AI Tech learning guide

The Difference Between AI, Machine Learning, and Deep Learning

Artificial intelligence, machine learning, and deep learning are closely related, but they do not mean the same thing.

Artificial intelligence is the broadest category. It includes computer systems designed to perform tasks that appear to require intelligence.

Machine learning is one way to build artificial intelligence. Instead of programming every rule directly, developers train a model to recognize patterns in data.

Deep learning is a specialized type of machine learning that uses neural networks containing multiple processing layers.

The simplest way to remember the relationship is:

Artificial intelligence contains machine learning, and machine learning contains deep learning.

Or, written as a hierarchy:

Artificial Intelligence → Machine Learning → Deep Learning

Generative AI, large language models, AI personas, and many modern AI agents are built primarily with deep-learning methods, but they remain part of the much larger field of artificial intelligence.

This guide explains how the categories fit together, how each technology works, and why the differences still matter in 2026.

AI vs. Machine Learning vs. Deep Learning at a Glance

Term	Basic meaning	How it works	Common examples
Artificial intelligence	The broad field of creating machines that perform intelligent tasks	May use rules, search, logic, optimization, machine learning, or several methods together	Route planning, game-playing systems, expert systems, virtual assistants
Machine learning	A subset of AI in which models learn patterns from data	Algorithms adjust a model using examples rather than relying only on hard-coded rules	Spam filters, recommendations, fraud detection, demand forecasting
Deep learning	A subset of machine learning using multilayer neural networks	Networks learn increasingly complex representations from large amounts of data	Image recognition, speech recognition, LLMs, generative images
Generative AI	AI that creates new content	Often uses deep-learning models trained on text, images, audio, video, or code	Chat assistants, image generators, coding tools
Large language models	Deep-learning models trained to process and generate language	Usually use transformer neural networks and token prediction	GPT, Claude, Gemini, Grok, Llama
AI agents	Systems that pursue goals using models and tools	May plan, call tools, inspect results, and adjust their actions	Research agents, coding agents, workflow assistants

IBM describes the relationship as a set of nested categories: AI is the broadest field, machine learning is a subset of AI, and deep learning is a subset of machine learning built around multilayer neural networks.

A Simple Analogy: Transportation

Imagine that transportation represents artificial intelligence.

Transportation is the broad goal of moving people or objects. It includes cars, trains, airplanes, bicycles, ships, and walking.

Within transportation, motor vehicles represent machine learning. They are one important way of achieving the larger goal, but they are not the only way.

Within motor vehicles, electric cars represent deep learning. They are a more specialized category that uses a particular approach.

The relationship would be:

• Every electric car is a motor vehicle.
• Every motor vehicle is a form of transportation.
• Not every form of transportation is a motor vehicle.
• Not every motor vehicle is an electric car.

The same logic applies to AI:

• Every deep-learning system is a machine-learning system.
• Every machine-learning system is an AI system.
• Not every AI system uses machine learning.
• Not every machine-learning system uses deep learning.

This distinction is important because people often use “AI” as a label for every modern computer system, even when the system is using a much narrower technique.

What Is Artificial Intelligence?

Artificial intelligence is the broad field of developing computer systems that can perform sophisticated tasks associated with perception, reasoning, prediction, language, learning, planning, or decision-making.

Examples include systems that can:

• Recognize objects in photographs
• Translate languages
• Recommend products
• Plan a route
• Detect fraudulent transactions
• Play strategic games
• Generate computer code
• Interpret spoken instructions
• Control a robot
• Answer questions
• Create images or video
• Coordinate a multi-step workflow

Google’s machine-learning glossary defines AI broadly as a nonhuman program or model capable of solving sophisticated tasks, while NIST treats AI as a field that includes machine learning, autonomous systems, evaluation, hardware, and responsible-AI practices.

Artificial intelligence is therefore an objective or field, not one single technology.

An AI system might use:

• Handwritten rules
• Logical reasoning
• Search algorithms
• Mathematical optimization
• Probability
• Machine learning
• Neural networks
• Computer vision
• Language models
• Robotics
• Several of these methods together

Does All AI Use Machine Learning?

No.

Some AI systems rely on rules written directly by human developers.

Consider a simple diagnostic system:

1. If the engine temperature exceeds a specified value, display an overheating warning.
2. If oil pressure falls below a specified value, display a low-pressure warning.
3. If both happen together, recommend that the driver stop immediately.

The system makes decisions that may appear intelligent, but it does not need to learn from data. Its rules were programmed explicitly.

Other non-machine-learning approaches include:

• Decision trees written by experts
• Search algorithms
• Scheduling systems
• Game-playing logic
• Constraint solvers
• Symbolic reasoning
• Optimization systems
• Knowledge bases
• Traditional expert systems

These methods remain useful when the rules are well understood, the problem is stable, and predictable behavior is more important than adaptation.

Traditional Rule-Based AI

Early artificial intelligence frequently relied on symbolic rules.

A system might represent knowledge in statements such as:

If an animal has feathers, lays eggs, and has a beak, it is probably a bird.

A larger expert system could contain thousands of rules supplied by specialists.

Rule-based AI can work well when:

• The rules can be clearly defined.
• The number of exceptions is manageable.
• Decisions must be explained directly.
• Data is limited.
• Behavior must be highly predictable.

It becomes harder to maintain when:

• The problem contains millions of possible variations.
• Human experts cannot describe every relevant pattern.
• The environment changes frequently.
• Inputs include unstructured images, language, or audio.
• New rules conflict with older rules.

