What Are the Core Concepts Behind AI? From Data to Models Explained

Following the development trajectory of AI, this series introduces key technologies from Seq2Seq and RNN to Transformers and powerful GPT models, helping beginners and experienced developers understand the principles and implementation details of each step.

Artificial Intelligence (AI) refers to the ability of computers to perform decision‑making tasks that simulate human intelligence, such as natural language understanding, image recognition, problem solving, and reasoning. Machine Learning (ML) is a subset of AI that focuses on enabling computers to make decisions based on data, discovering patterns and using them for future predictions.

Deep Learning (DL) is a branch of ML that uses multi‑layer neural networks to handle complex data patterns, excelling at image and language tasks.

Machine Learning Basics

The core workflow of machine learning consists of three stages: 1) Memory – collecting and preparing data; 2) Formulation – building and training a model; 3) Prediction – using the trained model for inference.

In a house‑price prediction example, the dataset includes features such as area, location, number of bedrooms, etc., and the target label is the house price.

Structured data: tabular data where each row represents a house and each column a feature.

Unstructured data: images, text, etc., usually not stored in tables.

A model is a set of rules learned from data that can make predictions or decisions. Large models contain billions of parameters (weights and biases) that capture complex patterns.

DeepSeek’s 671B model has 671 billion parameters, enabling it to capture intricate data relationships and achieve high predictive accuracy.

Data Concepts

Data points (samples) consist of features (attributes) and, optionally, a label (target). Data can be classified by type:

Numerical data : values that can be used for mathematical operations (e.g., area, price).

Categorical data : values representing categories or states (e.g., region, house type).

Data can also be categorized by labeling:

Labeled data : each data point has an associated label.

Unlabeled data : data points lack labels, common in clustering tasks.

Linear Regression

Linear regression models the relationship between input features and a continuous target using the equation f(x)=ax+b, where a is the slope (weight) and b is the intercept (bias).

Supervised vs Unsupervised Learning

Supervised learning uses labeled data to learn a mapping from features to labels. It includes regression models (predicting continuous values) and classification models (assigning categories).

Unsupervised learning discovers hidden structures in unlabeled data. Common methods are clustering, dimensionality reduction, and generative algorithms.

Semi‑supervised learning combines a small amount of labeled data with a large amount of unlabeled data to improve model performance.

Reinforcement Learning

Reinforcement Learning (RL) studies how an agent interacts with an environment to maximize cumulative reward through trial‑and‑error, applied in game AI, autonomous driving, and robotics.

Feature Engineering

Feature engineering transforms raw data into useful inputs for models. Core tasks include feature selection, feature extraction, data cleaning, feature transformation, and encoding categorical variables.

Core Work

Feature selection : keep the most relevant features (e.g., area, location score, age for house‑price prediction).

Feature extraction : create new features automatically (e.g., using CNNs for image features).

Data cleaning : remove noise, fill missing values, correct outliers.

Feature transformation : normalize values to a common scale.

Encoding categories : convert categorical values to numeric (e.g., one‑hot encoding).

Tokenization, Vectorization, and Embedding

Tokenization splits text into basic units (characters, sub‑words, words, phrases). After tokenization, data must be converted to numbers.

Character‑level : split into individual characters.

Sub‑word level : split words into smaller units.

Word level : split sentences into words.

Phrase level : treat common multi‑word expressions as single tokens.

Vectorization transforms tokens into numeric vectors, enabling mathematical operations and batch processing. Traditional methods include Bag‑of‑Words, TF‑IDF, and one‑hot encoding, but they suffer from sparsity and lack of semantics.

Embedding is a dense, low‑dimensional representation that captures semantic relationships. Common embeddings:

Word2Vec : learns word vectors from context, placing similar words close in vector space.

BERT : provides contextualized embeddings where the same word has different vectors in different sentences.

from gensim.models import Word2Vec

sentences = [
    ["机器学习", "很", "有趣"],
    ["让", "我们", "一起", "学习"],
    ["机器学习", "是", "人工智能", "的", "一个", "分支"]
]

# Train Word2Vec model
model = Word2Vec(sentences, vector_size=50, window=2, min_count=1, workers=4)

# Get word vectors
word_vector_ml = model.wv['机器学习']
word_vector_ai = model.wv['人工智能']

print("机器学习的词向量：", word_vector_ml)
print("人工智能的词向量：", word_vector_ai)

# Compute similarity
similarity = model.wv.similarity('机器学习', '人工智能')
print("机器学习与人工智能的相似度：", similarity)

Output example:

机器学习的词向量： [0.00123456 0.00234567 ... 0.00345678]
人工智能的词向量： [0.00456789 0.00567890 ... 0.00678901]
机器学习与人工智能的相似度： 0.85

Why GPUs Are Essential for Large Models

GPUs excel at parallel matrix operations, which are the core of deep neural network training and inference. NVIDIA’s CUDA platform and cuDNN library enable developers to harness this capability, making GPUs indispensable for modern AI workloads.