Understanding AI

Artificial intelligence (AI) is the field of computer science concerned with building systems that perform tasks normally requiring human judgement. The word "normally" is doing significant work in that sentence. Every decade, the definition of what counts as intelligent shifts: tasks that once seemed remarkable (chess, image recognition, translation) become routine engineering once they are solved.

This definitional drift is not a bug in the field. It reflects a genuine pattern: once we understand exactly how a system achieves a result, we tend to stop calling the result "intelligent." This phenomenon, sometimes called the AI effect, means that public expectations about AI consistently run ahead of what deployed systems actually do.

Understanding AI properly requires separating three nested concepts. AI is the broadest category; machine learning (ML) is a subset; deep learning is a subset of machine learning. Each narrows the scope further, and each requires distinct mental models for working with it effectively.

The three nested fields can be understood as concentric rings. The outer ring, AI, covers any technique that enables machines to simulate aspects of intelligent behaviour. Rule-based systems from the 1970s that checked passport validity were AI, even though they contained no learning at all.

The middle ring, machine learning, covers systems that learn patterns from data rather than following hand-written rules. Instead of writing "if the email contains the word prize then mark as spam," an ML system examines thousands of examples and infers the patterns itself.

The inner ring, deep learning, uses neural networks with many layers. These layered networks learn hierarchical representations: pixels become edges, edges become shapes, shapes become objects. The breakthrough came when graphics processing units (GPUs) made training large networks economically viable around 2012.

Modern AI did not arrive suddenly in 2022. It is the product of seventy years of research, two extended periods of reduced funding known as "AI winters," and a series of empirical breakthroughs enabled by more computing power and more data. Knowing the trajectory helps you calibrate how reliable current systems actually are.

1950: The Turing Test. Alan Turing proposed a simple benchmark: if a human judge cannot reliably distinguish a machine's text responses from a human's, the machine has demonstrated intelligent behaviour. Turing deliberately side-stepped unanswerable questions about consciousness and focused on observable performance.

1974 to 1980: The first AI winter. Early optimism collapsed when rule-based systems proved brittle outside narrow topics. Funding dried up sharply across both the UK and the United States.

1997: Deep Blue defeats Kasparov. IBM's Deep Blue defeated world chess champion Garry Kasparov using search algorithms and evaluation functions, not machine learning. It demonstrated that narrow, superhuman capability was achievable in well-defined domains.

2012: AlexNet and the deep learning revolution. A neural network called AlexNet, developed at the University of Toronto by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, won the ImageNet challenge by a margin that shocked researchers. Error rates dropped by nearly 10 percentage points versus the prior year's best result.

2017 to present: The transformer era. Google researchers published "Attention Is All You Need" in 2017, introducing the transformer architecture. By 2020, GPT-3 demonstrated that scaling transformers with vast text data produced systems with broad language capability. ChatGPT launched in November 2022 and reached 100 million users in two months.

Media coverage of AI frequently conflates three distinct concepts that researchers treat as separate: narrow AI, general AI, and superintelligence. Conflating them leads to misaligned expectations about what current tools can and cannot do.

Narrow AI (Artificial Narrow Intelligence, ANI) is AI designed and trained for a specific task. It can be superhuman at that task and completely useless outside it. Every AI system in commercial use today is narrow AI: the model that detects fraud on your bank card, the voice assistant that sets a timer, the algorithm that recommends what to watch next.

General AI (Artificial General Intelligence, AGI) is a hypothetical system that can perform any intellectual task a human can, applying reasoning across domains without task-specific training. No such system exists. Researchers disagree sharply about whether AGI is decades away, centuries away, or impossible in principle.

Superintelligence is a further hypothetical: an AI system that exceeds human cognitive capability across all domains. It remains a topic of philosophical and speculative discussion, not an engineering specification.

Understanding limitations is as important as understanding capabilities. The systems you will build agents with have genuine and structural limitations that are not bugs to be patched. They are properties of how these systems work.

Hallucination. Large language models generate plausible-sounding text based on patterns in their training data. When asked about a topic for which training data is sparse, the model fills gaps with confident-sounding fabrications. This is called hallucination. The model has no concept of truth or falsehood; it produces statistically likely continuations, some of which happen to be false.

Causal reasoning. Statistical correlations in training data are not causal relationships. A model trained on data where ice cream sales and drowning incidents both peak in summer will represent that correlation, not the fact that neither causes the other.

Consistent behaviour. The same prompt can produce different outputs on different runs, particularly at higher temperature settings. Production systems must account for this variability, especially in safety-critical or compliance contexts.

Stable identity over time. Each conversation typically starts fresh, with no persistent memory by default. An LLM does not remember your name from a previous session unless the application explicitly stores and retrieves that information.