Tired of constantly hearing these buzz words? You are not alone.
In today’s tech-driven world, these terms have become increasingly common and are often used interchangeably, while they don’t mean the same thing. This sometimes creates confusion about their real meaning and actual application. In this short article, we will give a brief explanation of each one with the hope of clearing this ambiguity.
Before we start, here are some quick and recent statistics to understand the overall picture and adoption of such technologies across the globe:
- As end of 2023, global AI market was worth $196.63 billion, an increase of $60 billion from 2022.
- Global AI market is expected to reach $1.81 trillion by 2030.
- 9 in 10 organizations back AI to give them a competitive edge over rivals.
- Almost 100 million people will be working in the AI space by next year.
- Around 4 in 5 companies deem AI to be a top priority in their business strategy.
- Netflix’s recommendations technology is worth $1 billion in revenue annually.
Artificial Intelligence
The basic and simplest definition of AI is the ability of machines to mimic humans. AI is the broader field that includes all technologies that make machines capable of performing work requiring human intelligence and decision making. It aims to: create systems capable of simulating human cognitive functions such as learning, reasoning and adaptation by learning from previous events.
Machine learning, Deep Learning, and Generative AI are all subsets of AI.
Machine Learning
A branch of artificial intelligence that focuses on developing algorithms that allow systems to learn from data and make decisions based on patterns discovered in this data.
It depends on providing systems with data and training them to make decisions or predictions based on that data without the need for explicit upfront programming for each case.
Examples include: online products recommendations, credit scoring and financial forecasts.
Deep Learning
A branch of machine learning that uses deep (multi-layer) neural networks to solve complex problems requiring huge data sets analysis. It relies on multiple layers of neural networks that automatically extract features from raw data. It can deal with formats of structured data (numbers, short text and dates) and unstructured data (audio and video files). Neither Machine learning nor deep learning have generative capabilities like Generative AI.
Examples: image/ sound recognition, autonomous driving and customer service chatbots.
Generative AI
A branch of deep learning that focuses on creating new content (such as text, images, sounds) using generative neural networks, or simply creating more data following the rules and patterns the machine have learned. It relies on neural networks to generate data that resembles the original one.
For example, if you train it on pictures of dogs, it will learn that a dog has four legs, two ears and a tail and start from there creating in the same context. Generative AI produces striking realistic content, from writing to art and music, yet it requires substantial data to generate convincing outputs.
Examples include: text-to-image generation (DALL-E), text generating (Chat-GPT), generating music, etc.
In short, artificial intelligence is the most comprehensive term that includes all the ways that machines can become intelligent. Machine learning is part of artificial intelligence and has capabilities such as learning, perception, and understanding, but it extracts features manually and deals with structured data. While deep learning is part of machine learning (neural networks) and works in a more complex (multi-layer) way, extracts features automatically and deals effectively with unstructured data. Finally, generative AI is an application of deep learning that aims to create new and original content, mimicking humans’ ability to do so.