What’s the Difference Between AI and Machine Learning?
AI and ML are very popular terms used today. These technologies have entered many different industries, and they’ve become buzzwords that make something special. But in reality, most markets that advertise these technologies don’t know what they are or what they represent.
To make things even worse, many people think that Artificial Intelligence and Machine Learning are the same things. Just because people are using something daily doesn’t mean they understand it.
However, for customers and people looking to work with these technologies, it’s essential to understand the differences between these two. In one of our previous posts, we’ve explained what AI and ML are, and today we’re going to talk about how they are different and what are the clear distinctions you should remember.
Quick Definition of AI and ML
If you haven’t read our post on what these are (and we suggest you do), let’s quickly go over some basics. Artificial Intelligence is a program that has human-like intelligence and can adapt, act, and reason on its own.
There are many different types of AI, depending on how they are designed and what they are used for. On the other hand, Machine Learning is a precursor algorithm to narrow AI. These programs work on a trial and error basis.
They do a single task countless times and improve over time as they learn more about the data they are exposed to.
Most Notable Differences
| AI | ML |
| Creates machines and programs that mimic human intelligence. | A precursor to AI that enables programs to learn on their own, without additional coding. |
| The main focus of Artificial Intelligence is to create programs and machines with human-like capabilities. | The main focus of Machine Learning is to enable machines to learn effectively from data and give the desired output. |
| AI programs perform tasks similarly to humans. | ML programs perform designated tasks on datasets to give desired results. |
| AI has a broad range. | ML has a limited range. |
| AI has self-correction, reason, and learning capabilities. | ML has self-correction and learning capabilities only when exposed to data. |
The Differences in Approach
Artificial Intelligence
As the name itself implies, AI is the process of creating artificially intelligent computers. This computer science branch specializes in coding programs to be intelligent. It’s the science of making intelligent machines and programs.
The main motivation behind this approach is the scientists’ effort to create a technology that could simulate the intelligence exhibited by the human brain. So far, the brain has been the most powerful computing tool discovered by humanity.
Our brains have a Neocortex that allows us to remember things, function, think, and behave in a desired way. Our Neocortical memory stores spatial and temporal patterns. When we recall these patterns, our brain allows us to predict what will happen, what we will hear, or see.
That’s something that scientists want to replicate with Artificial Intelligence. However, even with all the research and technology available today, we still haven’t learned a lot about our brains. At the same time, coding all of this is a challenge of its own.
Machine Learning
This branch of computer science focuses on creating algorithms that can improve, learn, and eventually turn into Artificial Intelligence. With these algorithms, programs and computers can automatically learn without any management or being coded once again.
AI tries to create intelligence by starting from the top and going all the way down. During that process, there are a lot of changes, tweaks, new discoveries, and changes made within the code. Machine learning sets up an algorithm and focuses it on a single task.
The computer or the program then uses various data it’s exposed to learn from it. The algorithms are designed to understand the data that is fed to them. They can understand their features and attributes and can reach conclusions based on the information they’ve processed.
After some time, the algorithm trains itself through the same process. Once it has reached a satisfactory point, it can be texted without any additional programming. You only have to give the same type of data to the program and see what results come out.
Types of AI vs. Types of ML
Artificial Intelligence
There are many different ways AI can be classified. For the purpose of this post, we’ll look at the types of AI depending on their strength.
- Narrow Artificial intelligence is also known as weak AI: the only AI that has been fully developed and is used on a daily basis. It performs single tasks like driving, searching for data, speech recognition, and so on. It has many limitations and doesn’t mimic human intelligence, merely simulating human actions with designated parameters.
- Strong AI: also called Artificial general intelligence or deep AI. It’s a machine with general human intelligence through which it can learn or make decisions to solve problems. These programs can act, understand, and think similarly to humans. However, this technology hasn’t yet been fully developed. It’s still not possible to apply to a wide range of problems.
- Artificial Super Intelligence: this is still a theoretical technology that hasn’t even been developed in its primitive form. It’s a hypothetical human intelligence within a machine. Artificial Super Intelligence represents self-aware machines that have more capacity than humans.
Machine Learning
Machine learning technologies differ based on their algorithms. Here are some of the most common ones.
- Supervised Machine Learning: this learning technique is governed throughout. The main goal of this kind of algorithm is to predict outcomes through training labels and samples. During training, a programmer tells the algorithm what needs to be predicted.
- Unsupervised Machine Learning: there is no supervision or training sample and labels for these algorithms. They look for patterns or structures within data. When these are obvious, data is clustered, making the approach great for analysis or visualization.
- Reinforcement Machine Learning: these algorithms have an agent that quantifies results and takes actions within an environment. When the agent makes the desired action, it’s rewarded, and these rewards encourage it to do more of the same.
Conclusion
These are the key differences between Artificial Intelligence and Machine Learning at their fundamental levels. Even though they are different, they are closely tied together and are often combined within computer programs to achieve better results.