Artificial Intelligence: Glossary - the most important terms

A compilation of frequently used terms relevant to systems of Artificial Intelligence.

Printed terms are cross-references to other terms that are also explained in the AI glossary.

Further information on AI can be found in contributions on Dr. GDPR:

Dictionary of important AI terms

Term	Explanation
Artificial Intelligence	Also abbreviated as AI. AI is the development of computers and software that possess human-like abilities such as learning, problem-solving, language understanding and production, sensory perception, action control, and autonomous decision-making.
Model	An AI model is an electronic brain. Just like in a biological brain, it consists of a neural network (see ANN).
Large Language Model	Abbreviation for Large Language Model. A model that has been trained on a large number of text documents in order to be able to represent the grammar of one or more languages very well.
Foundation Model	A well-trained foundational model that can be fine-tuned for specific tasks or domains. Such models are often provided under a user-friendly license.
ANN	Abbreviation for Artificial Neural Network. Electronic representation of the biological model. Basis for AI models (see Model).
Translator	A crucial mathematical approach that has made high-performance AI applications like ChatGPT possible, and is responsible for the current success of AI. Exists since 2017. The precursor what developed by Jürgen Schmidhuber in the early 1990s. Consists of an Encoder-Decoder structure, where input is encoded into features that are then decoded into output. See also Embeddings.
Model size	The size of a AI model (see model) is given in shorthand form by the number of parameters (see parameters). Small models have only several hundred million parameters. A 200M model has 200 million parameters. Larger models have 13B or 60B parameters. The "B" stands for billion (English: "billion").
Parameter	As parameters, weights are called in an artificial neural network (see ANN). A weight defines the strength of the connection between two neurons.
ChatGPT	ChatBot based on GPT architecture (see GPT). Also has a programmable interface (API). Should have approximately 160B parameters (cf. model size). Problematic with regards to data protection, business secrets and confidential documents.
GPT	Abkürzung für Generative Pretrained Transformer. Siehe Generativ, Vortrainiert und Transformer.
Generative	Ability to create or produce something new. Part of the abbreviation GPT, which is also embedded in ChatGPT.
Well-trained	A pretrained model is a trained model. It has been trained. Instead of saying "trained", however, we say "pretrained". The background is that an already trained model can be further trained, which is called fine-tuning.
Fine-Tuning	Pre-trained models (see pre-trained) can be further "trained" for specific tasks (see downstream). This further training is called Fine-Tuning. Fine-Tuning has the great advantage that much less input data and resources (time, computational power, main memory) are required than to build a pre-trained AI model.
Layers	Refers to artificial neural networks (see ANN): The layers of a neural network are the input layer, the output layer, and the hidden layers between these two layers.
Input layer	First layer of an artificial neural network (see ANN). Input data (sensor data or user input) reaches the ANN through the input layer.
Output layer	Last layer of an artificial neural network (see ANN). The output layer announces the result of a machine learning algorithm.
Hidden Layers	Layers of a ANN, situated between input layer and output layer. It is here that magic happens, also referred to as intelligence. See human intelligence and artificial intelligence.
Hidden layers	See Hidden Layers.
Upstream Task	Task for which an AI model has been pre-trained. See also Downstream Task.
Downstream Task	Task for which a AI-model is prepared with the help of Fine-Tuning and which differs from the Upstream Task for which the AI-model what originally trained.
PyTorch	Most popular AI framework, based on the programming language Python.
Tensorflow	Besides being a well-known AI framework, PyTorch is from Google. It's considered more complex than PyTorch.
Array	Mathematical concept for data representation. See also Vector.
Delegation	Offloading parts of a machine learning model (see Model) onto a CPU (instead of GPU) or hard drive. Offloading resolves memory issues (see VRAM) with large ML models, but results in significantly slower computations.
CUDA	Abbreviation for Compute Unified Device Architecture. Software architecture of Nvidia, to use graphics cards (see GPU) for calculations.
Nvidia Corporation	Without wanting to advertise: World-leading provider for graphics cards that are particularly well-suited for AI calculations. See CUDA. There is no other manufacturer I know of whose graphics cards are supported as well in numerous AI frameworks (see PyTorch).
Python	Most popular programming language for AI applications.
Graphics Processing Unit	Abbreviation for Graphics Processing Unit. A GPU of a high-performance graphics card like from Nvidia contains thousands of cores. A core can solve a calculation task. Several cores can work simultaneously. For AI applications, billions of calculations can be executed on a GPU (graphics card) several times faster than on a CPU (normal processor). The graphics card is misused as a calculator in AI applications. It does not output images or text.
CPU