Artificial neural networks are among the most successful modern machine learning methods, in particular because their non-linear parametrization provides a flexible way to implement feature learning (see, e.g., Goodfellow et al., 2016, chapter 15).

In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward.

What does it mean to have speech without a speaker? For the first time, speech has been decoupled from consequence. We now live alongside AI systems that converse knowledgeably and persuasively--deploying claims about the world, explanations, advice, encouragement, apologies, and promises--while bearing no vulnerability for what they say. Millions of people already rely on chatbots powered by large language models, and have integrated these synthetic interlocutors into their personal and professional lives. An LLM's words shape our beliefs, decisions, and actions, yet no speaker stands behind them. This dynamic is already familiar in everyday use. A chatbot gets something wrong. When corrected, it apologizes and changes its answer.

However, such pessimism for out-of-sample data could be too restricted and sample inefficient, as not all out-of-sample(unseen) states are not generalizable [20].