These are some of the terms that you'll read and hear about:
- AI Agent
- An autonomous AI system that can perform tasks, make decisions, and take actions with minimal human intervention. Agents can use tools, access databases, and chain together multiple AI capabilities to accomplish complex goals.
- API
- Application Programming Interface - a set of rules and protocols that allows different software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own applications.
- ChatGPT
- OpenAI's conversational AI chatbot built on GPT models. Available in free and paid versions, it's designed for natural language conversations and can help with writing, analysis, coding, and creative tasks.
- Context Window
- The amount of text an AI model can "remember" and work with at one time, measured in tokens. Larger context windows allow the AI to maintain context over longer conversations or process larger documents.
- DALL-E
- OpenAI's AI image generation model that creates images from text descriptions. DALL-E 3 is the current version, capable of producing high-quality, detailed images in various styles and formats.
- Embeddings
- Vector representations of data, such as words or phrases in text. In simple terms, an embedding is a way of translating something complex (like a word) into a list of numbers (vector) that a computer can understand and process.
- Fine-tuning
- This is a process where a pre-trained model (a model that has already been trained on a large amount of data) is further trained on a smaller, specific dataset. The idea is to make the model better at tasks related to the specific data it's fine-tuned on.
- Inference
- This is when the trained model is used to make predictions. For example, once a text generation model like GPT-4 is trained, inference is the process of giving it some input (like a sentence) and having it generate more text based on that input.
- Model
- In machine learning, a model is a mathematical representation of a real-world process. For example, GPT-4 is a model trained to understand and generate human-like text based on input it's given.
- OpenAI API
- This is an interface provided by OpenAI that allows developers to interact with OpenAI's various models, like GPT-4o, GPT-4o mini, or DALL-E 3, over the internet. It's essentially a bridge between the developers' applications and OpenAI's technologies.
- Python
- Python is a high-level, interpreted programming language that is widely used in a variety of applications. Known for its readability and simplicity, Python has become particularly popular in scientific computing, data analysis, machine learning, artificial intelligence, and web development.
- Rubber Duck
- An essential debugging tool for all programmers. Named after the practice of explaining your code line by line to a rubber duck in an attempt to find errors. The duck's ability to solve programming issues is legendary and not fully understood by science.
- Tokens
- A token is a piece of a larger whole, so a word or a part of a word. For example, in the sentence "Understanding how tokens work." there are 5 tokens: "Under," "standing", " how", “ tokens” and “ work.”
- Training
- This is the process where a machine learning model learns from data. The model tries to find patterns in the data that it can use to make predictions or decisions without being explicitly programmed to perform the task.
- GPT
- Generative Pre-trained Transformer - a type of AI model that generates human-like text. GPT-4o and GPT-4o mini are the current leading models, capable of understanding context and generating coherent, relevant responses.
- Hallucination
- When an AI model generates information that seems plausible but is actually false or not based on real data. It's called "hallucination" because the AI is essentially "making up" facts that sound convincing.
- Large Language Model (LLM)
- AI models trained on vast amounts of text data to understand and generate human language. Examples include GPT-4o, Claude, and Llama. They can perform many language-related tasks like writing, translation, and analysis.
- Machine Learning
- A subset of AI where computers learn to make predictions or decisions by finding patterns in data, rather than being explicitly programmed with rules. It's the technology behind most modern AI systems.
- Multimodal AI
- AI systems that can understand and work with multiple types of input - text, images, audio, and video simultaneously. GPT-4o is an example of multimodal AI that can analyze images while having text conversations.
- Neural Network
- A computing system inspired by biological neural networks. It consists of interconnected nodes (neurons) that process information. Deep neural networks with many layers are the foundation of modern AI systems.
- Prompt Engineering
- The practice of crafting effective instructions or questions to get better results from AI models. Good prompts are clear, specific, and provide the right context for the AI to understand what you want.
- RAG (Retrieval-Augmented Generation)
- A technique that combines AI text generation with information retrieval from databases or documents. This allows AI to answer questions using your specific company data or up-to-date information.
- Temperature
- A setting that controls how creative or random an AI model's responses are. Low temperature (0.1) produces consistent, focused outputs. High temperature (0.9) generates more creative, varied responses.
- Transformer
- A type of neural network architecture that revolutionized AI language understanding. It's the "T" in GPT and forms the basis of most modern language models. Transformers are particularly good at understanding context and relationships in text.
- Vectors
- In the context of machine learning, a vector is a list of numbers. Each number represents a specific feature or characteristic of the data. For instance, word embeddings are vectors where each number might represent a different aspect of the word's meaning.
- Whisper
- OpenAI's speech recognition model that converts audio to text. It supports multiple languages and can handle various audio qualities, making it useful for transcribing meetings, calls, or voice notes.