Generative artificial intelligence (AI) is shaking things up in tech, driving big changes across industries by creating new content based on massive datasets. This technology is rewriting the rules on creativity, productivity and problem-solving. But as these models get smarter a tricky issue is starting to crop up: will we have enough data to keep fueling their growth? This interesting subject came to us from datanami in their article, “Are We Running Out of Training Data for GenAI?“
These generative AI models need huge amounts of data to pick up on patterns, structures and relationships. Take large language models (LLMs), for example – they’re trained on billions of words pulled from books, articles, websites and other sources. This data helps them understand and produce human-like text. Image-generating models work the same way, relying on vast collections of images to grasp shapes, colors and textures.
The performance of these AI models depends a lot on both the quality and quantity of the data they’re fed. The more data they get the better they become at being accurate, creative and aware of context. But as generative AI keeps advancing and spreading there’s a growing worry about how sustainable this data-heavy approach really is.
By finding new data sources, tightening ethical standards and exploring innovative training methods generative AI can keep growing even as data becomes a valuable resource.
Melody K. Smith
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