As generative AI continues to evolve, a key focus is on the data side of things. Having the best dataset is essential for AI projects to deliver more human-like responses to queries.
Without quality data inputs, the outputs generated by AI systems can be underwhelming. This has led to major players like Google, X, and OpenAI making efforts to secure high-quality data sources to enhance their AI capabilities.
Recent advancements include Meta launching a new web crawler named the “Meta External Agent” to gather more data from the open web for its Llama models. This move highlights the increasing importance of improving data ingestion processes in order to enhance AI resources.
Google, a pioneer in web scraping for Search results, already has a wealth of data. However, many publishers are now blocking crawlers from AI companies like OpenAI to protect their data.
Meta’s new crawler is not facing mass blocking yet, providing a valuable source of inputs for training its large language models. With billions of active users, Meta already has vast public Facebook and IG posts as a data source.
While Google has a robust question base, it sources answers from third-party websites, making deals like the one with Reddit crucial for training LLMs. Similarly, X focuses on real-time updates for its Grok chatbot, aiming to provide relevant, up-to-date information directly from X posts.
Social platforms like X and Meta are incentivizing users to pose engaging questions that fuel responses, aligning users around providing the necessary data for AI advancements.
Social platforms are now incentivizing users to ask questions and drive engagement, as human answers play a key role in developing more human-like AI responses.
Tools like Answer the Public can help individuals and businesses identify common search queries related to their chosen keywords, providing insights to boost social media engagement.