Machine learning developed partly because many important problems are too complicated to solve through hand-written rules alone.

What Is Machine Learning?

Machine learning is a subset of artificial intelligence in which software is trained to make useful predictions or generate outputs based on patterns in data.

Instead of programming every decision rule, developers provide examples and a learning process.

Google defines machine learning as the process of training a software model to make useful predictions or generate content from data. IBM similarly describes machine learning as the branch of AI in which algorithms learn patterns from training data and use them to make inferences about new data.

Suppose a company wants to identify spam email.

A traditional rule-based system might contain instructions such as:

• Mark the email as spam if the subject contains “free money.”
• Mark it as spam if it contains more than five links.
• Mark it as spam if the sender is on a blocked list.

These rules may catch some spam, but senders can change their wording.

A machine-learning system is trained using many examples labeled:

• Spam
• Not spam

The model examines patterns in the examples and learns which combinations of words, links, senders, formatting, and other features are associated with spam.

When a new message arrives, the model predicts whether it belongs to the spam category.

A Simple Machine-Learning Analogy

Imagine teaching someone to identify apples.

One approach is to provide a detailed rulebook:

• Apples are usually round.
• They can be red, green, or yellow.
• They grow on trees.
• They often have a stem.
• Their skin is relatively smooth.

That resembles traditional programming.

Another approach is to show the learner thousands of labeled photographs:

• Apple
• Not an apple
• Apple
• Pear
• Apple
• Tomato

Over time, the learner recognizes visual patterns that distinguish apples from other objects.

That resembles machine learning.

The model does not necessarily learn the same written rules a human would use. It develops mathematical relationships that help it make accurate predictions.

The Core Parts of Machine Learning

Most machine-learning projects involve several basic elements.

Data

Data provides examples from which the model can learn.

It might include:

• Images
• Numbers
• Customer records
• Sensor readings
• Audio recordings
• Written documents
• Medical scans
• Transactions
• Product interactions
• Weather observations

The quality and relevance of the data strongly influence the quality of the resulting model.

Features

Features are the measurable characteristics used as inputs.

For a house-price model, features might include:

• Square footage
• Number of bedrooms
• Neighborhood
• Lot size
• Age of the house
• Distance from a city center

For an email filter, features might include words, sender information, links, attachments, and message structure.

Labels

A label is the outcome a supervised model is trained to predict.

Examples include:

• Spam or not spam
• Fraudulent or legitimate
• Cat, dog, or bird
• House sale price
• Customer likely to cancel or remain

Not every form of machine learning uses labels.

Algorithm

The algorithm is the mathematical procedure used to train the model.

Examples include:

• Linear regression
• Logistic regression
• Decision trees
• Random forests
• Support-vector machines
• Neural networks
• Clustering algorithms

Model

The model is the learned mathematical representation produced through training.

The algorithm is the learning method. The model is the result that makes predictions.

Training

Training is the process of adjusting the model using data.

The system makes predictions, compares them with expected results, measures error, and modifies the model to reduce similar errors.

Inference

Inference is the use of a trained model on new information.

Training might teach the model what fraudulent transactions look like. Inference occurs when it evaluates a new transaction.

Machine Learning vs. Traditional Programming

Traditional programming	Machine learning
Developers write explicit rules	The model learns patterns from data
Best when logic is known	Best when patterns are difficult to describe manually
Usually produces predictable behavior	Produces probabilistic predictions
Changes require rule revisions	Changes may require new data or retraining
Easier to trace step by step	Can be harder to interpret
Often works with structured inputs	Can process highly complex data
Does not require a training dataset	Requires relevant training and evaluation data

Traditional programming follows a structure like:

Rules + Input → Output

Machine learning follows a structure like:

Examples + Expected Outputs → Learned Model

The trained model can then be used as:

New Input + Learned Model → Prediction

Neither approach is universally superior.

A tax calculator may be better implemented with explicit rules. A photo-recognition system may be better implemented with machine learning.

Many real applications use both.

Major Types of Machine Learning

Machine learning includes several major training approaches.

Google’s current introductory material categorizes machine-learning systems into supervised, unsupervised, reinforcement, and generative approaches, each of which learns through a different kind of signal or objective.

Supervised Learning

Supervised learning uses labeled examples.

The model is shown both the input and the expected answer.

For example:

Input	Label
Email content	Spam
House characteristics	Sale price
Medical image	Condition detected
Customer activity	Likely to cancel
Photograph	Dog

The goal is to learn a relationship that generalizes to new cases.

Supervised learning commonly handles two types of problems.

Classification

Classification predicts a category.

Examples include:

• Spam or not spam
• Fraudulent or legitimate
• Positive, negative, or neutral
• Cat, dog, or bird
• Customer likely or unlikely to leave

Regression

Regression predicts a numerical value.

Examples include:

• House price
• Energy demand
• Delivery time
• Temperature
• Expected sales

Google’s machine-learning materials describe supervised learning as training on labeled data, with regression used for numerical predictions and classification used for categories.

Unsupervised Learning

Unsupervised learning uses data without predetermined labels.

The system looks for structure, similarities, patterns, or groupings.

A retailer might provide customer purchasing records without defining customer types in advance. An unsupervised model could identify groups such as:

• Frequent budget shoppers
• Occasional premium customers
• Seasonal buyers
• Customers focused on one product category

The system discovers groupings based on mathematical similarities.

