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34 SMT007 MAGAZINE I MAY 2024 Natural language processing (NLP): NLP uses an understanding of the structure, gram- mar, and meaning in words to help comput- ers "understand and comprehend" language. NLP requires a large corpus of text (usually half a million words). NLP technologies help in many situations that include scanning texts to turn them into editable text (optical char- acter recognition), speech to text, voice-based computer help systems, grammatical correc- tion (like auto-correct or Grammarly), sum- marizing texts, and others. Neural networks (NN): Neural networks, also called artificial neural networks (ANN) are a subset of ML algorithms. ey were inspired by the interconnections of neurons and syn- apses in the human brain. In a neural network, aer data enter the first layer, the data go through a hidden layer of nodes where calcu- lations that adjust the strength of connections in the nodes are performed and then go to an output layer. Overfitting: Overfitting is a problem that occurs when a model is too complex, per- forming well on the training data but poorly on unseen data. Example: A model that has memorized the training data instead of learn- ing general patterns and thus performs poorly on new data. Predictive analytics: Predictive analytics is a type of analytics that uses technology to pre- dict what will happen in a specific time frame based on historical data and patterns. Prescriptive analytics: Prescriptive analytics is a type of analytics that uses technology to analyze data for factors such as possible situ- ations and scenarios, past and present perfor- mance, and other resources to help organiza- tions make better strategic decisions. Structured data: Structured data is data that is defined and searchable. is includes data like phone numbers, dates, and product SKUs. Training data: is is the data used to train the algorithm or machine learning model. is data has been generated by humans in their work (or other contexts) in the past. While it sounds simple, training data selec- tion is important because the wrong data can perpetuate systemic biases. If you are train- ing a system to help with hiring people, and you use data from existing companies, you will be training that system to hire the kind of people who are already there. Algorithms take on the biases that are already inside the data. Transformer models: Used in GenAI (the T in GPT stands for Transformer), transformer models are a type of language model. ey are neural networks and are also classified as deep learning models. ey allow AI systems to determine and focus on important parts of the input and output using a self-attention mecha- nism to help. Unstructured data: Unstructured data is data that is undefined and difficult to search. is includes audio, photo, and video content. Most of the data in the world is unstructured. User experience design/user interface design (UX/UI): User-experience/user-interface design refers to users' overall experience with a prod- uct. ese approaches are not limited to AI work. Product designers implement UX/ UI approaches to design and understand the experiences their users have with their tech- nologies. SMT007 Resources • Glossary of Artificial Intelligence Terms for Educators, by Pati Ruiz and Judi Fusco, CIRCLS. • "Artificial Intelligence (AI) Terms: A to Z Glossary," Coursera.org. • "AI Terms Explained," Moveworks.com.