Machine learning vs AI vs deep learning: The differences explained

In today’s digital age, terms like machine learning, deep learning, and AI are often used interchangeably, leading to a common misconception that they all mean the same thing. However, these terms have distinct technical differences that are important to understand. This article aims to explore these terms in detail, but feel free to check out the video above as well.

What is machine learning and deep learning?

Machine learning is a subfield of computer science that emphasizes the development of algorithms and statistical models. These models enable computers to perform tasks without explicit instructions, relying instead on patterns and inference. Unlike traditional computer programs where you specify the steps, machine learning presents examples from which the system learns, deciphering the relationship between different elements in the example.

Machine learning involves two distinct phases: training and inference. A computer algorithm analyzes many samples or training data to extract relevant features and patterns during the training stage. This data can include numbers, text, images, speech, and videos. The models analyze the data, identify different features in the dataset, and learn to distinguish one thing from another.

There are different methods of conducting the training stage. The first one, supervised learning, involves learning that explicitly maps the input to the output. Other types of training include unsupervised learning, where the patterns are not labeled, and reinforcement learning.

Inference, the second stage, is the output stage. Here, the model, drawing from everything it learned, is queried about something not included in the training data.

Numerous models can be used, and not all are neural networks. However, neural networks, which mimic how the neurons in the brain work, are pretty popular today. These digital neurons are arranged in layers, each having weights and biases. The network adjusts these weights and biases during the learning phase to produce the correct answer.

There are various types of neural networks beyond classic examples, including convolutional neural networks, recurrent neural networks (RNNs) like long short-term memory networks (LSTMs), and more recently, transformer networks. Deep learning relates to neural networks, with the term “deep” referring to the number of layers inside the network.

Many machine learning systems we use daily, such as face detection, speech recognition, object detection, and more, are all types of machine learning, not AI. However, due to marketing strategies, these are often labeled as AI. AI, which originally referred to human-like intelligence in machines, now refers to any aspect of technology that partially shares attributes with human intelligence. In this sense, AI is very narrow and is essentially machine learning.

The Turing Test, a game where three people communicate via text messages, has been made obsolete by Language Models (LLMs) as they can imitate without thinking, thus invalidating the imitation game to answer the original question, “Can machines think?” This leads us to Artificial General Intelligence (AGI), a term used to describe a type of artificial intelligence that is as versatile and capable as a human. AGI is currently a theoretical idea with no existing systems. To be considered AGI, a system must learn and apply its intelligence to various problems, even those it hasn’t encountered before. A true human AGI would need to possess consciousness and self-awareness.