Artificial intelligence (AI) key terms and their meanings

AI (artificial intelligence)

The simulation of human intelligence processes by machines or computer systems. This can include learning, reasoning, problem-solving, and decision-making.

Machine learning (ML)

A subfield of AI that allows machines to learn from data without explicit programming. This is done by algorithms that can identify patterns and relationships in data, and then use those patterns to make predictions or decisions on new data.

Deep learning (DL)

A type of machine learning that uses artificial neural networks (ANNs) to learn from data. ANNs are inspired by the structure and function of the human brain, and they can learn complex relationships in data that would be difficult for traditional machine learning algorithms to identify.

Neural Network

A large computer network, designed to mimic a human brain. This is used for both computations and AI model training. Related terms Convolutional neural networks (CNNs), Recurrent neural networks (RNNs)

Algorithm

A set of instructions that a computer program follows to complete a task. In AI, algorithms are often used to train machine learning models and make predictions.

Natural language processing (NLP)

A subfield of AI that deals with the interaction between computers and human language. NLP tasks include speech recognition, machine translation, and chatbots.

Computer vision

A subfield of AI that allows machines to extract information from images and videos. Computer vision tasks include object recognition, image classification, and video analysis.

Chatbots

Computer programs that are designed to simulate conversation with humans. Chatbots can be used for a variety of purposes, such as customer service, information retrieval, and entertainment.

Data

The information that is used to train machine learning models. Data can be in the form of text, images, videos, audio, or other formats.

Big data

Large and complex datasets that are difficult to process with traditional tools. Big data can be used to train more accurate and powerful machine learning models.

Data Science

A field that combines statistics, computer science, and domain knowledge to extract insights from data. Data science is critical for developing and deploying effective AI systems.

Data Mining

The process of discovering patterns and knowledge from large datasets. In the context of AI, data mining often involves using machine learning algorithms to analyse and extract valuable information from data. Process of deciphering large datasets to find new ways to improve an AI model.

General intelligence (AGI)

A hypothetical type of AI that would be capable of understanding and reasoning about the world in the same way that humans do. AGI is still a long way off, but it is a goal that many AI researchers are striving for.

Cognitive computing?

A field of AI that aims to create machines that can simulate human thought processes.

Machine translation?

A technology that allows computers to translate text from one language to another.

Pattern recognition

This refers to a field within AI, that deals with finding and decoding similar patterns or trends in data.

Predictive?Analysis

The ability of an AI model to decipher data points and output detailed analytics and predictions based on it. Predict future events based on historical data.

Reinforcement learning

?

A method of teaching AI, by encouraging it to find an answer without any set parameters. The AI is graded on its output by a human handler, which helps it improve the next result, until the desired output is reached.

Turing Test

?

A test devised by mathematician Alan Turing that tests the AI in various fields to see if it can pass itself off as a human.

Backward Chaining

?

Backward Chaining is a method by which AI models start with a desired output and work in reverse to find data to support it.

Forward Chaining

A method by which an AI model works with a given problem to find a solution. This is done by analysing data sets and finding points that are relevant to the problem.

?

AI Accelerator

?

A hardware chip or micro-processor, designed for general purpose AI applications. These can be used to train AI models, or in larger neural networks.

Corpus

?

A dataset of written or spoken material that is used to train linguistic AI models for performing tasks.

Abductive logic programming (ALP)

?

Abductive reasoning is a form of logical reasoning that seeks the answer to a question, while using the most simple and straight forward way to derive it. In AI, ALP is a knowledge-representation framework that is used to solve problems based on Abductive reasoning principles

Hyperparameter?

?

Manually set values for AI models that will affect how it learns.

Expert Systems?

AI systems that are designed to emulate the decision-making abilities of human experts in a specific domain. Expert systems are typically rule-based and rely on a knowledge base of facts and rules.

Agent-Based Modelling?

A computational modelling technique that simulates the behaviour of agents in an environment. Agent-based modelling is used to study complex systems, such as economies, ecosystems, and social networks

Superintelligence?

An intelligence that is far superior to human intelligence in all aspects. Superintelligence is a hypothetical concept that raises many ethical and philosophical questions.

?

Explainable AI (XAI)?

A field of research that focuses on making AI models more understandable and interpretable. XAI is important for building trust in AI systems and ensuring that they are not biased or discriminatory.

AI Ethics?

The study of the ethical implications of AI. AI ethics is concerned with issues such as bias, fairness, accountability, and transparency.

Bias

An unintended prejudice or preconception that can affect the results of a machine learning model. Bias can arise from the data that the model is trained on, the algorithms that are used, or the decisions that are made by humans.

Strong AI?is essentially AI that is capable of human-level, general intelligence. In other words, it’s just another way to say “artificial general intelligence.”?

?

Weak AI, refers to the narrow use of widely available AI technology, like machine learning or deep learning, to perform very specific tasks, such as playing chess, recommending songs, or steering cars. Also known as Artificial Narrow Intelligence (ANI), weak AI is essentially the kind of AI we use daily.

This is just a brief overview of some of the key terms in AI. As the field continues to evolve, new terms will emerge and some existing terms may take on new meanings. It is important to stay up-to-date on the latest developments in AI so that you can understand how this powerful technology is transforming our world