AI, ML, Deep Learning, LLMs, and Algorithms: The Differences

01. What This Clears Up

People routinely say "AI" when they mean an LLM, "algorithm" when they mean a trained model, and "deep learning" when they mean machine learning. These conflations matter because they obscure what is actually happening inside a system. A fraud-detection model at a bank and GPT-4 are both "AI," but they are built with different techniques, for different goals, with different failure modes. This file maps the terms to each other so the distinctions stay clear.

For a deeper treatment of how machine learning works, see Machine Learning Basics.
For the current landscape of AI types, see Types of AI.

02. The Nesting Model (AI > ML > DL > LLMs)

Think of these as concentric rings. Each inner ring is a subset of the one outside it.

Artificial Intelligence is the outermost ring. It is the goal: building systems that exhibit intelligent behavior, make decisions, or solve problems without continuous human instruction. It is a field of computer science, not a single technique. AI encompasses rule-based expert systems, search algorithms, optimization methods, machine learning, and more.

Machine Learning sits inside that ring. ML is the dominant modern approach to AI. Instead of writing explicit rules ("if email contains 'prize money,' mark as spam"), you provide an algorithm and a dataset, and the system learns the rules from examples. ML provides the backbone of most modern AI systems, from forecasting models to recommendation engines to self-driving vehicles (IBM, 2024).

Deep Learning sits inside ML. It is ML that uses artificial neural networks with many layers. Those layers are what "deep" refers to. Deep learning does not require manual feature engineering: the network learns relevant patterns directly from raw data. It is computationally expensive and data-hungry compared to classical ML, but it reaches state-of-the-art performance across nearly every domain in which AI is applied.

Transformers sit inside deep learning. The transformer architecture, introduced in 2017, uses an attention mechanism that allows the model to focus on the parts of an input most relevant to the current task. It became the dominant architecture for language tasks and has since been extended to images, audio, and code.

Large Language Models sit at the center. An LLM is a deep learning model built on the transformer architecture, trained on massive text corpora using next-token prediction as its core objective. LLMs are also a variety of foundation model (see definitions below). They are also a primary driver of the current generative AI moment.

AI
 └── Machine Learning
      └── Deep Learning
           └── Transformer models
                └── Large Language Models (LLMs)

Every LLM is a transformer. Every transformer used for language is a deep learning model. Every deep learning model is a machine learning model. Every ML model is an AI system. The reverse does not hold at any level.

03. Algorithm vs Model (The Distinction People Miss)

These two words are used interchangeably in everyday conversation and that is almost always wrong.

An algorithm is a procedure: a defined set of mathematical operations that, given data, adjusts its parameters to minimize error or maximize reward. Gradient descent is an algorithm. Backpropagation is an algorithm. A decision tree learning procedure is an algorithm. The algorithm itself contains no learned knowledge.

A model is the trained artifact that results from running a learning algorithm on a specific dataset. After training is complete, the algorithm's adjusted parameters are frozen into the model. The model can then make predictions on new data. GPT-4 is a model. ResNet-50 is a model. The spam filter deployed in your email client is a model.

The relationship: you run an algorithm on data to produce a model. The algorithm is the recipe. The model is the cake.

Why this matters in practice: when someone says "the algorithm decided," they usually mean the model decided. The algorithm was involved during training and is no longer active during deployment. Confusing the two muddies accountability, explainability, and debugging.

04. Term-by-Term Definitions

Artificial Intelligence (AI):
The field of computer science concerned with building systems that can perform tasks typically requiring human intelligence, including reasoning, learning, perception, and language understanding (Google Cloud, 2024). AI predates machine learning: rule-based expert systems, symbolic logic programs, and search algorithms are all AI. The field began formally at the Dartmouth Summer Research Project in 1956.

Machine Learning (ML):
The subset of AI focused on algorithms that learn patterns from data and generalize those patterns to new inputs, without being explicitly programmed for every case (IBM, 2024). The key difference from classical AI: the logic is not hard-coded; it is inferred from examples. Arthur Samuel coined the term in 1959.
See Machine Learning Basics for how supervised, unsupervised, and reinforcement learning work.

Deep Learning (DL):
A subset of ML that uses neural networks with many hidden layers (IBM, 2024). The "depth" refers to the number of layers between input and output. Deep learning automates feature extraction: the network figures out which aspects of the raw data matter, rather than requiring human engineers to define those features in advance. GPUs made deep learning practical at scale in the early 2010s.

Neural Network:
The computational structure underlying deep learning. Loosely inspired by the brain, it consists of interconnected layers of nodes (neurons), each performing a nonlinear mathematical operation. Connections have numerical weights; those weights are what training adjusts. A shallow neural network has one or two hidden layers. A deep neural network has many. Neural networks are not a separate category from deep learning; they are the mechanism by which deep learning operates.

Large Language Model (LLM):
A deep learning model built on the transformer architecture and trained on large volumes of text data, using next-token prediction as its primary training objective (IBM, 2024). After training, LLMs can understand context, generate fluent text, answer questions, translate, summarize, and write code. Examples include GPT-4, Claude, Gemini, and Llama. LLMs are also foundation models.
For a detailed treatment, see What Is a Large Language Model?.

