Evolution of Data Sets in AI Training The importance of data sets in training AI models is discussed, highlighting the evolution from custom-built data sets to large-scale unstructured data like Common Crawl and Lyon.
Curating Large-Scale Data Sets Challenges with creating and curating massive data sets for modern machine learning models are explored, emphasizing the need for human involvement in labeling but also acknowledging limitations due to scale.
Generative Models & Ethical Concerns Discussion on how generative models like GPT rely on vast amounts of diverse training data, leading to challenges in curation and attribution as well as potential ethical concerns around using publicly available images without explicit consent.
Fine-Tuning Models with Human-Generated Datasets Exploration into the process of fine-tuning AI models with specific structured datasets created by humans through conversations or interactions rather than relying solely on internet-scraped content. The role of curated human-generated datasets is crucial for model performance optimization.