資料內(nèi)容：
Terminology in AI is a fast-moving topic, and the
same term can have multiple meanings. The
glossary below should be viewed as a snapshot
of contemporary definitions.
Artificial intelligence system: a machine-based
system that, for explicit or implicit objectives,
infers, from the input it receives, how to
generate outputs such as predictions, content,
recommendations or decisions that can influence
physical or virtual environments. Different AI
systems vary in their levels of autonomy and
adaptiveness after deployment.1
Causal AI: AI models that identify and analyse
causal relationships in data, enabling predictions
and decisions based on these relationships.
Causal inference models provide responsible AI
benefits, including explainability and bias reduction
through formalizations of fairness, as well as
contextualisation for model reasoning and outputs.
The intersection and exploration of causal and
generative AI models is a new conversation.
Fine-tuning: The process of adapting a pre-trained
model to perform a specific task by conducting
additional training while updating the model’s
existing parameters.
Foundation model: A foundation model is an
AI model that can be adapted to a wide range
of downstream tasks. Foundation models
are typically large-scale (e.g. billions of parameters)
generative models trained on a vast array
of data, encompassing both labelled and
unlabelled datasets
