Applications
55 articles in this section
Adversarial Machine Learning
Adversarial machine learning is the study of attacks that exploit weaknesses in machine learning models, such as crafted inputs that cause misclassification, and of the defences designed to make models more robust.
Agentic AI
Agentic AI refers to artificial intelligence systems designed to pursue goals autonomously over extended time horizons by perceiving their environment, reasoning about actions, executing multi-step plans, and learning from outcomes without requiring continuous human instruction.
Agentic RAG
Agentic RAG is an approach to retrieval-augmented generation in which autonomous AI agents dynamically decide when, what, and how to retrieve information, applying planning, reflection, and tool use rather than following a fixed retrieve-then-generate pipeline.
AI Agents
Autonomous AI systems that perceive their environment, reason over goals, select and execute actions using external tools, and operate across multi-step tasks with minimal human intervention.
AI Automation
AI automation combines artificial intelligence with robotic process automation and workflow orchestration to eliminate repetitive tasks, reduce errors, and free human workers for higher-value work.
AI Drug Discovery
AI drug discovery applies machine learning, deep learning, and generative modelling to accelerate the identification, design, and optimisation of therapeutic compounds across the pharmaceutical pipeline.
AI Memory
AI memory refers to the mechanisms that allow artificial intelligence agents to retain, retrieve, and use information across interactions, extending capability beyond a single context window.
AI Music Generation
AI music generation is the use of machine learning models to compose, arrange, or produce music from text prompts or other inputs, spanning full songs with vocals, instrumental tracks, and sound design.
AI Red Teaming
A structured adversarial evaluation practice in which testers attempt to elicit harmful, unsafe, or policy-violating behaviour from AI systems in order to surface risks before deployment.
AI Safety
AI safety is a field of research and practice concerned with the development of artificial intelligence systems that behave reliably, avoid harmful outputs, and remain aligned with human values, especially as systems become more capable.
AI Video Generation
AI video generation refers to the automated creation of video content from text prompts, images, or other inputs using generative neural networks, enabling synthetic video production without cameras or traditional animation.
AI Watermarking
AI watermarking refers to techniques for embedding detectable signals into AI-generated content to establish provenance, enable detection, and support content authenticity verification across images, audio, video, and text.
AlphaFold
AlphaFold is an AI system developed by Google DeepMind that predicts the three-dimensional structure of proteins from their amino acid sequences, achieving accuracy comparable to experimental methods.
Anomaly Detection
A class of machine learning techniques that identifies rare events, observations, or patterns that differ significantly from the majority of data, used for fraud, intrusion, and fault detection.
Autonomous Agents
Autonomous AI agents are software systems that use large language models as a reasoning core, enabling them to plan multi-step tasks, use external tools, maintain memory, and take actions to achieve goals with minimal human intervention.
Chain-of-Thought Prompting
A prompt engineering technique that improves large language model reasoning on complex tasks by instructing the model to generate explicit intermediate reasoning steps before arriving at a final answer.
Chatbot
A chatbot is a software application designed to simulate human conversation through text or voice, ranging from simple rule-based systems to sophisticated AI assistants powered by large language models.
Code Generation
AI code generation is the use of large language models to automatically produce, complete, or transform source code from natural language descriptions, enabling assisted and autonomous software development.
Computer Vision
Computer vision is the field of artificial intelligence that enables machines to interpret and act upon visual information from the world — including images, video, and depth data.
Explainable AI
Explainable AI (XAI) refers to methods and techniques that make the decisions and predictions of artificial intelligence systems interpretable and understandable to human users, addressing the opacity of complex machine learning models.
Face Recognition
Face recognition is a biometric technology that identifies or verifies individuals by analysing facial features from images or video, widely used in security, banking, and immigration.
Fine-Tuning
The process of further training a pre-trained machine learning model on a smaller, task-specific dataset to adapt its weights for a particular domain, task, or desired behaviour.
Fraud Detection
Fraud detection is the application of data analysis, machine learning, and AI to identify deceptive or unauthorised transactions, activities, and behaviours in financial, digital, and commercial systems.
Function Calling
Function calling is the structured mechanism by which a large language model returns a JSON-formatted invocation of a named function with typed arguments, enabling reliable integration of LLMs with external systems.
Generative AI
Generative AI refers to artificial intelligence systems capable of producing new content — text, images, audio, video, or code — by learning the underlying distribution of training data.
Hybrid Search
Hybrid search is a retrieval technique that combines sparse keyword-based search (typically BM25) with dense vector semantic search to achieve superior recall and precision over either method alone.
Image Segmentation
A computer vision task that partitions an image into meaningful regions by assigning a class label to every pixel, enabling pixel-level understanding of visual scenes.
In-Context Learning
In-context learning is the ability of large language models to perform new tasks by conditioning on examples or instructions provided within the input prompt, without updating model weights.
