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AI machine learning, and deep learning are all terms that you may have heard in the context of computer science and technology. While these terms are often used interchangeably, they actually refer to distinct concepts and technologies. In this article, we'll explore the differences between AI, machine learning, and deep learning, and explain what each one is and how it works.
AI vs. Machine Learning vs. Deep Learning: What's the Difference?
AI, or artificial intelligence, is a broad term that refers to the ability of machines to perform tasks that would typically require human intelligence. This can include things like recognizing speech, understanding natural language, making decisions, and even exhibiting creativity. AI can be divided into two main categories: narrow AI and general AI.
Narrow AI refers to machines that are designed to perform specific tasks, such as image recognition or language translation. This is also known as weak AI.
General AI, on the other hand, refers to machines that have the ability to perform any intellectual task that a human can and can also be referred to as strong AI.
Machine learning is a subset of AI that refers to the ability of machines to learn from data and improve their performance over time. Machine learning algorithms use statistical models to analyse data and make predictions or decisions based on that data. These algorithms can be supervised, unsupervised, or semi-supervised, depending on the type of data they are analysing and the goal of the analysis.
Some examples of machine learning are the recommendation systems that Netflix, and Spotify use to recommend movies and music. Banks also use machine learning to analyse consumer spending patterns and detect fraud when the pattern looks out of place.
Deep learning is a subset of machine learning that refers to the use of neural networks, which are modelled after the structure of the human brain, to analyse data. Deep learning helps computers to learn by themselves. It's like teaching a computer to recognize things on its own without being specifically told what to look for.
Using the example of a dog, in traditional machine learning, we would have to tell the computer what features to look for, like a mouth, tail or fur. But with deep learning, the computer can learn by itself what a dog looks like by being shown lots of pictures of dogs.
Some examples of deep learning are facial recognition when your phone automatically tags your friends in the photo after you have taken a selfie with friends. Self-driving cars are another example; sensors like cameras and radar are used by the car to detect objects on the road, and deep learning enables the car to ‘understand’ what it's seeing and process this information in an instant.
An easy way to understand the differences
From Siri to self-driving cars
One way to understand the differences between these concepts is to look at some real-world examples. Siri, Apple's digital assistant, is an example of AI as it is designed to perform specific tasks like answering questions and setting reminders.
A recommendation system, like the one Netflix uses, is an example of machine learning. It uses your viewing history to make personalised recommendations about which films, series or documentaries to watch next.
A self-driving car like Tesla's Autopilot system is an example of deep learning, as it uses neural networks - its “brain” - to analyse data from sensors and make decisions about how to navigate the road and avoid obstacles.
Let’s end off by using the example of a self-driving car. AI is like the overall concept of a self-driving car, which refers to the ability of a car to drive itself without human intervention. Machine learning is like the specific instructions and processes that the car uses to learn how to drive. Deep learning is like the brain that the car uses to analyse data from its sensors and make decisions about how to drive.
The challenges and limitations of these systems
While AI, machine learning, and deep learning are all exciting and rapidly developing fields, they also come with their own set of challenges and limitations. One of the biggest challenges is the need for large amounts of high-quality data to train these systems on and with. Another challenge is the potential for bias and discrimination in the data that is used to train these systems.
Some common issues that are being worked through at the moment include hiring algorithms and facial recognition. Some companies have used AI to screen job applicants but these algorithms can be biassed against women and people of colour because they were trained on historical data that reflects past discrimination in the hiring process.
When it comes to facial recognition some of the algorithms have been shown to be less accurate at identifying people with darker skin tones, because they were trained on data sets that had more images of light-skinned people. This can lead to misidentification and potential harm to those who are misidentified.
In conclusion, AI, machine learning, and deep learning are all important concepts in the broader field of computer science and technology. While they are often used interchangeably, they are distinct concepts and technologies that have their own strengths and limitations. When we understand the differences between them, we can better appreciate the ways in which they are currently transforming our world, from self-driving cars to personalised recommendations on streaming platforms and more.