Unleashing the Power of AI: Your Guide to Key AI Terms and Concepts

Algorithm: A set of computational instructions enabling a computer to perform tasks like object recognition or predictive analytics. 
 
Artificial Intelligence (AI): A field in computer science focused on creating machines that replicate cognitive tasks such as problem-solving, reasoning, perception, and language understanding. 
 
Artificial General Intelligence (AGI): AI capable of solving a wide range of problems, similar to human cognitive abilities, rather than specializing in one specific task. 
 
GPT: Generative Pre-trained Transformer model, a deep learning language model used in natural language processing tasks. 
 
ChatGPT: A conversational AI model based on GPT, used for creating chatbots or interacting with users in natural language. 
 
Classification: Categorizing various AI forms based on features like data type, tasks, and employed techniques. 
 
Computing Power: The processing power available to a computer system, influencing the speed and accuracy of AI algorithms. 
 
Deep Learning: A machine learning type using multilayered neural networks to extract high-level patterns from data. 
 
Diffusion Model: A model learning the latent structure of data by simulating data point diffusion through latent space, useful in tasks like image generation and super-resolution. 
 
Fine-Tuning: Making small adjustments to an AI algorithm to enhance performance. 
 
Generative AI: A type of artificial intelligence that creates new content, such as text, images, or music, by learning patterns from existing data. 
 
Hallucinations: In AI, "hallucinations" refer to instances where the system generates incorrect or misleading information that appears plausible but lacks accuracy. 
 
Large Language Model (LLM): AI subset enabling machines to understand and process large text quantities. 
 
Machine Learning: AI subset allowing computers to discover data patterns to make predictions or decisions independently. 
 
Plugins: Extensions or add-ons that enhance the functionality of an AI system by providing additional features or integrating with other tools and services. 
 
Prompts: Inputs or queries given to an AI model that guides it in generating relevant responses or content based on the provided information. 
 
Responsible AI: Designing AI applications to minimize bias, preserve privacy, and comply with regulatory standards. 
 
Synthetic Data: Artificially generated data used as a substitute for real-time data to train AI algorithms. 
 
Transformers: Neural networks using self-attention mechanisms to process large data sequences. 
 
Training Data: Data set used to "train" an AI algorithm, enhancing its performance. 
 
Workflow Automation: Using software technologies to automate recurring tasks within business processes.