This paper aims to present a landscape of AI governance for and from the Global South,
advanced by critical and decolonial-informed practitioners and scholars, and contrast this …

Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement
Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

Should we care whether AI systems have representations of the world that are similar to
those of humans? We provide an information-theoretic analysis that suggests that there …

Recommender systems are the algorithms which select, filter, and personalize content
across many of the world's largest platforms and apps. As such, their positive and negative …

Interpretable machine learning has exploded as an area of interest over the last decade,
sparked by the rise of increasingly large datasets and deep neural networks …

Increasingly complex and autonomous systems require machine ethics to maximize the
benefits and minimize the risks to society arising from the new technology. It is challenging …

Big models, exemplified by Large Language Models (LLMs), are models typically pre-
trained on massive data and comprised of enormous parameters, which not only obtain …

The book's core argument is that an artificial intelligence that could equal or exceed human
intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical …

Biological and artificial information processing systems form representations of the world
that they can use to categorize, reason, plan, navigate, and make decisions. To what extent …