Common unsupervised-learning tasks include:

• Clustering
• Anomaly detection
• Dimensionality reduction
• Pattern discovery
• Data segmentation

Google explains that supervised learning is appropriate when the desired value or category is known, while unsupervised learning is useful for discovering possible groupings within unlabeled data.

Semi-Supervised Learning

Semi-supervised learning combines a smaller amount of labeled data with a larger amount of unlabeled data.

Labeling data can be expensive.

For example, a hospital may have millions of medical images but only a limited number reviewed and labeled by specialists.

A semi-supervised approach attempts to benefit from both:

• The reliability of labeled examples
• The scale of unlabeled examples

Self-Supervised Learning

Self-supervised learning creates training signals from the data itself.

A language model can hide or predict parts of a sentence. An image system can learn by predicting missing sections or comparing different versions of the same image.

This allows models to learn from enormous datasets without requiring people to label every example manually.

Many modern foundation models and large language models rely heavily on self-supervised pretraining.

Reinforcement Learning

Reinforcement learning trains a system through rewards and penalties.

An agent takes an action, observes the result, and receives a signal indicating whether the action was helpful.

Examples include:

• Learning to play a game
• Controlling a robot
• Managing energy use
• Optimizing a sequence of decisions
• Navigating a simulated environment

A reinforcement-learning system must balance:

• Exploration: trying new actions
• Exploitation: repeating actions that have worked

The method is especially relevant when actions influence future conditions rather than producing one immediate prediction.

What Is Deep Learning?

Deep learning is a subset of machine learning based on neural networks containing multiple processing layers.

These systems are often called deep neural networks.

IBM defines deep learning as machine learning driven by multilayer neural networks and notes that these models power many leading applications in computer vision, generative AI, robotics, and other areas.

The word deep refers to the number of layers through which information passes.

A simple neural network may contain:

• An input layer
• One hidden layer
• An output layer

A deep neural network contains several or many hidden layers.

Each layer transforms the information and passes it to the next layer.

What Is a Neural Network?

A neural network is a machine-learning model made of connected mathematical units commonly called nodes or artificial neurons.

The name was inspired loosely by biological brains, but artificial neural networks are simplified mathematical systems rather than simulations of complete human thought.

Google describes neural networks as model architectures designed to identify nonlinear patterns in data. Hidden layers between the input and output allow the network to learn increasingly useful combinations of features.

A neural network might receive inputs such as:

• Pixel values from an image
• Words in a sentence
• Measurements from sensors
• Characteristics of a customer
• Samples from an audio recording

Connections between nodes have numerical weights.

During training, those weights are adjusted to improve the model’s predictions.

A Simple Neural-Network Analogy

Imagine a committee reviewing a photograph.

The first group looks for basic features:

• Edges
• Curves
• Colors
• Brightness

The next group combines those observations:

• Fur-like textures
• Circular shapes
• Eye-like patterns
• Ear-like shapes

A later group evaluates higher-level possibilities:

• Animal face
• Dog
• Particular breed

The final group produces a prediction.

Deep neural networks work differently in mathematical detail, but this analogy illustrates how several layers can transform simple inputs into increasingly complex representations.

How Deep Learning Recognizes an Image

Suppose a neural network is trained to recognize dogs.

The input layer receives pixel information.

Early layers may respond to:

• Lines
• Edges
• Color changes
• Simple textures

Middle layers may respond to:

• Eyes
• Fur
• Ears
• Snouts
• Body shapes

Later layers combine those patterns into a more complete representation of a dog.

Human developers do not necessarily specify every feature manually. The network learns useful representations during training.

This ability to learn features automatically is one of deep learning’s major advantages.

Machine Learning vs. Deep Learning

Machine learning	Deep learning
Broad category of systems that learn from data	Specialized category using multilayer neural networks
Includes regression, decision trees, random forests, clustering, and neural networks	Primarily relies on deep neural networks
Can work well with smaller structured datasets	Often benefits from large datasets
May require manually designed features	Can learn useful representations automatically
Often easier to interpret	Frequently more difficult to explain
Can train with modest computing resources	May require specialized processors and substantial computation
Strong for tables and structured business data	Strong for language, images, audio, and video
May be faster and cheaper	Can achieve greater performance on complex perception tasks

Deep learning is not always better than other machine-learning methods.

For a modest spreadsheet containing customer age, purchase totals, region, and account status, a decision tree or regression model may be sufficient.

For recognizing objects across millions of varied images, a deep neural network may be more appropriate.

Why Did Deep Learning Become So Important?

Deep learning became more practical because several conditions came together:

• Larger digital datasets
• More powerful processors
• Specialized graphics-processing hardware
• Improved training methods
• Better neural-network architectures
• Cloud computing
• Open research and software frameworks

Deep learning proved especially effective for data that is difficult to represent through manually selected features, including:

• Images
• Speech
• Natural language
• Video
• Complex sensor readings
• Biological sequences

The same broad approach eventually enabled foundation models capable of performing many tasks rather than one narrowly defined prediction.

Common Deep-Learning Architectures

Different neural-network designs are useful for different kinds of information.

Feedforward Neural Networks

Information moves from input toward output without a recurring loop.

These networks can be used for classification, regression, and general pattern recognition.

Convolutional Neural Networks

Convolutional neural networks, often called CNNs, became especially important in computer vision.

They can detect visual patterns across different parts of an image.

