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Basic concepts in the field of AI (Part 2)

Written by

Clara Bennett

Updated on:June-18th-2025

In 2017, Google published a paper titled "Attention is all you need" which proposed a new simple network architecture, Transformer, which is completely based on the self-attention mechanism to process the dependencies of the input sequence, and abandons the loop and convolution operations.

Transformer is a deep learning model that is mainly used to process sequence data such as text. It has shown excellent results in language understanding tasks such as translation or text generation. The core advantage of Transformer is that it can process all parts of the input sequence at the same time, which greatly speeds up the training process and improves the model's ability to handle long-distance dependencies.

The four core components of Transformer: self-attention mechanism, multi-head attention, position encoding, and feedforward network.

Self-attention mechanism: simulates the ability to evaluate and utilize the relationship between each word and other words in the sentence when processing each word.

Multi-head attention: Processing information from multiple “angles” or “subspaces” simultaneously helps capture the multi-faceted characteristics of sentences or data.

Positional encoding: Similar to a sequence-aware mechanism, it helps the Transformer model understand the position of words in a sentence even though it processes a group of words instead of one after another.

Feedforward network: In Transformer, the feedforward network is the part of the network that outputs the same operation for each position after the self-attention structure. This process is independent for each element in the sequence.

These core components of Transformer are independent and yet work together to effectively improve the model's ability to process sequence data, especially in understanding and generating language text. It is particularly suitable for processing natural language. It is good at capturing contextual relationships in text and has high parallel processing efficiency.

GPT: Generative Pre -trained Transformer

GPT (Generative Pre-trained Transformer) is an advanced natural language processing model that generates text similar to human writing. Each part of the name represents its unique function and structure. Let's explain each part step by step and use simple language and examples to help you understand.

Generative means that this model can generate content. Unlike models that can only classify or predict, GPT can create new sentences, paragraphs, and even entire articles.

Pre-trained

Pre-training: refers to pre-training the model on a large amount of text data. This enables the model to have rich language knowledge and understanding capabilities when it starts to apply specific tasks. For example: customer service question-and-answer system, answering professional pre-sales and after-sales questions.

1. Generative: The model can generate new text content.Simple explanation: Given a sentence start, the model can continue to write a story.
2. Pre-trained: The model is initially trained on a large amount of text.Simple explanation: The model has learned language knowledge from a large number of books and articles.
3. Transformer: An efficient neural network structure that is good at processing text.Simple explanation: The model can understand all the words in a sentence at the same time, which improves the efficiency of understanding.

BERT

BERT (Bidirectional Encoder Representations from Transformers) is a deep learning technology based on the Transformer model for natural language processing. One of the main innovations of BERT is its bidirectional training, which considers both the left and right contexts of each word in the text. This design makes BERT perform well in understanding the complex semantics of text.

• 1. Transformer-based architecture:

BERT adopts the multi-head self-attention and position encoding techniques in the Transformer model, using these techniques to capture the relationship between words and maintain word order information. However, BERT only uses the encoder part of the Transformer (without the decoder).

• 2. Two-way context understanding:

For example, if you are watching a certain clip of a movie, to understand the plot, you need to look at not only what happened before this clip, but also what happened after it. BERT uses bidirectionality (looking at the context of the text at the same time) to better understand the meaning of each word than traditional unidirectional models (processing text only from left to right or only from right to left).

• 3. Pre-training and fine-tuning:

Pre-training: First, BERT is trained on a large text library (such as Wikipedia) to learn the language patterns in the text. The learning tasks at this stage include "Masked Language Model" (MLM) and "Next Sentence Prediction" (NSP). In the MLM task, BERT randomly masks some words in the sentence and tries to predict them; in the NSH task, BERT tries to predict whether the second sentence is a reasonable follow-up to the first sentence.

Fine-tuning: After pre-training, BERT can be adapted to specific tasks, such as sentiment analysis, question answering, etc., through additional training. In this stage, BERT combines a small amount of task-specific data and adjusts its parameters to better perform the task.

The power of BERT lies in its bidirectional context understanding ability and flexible pre-training and fine-tuning strategies, which have enabled it to make revolutionary progress in many natural language processing tasks.

Vector Database

In the field of artificial intelligence (AI), especially when processing data such as natural language or images, it is often necessary to convert raw data into vector form. These vectors are usually called feature vectors, which are numerical representations of raw data and can be used for training and prediction of various machine learning models. In order to efficiently manage and retrieve these vectors, we will use a vector database.

