AI vs Machine Learning. If you’re wondering what the difference is, then you’re certainly not alone.
We’re going to take a look at the definitions of artificial intelligence, machine learning and deep learning, and finally settle the AI vs Machine Learning debate.
Often, especially in the media, deep learning, AI and machine learning are portrayed as the same thing. They're frequently used interchangeably, even by practitioners of analytics.
Any device that has the ability to perceive its environment and takes actions to maximise its chance of success at achieving a goal can be said to have some kind of artificial intelligence, which is more commonly referred to as AI.
More specifically, AI is classed as when a machine has “cognitive” capabilities, such as problem-solving and learning by example, usually associated with the benchmark human level of reasoning, vision and speech.
What is AI?
AI has three different levels:
- Narrow AI, when a computer can perform one task much better than a human - this is the level of AI technology that we’re currently at
- General AI, which is when a machine can successfully perform any intellectual task that a human being can
- Strong AI, when machines can beat humans in lots of tasks
One of our favourite early developments in AI is the perceptron. This was a single layer artificial neural network designed for image recognition in the late 1950s.
AI enthusiasts like the perceptron example because it is a simple artificial neural network that can be used to represent logistic regression, a very common algorithm in statistical modelling.
But why are they called neural networks?
Neural networks were named because the first practitioners of AI thought that these interconnected nodes looked like the human neural system.
Humans have neural networks in our nervous system. These are the natural neural nets, while the perceptron is a rudimentary form of the artificial ones.
Nowadays, the analogy of the human brain is out of fashion. Current practices in AI do not actually mimic the way that the brain processes information. What makes AI so interesting is the fact that we don’t fully understand how AI does process information.
Two of biggest misconceptions around AI vs machine learning is that AI and machine learning are different terms for exactly the same thing - but this isn't true.
What is Machine Learning?
Machine learning is a subset of AI. This is where a body of research on supervised and unsupervised learning has flourished since the 1980’s.
Machine learning is where most of the applications of AI for business lie. You can find out more about this in our video ‘Can Machines Be Better Than Us?’.
What is Deep Learning?
Finally, as a subset of machine learning (and yet another cause of confusion in the AI vs machine learning discussion), we have deep learning. It is called deep because it makes use of deep artificial neural networks.
This means that it makes use of neural nets with more than one hidden layer, which exist between the input layer and the output.
Deep learning applications include:
- Self-driving cars
- Text sentiment analysis
- Recommendation engines
- Language recognition
In this sense, the 1950s perceptron is an example of a shallow neural net.
The field of deep learning is particularly responsible for all the advancements that have recently been made in image recognition. Did you know that every image can be represented as pixels in just the three colours of red, green and blue? So if you can represent an image as numerical data, we can process this with deep learning.
Hopefully, that’s helped to clear up some of the confusion about AI vs machine learning! Essentially, deep learning is a subset of machine learning, which is a subset of AI.
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And here's out video on the difference between AI and machine learning!