So you’ve heard these AI terms and nodded along; let’s fix that
So you’ve heard these AI terms and nodded along; let’s fix that
Publish Date: 2026-05-09 17:45:00
Source Domain: techcrunch.com
- Artificial intelligence (AI) is revolutionizing the world and introducing new terms that can make even skilled professionals feel uncertain.
- A glossary is introduced to help clarify these unfamiliar terms and will be updated as the field evolves.
- Artificial General Intelligence (AGI) aims to create AI more capable than average humans in many tasks.
- An AI agent can perform a series of tasks autonomously, such as filing expenses or booking reservations, although the infrastructure is still developing.
- API endpoints are like ‘buttons’ that enable applications to interact and automate processes independently.
- Chain-of-thought reasoning is a method used in large language models to break down complex problems into manageable steps for better accuracy.
- Coding agents are specialized AI agents that handle writing, testing, and debugging code autonomously.
- Compute refers to the computational power provided by hardware like GPUs and CPUs that drives AI operations.
- Deep learning is a subset of machine learning that uses multi-layered neural networks to make complex correlations and improve on its own outputs.
- Diffusion systems in AI models aim to learn how to restore data from noise, enabling data generation applications.
- Distillation is a technique used to train smaller AI models based on larger ones, enabling efficiency while retaining most of the knowledge.
- Fine-tuning is the process of further training an AI model on specialty data to optimize it for specific tasks.
- Generative Adversarial Networks (GANs) involve two competing neural networks that optimize AI outputs to be more realistic.
- Hallucination refers to AI fabricating incorrect information, posing risks to AI quality and safety.
- Inference is the AI process of making predictions or conclusions from previously learned data.
- Large language models (LLMs) process natural language inputs to generate responses, used in popular AI assistants.
- Memory cache improves inference through caching, reducing the computational effort needed for repetitive tasks.
- Neural networks are the algorithmic structures underlying AI capabilities, inspired by the human brain’s pathway connections.
- Open source refers to publicly available AI models or code, fostering global collaboration and progress.
- Parallelization in AI denotes the ability to perform multiple tasks simultaneously, enabling more complex models.
- RAMageddon refers to the shortage and increasing cost of RAM chips, affecting various tech industries because of AI’s growing needs.
- Reinforcement learning trains AI through rewards for correct actions, letting AI models learn and adapt on their own.
- Tokens are the basic units of text that AI language models break down language into for processing, influencing cost in enterprise AI use.
- Training in AI involves feeding data into the model to learn patterns and generate useful outputs through iterative adjustments.
- Transfer learning leverages previously trained models to develop new models for related tasks, saving resource and time.
- Weights in AI training dictate the importance of different features, shaping model outputs by adjusting their influence through training.
- Validation loss measures an AI model’s learning effectiveness during training, helping identify issues like overfitting.