Applications include:

• Object recognition
• Medical-image analysis
• Facial analysis
• Manufacturing inspection
• Satellite imagery
• Document processing

Recurrent Neural Networks

Recurrent neural networks, or RNNs, were designed for sequences.

They were used for:

• Language
• Speech
• Time-series data
• Handwriting
• Sequential prediction

Variants such as long short-term memory networks helped models maintain information across longer sequences.

Transformers

Transformers use attention mechanisms to evaluate relationships among parts of an input.

They became especially important for language and now support many image, audio, video, and multimodal systems.

Modern large language models are generally built with transformer-based deep neural networks.

Read Large Language Models Explained Simply for a fuller explanation.

Generative Adversarial Networks

Generative adversarial networks, or GANs, use two competing neural networks.

One produces content, while the other evaluates whether it resembles real training examples.

GANs have been used for:

• Image generation
• Image restoration
• Synthetic data
• Style transfer
• Visual simulation

Diffusion Models

Diffusion models learn to generate content by reversing a process that adds noise to data.

They became a major foundation for modern image generation and are also used in video, audio, and scientific applications.

Read What Is Generative AI? for more about diffusion and content-generation systems.

Everyday Examples of Artificial Intelligence

Not every use of AI involves deep learning.

Examples of the broader AI category include:

Route Planning

Navigation software evaluates road networks, distance, traffic, and constraints to find a route.

It may combine search, optimization, current data, and machine-learning predictions.

Scheduling

A scheduling system may assign workers, classrooms, aircraft, or deliveries while satisfying large numbers of constraints.

Game-Playing Systems

A game system may search possible moves, estimate outcomes, and select a strategy.

Modern systems may combine search with machine learning or reinforcement learning.

Expert Systems

An expert system applies a collection of rules to help classify a situation or recommend an action.

Robotics

A robot may combine planning, control systems, computer vision, sensors, and machine learning.

These examples show why AI is larger than machine learning.

Everyday Examples of Machine Learning

Email Spam Filtering

The model learns patterns associated with unwanted messages.

Product Recommendations

Recommendation models analyze user activity, product relationships, and patterns among similar users.

Google describes collaborative filtering as learning relationships among users and items to generate recommendations, including items a user may not have encountered independently.

Fraud Detection

A model identifies transactions that differ from normal patterns.

Demand Forecasting

Businesses use historical sales, seasons, promotions, and outside conditions to estimate future demand.

Credit-Risk Prediction

Models estimate the likelihood that a loan will be repaid.

Such systems require careful evaluation because mistakes or biased data can affect access to important opportunities.

Predictive Maintenance

Sensors collect temperature, vibration, pressure, and performance data from equipment. Models identify signs that a component may fail.

Customer Segmentation

Unsupervised models group customers according to behavior or characteristics.

Everyday Examples of Deep Learning

Image Recognition

Deep neural networks can identify objects, scenes, handwriting, defects, and visual patterns.

Speech Recognition

Audio models convert speech into text and distinguish patterns across voices, accents, noise levels, and speaking styles.

Language Translation

Transformer-based networks analyze relationships between languages and generate translated text.

Large Language Models

LLMs use deep-learning architectures to interpret and generate language.

Image Generation

Diffusion and related deep-learning models create images from written prompts or other visual inputs.

Voice Generation

Deep-learning systems produce speech, narration, sound effects, or cloned voices.

Autonomous-System Perception

Vehicles and robots use deep-learning models to interpret cameras, sensors, objects, roads, and surrounding movement.

Where Does Generative AI Fit?

Generative AI is part of artificial intelligence.

Most leading generative AI systems are also built with machine learning and deep learning.

The hierarchy usually looks like this:

Artificial Intelligence → Machine Learning → Deep Learning → Generative Models → Large Language Models or Diffusion Models

Generative AI produces new content, including:

• Text
• Images
• Video
• Audio
• Music
• Computer code

NIST defines generative AI as a class of AI models that learns characteristics of input data and produces derived synthetic content. Google likewise describes generative models as capable of responding to prompts with text, code, images, video, and other outputs.

Not every machine-learning system is generative.

A fraud model that returns a risk score is predictive. A language model that drafts an explanation is generative.

Predictive AI vs. Generative AI

Predictive AI	Generative AI
Estimates an outcome	Produces new content
Classifies, ranks, or forecasts	Writes, draws, speaks, composes, or codes
Often returns a score or category	Often returns text, images, audio, or video
Example: Will this customer leave?	Example: Draft a retention message
Example: Is this transaction fraudulent?	Example: Explain why it appears suspicious
Example: What will sales be next month?	Example: Write a sales forecast report

The same product may contain both.

A customer-support platform might use predictive machine learning to classify a request and generative AI to draft the response.

Where Do Large Language Models Fit?

A large language model is generally:

• Artificial intelligence
• Machine learning
• Deep learning
• Generative AI
• A language-focused foundation model

This means an LLM belongs to several nested categories at the same time.

A model such as GPT, Claude, Gemini, Grok, or Llama is not a separate alternative to machine learning. It is an advanced application of machine learning and deep learning.

The hierarchy can be written as:

AI → Machine Learning → Deep Learning → Transformer Models → Large Language Models

LLMs are trained to recognize and generate patterns in tokens. They may also be adapted to work with images, audio, tools, and other forms of information.

Where Do AI Personas Fit?

An AI persona is usually not a new category of learning algorithm.

It is an experience built around one or more AI models.