Vector databases are databases designed specifically to store, manage, and retrieve vector data. In traditional databases, data is usually stored in tables, such as rows of data records. Vector databases are more suitable for processing data in the form of multidimensional arrays. They can support complex queries on these vector collections, such as finding the vector that is most similar to a given vector.

Why do we need a vector database?

1. Efficient retrieval: In AI applications such as recommendation systems or image recognition, it is important to quickly find historical data that is similar to the input data. Vector databases accelerate this "nearest neighbor" search by optimizing the data structure.

2. Large-scale storage: AI training and applications often involve a large amount of vector data. Traditional databases are not efficient in processing such large-scale high-dimensional data. Vector databases are designed specifically for this need and provide better storage solutions.

3. Dynamic update: In many application scenarios, vector data needs to be continuously updated or expanded, and vector databases can efficiently handle these dynamically changing data sets.

Embeddings

Embeddings is a commonly used technique, especially in the fields of natural language processing (NLP) and machine learning, to convert non-numerical data such as text and images into numerical vectors that can be better understood and processed by computers.

These vectors are not random numbers, but are learned to capture and express important features and relationships of the original data. For example, in text processing, the embedding vector of a word captures the grammatical and semantic characteristics of the word.

core:

1. Dimensionality reduction: Raw data such as words, user IDs, or product IDs may have thousands of unique values. If these data are processed directly, it requires a lot of space and computing resources. Embedding can compress these large-scale categorical data into a smaller, continuous numerical space.

2. Capturing relationships: Embeddings learn relationships in the data through training, for example, in text, words that often co-occur will be closer to each other in the vector space.

Through embedding technology, we can more effectively process and analyze various complex data. It is an effective way to convert a large number of complex data points into easy-to-operate numerical forms. It is also widely used in other forms of machine learning tasks.

LLM (Large Language Model)

1. Large scale:

Meaning: The model contains a large number of parameters (usually billions to tens of billions of parameters), which are like the connections in the brain, helping the model understand and generate language.

Simple explanation: A large language model is like having a very large and complex brain.

2. Language:

Meaning: The model specializes in processing natural languages (such as English, Chinese).

Simple explanation: The model is very good at understanding and generating human language, such as writing articles and answering questions.

3. Model:

Meaning: A model is a system trained through machine learning techniques to generate reasonable outputs based on inputs.

Simple explanation: The model is like a very smart robot that can respond to what you say or write.

Key points to understand about large language models:

1. Large amount of data training:

Meaning: Large language models are trained on massive amounts of text data, which comes from the Internet, books, articles, etc.

Simple explanation: The model learns from a large number of books and articles and accumulates rich language knowledge.

2. Complex structure:

What it means: A large language model has a deep neural network structure, which enables it to understand and generate complex language patterns.

Simple explanation: there are many "layers" within the model, each responsible for handling different aspects of the language, just like a large team, each doing its own job.

Model Size

The "175B" in the GPT-3 large model refers to the number of parameters contained in the model, which is 175 billion. These parameters mainly include weights and biases, which are continuously updated during the model training process to optimize the performance of the model.

1. Parameters:

Definition: Parameters are adjustable values in a model, such as weights or biases. They are connected through the layers and nodes of a neural network and determine how input data is processed.

Function: During the training process, the model adjusts these parameters to minimize the prediction error, thereby improving performance on various tasks.

2.175 billion parameters:

What it means: GPT-3 has 175 billion parameters. This is a very large number, indicating that the model has a very high capacity to learn and understand complex data patterns.

Performance improvement: With so many parameters, GPT-3 performs very well in tasks such as generating text, answering questions, and translating languages, because the model can capture more language details and complex contextual relationships.

Parameter quantity meaning:

1. Higher expressive ability:

Complex patterns: More parameters allow the model to learn and represent complex patterns and details in the data. This is very important for different natural language tasks such as text generation, question answering, translation, etc.

2. Improve generalization ability:

Diverse data adaptation: Models with a large number of parameters can handle and adapt to a more diverse range of data types and tasks, and have greater versatility and robustness.

3. Improve performance:

Precise predictions: More parameters generally mean that the model can provide higher prediction accuracy and generation quality, especially when dealing with ambiguous or complex language tasks.

Training and resource requirements

1. Computing resources:

High demand: Training such a large model requires very powerful computing resources, including a large number of GPUs or TPUs. This requires powerful hardware support and a large amount of power supply.

2. Time and cost:

Time-consuming: Training a model takes a long time, which can last for weeks or even months.

High cost: Due to the huge hardware and power consumption, the cost of training and deploying large models is also very high