The Alignment Problem in artificial intelligence refers to the challenge of ensuring that AI systems act in ways that are aligned with human values and intentions. This problem arises from the difficulty in defining objectives in specific ways and subsequently encoding complex ethical principles and preferences into machine-operable formats. As AI systems become more autonomous and integrated into various aspects of daily life, the stakes of misalignment increase, potentially leading to unintended consequences.

Reinforcement Learning is a “type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences” (Bhatt, Shweta. “Reinforcement Learning 101.” Medium, 19 Mar. 2018). Unlike supervised learning where the model is trained with the correct answer, in reinforcement learning, the agent learns from the consequences of its actions through rewards or penalties. The concept of reinforcement learning was developed by B.F. Skinner, whose wartime experiments on pigeons used external rewards to “sculpt” the pigeons’ behavior.

Word embedding is a technique in language processing where words are mapped to vectors of numbers. This process captures the semantic relationships between words. Common models used to generate word embeddings include Google’s word2vec and Stanford’s GloVe, which use large datasets of text to learn these representations.