A persona may combine:

• An LLM
• System instructions
• A name and visual identity
• A subject focus
• Curated knowledge
• Communication rules
• Search or retrieval
• Specialized tools
• Safety boundaries

The model supplies the broad language capability. The persona design determines how that capability is presented and focused.

For example, one underlying model could support:

• A physics tutor
• A history guide
• A human-evolution explorer
• A career coach
• A central network guide

Read What Are AI Personas?.

Where Do AI Agents Fit?

Agentic AI is also not necessarily a separate learning method.

An AI agent is a system that uses models, tools, instructions, memory, and permissions to pursue a goal.

An agent may use an LLM to:

• Interpret a request
• Create a plan
• Choose a tool
• Evaluate a result
• Determine the next step
• Explain what it did

The agentic system sits above the model.

A useful hierarchy is:

Artificial Intelligence → Machine Learning → Deep Learning → Large Language Model → Agentic Application

The agent may also use traditional software, search, databases, rules, and external services.

Read What Is Agentic AI?.

AI Model vs. AI System

An AI model is one mathematical component.

An AI system is the complete product or workflow containing that model.

A system may include:

• One or more models
• User-interface software
• Business rules
• Databases
• Search
• Uploaded documents
• User accounts
• Safety filters
• Human approval
• Logging
• External tools

A chatbot is not simply an LLM. A self-driving vehicle is not simply a vision model. A recommendation platform is not simply one algorithm.

This distinction matters because the behavior of a complete product depends on far more than the model alone.

NIST defines an AI model as a component of an information system that uses computational, statistical, or machine-learning techniques to produce outputs from inputs.

Narrow AI vs. General AI

Nearly all practical AI systems available today are forms of narrow AI.

Narrow AI is built to perform particular tasks, even when those tasks are broad enough to include writing, analysis, coding, and image interpretation.

Examples include:

• Recommendation systems
• Fraud detectors
• Language models
• Image generators
• Navigation systems
• AI tutors
• Coding agents

Artificial general intelligence, commonly abbreviated AGI, would refer to a much more general form of machine intelligence capable of transferring understanding and competence across a wide range of situations at or beyond human levels.

There is no universally accepted test or threshold that establishes when AGI has been achieved.

Claims about AGI should therefore be evaluated according to specific capabilities and evidence rather than marketing language.

Algorithms, Models, and Applications

These terms are often confused.

Algorithm

An algorithm is a procedure for solving a problem or performing a computation.

Machine-Learning Algorithm

A machine-learning algorithm is a procedure used to train a model from data.

Model

A model is the learned mathematical structure that produces predictions or outputs.

Architecture

An architecture is the general design of a model.

Examples include:

• Decision tree
• Neural network
• Transformer
• Convolutional network

Application

An application is the user-facing product or system.

For example:

• Transformer: architecture
• GPT-family model: trained model family
• Chat assistant: application
• Named educational guide: persona-based experience

Keeping these levels separate prevents many common misunderstandings.

Training vs. Inference

All three fields—AI, machine learning, and deep learning—often involve two separate phases.

Training

Training creates or adjusts the model.

The system:

1. Receives examples.
2. Produces predictions.
3. Measures errors.
4. Adjusts internal values.
5. Repeats the process.

Google describes loss as a numerical measure of how wrong a model’s predictions are. Training aims to adjust the model so that loss decreases on relevant examples.

Inference

Inference uses the trained model.

Examples include:

• Classifying a new email
• Recognizing a new photograph
• Predicting a house price
• Generating an answer
• Detecting an unusual transaction

Training may require substantial data and computation. Inference is what users experience when they interact with the deployed system.

Features and Feature Engineering

Traditional machine-learning systems often depend on feature engineering.

Feature engineering means selecting or transforming the information the model should use.

For a credit-risk model, developers might create features such as:

• Debt-to-income ratio
• Payment history
• Length of account history
• Number of recent applications

For a weather model, features might include:

• Temperature
• Humidity
• Air pressure
• Wind speed
• Time of year

The choice of features can strongly affect performance.

Deep learning can reduce the need for manual feature engineering because neural networks can learn useful representations directly from raw or lightly processed data.

That advantage comes with trade-offs involving computing requirements, explainability, and the amount of training data needed.

Parameters and Hyperparameters

A parameter is a value learned during training.

Examples include:

• A regression coefficient
• A decision-tree split
• A neural-network weight

A hyperparameter is chosen by developers before or during the training process.

Examples include:

• Learning rate
• Tree depth
• Number of layers
• Batch size
• Number of training iterations

Training adjusts parameters. Developers or automated tuning processes select hyperparameters.

Overfitting and Generalization

A model should not merely memorize its training examples.

It should generalize to new cases.

Overfitting occurs when a model performs well on training data but poorly on new data.

Imagine a student who memorizes the exact answers on one practice examination but cannot solve the same kinds of problems when the wording changes.

That resembles overfitting.

Developers reduce overfitting through techniques such as:

• Separate training and test data
• Cross-validation
• Regularization
• Simpler models
• More representative data
• Early stopping
• Data augmentation

A more complicated model is not automatically a better model.

How Machine-Learning Models Are Evaluated

Accuracy alone is not always sufficient.

Suppose only one out of every 1,000 financial transactions is fraudulent.

A model that labels every transaction as legitimate would be 99.9 percent accurate—but completely useless for detecting fraud.

Other evaluation measures include:

• Precision
• Recall
• False-positive rate
• False-negative rate
• F1 score
• Area under the ROC curve
• Mean absolute error
• Mean squared error
• Calibration
• Performance across different groups

The correct measure depends on the consequences of different errors.

