4/22/24, 1:25 PM 50 Al Terms Every Beginner Should Know | TELUS International Model: A broad term referring to the product of Al training, created by running a machine learning algorithm on training data. Neural network: Also called a neural net, a neural network is a computer system designed to function like the human brain. Although researchers are still working on creating a machine model of the human brain, existing neural networks can perform many tasks involving speech, vision and board game strategy. Natural language generation (NLG): This refers to the process by which a machine turns structured data into text or speech that humans can understand. Essentially, NLG is concerned with what a machine writes or says as the end part of the communication process.

Natural language processing (NLP): The umbrella term for any machines ability to perform conversational tasks, such as recognizing what is said to it, understanding the intended meaning and responding intelligibly. Natural language understanding (NLU): As a subset of natural language processing, natural language understanding deals with helping machines to recognize the intended meaning of language taking into account its subtle nuances and any grammatical errors. Overfitting: An important Al term, overfitting is a symptom of machine learning training in which an algorithm is only able to work on or identify specific examples present in the training data. A working model should be able to use the general trends behind the data to work on new examples. Parameter: A variable inside the model that helps it to make predictions. A parameters value can be estimated using data and they are usually not set by the person running the model.

Pattern recognition: The distinction between pattern recognition and machine learning is 4/22/24, 1:25 PM 50 Al Terms Every Beginner Should Know | TELUS International Semantic annotation: Tagging different search queries or products with the goal of improving the relevance of a search engine. often with the goal of determining the writers attitude towards something. Strong Al: This field of research is focused on developing Al that is equal to the human mind when it comes to ability. General A/is a similar term often used interchangeably. Supervised learning: This is a type of machine learning where structured datasets, with inputs and labels, are used to train and develop an algorithm. Test data: The unlabeled data used to check that a machine learning model is able to perform its assigned task.

Training data: This refers to all of the data used during the process of training a machine learning algorithm, as well as the specific dataset used for training rather than testing. Transfer learning: This method of learning involves spending time teaching a machine to do arelated task, then allowing it to return to its original work with improved accuracy. One potential example of this is taking a model that analyzes sentiment in product reviews and asking it to analyze tweets for a week. Turing test: Named after Alan Turing, famed mathematician, computer scientist and logician, this tests a machines ability to pass for a human, particularly in the fields of language and behavior. After being graded by a human, the machine passes if its output is indistinguishable from that of human participants. Unsupervised learning: This is a form of training where the algorithm is asked to make inferences from datasets that dont contain labels. These inferences are what help it to learn.