Explainer

Artificial Intelligence Explained

A plain-English guide to AI – how it works, what the key concepts mean, and why it matters. No hype, no jargon – just the essentials.

Key Facts

ChatGPT reached 100 million users in two months after launch (Jan 2023) – the fastest adoption of any consumer application in history.

AI training compute is doubling roughly every 6 months. The compute used for frontier models has increased ~10 billion-fold since 2010.

Global investment in AI reached over $200 billion in 2025, with the US accounting for roughly two-thirds of venture funding.

LLMs can now pass the bar exam, medical licensing exams, and graduate-level science tests – often scoring in the top percentiles.

AI systems can now generate photorealistic images, fluent text, working code, and even short videos from text descriptions alone.

Over 50 countries have introduced or proposed AI regulation. The EU AI Act (2024) is the world's first comprehensive AI law.

AI agents that can autonomously browse the web, write code, and complete multi-step tasks are rapidly advancing in 2025–26.

An estimated 300 million jobs could be affected by generative AI, though many new roles are also being created.

How Modern AI Works

At its core, modern AI is pattern recognition at scale. A neural network is shown billions of examples – text, images, or other data – and learns the statistical patterns within them. It doesn't "understand" in the human sense; it builds an extraordinarily sophisticated model of what typically follows what.

Large language models (LLMs) like GPT-4, Claude, and Gemini are trained by reading trillions of words from the internet, books, and code. They learn to predict the next word in a sequence – but this simple objective, at sufficient scale, produces systems that can write essays, solve maths problems, generate code, and engage in nuanced conversation.

The transformer architecture (introduced in 2017) made this possible. Its "attention mechanism" lets the model consider the relationship between every word and every other word in a passage simultaneously, capturing context far better than earlier approaches. Virtually all frontier AI models today are based on transformers.

Training these models requires immense compute – thousands of specialised GPUs running for months, consuming megawatts of electricity. This has created a concentration of AI capability among a handful of well-funded labs (OpenAI, Google DeepMind, Anthropic, Meta, xAI) and a growing debate about the environmental and economic costs.

Once trained, models are made safer through reinforcement learning from human feedback (RLHF) – human evaluators rate responses, and the model learns to prefer answers humans find helpful, accurate, and harmless. This is an active area of research, because aligning increasingly capable systems with human values becomes harder as capabilities grow.

The AI Landscape in 2025–26

AI development is moving at an unprecedented pace. Key trends shaping the field right now:

AI Agents

Systems that can autonomously plan, use tools, browse the web, write code, and complete multi-step tasks are the defining frontier. Companies are racing to build agents that act reliably on behalf of users.

Multimodal models

Frontier models now process text, images, audio, and video natively. This enables applications from visual question-answering to real-time voice assistants.

Reasoning models

A new class of models (like OpenAI's o-series and DeepSeek-R1) that 'think step by step' before answering, dramatically improving performance on maths, science, and complex logic tasks.

Open-source surge

Meta's Llama, Mistral, and DeepSeek have demonstrated that open-weight models can rival proprietary ones, democratising access but also raising safety questions.

AI regulation

The EU AI Act, US executive orders, and UK AI Safety Institute mark the beginning of serious AI governance. Balancing innovation with safety is the central policy challenge.

Scaling debate

Whether simply making models bigger continues to improve them ('scaling laws') or whether new architectures are needed is one of the biggest open questions in the field.

Glossary

Artificial intelligence (AI)

The broad field of computer science focused on creating systems that can perform tasks typically requiring human intelligence – recognising images, understanding language, making decisions, and generating content.

Machine learning (ML)

A subset of AI where systems learn patterns from data rather than being explicitly programmed. Instead of writing rules by hand, you feed examples and the algorithm finds the rules itself.

Deep learning

A subset of machine learning that uses artificial neural networks with many layers. Deep learning drives most modern AI breakthroughs – from image recognition to language models.

