2025 Year in Review: Spatial Mapping & HDVO Architecture Báo Cáo Tổng Kết Thành Quả Công Nghệ 2025 CODE AT THE SPEED OF THOUGHT Chuẩn hóa kiến trúc HDVO thông qua S-prompting cho các hệ thống vận hành giả thiết (phân tích hoặc kiểm thử), ở quy mô vượt ngưỡng thử nghiệm — nơi bắt đầu phát sinh hệ quả ngữ nghĩa của một ứng dụng thực (~5.000 dòng mã). 5.000+ Dòng mã / Ứng dụng Mốc quy mô mới cho các ứng dụng Single Page phức tạp điều phối bởi AI. 150+ ...
August 29, 2004 | [Works on HubPages] Data science without domain knowledge: An analysis reveals two salary policies in the AI/ML and Big Data fields
You might find it hard to believe in the capabilities of a data scientist/analyst without any domain knowledge, but the true goal of data science is to create expertise where none exists. In fact, the places where data science thrives the most should be those untouched by prior domain knowledge. This is very different from building a chatbot based on an enormous collection of conversations, gathered from previous interactions between human agents and customers. So, does it mean that simply having a massive dataset that covers every aspect of life is the answer? Like ChatGPT-5, 6, 7, or even ∞? The answer is, unfortunately, NO! Absolutely not! An Example Imagine you’re skeptical. Copy the section above and ask ChatGPT to complete it with the prompt, “Complete the following Medium story.” It becomes clear that if you’re under the impression that “in the future, ChatGPT will have enough domain knowledge to replace data scientists/analysts in business analysis,” the story generated by Chat...