AI TAXMAN is programmed through ChatGPT's API. We have developed a special purpose chat box that interacts with ChatGPT
How ChatGPT Works: An Overview
ChatGPT is an advanced AI system built on the principles of machine learning, specifically leveraging a model architecture called a transformer. It operates as a large language model (LLM), trained to understand and generate human-like text based on input it receives. Here’s an outline of how it works:
1. Understanding Input (Natural Language Processing - NLP):
When you type a question or statement, ChatGPT processes the input to understand its meaning.
It breaks the input into smaller components called tokens (e.g., words, phrases, or characters).
Using its training data, it predicts the context, tone, and intent of your message.
2. Generating Responses (Probability and Prediction):
ChatGPT generates responses by predicting the most likely sequence of words that align with the given input.
Each word is chosen based on probabilities calculated from patterns it learned during training. For example, if you ask about "machine learning," the system considers the context and predicts words related to the topic.
3. Training ChatGPT (Machine Learning Basics):
ChatGPT was trained using supervised learning and reinforcement learning techniques:
Supervised Learning:
The model was trained on vast amounts of text data from books, websites, and articles.
It learned grammar, facts, context, and patterns from this dataset.
Reinforcement Learning with Human Feedback (RLHF):
After initial training, the model was fine-tuned using human feedback to improve the quality of its responses.
Human reviewers rated outputs, helping the system understand better responses.
4. Transformer Architecture:
ChatGPT is based on the transformer neural network architecture. This system uses mechanisms called self-attention to focus on different parts of the input when generating a response.
For example, in a long question, the transformer identifies key parts (e.g., subjects, verbs, context) to provide a coherent answer.
5. Continuous Learning (Pretrained, but Not Self-Learning):
ChatGPT doesn't learn from individual conversations in real-time to ensure privacy and reliability.
It relies on patterns from the training phase to generate answers, meaning it cannot "remember" personal data unless explicitly shared within the current session.
Basics of Machine Learning (ML)
Machine learning is a subset of artificial intelligence where systems learn from data to improve their performance over time without being explicitly programmed.
1. Core Concepts:
Data: ML models are trained using large datasets that help them learn patterns and relationships.
Training: The model identifies trends in the data and adjusts its parameters to predict or classify future data accurately.
Testing: Once trained, the model is tested on unseen data to ensure it performs well.
2. Types of Machine Learning:
Supervised Learning: The model learns from labeled datasets (e.g., images with captions or questions with answers).
Unsupervised Learning: The model identifies patterns or clusters in data without labels.
Reinforcement Learning: The model learns by receiving rewards or penalties based on its actions in an environment.
3. Key Components:
Neural Networks: Layers of artificial neurons process data, with each layer learning more abstract features.
Backpropagation: Errors in predictions are sent back through the network, adjusting weights to improve accuracy.
Optimization Algorithms: Techniques like gradient descent minimize the error in predictions.
