Understanding AI: From Brain Differences to Data Science Practices and Large Model Applications

Human Brain vs. Artificial Intelligence – AI today cannot generate self‑awareness because it is fundamentally based on logical, low‑dimensional processes, unlike human consciousness which operates at a higher dimensional level.

Data Science in Large Models – The workflow includes data preprocessing (cleaning, handling missing values, normalization), exploratory data analysis (visualization, statistical tests), modeling and evaluation (linear regression to deep neural networks, cross‑validation, ROC, confusion matrix), posterior analysis (feature contribution, hidden‑layer visualization), experiment design and hypothesis testing (e.g., A/B testing), and effective communication through visualization.

General Applications of Large Models – Natural language processing (text generation, Q&A, sentiment analysis), knowledge‑graph construction, personalized recommendation, intelligent customer service, content creation, education tutoring, medical consultation, market forecasting, etc.

Vertical Applications in Enterprises – Human resources (resume screening, satisfaction surveys), administrative management (meeting‑room booking, expense processing), product‑research teams (project tracking, code review), teachers (classroom management, student tracking), research staff (course design, literature gathering), smart scheduling, knowledge‑base construction, IT monitoring, legal contract review, finance (billing, budgeting, forecasting).

Machine‑Learning Workflow – Steps: data acquisition, basic processing, feature engineering, model training, evaluation (precision, recall, F1), offline testing, online validation, and deployment.

Transformer and Fine‑Tuning – Overview of self‑attention, autoregressive generation, and the four‑stage training pipeline (pre‑training, supervised fine‑tuning, reward modeling, reinforcement learning). Detailed fine‑tuning procedure includes selecting a pre‑trained model, data preparation (train/validation/test split), freezing weights, defining update scope (LoRA on attention matrices), initializing parameters, training, adjusting hyper‑parameters, testing, and iterative training.

Embedding and Retrieval – Convert text to vectors for similarity search, build a knowledge base, perform semantic retrieval, and inject retrieved chunks into the model for answering user queries.

Dataset Construction – Sources: public datasets, web crawling, crowdsourcing, partner data, user‑generated content, simulation environments, sensor collection, and synthetic data generation with LLMs. Emphasizes proper labeling, splitting ratios, and the role of negative samples.