**Algorithm**
A step-by-step set of instructions that a computer follows to solve a specific problem or perform a task, such as recognizing patterns or making decisions.
**[[Artificial General Intelligence (AGI)]]**
A hypothetical form of AI that can perform any intellectual task a human can, exhibiting broad, adaptable intelligence—unlike current "narrow" AI, which is specialized for specific tasks.
**[[Artificial Intelligence (AI)]]**
Technology that enables computers or machines to simulate human intelligence, performing tasks like learning, reasoning, and problem-solving that would otherwise require human input.
**Autonomous Agent**
An AI system capable of performing tasks or making decisions independently, often in dynamic or unpredictable environments, like self-driving cars.
**Bias**
Systematic errors in AI outputs caused by prejudices or imbalances in the training data, which can lead to unfair or inaccurate results.
**Chatbot**
A software application that mimics human conversation through text or voice, often used for customer service or virtual assistance.
**Context Window**
The context window is the amount of text (measured in tokens) that an AI language model can consider at one time when generating or analyzing responses. A larger context window allows the model to "remember" and use more information from the conversation or document, improving coherence and relevance in its outputs.
**Dataset**
A collection of digital information (text, images, numbers, etc.) used to train, test, and validate AI models.
**[[Deep Learning]]**
A type of machine learning that uses artificial neural networks with many layers to recognize complex patterns in data, such as images or text, inspired by the human brain.
**[[Generative AI]]**
AI models that can create new content—such as text, images, or music—by learning patterns from existing data and generating outputs that resemble the training material.
**Hallucination**
When an AI system generates incorrect or fabricated information that appears plausible but is not based on real data.
**Hyperparameter**
A configuration value set before training an AI model that influences how learning occurs, such as learning rate or number of layers in a neural network.
**Inference**
The process by which an AI model generates a response or prediction based on input data, such as answering a question or classifying an image.
**[[Large Language Model (LLM)]]**
A type of AI model trained on massive amounts of text data to understand and generate human-like language; examples include [[ChatGPT]] and Gemini.
**[[Machine Learning (ML)]]**
A subset of AI that allows systems to learn from data and improve over time without being explicitly programmed for each task.
**[[Natural Language Processing (NLP)]]**
A field of AI focused on enabling machines to understand, interpret, and generate human language, as seen in chatbots and language translation tools.
**[[Neural Network]]**
A computational model made up of layers of interconnected nodes ("neurons") that processes data by learning patterns, mimicking how the human brain works.
**Retrieval-Augmented Generation (RAG)**
A technique where AI models enhance their outputs by retrieving relevant information from external sources during the generation process, improving accuracy and relevance.
**Token**
A segment of text (such as a word or part of a word) that AI models process when analyzing or generating language; the number of tokens affects the complexity of model outputs.
**Training**
The process of teaching an AI model by feeding it data so it can learn patterns and improve its performance on specific tasks.
## See also
### Companies
- [[Canva]]
- [[Facebook]]
- [[Google]]
- [[Ideogram]]
- [[Leonardo.ai]]
- [[Microsoft]]
- [[OpenAI]]
- [[Palantir]]
- [[Twitter]] / [[Twitter (X) Timeline]]
### People
- [[David Sacks]]
- [[Elon Musk]]
- [[Jack Dorsey]]
- [[JD Vance]] -- via [[Peter Thiel]] and [[Palantir]]
- [[Jeff Yass]]
- [[Nick Bostrom]]
- [[Peter Thiel]]
- [[Sam Bankman-Fried]]