Generative AI:
A category of AI models that produce new content (text, images, audio, video, code) rather than only classifying or predicting. Generative AI is not a separate architecture; it describes the output type. LLMs are generative AI. Diffusion models for images (Stable Diffusion, DALL-E) are generative AI. Not all generative models are LLMs, and not all LLMs are used purely generatively. The contrasting category is discriminative or predictive AI, which outputs a label, category, or numerical prediction rather than generating new content. A spam filter is discriminative. ChatGPT is generative.
See Generative AI.

Foundation Model:
A large model trained on broad data at scale such that it can be adapted to a wide range of downstream tasks (Stanford HAI / CRFM, 2021). The term was coined by Stanford researchers to describe a new paradigm where a single pre-trained model, such as BERT or GPT-3, serves as a reusable base for many applications via fine-tuning or prompting. LLMs are foundation models. So are large vision models, multimodal models, and some code models. Not all foundation models are LLMs.

Data Science:
A field that combines statistics, programming, and domain knowledge to extract insights from data. Data science overlaps with ML: data scientists use ML algorithms as tools, and ML engineers productionize the models data scientists build. The key difference is orientation: data science is primarily about understanding data and answering questions; ML is about building systems that learn and generalize. A data scientist might use linear regression to forecast quarterly revenue without caring whether the result is deployed in production. An ML engineer cares deeply about deployment, monitoring, and model drift. In practice, many practitioners work across both.

Natural Language Processing (NLP):
A subfield of AI and computer science concerned with enabling machines to understand, interpret, and generate human language (IBM, 2024). NLP is the problem domain. Deep learning and transformers are current dominant methods for solving NLP tasks. LLMs are the most powerful NLP tools available today but NLP also includes older approaches such as rule-based parsers and statistical methods. Translation, sentiment analysis, speech recognition, and question answering are all NLP tasks.

Computer Vision:
The subfield of AI concerned with enabling machines to extract meaning from images and video (Google Cloud, 2024). Tasks include image classification, object detection, image segmentation, and optical character recognition. Like NLP, computer vision is a problem domain. Deep learning (specifically convolutional neural networks, and more recently transformers) provides the primary solution methods.

05. Comparison Table

06. Which Term to Use When

Use AI when you are speaking about the field broadly, about a system whose internals do not matter for the conversation, or about the policy or societal dimension. "AI regulation," "AI literacy," "deploying AI in healthcare" are all appropriate uses.

Use machine learning when you are talking about a system that was trained on data, especially if you are contrasting it with rule-based software or discussing training, generalization, or model performance.

Use deep learning when the multi-layer neural network architecture is specifically relevant, usually in technical discussions about model design, compute requirements, or why deep learning outperforms classical ML on a given task.

Use LLM when you are specifically referring to transformer-based text models. "The LLM hallucinated" is more precise than "the AI hallucinated" because it tells you the failure mode is specific to autoregressive text generation.

Use algorithm when describing a training procedure, a learning rule, or the mathematical process. Use model when describing the trained artifact being deployed, evaluated, or queried.

Use generative AI when the ability to produce novel content is what matters for the context, and when you want to include image, audio, and video generation alongside text.

Avoid substituting "AI" for the more specific term when precision matters. Saying "the AI decided to reject your loan application" hides whether it was a simple logistic regression model or an LLM, which are very different systems with very different interpretability profiles.

07. Common Confusions

"AI and ML are the same thing."
No. All ML is AI, but not all AI is ML. Symbolic AI systems, rule-based expert systems, and search algorithms are AI without being ML. The terms became nearly synonymous in popular usage because ML is now the dominant approach, but the distinction matters when evaluating older systems or non-learned approaches.

"All AI is machine learning."
No. Rules-based systems, classical planning algorithms, and symbolic reasoning systems are AI that do not learn from data. A thermostat with programmed temperature rules is a trivial AI. An expert system diagnosing diseases from symptom rules is AI. Neither uses machine learning.

"LLM equals AI."
No. LLMs are a specific, recent technology that sits several layers deep inside the AI hierarchy. Treating them as synonymous with AI erases decades of prior work and obscures the difference between language models and other AI systems such as recommendation engines, fraud detectors, or robotic control systems.

"Algorithm equals model."
No. The algorithm is the training procedure. The model is the result. The algorithm ceases to operate once training is complete. Calling a deployed model "the algorithm" confuses the static artifact with the dynamic process that created it.

"Deep learning is the same as AI."
No. Deep learning is a subset of ML, which is a subset of AI. There are ML models that are not deep learning (random forests, support vector machines, linear regression). There are AI systems that are not even ML. Deep learning is not a synonym for AI; it is one technique within it.

"Generative AI is a new type of AI."
Partly. Generative models have existed for decades (GANs date to 2014). What changed around 2022 was scale: foundation models trained on internet-scale data produced output quality good enough for mass adoption. The category is not new; the capability threshold crossed is.

"Foundation model means LLM."
No. Foundation models include vision models (CLIP), image generation models (Stable Diffusion), multimodal models, and code models. LLMs are foundation models, but foundation models are not exclusively LLMs. The defining characteristic of a foundation model is that it is trained broadly and adapted to specific tasks, not that it handles text.