What are AI, Machine Learning and Deep Learning? What are the differences among these three terms?

In today’s technological era, the terms AI, Machine Learning, and Deep Learning are increasingly common. Many people even use them interchangeably, but in reality, these are three closely related yet distinct concepts.

For example, when Google’s AlphaGo defeated Go champion Lee Sedol in 2016, the media alternated between using the terms AI, machine learning, and deep learning to describe this victory. In fact, AI, machine learning, and deep learning all contributed to AlphaGo’s success, but they are not the same thing.

This article will help you clearly understand the differences between AI, Machine Learning, and Deep Learning, as well as their relationships. Let’s explore the details together with INVIAI!

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is a broad field of computer science focused on creating systems that can simulate human intelligence and cognitive functions.

In other words, AI encompasses all techniques that enable computers to perform tasks that normally require human intelligence, such as problem-solving, decision-making, environmental perception, language understanding, and more. AI is not limited to data-driven learning methods but also includes rule-based or knowledge-based systems programmed by humans.

In practice, AI systems can be designed in various ways: rule-based, expert knowledge-based, or data-driven with self-learning capabilities. We generally classify AI into two main categories:

• Narrow AI (Weak AI): Artificial intelligence with a limited scope, proficient in a specific task (e.g., playing chess, facial recognition). Most AI systems today fall into this category.
• General AI (Strong AI): Artificial intelligence capable of understanding and performing any intellectual task that a human can do. This remains a future goal and does not yet exist in reality.

>>> Click to learn more about: What is AI?

What is Machine Learning?

Machine Learning (ML) is a subset of AI focused on developing algorithms and statistical models that allow computers to learn from data and gradually improve accuracy without explicit step-by-step programming. Instead of humans writing all instructions, ML algorithms analyze input data to extract patterns and make predictions or decisions when encountering new data.

A classic definition by Arthur Samuel in 1959 describes Machine Learning as “the field of study that gives computers the ability to learn without being explicitly programmed.” ML algorithms are generally divided into several main types:

• Supervised learning: Models trained on labeled datasets (e.g., predicting house prices from past data with known values).
• Unsupervised learning: Models that find structures or groups in unlabeled data (e.g., clustering customers into groups with similar behaviors).
• Reinforcement learning: Models that interact with the environment and learn behaviors through rewards or penalties (e.g., AI improving game skills through repeated play).

It is important to note that not all AI systems are Machine Learning, but all Machine Learning algorithms fall under AI. AI is broader than ML – similar to how all squares are rectangles, but not all rectangles are squares.

Many traditional AI systems, such as chess programs based on search algorithms, do not “learn” from data but follow rules programmed by humans – these are considered AI but not ML.

What is Deep Learning?

Deep Learning (DL) is a specialized branch of Machine Learning that uses multi-layer artificial neural networks to learn from data.

The term “deep” refers to networks with many hidden layers (usually more than three) – this multi-layered structure allows the model to learn complex features at high levels of abstraction. Deep Learning is inspired by the human brain’s functioning, with artificial “neurons” connected to mimic biological neural networks.

What makes Deep Learning powerful is its ability to automatically extract features from raw data: deep learning models can discover important patterns and characteristics without requiring humans to provide predefined input features. This makes Deep Learning especially effective for complex data types such as images, audio, and natural language – where manually defining useful features is very challenging.

However, to achieve high performance, deep learning models typically require very large datasets and powerful computational resources (GPUs, TPUs, etc.) for training. In return, with sufficient data and computing power, Deep Learning can excel in tasks like image recognition, speech recognition, machine translation, gaming, and even match or surpass human-level performance in some areas.

The Relationship Between AI, Machine Learning, and Deep Learning

As mentioned, Deep Learning ⊂ Machine Learning ⊂ AI: AI is the broadest field, Machine Learning is a subset of AI, and Deep Learning is a part of Machine Learning. This means all deep learning algorithms are machine learning algorithms, and all machine learning methods belong to AI.

However, the reverse is not always true – not all AI systems use machine learning, and machine learning itself is just one of many approaches to realizing AI.

For example, an AI system might rely solely on a set of human-programmed rules (without machine learning), such as an AI program classifying fruits based on barcode labels. Conversely, when problems become more complex and data more abundant, machine learning and deep learning methods are needed for better results.

Main Differences Between AI, Machine Learning, and Deep Learning

Although they have a hierarchical relationship as above, AI, ML, and DL have clear differences in scope, operation, and technical requirements:

Scope

AI is a general concept that includes all methods enabling machines to simulate intelligence (both rule-based and data-driven). Machine Learning narrows this down to AI methods based on machines learning from data. Deep Learning narrows further – it is a subset of ML that uses multi-layer neural networks to learn, so DL is both ML and AI.

