traditional verses generative ai comparison
0 likes9 views
The document compares generative AI and traditional machine learning, highlighting that generative AI works with unstructured text and learns from natural language, while traditional ML is focused on structured data. It notes that generative models use complex, layered structures that may lack explainability, whereas traditional ML follows clear mathematical frameworks and is more explainable. Both approaches can be fine-tuned for specific applications, but they differ in their training methodologies and domains.
1 of 1
Download to read offline
More Related Content
More from Emily Kunka, MS, CCRP (16)
Ad
Recently uploaded (20)
Ad
traditional verses generative ai comparison
- 1. 1 2 3 4 5 1 2 3 4 5 COMPARISON GENERATIVE AI TRADITIONAL ML DEALS WITH UNSTRUCTURED TEXT FROM BOOKS, ARTICLES, THE INTERNET LEARNS FROM NATURAL LANGUAGE (TRILLIONS OF WORDS) GENERAL PURPOSE, MANY DOMAIN, BUT CAN BE FINE-TUNED FOR SPECIFIC APPLICATIONS LEARNS LANGUAGE PATTERNS, CONTEXT, AND SEMANTICS MORE COMPLEX, LAYERED, NONLINEAR MODELS CONSIDERED TO BE LESS EXPLAINABLE TRAINED USING STRUCTURED NUMERICAL AND CATEGORICAL DATA (DISCRETE UNITS) IN TABULAR FORMAT MANUAL ANNOTATION AND LABELING FOR SUPERVISED LEARNING AND UNLABELED DATA IN SELF-SUPERVISED LEARNING GENERALLY SPECIFIC TO A SINGLE TASK AND A SINGLE DOMAIN GROUNDED IN STATISTICAL, PROBABLISTIC, AND OPTIMIZATION THEORIES CLEAR MATHEMATICAL FRAMEWORK, CONSIDERED TO BE MORE EXPLAINABLE EMILY LEWIS INNOVATION