligence and one of the techniques available for realizing AI.
Where AI and Machine Learning meet
One of the most interesting recent developments in AI and Machine Learning can be found in their conjunction. This combination of AI and Machine Learning profits from the flexibility of AI, combined with the power of Machine Learning to learn from past results based on the vast amounts of data that are available today. Algorithms like Neural Networks and Deep Learning and Evolutionary Algorithms are where these two meet.
Different meanings to different people
One of the main reasons why AI and Machine Learning are used interchangeably is because it depends on who you talk to. A big part of the confusion is because Machine Learning and AI mean different things to different users.
In media and marketing, everything is AI
Given that AI is a sexier term than Machine Learning right now, in media and marketing, the term Artificial Intelligence is used most often as covered over at runrex.com. However, the range of things the media refers to as AI is very wide:
Expert Systems
Process Automation
Deep Learning
Machine Learning
Reinforcement Learning, etc.
In academia, Machine Learning is a subfield of AI
To academics and people who have studied data science, as the gurus over at guttulus.com, Machine Learning is a subfield of the much larger field of Artificial Intelligence as already mentioned earlier. AI refers to a very large field of research that encompasses several techniques aimed at developing computers that can learn and solve problems while Machine Learning is the field of AI concerned with learning from data on its own.
In business, Machine Learning and AI are approximately the same thing
On the other hand, in business, AI and Machine Learning usually refer to the same thing. From discussions over at runrex.com, this is because most business applications of AI amount to Supervised Learning, which is a subfield of Machine Learning.
Machine learning doesn’t always have to be AI
Even though, as mentioned earlier, Machine Learning is a subfield of the much larger field of Artificial Intelligence, Machine Learning doesn’t always have to be AI. This is because there is a huge field of “traditional AI” Machine Learning containing many tools that have been used for years by statisticians and data scientists. Many of these tools are still extensively used today, like Regression, K-Nearest Neighbors, among others.
Key differences between Artificial Intelligence and Machine Learning
Now that we have a basic understanding of what these two are about and why they are usually used interchangeably with each other, let us dive straight into the key differences between them.
The difference in how they work
While Artificial Intelligence is a technology that enables a machine to simulate human behavior, Machine Learning is a subset of AI which allows a machine to automatically learn from past data without being explicitly programmed to do so.
The difference in goals
As the subject matter experts over at guttulus.com point out, the goal of AI is to make a smart computer system like humans to solve complex problems. The goal of Machine Learning on the other hand is to allow machines to learn from data so that they can give accurate output.
The difference in outcomes
In Artificial Intelligence, we make intelligent systems to perform any task as a human would as covered over at runrex.com. In Machine Learning, we teach machines with data to perform a particular task and give an accurate result.
The difference in subsets
Machine Learning and Deep Learning are the two main subsets of Artificial Intelligence, while on the other hand, Deep Learning is the main subset of Machine Learning as articulated in discussions on the same over at guttulus.com.
The difference is scope
As has already been mentioned, AI is a very large field of research encompassing several techniques aimed at developing computers that can learn and solve problems, including:
Computer Vision
Robotics
Natural Language Processing, etc.
Therefore, another key difference between the two is that, while AI has a very wide range of scope, Machine Learning has a more limited scope given that it is a subset of AI.
The difference in the flexibility.
While Artificial Intelligence is working to create an intelligent system that can perform various complex tasks, Machine Learning is working to create machines that can perform only those specific tasks for which they are trained, offering less flexibility.
The difference in the main concern
As is revealed in discussions on the same over at runrex.com, while Artificial Intelligence systems are concerned about maximizing the chances of success, Machine Learning is mainly concerned about accuracy and patterns.
The difference in applications
As the gurus over at guttulus.com point out, the main applications of AI are Siri, Customer support using chatbots, Expert Systems, Online game playing, intelligent humanoid robots, etc. On the other hand, the main applications of Machine Learning are Online recommender systems, Google search algorithms, Facebook auto friend tagging suggestions, etc.
The difference in the type
From discussions over at runrex.com, AI can be divided into three types: Weak AI, General AI, and Strong AI. Machine Learning, on the other hand, can be divided into mainly three types: Supervised learning, Unsupervised learning, Reinforcement learning.
The role played by new data
While AI includes learning, reasoning, and self-correction, Machine Learning includes learning and self-correction when introduced with new data.
The difference in data used
AI completely deals with structured, semi-structured, and unstructured data while Machine Learning deals with structured and semi-structured data alone.
This article is only the tip of a very large iceberg as far as this topic is concerned, and you can glean more insights on the same by checking out runrex.com and guttulus.com.
