1.
The field of computer science concerned with building machines capable of performing tasks that typically require human intelligence.
2.
A subfield of AI where algorithms learn data without being explicitly programmed.
3.
A subset of machine learning that uses artificial neural networks with multiple layers to analyze data.
4.
A field of AI that enables computers to understand, interpret, and generate human language.
5.
A set of rules or instructions that a machine follows to perform a task.
6.
An AI-powered program designed to simulate conversations with humans.
7.
A type of AI model trained on massive amounts of text data to generate human-like text.
8.
A type of AI that can create new content, such as text, images, or music.
9.
A specific type of LLM that uses a transformer architecture.
10.
A type of neural network architecture used in many LLMs.
11.
A systematic error or flaw in an AI model's predictions, often due to biased training data.
12.
The data used to train an AI model.
13.
When an AI model generates incorrect or nonsensical information.
14.
A type of AI model inspired by the structure of the human brain.
15.
The input text given to an AI model to guide its output.
16.
A variable that is adjusted during training to optimize a model's performance.
17.
A type of machine learning where an agent learns to make decisions by receiving rewards or penalties for its actions.
18.
A type of machine learning where a model is trained on labeled data.
19.
A type of machine learning where a model is trained on unlabled data.
20.
A type of reinforcement learning where human feedback is used to guide the learning process.
21.
The closely coordinated responses from human scientists during a machine's learning process.
22.
Issues that AI stakeholders must consider to ensure that the technology is developed and used responsibly.
23.
A set of protocols that determine how two software applications will interact.
24.
The large data sets that AI study to reveal patterns and trends.
25.
A computerized model that focuses on mimicking human thought processess.
26.
How computers can gain understanding through images and videos.
27.
Closely examining data to identify patterns and glean insights.
28.
When an AI system shows unpredictable or unintended capabilities that only occur when individual parts interact as a wider whole.
29.
Mechanisms and frameworks designed to ensure that AI systems operate within ethical, legal, and technical boundaries.
30.
An incorrect response or false information from an AI system that is presented as fact.
31.
Control the learning process and determine the values of model parameters.
32.
A type of AI system that receives knowledge from real-time events and stores it in a database to make better predictions.
33.
A type of analytics that uses technology to predict what will happen in a specific time frame based on historical data and patterns.
34.
A type of analytics that uses technology to analyze data for factors such as possible situations and scenarios, past and present performance, and other resources to help make better strategic decisions.
35.
The process of using AI to analyze the tone and opinion of a given text.
36.
Data that is defined and searchable.
37.
Evaluates a machine's ability to exhibit intelligence equal to humans.
