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30 SMT007 MAGAZINE I MAY 2024 Algorithm: Algorithms are the "brains" of an AI system, and what determines decisions. In other words, algorithms are the rules for what actions a computer takes. e AI system is based on algorithms. Machine learning algo- rithms can discover their own rules (see Adap- tive learning and Machine learning for more) or be rule-based, where human programmers give the rules. Big data: Refers to the large data sets that can be studied to reveal patterns and trends to sup- port business decisions. It's called "big" data because organizations can now gather massive amounts of complex data using data collection tools and systems. Big data can be collected very quickly and stored in a variety of formats. Black boxes: Many machine learning algo- rithms are "black boxes," meaning that we don't have an understanding of how a system is using features of the data when making deci- sions (generally, we do know what features are used but not how they are used). ere are cur- rently two primary ways to pull back the cur- tain on the black boxes of AI algorithms: inter- pretable machine learning and explainable machine learning (see definitions). Chat-based generative pre-trained trans- former (ChatGPT) models: A system built with a neural network transformer style of AI model, these systems work well with natural language processing tasks (see definitions for Neural networks and Natural language pro- cessing). In this case, the model: 1) can gen- erate responses to questions (Generative); 2) was trained in advance on a large amount of the written material available on the web (Pre- trained); 3) and can process sentences dif- ferently than other types of models (Trans- former). Computer vision: Computer vision is a set of computational challenges concerned with teaching computers how to understand visual information, including objects, pictures, scenes, and movement (including video). Computer vision (oen thought of as an AI problem) uses techniques like machine learn- ing to achieve this goal. Data mining: Data mining is the process of sorting through large data sets to identify pat- terns that can improve models or solve prob- lems. Deep learning: Deep learning models are a subset of neural networks. With multiple hid- den layers, deep learning algorithms are poten- tially able to recognize more subtle and com- plex patterns. Like neural networks, deep learning algorithms involve interconnected nodes where weights are adjusted, but as men- tioned earlier, there are more layers and more calculations that can make adjustments to the output to determine each decision. e deci- sions made by deep learning models are oen very difficult to interpret as there are many hidden layers doing different calculations that are not easily translatable into English rules (or another human-readable language). Explainable machine learning (XML) or explainable AI (XAI): Researchers have devel- oped a set of processes and methods that allow humans to better understand the results and outputs of machine learning algorithms. is helps developers of AI-mediated tools under- stand how the systems they design work and can help them ensure that they work correctly and are meeting requirements and regulatory standards. Foundation models: Foundation models rep- resent a large amount of data that can be used as a foundation for developing other mod- els. For example, generative AI systems use large language foundation models. Foundation models can speed up the development of new systems, but there is controversy about issues of trustworthiness and bias in the data. Generative AI (GenAI): A type of machine learning that generates content, currently such as text, images, music, videos, and can cre- ate 3D models from 2D input. See ChatGPT