Types of artificial intelligence

The UK Government’s 2023 policy paper on ‘A pro-innovation approach to AI regulation’ defined AI, AI systems or AI technologies as “products and services that are ‘adaptable’ and ‘autonomous’.

Types of artificial intelligence include:

• Deep learning. A subset of machine learning that uses artificial neural networks to recognise patterns in data and provide a suitable output, for example, a prediction (UK Post Brief 57 'Artificial intelligence: An explainer'). Deep learning is suitable for complex learning tasks, and has improved AI capabilities in tasks such as voice and image recognition, object detection and autonomous driving (UK Post Note 633 Interpretable Machine Learning).
• Frontier AI. Defined by the Government Office for Science as ‘highly capable general-purpose AI models that can perform a wide variety of tasks and match or exceed the capabilities present in today’s most advanced models’. Currently, this primarily encompasses a few large language models including: ChatGPT (OpenAI), Claude (Anthropic), and Bard (Google).
• General AI. Often referred to as Artificial general intelligence, strong AI or broad AI, this often refers to a theoretical form of AI that can achieve human-level or higher performance across most cognitive tasks. See also Superintelligence below.
• General-purpose AI.Often refers to AI models that can be adapted to a wide range of applications (such as Foundation Models). See also narrow AI below..
• Generative AI. An AI model that generates text, images, audio, video or other media in response to user prompts. It uses machine learning techniques to create new data that has similar characteristics to the data it was trained on. Generative AI applications include chatbots, photo and video filters, and virtual assistants.
• Machine learning. AI that allows a system to learn and improve from examples without all its instructions being explicitly programmed (UK Post Note 633 Interpretable Machine Learning). Machine learning systems learn by finding patterns in training datasets. They then create a model (with algorithms) encompassing their findings. This model is then typically applied to new data to make predictions or provide other useful outputs, such as translating text. Training machine learning systems for specific applications can involve different forms of learning, such as supervised, unsupervised, semi-supervised and reinforcement learning.
• Narrow AI. Sometimes known as weak AI, these AI models are designed to perform a specific task (such as speech recognition) and cannot be adapted to other tasks. See also general-purpose AI above.
• Responsible AI. Often refers to the practice of designing, developing, and deploying AI with certain values, such as being trustworthy, ethical, transparent, explainable, fair, robust and upholding privacy rights.
• Superintelligence. A theoretical form of AI that has intelligence greater than humans and exceeds their cognitive performance in most domains. See also artificial general intelligence above.

Ref: The UK government's 'Artificial intelligence (AI) glossary' updated January 2024.

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