Google’s classification guidance emphasizes that decision thresholds and evaluation metrics should reflect the relative costs of false positives and false negatives.

The Importance of Training Data

Machine-learning systems learn from examples.

If the examples are incomplete, unrepresentative, outdated, mislabeled, or biased, the resulting model may perform poorly.

Common data problems include:

• Missing values
• Incorrect labels
• Duplicates
• Historical bias
• Uneven representation
• Data collected for a different purpose
• Changes over time
• Privacy violations
• Information leakage
• Imbalanced classes

A technically sophisticated deep-learning model cannot automatically repair a fundamentally unsuitable dataset.

Bias in AI and Machine Learning

Bias can enter at many stages:

• Defining the problem
• Collecting data
• Selecting labels
• Choosing features
• Designing the model
• Setting thresholds
• Interpreting outputs
• Deciding where the system is used

Google’s responsible-AI glossary notes that bias can affect the collection and interpretation of data, model design, and the way people interact with AI systems.

A model can reproduce patterns found in historical data even when those patterns reflect unfair treatment.

Responsible use therefore requires more than removing offensive outputs. It includes testing who benefits, who may be harmed, and whether performance differs across relevant groups.

Explainability

Some AI systems are easier to explain than others.

A small decision tree may show exactly which rule led to an outcome.

A deep neural network containing billions of learned values may be much harder to interpret.

Explainability matters when AI affects:

• Employment
• Credit
• Healthcare
• Education
• Insurance
• Criminal justice
• Public benefits
• Safety-critical systems

NIST identifies explainability and evaluation as important parts of building trustworthy AI systems and helping people understand how AI-supported decisions are produced.

The most accurate model is not always the best choice if no one can meaningfully review its behavior in a high-stakes setting.

AI Risk Is a System Problem

Risk does not come only from the learning algorithm.

It can result from:

• Poorly defined goals
• Weak data
• Incorrect labels
• Model errors
• Unsafe tool access
• Misleading user interfaces
• Excessive automation
• Inadequate monitoring
• Poor human oversight
• Use in an inappropriate setting

NIST’s AI Risk Management Framework treats risk management as an ongoing activity across the design, development, deployment, use, and evaluation of AI systems.

A highly accurate model can still be part of a harmful system if it is used for the wrong purpose or trusted beyond its demonstrated capabilities.

Which Technology Should Be Used?

The best approach depends on the problem.

Use Traditional Rules When:

• The logic is clear.
• Behavior must be predictable.
• Regulations define the decision precisely.
• The number of cases is manageable.
• There is little suitable training data.
• Errors must be traced directly.

Example: calculating sales tax according to a fixed rule.

Use Traditional Machine Learning When:

• The data is structured.
• The outcome is measurable.
• A simpler model performs well.
• Interpretability matters.
• Computing resources are limited.
• The dataset is not enormous.

Example: predicting customer cancellation from account records.

Use Deep Learning When:

• The inputs are images, audio, video, or language.
• Useful features are difficult to define manually.
• Large representative datasets are available.
• High predictive performance justifies the complexity.
• Adequate computing resources are available.

Example: identifying objects in road-camera footage.

Use Generative AI When:

• The task requires creating or transforming content.
• Natural-language interaction is valuable.
• The output can be reviewed.
• Some variation is acceptable.
• The system has appropriate grounding and safeguards.

Example: drafting a report from verified source material.

Use an AI Agent When:

• The goal requires several changing steps.
• Multiple tools must be coordinated.
• The system can be given limited permissions.
• Actions can be monitored or reversed.
• Human approval is available for important decisions.

Example: reviewing documents, preparing a comparison, and creating a draft presentation.

When Not to Use Machine Learning

Machine learning may be unnecessary when:

• A simple formula solves the problem.
• A database lookup returns the correct answer.
• Rules are stable and complete.
• The available data is unreliable.
• Errors cannot be tolerated.
• The outcome cannot be evaluated.
• The model would create more complexity than value.

Google’s guidance for machine-learning projects begins with problem framing: teams should first decide whether the real-world problem is suitable for ML rather than assuming an ML model is always required.

Using conventional software is not technologically backward. It is often the more responsible engineering decision.

Common Misconceptions

Misconception 1: AI and Machine Learning Are the Same

Machine learning is one part of AI.

AI also includes logic, search, planning, optimization, robotics, knowledge systems, and other approaches.

Misconception 2: All Machine Learning Is Deep Learning

Machine learning includes many models that are not deep neural networks.

Regression, decision trees, random forests, and clustering methods can solve important problems without deep learning.

Misconception 3: Deep Learning Is Always Better

Deep learning can be powerful, but it may be more expensive, harder to explain, and unnecessary for simple structured problems.

Misconception 4: AI Learns Like a Human

Machine-learning systems adjust mathematical relationships using data and optimization.

They do not necessarily learn through human understanding, consciousness, experience, or common sense.

Misconception 5: More Data Always Solves the Problem

More low-quality or irrelevant data may reinforce errors.

Representative, lawful, well-labeled, and task-appropriate data matters more than raw volume alone.

Misconception 6: A Highly Accurate Model Cannot Be Biased

Overall accuracy can hide poor performance for smaller or underrepresented groups.

Evaluation must examine relevant subgroups and error types.

Misconception 7: Generative AI Is a Separate Field from Machine Learning

Most modern generative AI is built through machine learning and deep learning.

