RLHF, or Reinforcement Learning from Human Feedback, is a training method that helps AI generate more natural and human-like responses by incorporating human feedback. By learning from how people evaluate its replies, AI can gradually improve the way it communicates, making interactions feel more relatable and friendly.
Title: [Episode 51] How Much Should AI Know? A Look into “Data Privacy” That Supports Convenience and Peace of Mind
Excerpt:
Between the convenience of AI and the concerns it raises lies the important concept of “data privacy.” It plays a key role in protecting personal information and is essential for building trustworthy AI.
Anthropic’s Claude Code is revolutionizing workflows by integrating AI across departments, enhancing collaboration and automating tasks for both technical and non-technical staff.
The phenomenon where AI confidently generates incorrect information, known as “hallucination,” highlights the limitations of language-based AI systems. Since these models learn patterns of words rather than actual knowledge, they can sometimes produce content that sounds plausible but is factually wrong. This issue is especially critical in fields like medicine and law, where accuracy is essential.
Agentic AI is revolutionizing financial services by enhancing efficiency and security through automation in customer service, fraud detection, and document processing.
AI governance refers to the rules and frameworks designed to ensure that artificial intelligence is used safely and fairly. It emphasizes transparency, accountability, and the need to adapt to rapid technological advancements. As AI becomes more integrated into our daily lives, governance plays a crucial role in building trust and ensuring that these technologies are developed and applied with care and responsibility.
Title: [Episode 48] Entering an Era Where We Can Ask AI “Why?” — The Trust and Assurance Aimed for by Explainable AI
Excerpt:
“Explainable AI,” which allows us to understand how AI makes its decisions, plays a key role in enhancing trust and safety. It is increasingly expected to be applied in various fields such as healthcare and finance.
AI systems can also exhibit “unfairness,” which means they may make decisions that disadvantage certain individuals or groups. To address this, the concept of “fairness” has become increasingly important. Fairness in AI involves efforts to reduce bias and ensure that decisions are made in a way that is just and equitable. Achieving fairness requires both technical approaches and ethical considerations, as developers strive to create AI systems that treat everyone with respect and impartiality.
As AI evolves, businesses are shifting towards hybrid systems that enhance reliability and predictability in complex applications, ensuring safer outcomes.
The reason why AI can make biased decisions lies in the data it learns from, which often reflects human society’s assumptions and prejudices. Understanding this helps us use AI more fairly.
