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.
LLMOps is a management approach designed to ensure the stable operation of large language models. As we move toward a society where AI works alongside humans, understanding this concept becomes an essential perspective.
Investing in AI infrastructure is crucial for the U.S. to maintain its competitive edge in technology, requiring robust energy and skilled workforce development.
Title: [Episode 44] What Is “MLOps” and Why It’s Essential Beyond Just Building AI
Excerpt:
MLOps is a crucial concept that ensures machine learning models continue to operate reliably even after they are built. It connects the development and deployment of AI in a smooth and sustainable way.
The reason why AI runs so smoothly on smartphones is thanks to a technology called “inference optimization.” This technique adjusts AI models to be lightweight and energy-efficient, allowing them to operate quickly even on devices with limited processing power.
Anthropic’s commitment to the EU’s AI Code of Practice underscores its dedication to responsible AI development and collaboration for enhanced safety and transparency.
The reason why AI runs smoothly on smartphones lies in a technology called “model compression.” This technique makes it possible to shrink large AI models, allowing powerful features to work efficiently even on palm-sized devices.
