AI Introduction and Teaching

Rivos chips, with their open RISC-V architecture, chiplet modular design, and high-energy-efficiency AI acceleration capabilities, have become a popular choice for AI startups seeking breakthroughs in high-performance computing. This article analyzes their technological innovations for applications such as large model training, edge inference, and AIaaS, and...

This article, from a news reporting perspective, focuses on five key techniques used by AI platforms and enterprises in model compression and inference acceleration: quantization, pruning, knowledge distillation, lightweight architecture design, compilers, and hardware acceleration. The content covers mainstream methodologies...

Human-feedback-based reinforcement learning (RLHF) is poised to become an indispensable core technology for large-scale model training and intelligent upgrades in the AI field by 2025. This article comprehensively reviews the fundamental principles of RLHF, its differences from traditional RL, key training processes, and mainstream application tools, and delves into data bottlenecks, reward models, etc.

Overfitting is a core challenge in machine learning, referring to a model's excessive fit to training data, which reduces its predictive ability for new data. As AI becomes increasingly prevalent in industries such as healthcare, finance, and e-commerce, overfitting not only affects decision accuracy but can also pose significant risks. This….