Learning Method and Human Intervention

In traditional machine learning, human involvement is significant – for example, engineers must select and extract suitable features from data to feed into the learning algorithms.

In contrast, deep learning largely automates feature extraction; multi-layer neural networks can learn important features at various abstraction levels from raw data, reducing dependence on human experts.

Simply put, for complex problems (e.g., image recognition), a traditional ML model might require engineers to provide features like shapes, colors, edges to identify objects, while a DL model can “see” the image and automatically learn those features.

Data Requirements

Machine learning algorithms usually perform well even with moderate or small datasets, provided the data is high quality and features are clear. Conversely, deep learning models often require very large datasets (millions of samples) to realize their advantages.

For instance, a deep learning-based speech recognition system may need to be trained on tens of thousands of hours of speech to achieve high accuracy. This makes deep learning especially suitable in the “big data” era, where over 80% of organizational data is unstructured (such as text and images) and requires deep learning methods for effective processing.

Computing Infrastructure Requirements

Because deep learning models are often very complex and must process huge amounts of data, training them demands high computational power. Traditional ML algorithms can run well on CPUs, even personal computers, while deep learning almost always requires GPU (or TPU, FPGA) support to accelerate parallel matrix computations.

Training deep learning models also takes significantly longer than simple ML models, sometimes requiring hours or days depending on data volume.

Performance and Accuracy

The ultimate goal of AI is to successfully solve the given task, not necessarily through learning from data. Meanwhile, machine learning aims to optimize prediction accuracy by learning from training datasets, accepting a trade-off in model “explainability.”

Deep learning can achieve very high accuracy, surpassing traditional ML methods if provided with sufficient data and computing power – many recognition tasks using deep learning have set record accuracies but come with high computational costs.

Suitable Applications

Machine Learning is often used for data analysis and prediction applications where data volume is moderate and computational demands are not too high. For example, ML is useful for predicting customer behavior, credit risk analysis, fraud detection, or spam filtering – tasks involving structured data that is not overly complex.

Conversely, Deep Learning excels in complex tasks requiring high accuracy and processing unstructured data such as image recognition, speech recognition, natural language processing, autonomous driving, etc. These fields often involve massive datasets and require models to “recognize” subtle features, which multi-layer neural networks handle well.

Practical Applications of AI, ML, and Deep Learning

To better understand the differences, let’s look at some typical application examples of each technology:

Artificial Intelligence (AI): AI is present in many smart systems around us, from predictive algorithms for user demand on Google, ride-hailing apps like Uber/Grab optimizing routes, to autopilot systems on commercial aircraft. Programs like Deep Blue playing chess or AlphaGo playing Go are also considered AI.

Note that some AI systems may not use machine learning, for example, AI controlling NPCs (non-player characters) in games might rely solely on fixed rules programmed by developers.

Machine Learning: Machine learning is widely applied across many fields. Typical examples include intelligent virtual assistants like Siri, Alexa, Google Assistant – they learn from user data to understand commands and respond appropriately. Email spam filters and malware detection software also use ML algorithms to identify junk mail based on learned email patterns.

Additionally, traditional ML is used in business forecasting, financial risk analysis, and many recommendation systems such as movie suggestions on Netflix or product recommendations on Amazon.

Deep Learning: Deep learning underpins recent breakthroughs in AI. Systems for speech recognition (e.g., converting speech to text, virtual assistants), image recognition (detecting objects, faces in photos), and self-driving cars analyzing real-time video all use deep learning to achieve high accuracy.

Deep Learning is also the foundational technology behind prominent generative AI models today, such as GPT-4 powering ChatGPT. These massive foundation models are trained on enormous text or image datasets, enabling them to generate new content and perform diverse tasks. In practice, applying powerful deep learning models like generative AI can accelerate value creation many times over compared to traditional methods.

In summary, AI, Machine Learning, and Deep Learning are not synonymous terms, but have a hierarchical relationship and clear distinctions.

AI is the broad picture of machine intelligence, within which Machine Learning and Deep Learning are important approaches to realize that goal. Machine Learning enables machines to learn from data and improve gradually, while Deep Learning goes deeper with multi-layer neural networks that can achieve superior power when large datasets are available.

Understanding the differences between AI, ML, and DL not only helps us use the correct terminology but also aids in choosing the right technological solutions: sometimes a simple machine learning model is sufficient to solve a problem, but complex problems require deep learning. In the future, as data grows and demands increase, deep learning is expected to continue playing a key role in driving new advances in the field of AI.