AI Model Fundamentals

I. A paradigm shift in intelligence — overview of AI’s technical evolution

    graph LR
    A1["Explicit rules<br/>(Rule-based)"] -- "Data-driven learning<br/>(Machine Learning)" --> B1["Autonomous intelligence<br/>(Generative AI)"]

Definition: the technical journey from simple logic circuits and rules, through systems that learn patterns from data on their own, to systems capable of human-level generation and reasoning.

Characteristics: ( Staged evolution ) a layered progression through statistics, machine learning, and deep learning to large language models ( Expanding generality ) moving from performance tuned for a narrow domain toward general-purpose AI ( AGI ) applicable across every industry

II. Detailed classification and mechanisms of AI technology

A. The mechanics of AI’s technical evolution

    flowchart TD
    subgraph S1["Classic AI & Statistics"]
        A2["Rule-based AI"] --> B2["Naïve Bayes"]
        B2 --> C2["HMM / MCMC"]
        D2["K-NN / SVM"] --- B2
    end

    subgraph S2["Machine Learning Evolution"]
        E2["Decision Tree"] --> F2["Ensemble / Random Forest"]
        G2["Genetic Algorithm"] --> H2["Optimization"]
    end

    subgraph S3["Connectionism to Deep Learning"]
        I2["Neural Network"] --> J2["Backpropagation"]
        J2 --> K2["Deep Learning"]
    end

    subgraph S4["Modern AI Era"]
        K2 --> L2["CNN (Vision)"]
        K2 --> M2["RNN (Sequence)"]
        M2 --> N2["NLP / Transformer"]
        N2 --> O2["LLM (Generative)"]
        O2 --> P2["Multimodal AI"]
        L2 --> P2
    end

    S1 -- "Learning from Data" --> S2
    S2 -- "Non-linear Mapping" --> S3
    S3 -- "Architecture Scaling" --> S4

    style S4 fill:#f5f3ff,stroke:#7c3aed,stroke-width:2px
    style O2 fill:#7c3aed,color:#fff
    style P2 fill:#7c3aed,color:#fff

B. Role and evolutionary stage of each major model

Stage	Key models	Core contribution & relationship
Stage 1: Rules & statistics	Rule-based, Naïve Bayes, HMM	Solve explicit problems by directly injecting human knowledge or relying on statistical probability
Stage 2: Feature-based learning	Decision Tree, SVM, K-NN	Extract features from data and classify by finding geometric/logical boundaries
Stage 3: Neural networks & optimization	Neural Network, Backpropagation	Mimic biological neurons and build complex learning systems via error backpropagation through differentiation
Stage 4: Deep learning ( DL )	Deep Learning, CNN, RNN	Stack layers deeply to automate high-level abstraction of data (specialized for images, time series)
Stage 5: Large models & generation	NLP, LLM, Multimodal AI	Achieve human-level language understanding and multi-sense integration via self-supervised learning and attention

III. Complementary relationships and trends across technologies

A. Interaction between technologies

Deterministic vs. probabilistic logic: the reliability of rule-based systems is being combined with the flexibility of neural networks into neuro-symbolic AI.
Global vs. local optimization: techniques such as genetic algorithms and MCMC are used for hyperparameter optimization to find global optima that backpropagation alone can miss.
From simple classification to complex generation: where SVM and K-NN focused on classifying structured data, Transformer-based LLMs evolved to learn the relationships between data and generate new content.

Study guide — The list in the left sidebar is ordered by the complexity and historical emergence of each technique, from foundational Rule-based AI through to the most recent Multimodal AI. Working through it in order will give you an organic understanding of both the foundations of AI technology and its most recent trends.