What does generative AI mean for Web3?

cointelegraph.com:

For decades, artificial intelligence (AI) has been a laymen’s lingo.

Current technological breakthroughs and advancements have created a surge of interest in a specific type of AI known as generative AI. With its unprecedented ability to generate new and distinctive content that aids human creativity, generative AI revolves around analysis, automation and generation of content. 

It is intriguing to learn how generative AI fits into the vernacular of all pragmatic applications. As per a BCG blog, the generative AI sector will gain an estimated 30% share of the whole AI market by 2025, which is equal to $60 billion of the total addressable AI market. 

Generative AI is a subset of machine learning that uses neural networks to generate new content. Unlike other AI systems programmed to perform specific tasks, Generative AI functions on large datasets and produces content that is new, unique and sometimes unpredictably informative. 

One of the most popular types of generative AI is generative adversarial networks (GANs). GANs consist of two neural networks: a generator and a discriminator. The generator creates new content, and the discriminator evaluates whether the content is real or fake. These networks continuously learn from each other, improving the quality of the generated content over time.

Generative AI has the potential to transform how we utilize AI, from producing realistic synthetic data for training AI models, to curating tailored content for customers. The quality of the content produced by GANs has subsequently increased over time. Today, GANs produce pictures and videos that can be nearly indistinguishable from the originals.

For instance, to speed up and lower the cost of the design process, businesses like H&M