According to @karpathy, the influential 2015 paper on 'style transfer' initiated a significant line of research in artificial intelligence, leading to advances in generative models and creative AI tools. This breakthrough has fostered the development of AI-powered NFT art platforms and algorithmic trading strategies, impacting the cryptocurrency market by driving demand for AI-generated digital assets and increasing volatility in related tokens (source: @karpathy, Twitter).

According to @AIatMeta, Meta has introduced Adjoint Sampling, a new learning algorithm designed to train generative models using scalar rewards. This technique, grounded in FAIR's recent theoretical work, enhances scalability and efficiency in AI model training (source: AI at Meta, May 20, 2025). For crypto traders, advancements like Adjoint Sampling could accelerate AI-driven blockchain analytics, smart contract optimization, and automated trading bots, increasing market volatility and opening new trading opportunities as AI-powered crypto tools proliferate.