So you’ve heard these AI terms and nodded along; let’s fix that

2026-05-09 · Source: AI News & Artificial Intelligence | TechCrunch · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Novice, long

Summary

This glossary defines key terminology in artificial intelligence, covering concepts from foundational AI types to specific operational mechanisms. It explains Artificial General Intelligence (AGI) as AI surpassing human capabilities across many tasks, noting varied definitions from OpenAI and Google DeepMind. The glossary details AI agents as tools performing multi-step tasks autonomously, and API endpoints as interfaces enabling program interaction. It clarifies chain-of-thought reasoning for LLMs, deep learning's neural network structure, and diffusion models for generative AI. Other terms include distillation for model efficiency, fine-tuning for task-specific optimization, GANs for realistic data generation, and "hallucination" for AI-generated inaccuracies. It also covers computational aspects like compute, inference, training, memory cache, parallelization, and the "RAMageddon" shortage, alongside core AI components like tokens, token throughput, transfer learning, weights, and validation loss.

Key takeaway

For software engineers and AI developers navigating the rapidly evolving AI landscape, understanding this core terminology is crucial. Familiarity with terms like "AI agent," "chain-of-thought," and "token throughput" will enable you to better design, implement, and optimize AI solutions. This knowledge also helps in evaluating model performance and addressing challenges like "hallucination" and "RAMageddon" in your projects.

Key insights

AI terminology encompasses foundational concepts, operational mechanisms, and critical performance metrics.

Principles

• AI systems learn patterns from data.
• Efficiency is crucial for AI deployment.
• AI development involves iterative refinement.

Method

AI models are trained by feeding data, adjusting weights, and using techniques like reinforcement learning or fine-tuning to optimize performance and reduce validation loss.

In practice

• Use chain-of-thought for complex LLM problems.
• Apply fine-tuning for domain-specific AI tasks.
• Monitor validation loss to prevent overfitting.

Topics

• Artificial General Intelligence
• AI Agents & Automation
• Large Language Models
• Deep Learning & Neural Networks
• Generative AI Techniques

Best for: AI Student, General Interest, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI News & Artificial Intelligence | TechCrunch.