AI Careers Glossary
25 key terms and concepts for navigating a career in artificial intelligence and machine learning.
A
AI Agent
An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve specified goals.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions.
AI Safety
The multidisciplinary field focused on preventing AI systems from causing unintended harm.
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E
F
Few-Shot Learning
A technique where AI models learn to perform tasks from only a small number of examples.
Fine-Tuning
The process of further training a pre-trained AI model on domain-specific data to improve its performance on particular tasks.
Foundation Model
A large-scale AI model trained on broad data that can be adapted to a wide range of downstream tasks.
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R
Reinforcement Learning
A machine learning paradigm where agents learn to make decisions by receiving rewards or penalties for their actions.
Retrieval-Augmented Generation (RAG)
A technique that enhances AI model responses by retrieving relevant information from external data sources before generating an answer.
T
Tokenization
The process of breaking text into smaller units (tokens) that language models can process.
Training Data
The curated datasets used to train machine learning models, directly influencing model capabilities and biases.
Transfer Learning
A technique where a model trained on one task is reused as the starting point for a model on a different but related task.
Transformer
The neural network architecture behind modern LLMs, using self-attention mechanisms to process sequences in parallel.