It is an application category within AI, not a replacement for the hierarchy.

Misconception 8: AI Personas Are New Foundation Models

Most personas are applications built around existing models, instructions, sources, and interfaces.

Misconception 9: An AI Agent Is a Conscious Digital Worker

An agent follows objectives, software instructions, model outputs, and permissions.

Goal-directed behavior does not prove consciousness or independent desire.

Misconception 10: AI Output Is Objective

AI output reflects data, design choices, model behavior, instructions, context, and the goals of the surrounding system.

It should be evaluated rather than accepted automatically.

The State of AI, Machine Learning, and Deep Learning in 2026

By 2026, artificial intelligence was no longer defined only by isolated prediction systems.

The field increasingly included:

• Multimodal foundation models
• Large language models
• Generative image and video systems
• Reasoning-oriented models
• Tool-using agents
• Smaller on-device models
• Robotics
• Scientific models
• Traditional predictive machine learning

The Stanford 2026 AI Index reported rapid improvement across language, image, video, speech, reasoning, robotics, and agentic systems. It also emphasized a “jagged frontier”: models could achieve exceptional results on some advanced benchmarks while still failing surprisingly simple tasks.

This unevenness is important.

A system that performs well on a difficult examination may still:

• Misread a basic diagram
• Misunderstand an unusual instruction
• Select the wrong tool
• Produce a false factual claim
• Fail when the environment changes
• Behave differently across similar prompts

Capability should therefore be assessed task by task.

Foundation Models

A foundation model is trained broadly and can be adapted to many tasks.

Earlier machine-learning models were often built for one purpose:

• Detect spam
• Predict demand
• Identify one type of object
• Estimate credit risk

Foundation models can support:

• Conversation
• Summarization
• Coding
• Image interpretation
• Research
• Document analysis
• Tool use
• Content generation

They are commonly adapted through:

• Prompting
• Retrieval
• Fine-tuning
• Tools
• Application-specific instructions

Foundation models have not eliminated traditional machine learning. Many organizations still use regression, decision trees, forecasting models, recommendation systems, and anomaly detectors because they are efficient and appropriate for structured tasks.

Multimodal AI

Multimodal systems can work with more than one type of information.

A model might process:

• Text
• Images
• Audio
• Video
• Documents
• Charts
• Screenshots

The same system may discuss a photograph, summarize a document, interpret a graph, and generate a written response.

Multimodal systems are usually built with deep learning, although a complete product may combine several models and traditional software components.

Smaller Models and On-Device AI

Not every AI application requires the largest available model.

Smaller models can offer:

• Faster responses
• Lower costs
• Reduced energy use
• Local processing
• Better privacy
• Easier deployment
• Specialization for one task

A phone, laptop, vehicle, or industrial device may run a compact model locally rather than sending every request to a remote data center.

The future of AI is likely to include combinations of large cloud models and smaller local models.

Industry and Transparency

The 2026 Stanford AI Index reported that industry produced more than 90 percent of notable AI models in 2025. It also found that several leading developers had stopped disclosing details such as complete training data, model sizes, training duration, and training code for their most resource-intensive systems.

This creates a challenge for independent evaluation.

A system can be widely used while outside researchers still lack complete information about:

• Training sources
• Data filtering
• Model size
• Energy use
• Safety testing
• Failure rates
• Development methods

Greater capability does not automatically produce greater transparency.

AI Adoption and Investment

The Stanford AI Index found that corporate AI investment more than doubled in 2025, with generative AI receiving a substantial portion of private funding.

Investment does not prove that every AI deployment is valuable.

Organizations still need to measure:

• Accuracy
• Productivity
• Costs
• Error rates
• Security
• User satisfaction
• Legal compliance
• Effects on workers and customers

An impressive demonstration may not translate into a dependable production system.

Why the Distinctions Still Matter

It may seem unnecessary to distinguish AI, machine learning, and deep learning when products increasingly combine all three.

The distinctions remain useful because they reveal:

• How the system was built
• What kind of data it requires
• How predictable it may be
• Which risks are likely
• How difficult it is to explain
• What computing resources it needs
• Whether another approach would be simpler

Calling everything “AI” hides important differences.

A rule-based scheduler, a fraud-prediction model, a language model, and an autonomous robot may all qualify as AI, but they require very different forms of testing and oversight.

A Complete AI Technology Hierarchy

The following structure shows how the major terms relate:

Artificial Intelligence

The broad field of building systems that perform sophisticated tasks.

Includes:

• Rule-based AI
• Search and planning
• Knowledge systems
• Optimization
• Robotics
• Machine learning

Machine Learning

AI systems trained to learn patterns from data.

Includes:

• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Self-supervised learning
• Reinforcement learning
• Neural networks

Deep Learning

Machine learning based on deep neural networks.

Includes:

• Convolutional networks
• Recurrent networks
• Transformers
• Generative adversarial networks
• Diffusion models

Generative AI

AI designed to create new content.

Includes:

• Text generation
• Image generation
• Audio generation
• Video generation
• Code generation

Large Language Models

Deep-learning models designed primarily to interpret and generate language.

Includes model families such as:

• GPT
• Claude
• Gemini
• Grok
• Llama
• Mistral

AI Applications

Products built around one or more models.

Includes:

• Chat assistants
• AI personas
• Search assistants
• Coding tools
• Educational tutors
• AI agents

Frequently Asked Questions

What is the difference between AI and machine learning?

