When you are thinking about machine learning or artificial intelligence, it is easy to get bogged down in complex computer details.
Confusingly, these two terms seem to be used interchangeably by some. This makes everything that bit more difficult to understand – they are not the same thing!
In a muddle? You are not alone! Keep reading to learn about AI and ML – what they are, and how they are different.
Artificial Intelligence and Machine Learning: The Definitions
Before we get into the differences between artificial intelligence (AI) and machine learning (ML), you need to understand what these terms mean.
Remember – they are not the same thing!
What Is Artificial Intelligence?
Artificial Intelligence is one of those concepts that everyone has a vague idea of, without being able to give a tangible definition.
Perhaps the most widely accepted definition comes from 2004, which states AI is the science and engineering of making intelligent machines.
It is widely associated with Turing’s questions on whether machines can think and the subsequent development of the Turing Test.
It is perhaps easier to break down the key differences between AI and human intelligence, in order to understand how it works.
In essence, the differences boil down to rationality.
A human thought process is human and then the human who had the thought acts like a human. This is fraught with potential issues and may even circumvent rationality altogether.
Ideally, however, you want a system that ‘thinks’ rationally and then follows this through to act rationally. This is what AI does.
As AI has developed, it has become increasingly sophisticated. AI machines are now able to do some tasks that would usually be only possible for humans to do.
This is because technology has evolved to an almost human level of discernment for that particular task.
As such, it can now be used for a wide range of mind-boggling applications. From identifying a cat image, to facial recognition, or a virtual assistant.
These tasks tend to be limited to the four types of AI that exist in the present classification. These are:
- Reactive AI
The most basic type is programmed to generate a predictable output from the input it receives.
This AI cannot learn actions or understand the future from the past.
- Limited memory AI
This AI develops from experience to build experimental functions based on actions or data. This is the type of AI used for car safety software.
- Theory of mind AI
This is the basis for the Turing Test and looks at the capabilities of a machine to try to get as close to the human mind as possible.
- Self-Aware AI
By far the most advanced type of AI, self-aware AI machines are able to understand emotions and will develop a level of consciousness.
These machines are most similar to the human mind.
What Is Machine Learning?
Machine learning is a type of data analysis that can automate analytical program building.
Technically, it is a branch of artificial intelligence and was founded on the idea that a system or machine can learn from data.
Of course, when we talk of machine learning it does not happen in the same way that a human learns.
Learning for machines is more about identifying patterns and analyzing data in order to streamline the decision-making process so that it is as efficient as possible.
This helps the machine make more accurate decisions with minimal human interaction.
Technological advancement means that machine learning now is not like it was when it began.
A machine’s ability to apply multiple complex calculations to vast amounts of data repeatedly and almost instantaneously has only come about in recent years. This development has radicalized this field of technology.
Google’s self-driving car, for example, is based on machine learning technology.
Browser cookies that help Amazon or Netflix recommend the perfect product or movie for you? Also machine learning.
A recent surge in data collection has dramatically increased the volumes of data that need to be quickly analyzed, bringing machine learning back to the forefront of technological research.
This increase in data has also increased the reliability of the outputs from machine learning systems.
This is because there is more for the machine to look at, allowing it to create more results, leading to quicker development, better outputs, and even more machine learning-based evolution.
It is a fairly complex circle of development!
What Are The Differences Between Artificial Intelligence And Machine Learning?
Given what we now know about AI and ML, it is perhaps not correct to say that there are differences between the two.
If one thing is part of another, are they different?
ML is part of AI, meaning that ML is a subset of a wider discipline that involves specific tasks and commands.
ML hardware ‘learns’ by using previous commands to streamline future processes. It is this ‘learning’ that makes it AI.
Uses Of Artificial Intelligence And Machine Learning
Still not sure how machine learning works? You may find it helpful to think about a real-world scenario where it is typically applied.
While the applications of machine learning are extensive, there is one common complex task that we are all familiar with.
Your email inboxes. Even if you are a reclusive internet user who never shares your email address, you will almost definitely get spam emails at some point.
Unwanted commercial emails clog up everyone’s inbox, taking up vital data space and occasionally delaying internet speed times.
More and more companies are using machine learning to help limit the volume of spam emails that customers receive.
In this instance, machine learning works by remembering which emails you do not interact with and moving them to an alternate email folder, so they don’t take up valuable space in your inbox.
This leaves your inbox free for important messages. Increasingly this software can also accurately detect and archive more nefarious emails – like attempts at fraud or phishing.
So, for emails, ML can help increase safety and make email software easier to use.
This is done by comparing all emails that your email address receives to a previously determined set of rules. If the email passes all of these rules, then it can go into your inbox.
If it does not pass then it is archived or moved to the spam folder. But the beauty of ML is that it will continuously develop these rules so that they are bespoke to you.
If, for example, you have stopped interacting with marketing emails from a company, you may notice that these emails are moved straight to your spam folder and don’t appear in your inbox at all.
Machine learning is part of artificial intelligence.
A system that deploys this type of AI is able to remember functions or commands and applies these ‘learned’ rules to future data outputs.
It is used a great deal for creating accurate computer modeling analysis. In a more universal, real-world application, it is used for filtering out unwanted or spam emails.