Artificial Intelligence (AI) is evolving at a rapid pace and has become a significant presence across various technological domains. Whether you’re drafting essays, preparing extensive research papers, or engaging in everyday digital tasks, AI tools like ChatGPT are increasingly becoming integral to our daily lives.
To maximise your experience with ChatGPT-like AI services, it’s essential to familiarise yourself with several key terms and concepts. Understanding these terms will enhance your ability to interact with AI effectively and leverage its capabilities to their fullest potential. Here’s a breakdown of the most important concepts to know before you start using such services. A fair understanding of these terms can enhance your experience and effectiveness when using AI tools like ChatGPT.
Prompt: The input or question you provide to the AI. The prompt guides the AI’s response. Basically, it is the instruction you type in box and get your response from AI for.
Training Data: The information and examples that the AI model was trained on. For example- ChatGPT, for instance, was trained on a vast dataset from the internet, which shapes its responses. Google’s Gemini is also trained from the wide variety of information available on Internet.
Model: The underlying algorithm that processes the input (prompt) and generates output (response). GPT-4 is the model used in ChatGPT.
Context: The surrounding information in a conversation that helps the AI understand and generate relevant responses. In a chat, context includes previous interactions and the current prompt. It is the surrounding information that helps the chatbot give accurate responses.
Token: A piece of text (like a word or part of a word) that the AI processes. AI models like GPT-4 handle inputs and outputs as sequences of tokens. For instance, in the sentence, FETechBytes is amazing, the tokens could be “FETechBytes,” “is,” and “amazing.”
Bias: AI can inherit biases from the data it was trained on. It’s important to recognise that responses might reflect these biases.
Fine-tuning: The process of adjusting a pre-trained model on a specific dataset or for a specific task to improve its performance in that area.
Inference: It is the process of the AI generating a response based on the input it receives.
Ethics in AI: Concerns about the responsible use of AI, including privacy, fairness, and the impact on society.
NLP (Natural Language Processing): A field of AI that focuses on the interaction between computers and humans through natural language.
Overfitting/Underfitting: In machine learning, overfitting occurs when a model is too closely aligned to specific data and doesn’t generalise well. Underfitting means the model is too simple to capture the underlying pattern in the data.
API (Application Programming Interface): A set of tools and protocols that allow different software programs to communicate. Many AI models can be accessed via an API.