AI, ML, and Deep Learning Explained: What's the Difference?

Ever since the launch of OpenAI's Chat‑GPT in November 2022, the AI ecosystem has grown exponentially. The tech industry has seen a surge in new AI startups, resulting in the release of numerous AI projects. This growth has sparked a global AI race, with start‑ups from various countries competing to deliver the next major technological breakthrough.

The industry has progressed from foundational large language models (LLMs) to secondary models, AI tools and now AI agents. Every few months, a new AI trend emerges, capturing the industry's attention.

As AI contends to solve new and wider problems, the user base of this technology also expands. Tech discussions are no longer confined to founders and developers; they now engage a wider audience. Before 2022, AI conversations were limited to select groups with niche interest in the technology. Today, a growing number of people actively seek updates on the latest developments in the AI ecosystem.

With this, several technical terms that were only used by the techies and founders are now used very commonly. However, the lack of clear definitions for some terms can often lead to confusion.

In this explainer article, we will explore the meanings and connotations of artificial intelligence (AI), deep learning and machine learning, three widely used terms in the AI ecosystem.

Also Read: Chips, Cash, Talent: Inside China's Grand Strategy to Win the AI Race

Artificial Intelligence (AI)

Artificial intelligence is a branch of computer science that seeks to develop systems that can carry out functions which would require human intelligence. Some of those functions include learning, thinking, solving issues, understanding languages and perception. The AI systems have been designed to examine information, identify patterns and make conclusions with minimal human intervention.

In practice, machine learning (ML) and deep learning (DL) are methods that drive AI capabilities, with DL providing the best outcomes in applications such as understanding languages and image recognition.

The terms "deep learning" and "machine learning" tend to be used interchangeably in AI conversations, but that is not entirely correct.

Machine Learning (ML)

Machine learning (ML) is a subset of AI that allows computers to automatically learn and improve their performance without being programmed. Rather than carrying out pre‑programmed instructions, ML systems analyse patterns in information to draw conclusions or make predictions.

Imagine training a computer to tell dogs and cats apart in photographs. It begins with collecting thousands of labelled photographs with some of dogs and some of cats. The ML programme examines those photographs to find distinguishing characteristics like ear shape or snout length. The model improves with more information presented to it. Once trained, the model can predict if a new photograph is of a cat or a dog based on what it has been taught.

Machine learning methods come in three types. Supervised learning constructs models using marked information, such as predicting house prices using house characteristics like location, size and years of construction. Unsupervised learning identifies patterns in unmarked information, such as classifying clients with comparable purchasing patterns. Reinforcement learning, whereby models can learn by trial and error, is used in self‑driving cars and gaming AI.

Machine learning drives several real‑world applications. Email providers filter out junk mail with machine learning and Netflix recommends content to watch based on viewing histories. Banks also utilise ML algorithms to identify fraudulent transactions based on unusual spending behaviours.

Deep Learning (DL)

Deep learning is a type of machine learning that mimics human learning. It employs multilayer artificial neural networks to process information and make intelligent decisions.

Deep learning processes information in layers of nodes that mimic those in the human brain. Each of those layers will identify different information features. For example, in image recognition, the first layer can recognise edges, the second can recognise shapes and later layers can recognise more intricate things like face structures.

For instance, consider facial recognition. A deep learning algorithm is trained on a large number of facial pictures. It learns to recognise general patterns like curves and lines to begin with. As it keeps processing more information, it learns to recognise more detailed patterns like eyes, noses and later full faces. This hierarchical learning mechanism enables deep learning to be extremely effective at processing complicated tasks.

Deep learning thrives in applications that demand massive amounts of information and complex pattern recognition. It drives technology such as voice assistants (e.g., Siri and Alexa), translation software and self‑driving cars that can recognise and interpret their environments.

Deep learning is frequently applied in industries such as healthcare, finance and entertainment due to its capacity to handle big datasets and identify intricate patterns. Its capacity to simulate human cognition enables it to surpass traditional machine learning algorithms in image recognition, speech processing and natural language processing.

Also Read: AI Agents Explained: How They Differ from AI Assistants and Bots

Difference Between AI, Deep Learning & Machine Learning

ML and DL both belong to AI but differ in their learning and information processing approaches. ML models make use of formalised information, whereby human beings determine important attributes that will be learnt by the system. For example, house prices forecasting may demand inputting information on location, size and years of construction. ML models are more direct, quicker to train and function effectively with smaller datasets and thus best utilised in applications such as spam detection and sales forecasting.

Deep learning uses layered neural networks to automatically identify patterns in raw data. It is best used in complicated operations like facial recognition, voice assistants and translation. DL models may be more efficient and accurate at such operations but require much more datasets and processing power than ML models.