Artificial Intelligence (AI) has revolutionized the way businesses operate, offering powerful tools to analyze data, automate processes, and enhance decision-making. At Authenticx, we utilize a diverse range of AI models to deliver actionable insights and improve outcomes for our clients.
Selecting the right model for the right task is crucial. In this article, we'll explore the various AI models we offer and their unique capabilities and applications.
AI Model Types
Generative AI (GenAI)
Definition: AI that produces new content based on input data, generating text, images, or other media by learning patterns from large datasets.
Application: Used for creating original text, images, audio, or video by learning from and reacting to existing data patterns.
Large Language Models (LLM)
Definition: AI models trained on vast datasets to understand and generate human-like text by predicting the next word or phrase based on context.
Application: Ideal for tasks involving text comprehension, summarization, translation, question answering, and content generation.
Natural Language Understanding (NLU)
Definition: A branch of AI that focuses on interpreting the meaning, intent, and context behind text or speech, beyond simple pattern matching.
Application: Used for tasks like sentiment analysis, intent recognition, context understanding, and extracting structured information from unstructured text.
Deep Learning (DL)
Definition: This model type trains on layered datasets to develop "neural pathway"-like capabilities, allowing it to analyze and understand content similarly to a human analyst.
Application: Effective for complex data analysis, especially when interpreting intricate patterns or making detailed predictions.
Machine Learning (ML)
Definition: A type of AI where models are trained on large datasets to make predictions or decisions without being explicitly programmed, improving over time as they are exposed to more data.
Application: Suitable for structured data tasks like classification, regression, and predictive analytics. ML models can adapt and improve based on new information but are often less suited for handling unstructured data like long-form text.
Additional AI Terminology
Data Labeling: The process of providing structure to data by creating labels that models can learn to apply, enabling the identification of specific information within the data.
Drift: Refers to an unacceptable level of inaccuracy in the results provided by a model when compared to human scoring. Addressing drift is crucial for maintaining model reliability.
Inter-Rater Reliability (IRR): A measure of consistency among human analysts' responses, indicating how much discrepancy exists between their evaluations. High IRR is required to build an effective AI model that can reliably match human judgement.
Confusion Matrix: A tool used to determine a model's accuracy by comparing its outputs to human responses, allowing for a clear understanding of where the model performs well and where improvements are needed.
ℹ️ To learn about how these models are utilized in the Authenticx platform, take a look at our Authenticx AI Models article.