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How do Machine Learning, Deep Learning, Tokenization, LLM, RAG, Agentic AI, Vector Databases, Weights, Bias and Training relate to each other?
In previous articles we have discovered what lies behind the scenes of artificial intelligence. The different key concepts that we have explored fit together to give life to advanced systems.
In this article we will correlate all the concepts described in the previous articles (listed below) to understand how they form the great gear of artificial intelligence:
- Simple Guide to Artificial Intelligence
- Machine Learning and Deep Learning
- How does ChatGPT work? Tokenization
- Where Does an AI Model Store Data?
- Overcoming static AI models with RAG
- What LLM are
- LM Studio: A Local alternative for using LLMs
- What is Agentic AI?
- What are vector databases?
Machine Learning and Deep Learning: The Foundations of AI
Machine Learning (ML) is the foundation of modern artificial intelligence, allowing computers to learn from data without being explicitly programmed. Within this, Deep Learning (DL) is distinguished by the use of deep neural networks, capable of processing complex information such as images and text.
It all comes down to AI models, which are the heart of artificial intelligence systems. An AI model is a mathematical-statistical system, often based on neural networks, that learns from data to perform specific tasks such as recognizing images, translating languages, or generating text.
Weights, Bias and Training: The core of an AI Model
An AI model is defined by its parameters, specifically its weights and biases, which determine how the model transforms inputs into outputs. Training involves updating these parameters to improve the model’s accuracy, usually through backpropagation and optimization using algorithms such as gradient descent. Training an AI model involves feeding it large amounts of data, allowing it to adjust its parameters through optimization algorithms. This process allows the model to improve its ability to make predictions or generate consistent and accurate outputs.
Tokenization and Vector Databases: Data Management
For an AI model to understand and generate text, a tokenization process is needed, which transforms words and sentences into numerical representations.
Tokenization is essential for the functioning of LLMs and for the training phase.
Vector databases, on the other hand, allow you to store and retrieve data based on semantic similarity, which is essential for improving the efficiency of information search.
Vector databases improve information retrieval capability in RAG systems.
It all relies on vectors, which are the numerical representation of data in a multidimensional space. Each word, token, image, or concept is transformed into a vector, which is a set of numbers (e.g. [10, 21, 34]) that describe its characteristics and relationships with other elements. This representation allows AI models to compare, classify, and generate information based on the similarity between vectors, making techniques like information retrieval in RAG possible.
Large Language Models (LLM) and RAG: The Evolution of Conversational Intelligence
Large Language Models (LLM), such as GPT, are AI models that use Deep Learning techniques to understand and generate text fluently. However, they often suffer from limitations related to static knowledge and hallucination (generation of inaccurate information). To mitigate these problems, Retrieval-Augmented Generation (RAG) is used, which combines an LLM with a vector database to retrieve relevant information in real time.
Agentic AI: The future of Autonomous Intelligence
Agentic AI represents the evolution of AI systems, enabling systems called “agents” to autonomously perform tasks, make decisions, and interact with the outside world. These agents leverage LLM, vector databases, and RAG mechanisms to improve their operational capabilities.
AI: A Complete System
The integration of the concepts described in this article allows to build a complete and autonomous AI system, where each component contributes to making the system more efficient and intelligent:
AI agents use LLM to understand and generate text.
The RAG allows agents to access up-to-date information.
Vector databases enable agents to store and retrieve knowledge efficiently.
In summary
Artificial intelligence is not a set of isolated concepts, but an interconnected ecosystem where each element plays a fundamental role:
- Machine Learning and Deep Learning provide the computational foundations
- Tokenization and vector databases manage data
- Training and optimization of weights and biases ensure high performance.
- LLMs and RAG enhance language understanding
Finally, Agentic AI represents the next step towards increasingly autonomous and intelligent systems.
Understanding how these elements work together is essential to fully exploiting the potential of AI in the real world.
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