The only AI glossary you’ll need this year
Summary
This comprehensive AI glossary provides plain-English definitions for essential artificial intelligence terms frequently encountered in product meetings, pitches, and industry discussions. It covers fundamental concepts like Artificial General Intelligence (AGI), Large Language Models (LLMs), and Neural Networks, alongside operational aspects such as Inference, Training, and Parallelization. The glossary also clarifies specialized techniques like Chain-of-Thought reasoning, Deep Learning, Diffusion, Distillation, Fine-tuning, and Reinforcement Learning. Key architectural patterns like Generative Adversarial Networks (GANs) and Mixture of Experts (MoE) are explained, alongside critical infrastructure components like API endpoints, Compute, and Memory Cache. It also addresses industry challenges such as Hallucination and the RAMageddon memory shortage, aiming to equip technical and professional readers with a clear understanding of the evolving AI landscape.
Key takeaway
For AI engineers and technical professionals navigating the rapid evolution of AI, this glossary serves as a vital reference to quickly grasp new terminology. You should consult it to clarify concepts like AGI, LLMs, or MoE architectures, ensuring precise communication and informed decision-making in project planning and system design. Understanding these terms will help you assess new technologies and avoid misinterpretations in a fast-paced environment.
Key insights
This glossary demystifies complex AI terminology, providing clear definitions for technical and professional readers navigating the rapidly evolving field.
Principles
- AI agents automate multi-step tasks using various AI systems.
- Deep learning models identify data characteristics autonomously.
- Hallucinations arise from gaps in training data.
In practice
- Distillation creates smaller, more efficient AI models.
- Fine-tuning optimizes LLMs for specific domain tasks.
- Token throughput is crucial for serving many users simultaneously.
Topics
- Artificial General Intelligence
- Large Language Models
- Deep Learning
- AI Agents
- Model Training
- Inference Optimization
Best for: AI Student, AI Engineer, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by AI News & Artificial Intelligence | TechCrunch.