The only AI glossary you’ll need this year
The only AI glossary you’ll need this year
Publish Date: 2026-07-03 17:20:00
Source Domain: techcrunch.com
Here’s a summary of the key points from the article on AI terminology, presented as an unordered list:
– The emergence of new language and terms associated with developments in artificial intelligence (AI).
– Efforts to create a glossary in plain language to help users understand the jargon used in AI industry, whether in building, investing, or just following the trends.
– Concept of Large Language Models (LLMs) and their use in popular AI assistants, their structure, and how they are trained.
– The importance of compute—the computational power—as the backbone of the AI industry.
– Overview of Deep Learning, explaining its neural network architecture inspired by the human brain’s pathways, and its complexities and resource requirements.
– Clarification of Diffusion technology used in generating data like art, music, and text.
– Explanation of Distillation, a technique to create smaller, efficient AI models from larger ones.
– Fine-tuning described as improving AI models on specific tasks through targeted data inputs.
– Generative Adversarial Networks (GANs) and their role in creating realistic generative AI outputs.
– Hallucination in AI refers to the inaccurate generation of information by AI models.
– The process of Inference, where AI models produce predictions or conclusions from the data they’ve been trained on.
– The role of Memory Cache in optimizing AI operations and speeding up response times.
– The Model Context Protocol (MCP) as a standard for connectivity between AI models and external data sources and tools.
– Mixture of Experts as a model structure that uses specialized sub-networks for efficiency.
– The significance of Neural Networks, the algorithmic backbone of deep learning and AI advancements.
– The debate over open source versus closed source in AI, highlighting the collaborative aspect of open sourcing.
– Parallelization in AI as an essential strategy for managing large-scale computations and its growing importance.
– The term RAMageddon highlighting the supply issue of random access memory impacting various tech industries.
– Recursive Self-Improvement (RSI) in AI, which involves models improving themselves without human interference.
– Reinforcement Learning as a method for training AI based on success feedback, especially useful for game playing and robotics.
– Tokens as the basic data units in AI that represent chunks of processed text, crucial for human-AI interaction.
– The process of Transfer Learning where knowledge from one task is applied to another, offering cost-effective model development.
– Validation Loss as an indicator of an AI model’s learning progress during training.
– Weights in AI models determine the importance given to various data features during the training process.
