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Artificial intelligence (AI) is progressively transforming the professional landscape, with Chief Information Officers (CIOs) being at the forefront of steering this evolution. Generative AI, in particular, is gaining increasing interest, especially within the Microsoft 365 ecosystem, where AI-based tools such as Copilot and Azure OpenAI promise to enhance productivity and simplify data management. However, mastering these new technologies requires understanding the underlying vocabulary and concepts. This glossary aims to assist CIOs and their teams by providing essential knowledge to navigate the complex world of generative AI.
1. General AI Terminology
Artificial Intelligence (AI): The simulation of human intelligence by machines, often through algorithms and neural networks.
Machine Learning: A subset of AI that enables systems to learn and improve without being explicitly programmed.
Deep Learning: A machine learning method using artificial neural networks to analyze data across multiple layers.
Model: A set of algorithms and structures used to process data and generate predictions or outcomes.
Artificial Neural Network (ANN): An AI model inspired by the human brain, used for processing complex information.
Training Data: A dataset used to teach a model how to make predictions or classifications.
2. Generative AI Technologies and Models
Language Model: An algorithm capable of generating coherent text based on large amounts of textual data.
GPT (Generative Pre-trained Transformer): A family of language models used to autonomously generate text, such as GPT-4.
Transformer: An AI architecture primarily used in natural language processing models, efficient for sequential tasks.
Data Augmentation: A technique used to increase the size of a dataset by creating new artificial data from existing data.
Prompting: A technique where text instructions are given to a model to generate a specific response.
Fine-tuning: The process of adapting a pre-trained AI model to a specific task by refining its parameters with a targeted dataset.
3. Microsoft AI Services and Tools
Azure OpenAI: Microsoft’s cloud service providing access to OpenAI models, such as GPT-4, in a secure environment, with direct integration into enterprise data.
Azure Machine Learning: A Microsoft platform for creating, training, and deploying AI models at scale, enabling complete AI lifecycle management.
Cognitive Services: A set of Microsoft Azure APIs that add AI functionalities to applications, such as image recognition, text translation, or sentiment analysis.
Microsoft Power Automate: A workflow automation tool that uses AI to automate tasks within Microsoft 365, SharePoint, and other services.
Power BI with AI: A data analysis platform that integrates AI capabilities for predictive and automated analytics.
Microsoft Copilot: A feature integrated into Word, Excel, and Teams that uses generative AI to assist users in content creation, report writing, or data analysis.