Millan Kaul

2 minute read

Written by - Millan Kaul

20 AI acronyms and keywords with definitions and Example Use

20 AI acronyms and keywords with succinct definitions sourced only from official AI company and chip provider blogs:

Acronym / Keyword Definition Example Use Reference Link
AGI Artificial General Intelligence: AI with human-level cognitive abilities across diverse tasks. Researchers aim to develop AGI capable of performing any intellectual task a human can. Shelf AI Glossary
API Application Programming Interface: Protocols for building and interacting with software applications. Developers use APIs to integrate AI features into their apps seamlessly. NVIDIA AI Workbench
Attention Mechanism Neural network component enabling focus on important parts of input data. Transformers use attention mechanisms to understand relationships between words in a sentence. Hugging Face Glossary
Benchmark Standardized tests to evaluate AI model performance on given tasks. The team used ImageNet as a benchmark to evaluate image recognition accuracy. NVIDIA Blog
Chatbot AI system simulating conversation using natural language processing. Many websites use chatbots to provide 24/7 customer support. OpenAI Blog
Data Labeling Tagging data with meaningful information for supervised model training. Accurate data labeling improved the model’s object detection in images. NVIDIA Blog
Embeddings Numeric vectors capturing semantic meaning of inputs for model use. Embeddings helped the recommendation system find similar products. NVIDIA Blog - Knowledge Graphs
Explainability AI’s ability to provide understandable reasons behind outputs. Explainability tools showed why the model flagged fraudulent transactions. Microsoft AI Training
Fine-tuning Adapting a pre-trained model on specific data to improve task accuracy. Fine-tuning the model on legal texts increased contract review accuracy. NVIDIA Blog
Inference Applying a trained model to new data to generate predictions. Inference was done in real-time to detect defects on the manufacturing line. NVIDIA Blog
LoRA Low-Rank Adaptation: Efficient technique to fine-tune large models with fewer parameters. LoRA reduced training costs while fine-tuning the large language model. Hugging Face Blog
Model Compression Techniques to reduce model size for faster deployment without much loss in accuracy. Model compression enabled AI to run efficiently on mobile devices. NVIDIA Blog
Multi-modal AI AI that processes and integrates multiple data types such as text, images, and audio. Multi-modal AI generated captions using both image and text inputs. OpenAI Blog
Overfitting When a model learns training data too well including noise, reducing generalization to new data. Early stopping was used to prevent overfitting during training. NVIDIA MLOps Blog
Prompt Engineering Designing inputs (“prompts”) to guide AI model’s responses effectively. Prompt engineering improved chatbot responses for customer queries. OpenAI Blog
Reinforcement Learning Training agents to make decisions by maximizing rewards through trial and error. Reinforcement learning helped teach the AI to play and win chess. Hugging Face Glossary
Self-Supervised Learning Model training using data’s own structure as supervision, reducing need for manual labels. Self-supervised learning enabled pretraining on large unlabeled datasets. Shelf AI Glossary
Transformer Neural network architecture using self-attention mechanisms, fundamental for LLMs. The GPT series is based on the transformer architecture. OpenAI GPT OSS
Zero-shot Learning Model performing tasks without direct training examples, generalizing from related knowledge. Zero-shot learning allowed text classification of unseen categories. NVIDIA Blog
Synthetic Data Artificially generated data used to augment real datasets or replace sensitive data. Synthetic data was used to train models without risking privacy breaches. NVIDIA Blog
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