Understanding Artificial Intelligence

In brief: Artificial intelligence (AI) refers to all technologies that enable machines to simulate human cognitive abilities. In 2025, the global AI market is worth over 180 billion dollars, with an annual growth rate of 37%. This guide covers the fundamentals, the different branches, concrete applications and the ethical challenges of this technology.

Reading time: 8 minutes

Table of Contents

• Definition and fundamental principles of artificial intelligence
• The different branches of artificial intelligence
• How artificial intelligence works
• Concrete applications of artificial intelligence
• Artificial intelligence and search engines
• Ethical challenges and limitations of artificial intelligence

Definition and fundamental principles of artificial intelligence

Understanding artificial intelligence begins with grasping its definition. AI encompasses the computing techniques that enable machines to perform tasks normally requiring human intelligence: recognizing images, understanding natural language, making decisions or generating content.

Origin and historical evolution

The term artificial intelligence was first used in 1956 at the Dartmouth Conference, organized by John McCarthy. Since then, the field has gone through several phases of enthusiasm and disappointment, often referred to as “AI winters.” The current boom is driven by three factors:

• The massive availability of data (big data)
• The increase in computing power (GPUs, cloud computing)
• Algorithmic advances, particularly in deep learning

Narrow AI vs General AI

It is important to distinguish two fundamental concepts:

Type	Definition	Example	Current status
Narrow AI (weak AI)	System specialized in a specific task	Voice recognition, recommendation	Operational and widely deployed
General AI (strong AI)	System capable of reasoning like a human on any subject	None to date	Theoretical, does not yet exist
Super AI	Intelligence surpassing humans in all domains	Science fiction	Hypothetical

All current applications fall under narrow AI. No existing system possesses consciousness or genuine understanding.

The different branches of artificial intelligence

To properly understand artificial intelligence, it is essential to know its sub-fields. Each one addresses a specific type of problem.

Machine learning

Machine learning is the most widespread branch of AI. It involves algorithms that learn from data without being explicitly programmed for each case. There are three main approaches:

• Supervised learning: the algorithm learns from labeled examples (email classification, fraud detection)
• Unsupervised learning: the algorithm identifies structures in unlabeled data (customer segmentation, clustering)
• Reinforcement learning: the algorithm learns through trial and error by maximizing a reward (robotics, games)

According to a McKinsey study, 65% of companies regularly use generative AI in 2024, double compared to the previous year.

Deep learning

Deep learning is a subset of machine learning. It uses artificial neural networks composed of multiple layers (hence the term “deep”). This technology excels at processing images, sounds and text. Language models like GPT-4 or Claude are based on deep learning architectures called transformers.

Natural Language Processing (NLP)

NLP (Natural Language Processing) enables machines to understand, interpret and generate human language. This is the branch that powers chatbots, voice assistants and AI-powered answer engines. To explore the impact of NLP on search further, it is useful to consult the comparison of AI engines such as Perplexity, ChatGPT and Google SGE.

How artificial intelligence works

The operation of artificial intelligence relies on an iterative cycle of data, training and prediction. Understanding this cycle helps demystify the technology.

The learning cycle

An AI model generally follows these steps:

1. Data collection: gathering a sufficient volume of relevant data
2. Preparation: cleaning, annotating and structuring the data
3. Training: the model adjusts its internal parameters to minimize errors
4. Evaluation: testing performance on unseen data
5. Deployment: putting the model into production

“What differentiates modern artificial intelligence from traditional programming is that instead of coding explicit rules, you provide data and the system learns the rules itself.” — Andrew Ng, co-founder of Coursera and AI researcher

Neural networks explained

A neural network is inspired by the functioning of the human brain. Each artificial neuron receives inputs, applies a mathematical function and transmits a result. Successive layers allow the network to detect increasingly complex patterns. A model like GPT-4 has hundreds of billions of parameters adjusted during training.

Component	Role	Human analogy
Artificial neuron	Basic computational unit	Biological neuron
Weights	Strength of the connection between neurons	Synapse
Input layer	Reception of raw data	Sensory organs
Hidden layers	Feature extraction	Cerebral cortex
Output layer	Final prediction	Decision / action

Concrete applications of artificial intelligence

Artificial intelligence is transforming numerous sectors of activity. Its applications continue to expand as the technology matures.

Health and medicine

In medicine, AI can detect cancers in medical images with accuracy comparable to that of radiologists. AI systems analyze patient records to suggest diagnoses and identify risks. According to an Accenture report, AI in healthcare could generate savings of $150 billion per year in the United States by 2026.

Digital marketing and SEO

Generative AI is revolutionizing content marketing. Search engines now integrate AI-generated responses into their results. This phenomenon, known as Generative Engine Optimization (GEO), is transforming the way you need to optimize your content for generative AI. To fully grasp this new paradigm, it is recommended to understand what GEO is and its impact on online visibility.

Finance and insurance

Financial institutions use AI for real-time fraud detection, algorithmic trading and credit risk assessment. In 2024, 80% of banks report having deployed at least one AI project in production.

Industry and logistics

AI optimizes production chains, predicts machine failures (predictive maintenance) and improves inventory management. Autonomous vehicles, although still in an advanced development phase, represent one of the most ambitious applications of artificial intelligence.

Artificial intelligence and search engines

The impact of AI on search engines represents a major turning point for online visibility. It is fundamental to understand artificial intelligence in this context to adapt your digital strategy.

The GEO era

Traditional search engines are evolving into answer engines powered by AI. Google with SGE (Search Generative Experience), Perplexity AI and ChatGPT with its search functionality are profoundly changing user behavior. This transformation requires rethinking traditional SEO toward a GEO (Generative Engine Optimization) approach.

The importance of structured data

For AI to understand and correctly cite content, structured data and Schema.org play a determining role. JSON-LD markup helps algorithms identify entities, relationships and factual information on a page. E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) remains a central criterion for AI systems that evaluate source quality.

Adapting your content strategy

AI is changing expectations for content. Generative engines favor information that is:

• Factual and data-driven: statistics, verifiable data
• Well-structured: hierarchical headings, lists, tables
• From reliable sources: identified author, authoritative site, sourced citations

Ethical challenges and limitations of artificial intelligence

Understanding artificial intelligence also means knowing its limitations and risks. The technology raises fundamental questions about society.

Algorithmic bias

AI models reproduce and amplify biases present in training data. A recruitment algorithm trained on historical data can discriminate against certain profiles. According to an MIT study, facial recognition systems show error rates 35% higher on dark-skinned faces than on light-skinned faces.

Intellectual property and transparency

Generative AI raises unprecedented questions regarding copyright. Content generated by models trained on protected corpora is the subject of multiple legal proceedings. The European Union adopted the AI Act in 2024, the first comprehensive regulatory framework on artificial intelligence, imposing obligations of transparency and compliance.

Environmental impact

Training large-scale AI models consumes considerable energy resources. The training of GPT-3 emitted approximately 552 tonnes of CO2, the equivalent of 123 round-trip flights between Paris and New York. Industry players are investing in more efficient computing solutions, but the environmental cost remains a major challenge.

The future of artificial intelligence

The outlook for artificial intelligence in the coming years includes the democratization of AI tools for small businesses, improved model explainability and the development of multimodal AI capable of simultaneously processing text, images, video and audio. The main challenge remains balancing technological innovation with societal responsibility.

Further reading

AI is also transforming media buying and visual creation: discover our ranking of the best programmatic advertising agencies and the best AI visual creation agencies leveraging these technologies daily.