Artificial intelligence is the broad field of building systems that perform sophisticated tasks.

Machine learning is one method of building AI by training models to learn patterns from data.

What is the difference between machine learning and deep learning?

Machine learning includes many methods for learning from data.

Deep learning is a specialized type of machine learning that uses multilayer neural networks.

Is deep learning a type of AI?

Yes.

Deep learning is a type of machine learning, and machine learning is a type of artificial intelligence.

Is all AI machine learning?

No.

Some AI systems rely on rules, search, logic, planning, optimization, or other programmed methods.

Is all machine learning deep learning?

No.

Regression, decision trees, random forests, clustering algorithms, and many other machine-learning techniques do not require deep neural networks.

Is ChatGPT AI, machine learning, or deep learning?

It is part of all three categories.

ChatGPT is an AI application built around models created with machine learning and deep learning. Its underlying language models use transformer-based neural networks.

Are large language models deep learning?

Yes.

Modern LLMs are generally deep-learning models based on transformer neural networks.

Is generative AI machine learning?

Most modern generative AI systems are built using machine learning and deep learning.

Generative AI describes what the system does—creates content—while machine learning and deep learning describe how it is built.

Are AI personas machine-learning models?

An AI persona is usually an application layer built around an existing model.

It may combine an LLM with instructions, curated knowledge, a name, a voice, tools, and a user interface.

Are AI agents a kind of deep learning?

An agent is a complete system rather than one learning method.

It may use deep-learning models, traditional software, search, databases, rules, and external tools.

What is the simplest example of artificial intelligence?

A rule-based system that selects actions according to defined conditions can qualify as AI when it performs a task associated with reasoning or decision-making.

What is the simplest example of machine learning?

A model trained on labeled email examples to classify new messages as spam or not spam is a common example.

What is the simplest example of deep learning?

A multilayer neural network trained to recognize objects in photographs is a common example.

Which is better: machine learning or deep learning?

Neither is universally better.

Deep learning is often effective for language, images, audio, and other complex data. Simpler machine-learning models may be faster, cheaper, easier to explain, and more effective for structured datasets.

Does machine learning require big data?

Not always.

Some models can learn from relatively small datasets. The amount of data required depends on the task, model complexity, data quality, and desired performance.

Does deep learning require big data?

Deep-learning systems often benefit from large datasets, although transfer learning and pretrained foundation models can reduce the amount of task-specific data required.

What is a neural network?

A neural network is a machine-learning architecture made of connected mathematical nodes.

Training adjusts the connections so the network becomes better at recognizing patterns or producing outputs.

Why is it called deep learning?

“Deep” refers to the multiple hidden layers within a neural network.

Can machine learning think?

Machine-learning systems perform mathematical computations that can produce useful predictions and reasoning-like behavior.

That does not establish human consciousness, personal understanding, emotion, or self-awareness.

Can AI learn on its own?

A model can adjust through a training process or gather feedback within a defined environment.

Humans still design the architecture, objectives, data pipeline, permissions, evaluation, and deployment conditions.

What is an algorithm?

An algorithm is a procedure or set of steps for solving a problem.

A machine-learning algorithm is used to train a model from data.

What is an AI model?

An AI model is a mathematical or computational component that transforms inputs into predictions, classifications, generated content, or other outputs.

What is the difference between a model and an AI system?

A model is one component.

An AI system includes the model along with data, software, interfaces, tools, rules, security controls, and human processes.

Is a recommendation system artificial intelligence?

It can be.

Many recommendation systems use machine learning to predict which items a user may prefer. Others may include manually defined rules.

Is a search engine artificial intelligence?

A search engine may combine traditional information retrieval, ranking algorithms, machine learning, language models, and other AI techniques.

Is automation the same as AI?

No.

Automation performs tasks without repeated human action. It may follow fixed rules and contain no AI.

AI can be used within automation when the task requires prediction, interpretation, planning, or adaptation.

What should I learn first?

Begin with the hierarchy:

1. Artificial intelligence is the broad field.
2. Machine learning learns patterns from data.
3. Deep learning uses multilayer neural networks.
4. Generative AI creates content.
5. LLMs generate and interpret language.
6. Personas and agents are applications built around models.

Understanding the Complete Picture

Artificial intelligence is not one invention.

It is a broad collection of goals, methods, models, and applications.

Machine learning changed AI by allowing computers to learn patterns from data rather than depending entirely on hand-written rules.

Deep learning expanded those capabilities by allowing multilayer neural networks to learn complex representations from images, language, audio, video, and other data.

Generative AI then used those methods to create new content.

Large language models made generative AI conversational.

AI personas gave those models specialized identities and educational purposes.

AI agents connected models to tools and multi-step actions.

The full relationship is:

AI is the field. Machine learning is an approach. Deep learning is a specialized approach. Generative AI and LLMs are model categories and applications. Personas and agents are experiences built around those models.

Understanding these distinctions makes it easier to evaluate what an AI product actually does, how it was built, what limitations it may have, and whether it is the right tool for the task.

Talk to Hub to compare any two AI concepts or request a personalized path through the series.

Complete the AI Concepts Series

This page completes the six-part AI Concepts Series:

• What Is Generative AI?
• What Are AI Personas?
• What Is Agentic AI?
• Large Language Models Explained Simply
• What Is Prompt Engineering?
• AI vs. Machine Learning vs. Deep Learning

Continue exploring through the AI Concepts Hub, where you can follow the series in order, compare related terms, and discover specialized AI personas across the network.