Neural network

A computing architecture loosely inspired by the brain, consisting of layers of interconnected nodes (neurons) that process information. Each connection has a weight that adjusts during training.

Large language model (LLM)

A neural network trained on vast amounts of text to predict and generate language. Examples include GPT-4, Claude, Gemini, and Llama. LLMs power chatbots, coding assistants, and content generation tools.

Transformer

The neural network architecture behind modern LLMs, introduced in 2017. Transformers use an 'attention mechanism' that lets the model weigh the relevance of every word in a sequence against every other word, enabling much better understanding of context.

Training

The process of feeding data to a model so it can learn patterns. Training large models requires enormous compute power – GPT-4-class models are estimated to cost over $100 million to train.

Inference

Using a trained model to make predictions or generate output. When you ask ChatGPT a question, that's inference. It's much cheaper than training but still requires significant compute at scale.

Parameters

The internal variables (weights) a model adjusts during training. GPT-4 is estimated to have over 1 trillion parameters. More parameters generally means greater capability but also higher cost.

Fine-tuning

Taking a pre-trained model and further training it on a specific dataset to specialise its behaviour – for example, training a general LLM on medical literature to create a healthcare assistant.

Prompt engineering

The practice of crafting input text (prompts) to get better outputs from AI models. Small changes in phrasing can dramatically alter responses.

Retrieval-augmented generation (RAG)

A technique where an LLM is given access to external documents or databases at query time, so it can base answers on up-to-date, specific information rather than relying solely on training data.

Hallucination

When an AI model generates plausible-sounding but factually incorrect information. LLMs predict likely text – they don't 'know' facts – so they can confidently state things that are wrong.

Foundation model

A large model trained on broad data that can be adapted for many downstream tasks. GPT-4, Claude, and Gemini are foundation models – they weren't built for one purpose but can be applied to many.

Multimodal AI

Models that can process and generate multiple types of data – text, images, audio, video. GPT-4o and Gemini are multimodal, able to 'see' images and 'hear' audio alongside text.

Generative AI

AI systems that create new content – text, images, music, code, video. Distinguished from 'analytical' AI that classifies or predicts. DALL-E, Midjourney, and Sora are generative AI tools.

Artificial general intelligence (AGI)

A hypothetical AI system that matches or exceeds human cognitive ability across all domains. Current AI is 'narrow' – excellent at specific tasks but lacking general reasoning. AGI timelines are hotly debated.

AI alignment

The challenge of ensuring AI systems pursue goals that are beneficial to humans. As models become more capable, ensuring they remain safe, honest, and controllable becomes increasingly critical.

Reinforcement learning from human feedback (RLHF)

A training technique where human evaluators rank model outputs and the model learns to prefer responses humans rate highly. Used to make LLMs more helpful, harmless, and honest.

AI agent

An AI system that can take autonomous actions – browsing the web, writing and running code, calling APIs – to accomplish goals with minimal human intervention. A major frontier in 2025–26.

Compute

The processing power required to train and run AI models, typically measured in GPU-hours or FLOPS. Access to compute is a key bottleneck and competitive advantage in AI development.

GPU (graphics processing unit)

Originally designed for rendering graphics, GPUs are now the primary hardware for training neural networks because they excel at the parallel matrix calculations AI requires. NVIDIA dominates this market.

Open-source vs closed-source AI

Open-source models (Llama, Mistral) release their weights publicly, allowing anyone to run and modify them. Closed-source models (GPT-4, Claude) are accessible only via APIs. The debate over which approach is safer and more beneficial is ongoing.

Tokens

The basic units LLMs process – roughly ¾ of a word in English. Model pricing, context windows, and speed are all measured in tokens. GPT-4 Turbo has a 128K-token context window.

Benchmark

A standardised test used to measure AI capabilities – e.g. MMLU (broad knowledge), HumanEval (coding), ARC (reasoning). Models are compared by their benchmark scores, though real-world performance often differs.

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