LoRA (Low-Rank Adaptation)
LoRA is a parameter-efficient fine-tuning technique that adapts large pre-trained models by injecting small trainable low-rank matrices into transformer layers, drastically reducing the number of trainable parameters without sacrificing performance.
Machine Translation
Machine translation is the automated conversion of text or speech from one natural language into another using rule-based, statistical, or neural systems.
Multi-Agent Systems
Multi-agent systems in AI are architectures in which multiple autonomous AI agents, each with specialised capabilities, collaborate through communication and coordination to complete complex tasks that exceed the capability of any single agent.
Named Entity Recognition
Named entity recognition (NER) is a natural language processing task that identifies and classifies named entities in text — such as people, organisations, locations, and dates — into predefined categories.
Object Detection
Object detection is a computer vision task that involves identifying the location and category of one or more objects within an image or video frame, producing bounding boxes and class labels for each detected instance.
Optical Character Recognition
A computer vision technology that converts images of typed, handwritten, or printed text into machine-readable digital text, increasingly powered by deep learning and transformer-based vision models.
Pose Estimation
Pose estimation is the computer vision task of detecting and tracking the position and orientation of human bodies, hands, or objects from images or video, typically by locating keypoints such as joints.
Predictive Maintenance
Predictive maintenance is the use of sensor data, statistical modelling, and machine learning to forecast equipment failures before they occur, enabling repairs to be scheduled precisely when needed.
Prompt Engineering
The practice of designing and optimising input instructions given to large language models to elicit accurate, relevant, and well-structured outputs for a given task or application.
Question Answering
Question answering is the natural language processing task of producing accurate answers to questions posed in natural language, often using information retrieval, reading comprehension, or large language models.
ReAct (Reasoning and Acting)
ReAct is a prompting framework that interleaves reasoning traces with task actions, letting a language model plan, call external tools, and incorporate observations to solve problems more reliably.
Recommendation System
A recommendation system is an information filtering algorithm that predicts and surfaces items — such as products, content, or services — that a particular user is likely to find relevant, based on past behaviour, item attributes, or the preferences of similar users.
Reinforcement Learning from AI Feedback
Reinforcement Learning from AI Feedback (RLAIF) is an alignment technique in which an AI model provides preference labels to train a reward model, replacing or supplementing expensive human annotation used in RLHF.
Reranking
Reranking is a two-stage information retrieval technique in which a fast first-stage retriever generates candidate documents, and a more accurate but computationally expensive model re-scores and reorders them.
Retrieval-Augmented Generation
A technique that enhances large language model outputs by retrieving relevant documents from an external knowledge base at inference time, grounding responses in up-to-date and domain-specific information.
Reward Modeling
Reward modeling is the process of training a neural network to predict human or AI preferences over model outputs, providing a scalable reward signal for reinforcement learning-based alignment of language models.
Semantic Search
Semantic search is a search paradigm that retrieves results based on the meaning and intent of a query rather than exact keyword matches, using vector embeddings to measure conceptual similarity between text.
Sentiment Analysis
Sentiment analysis is a natural language processing technique that automatically identifies and classifies the emotional tone of text as positive, negative, or neutral, and is widely used in customer feedback, social media monitoring, and financial analysis.
Speech Recognition
Speech recognition, or automatic speech recognition (ASR), is the technology that enables computers to identify and transcribe spoken language into text using acoustic models, language models, and deep learning architectures.
Text Summarisation
Text summarisation is the natural language processing task of producing a shorter version of a document that preserves its key information, using extractive or abstractive techniques.
Text-to-Speech
Text-to-speech is the technology that converts written text into synthesised spoken audio using rule-based, concatenative, or neural network methods.
Time Series Forecasting
Time series forecasting is the application of statistical and machine learning methods to predict future values of a sequence of observations indexed in time, such as sales, demand, electricity load, or financial prices.
Tool Use
Tool use in AI refers to the capability of language models to invoke external functions, APIs, or services to retrieve information, perform actions, or extend their abilities beyond text generation.
Tree of Thoughts
Tree of Thoughts (ToT) is a prompting framework that lets large language models explore and evaluate multiple intermediate reasoning paths in a branching search, improving performance on tasks requiring planning.
Vibe Coding
Vibe coding is an AI-assisted software development practice in which a developer describes intent in natural language and a large language model generates the code, with the human guiding and testing rather than writing it directly.
Whisper
Whisper is an open-source automatic speech recognition system developed by OpenAI, trained on 680,000 hours of multilingual audio data and capable of transcription, translation, and language identification across nearly 100 languages.
YOLO (You Only Look Once)
YOLO is a family of real-time object detection models that frame detection as a single regression problem, predicting bounding boxes and class probabilities directly from an image in one network pass.