Fundamental Truths

Learning from Data

AI systems improve their performance by processing data, identifying patterns, and adjusting their operations based on new information. Depending on the system's design and objectives, this learning process can be supervised, unsupervised, or reinforced.

Pattern Recognition

A central function of AI is the ability to recognise patterns within data. This capability enables AI to perform tasks such as image and speech recognition, natural language processing, and predictive analytics.

Decision-Making

AI systems make decisions by analysing data and selecting actions that align with predefined goals or optimise specific outcomes. These decisions are based on algorithms that weigh various factors to determine the best course of action.

Adaptability

AI systems can adapt to new data or changes in their environment. This adaptability allows them to maintain performance over time and adjust to evolving conditions without explicit reprogramming.

Autonomy

Many AI systems operate with a degree of autonomy, performing tasks without continuous human intervention. This autonomy ranges from simple automated responses to complex decision-making processes in dynamic environments.

Generalisation

AI systems generalise from their training data to make predictions or decisions about new, unseen data. Effective generalisation is crucial for AI to function reliably in real-world scenarios.

Perception

AI systems often include perceptual capabilities, allowing them to interpret sensory data such as visual, auditory, or textual information. This perception enables interaction with the environment and is essential for tasks like object recognition and language understanding.

Reasoning and Problem-Solving

AI systems apply logical reasoning to solve problems, draw inferences, and make decisions. This reasoning can involve complex computations and the application of rules or heuristics to navigate challenges.