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.
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.
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.
Cloud AI is a technology that allows us to access powerful computers and artificial intelligence systems via the internet, making our daily lives more convenient. This technology plays a significant role behind the scenes in many of the services we use every day, often without us even realizing it. As it continues to evolve, Cloud AI is expected to bring even more innovation and comfort to our lives in the future.
TPU (Tensor Processing Unit) is a specialized computing chip designed specifically for AI. It enables high-speed processing tailored for deep learning, playing a crucial role in advancing AI services.
GPU is a crucial component that supports AI learning and image processing. With its ability to perform parallel computations, it can efficiently handle large volumes of data.
Cosine similarity is a key technique used to quantify how similar two pieces of information—such as texts or images—are. It plays an important role in helping search engines and AI systems present relevant results by measuring the closeness between data in numerical form.
By utilizing RAG (Retrieval-Augmented Generation), AI systems can look up relevant information before generating a response. This approach enables them to provide more accurate and reliable answers, enhancing the overall quality of information delivery.
