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Artificial Intelligence (AI): A broad term encompassing systems that mimic human intelligence, including speech recognition, decision-making, visual perception, and language translation.
Natural Language Processing (NLP): The field of AI focused on the interaction between computers and humans using natural language, with the goal of understanding and making sense of human language.
Machine Learning (ML): A type of AI that enables systems to learn and improve from experience without explicit programming, using data to learn and make predictions.
Deep Learning: A subset of machine learning that uses artificial neural networks with representation learning, achieving high accuracy and performance in various tasks.
Generative Pre-training Transformer (GPT): An autoregressive language prediction model using deep learning to produce human-like text, serving as the basis for ChatGPT.
ChatGPT: An AI program developed by OpenAI that generates human-like text responses based on given prompts, utilizing the GPT model.
Transformer: A model architecture introduced in “Attention is All You Need” that uses self-attention mechanisms and has been applied in models like GPT.
Autoregressive Model: A statistical model that predicts future values based on past values, used by ChatGPT to predict the next word in a sentence.
Prompt: The input given to ChatGPT, to which it responds.
Token: A unit of text, such as a word or sentence, used as building blocks in Natural Language Processing.
Fine-Tuning: The process of adapting the model to specific tasks after initial training, such as question answering or language translation.
Context Window: The recent conversation history that ChatGPT considers when generating a response.
Zero-Shot Learning: The model’s ability to understand and respond appropriately to tasks it hasn’t been explicitly trained on.
One-Shot Learning: The model’s ability to learn a task from a single example during training.
Few-Shot Learning: The model’s ability to learn a task from a small number of examples during training.
Attention Mechanism: A technique in deep learning where the model assigns different weights to different words or features when processing data.
Reinforcement Learning from Human Feedback (RLHF): A fine-tuning method where the model learns from feedback provided by humans.
Supervised Fine-Tuning: The initial step in fine-tuning, where human AI trainers provide conversations with both user and AI roles to the model.
Reward Models: Models used to rank different responses generated by ChatGPT.
API (Application Programming Interface): A means for different software programs to interact, allowing developers to integrate ChatGPT into their applications or services.
AI Trainer: Humans who guide the AI model during the fine-tuning process by providing feedback, ranking responses, and writing example dialogues.
Safety Measures: Precautions taken to ensure the AI behaves in a safe, ethical, and privacy-respecting manner.
OpenAI: The artificial intelligence lab responsible for developing GPT-3 and ChatGPT, working toward the benefit of humanity with artificial general intelligence (AGI).
Scaling Laws: Observations that AI models tend to improve with more data, computation, and increased size.
Bias in AI: Instances where AI systems may exhibit biases in their responses due to biases present in their training data, which OpenAI aims to reduce.
Moderation Tools: Tools provided to developers to control the behavior of the model in their applications and services.
User Interface (UI): The point of interaction and communication between humans and computers in a device, application, or website.
Model Card: Documentation providing detailed information about a machine learning model’s performance, limitations, and ideal use cases.
Language Model: A model that predicts the next word or sequence of words in a sentence using mathematical and probabilistic frameworks.
Decoding Rules: