10 Foundational AI Building Block Concepts for Testers and Developers on https://qualitywithmillan.github.io

10 Foundational AI Building Block.md

#	Building Block Concept	Short Description	Reference Link
1	Foundation Models	Large AI neural networks trained on massive raw data, enabling broad understanding and generation tasks.	NVIDIA Blog - Foundation Models
2	Machine Learning Basics	Core ML concepts like supervised, unsupervised learning foundational to AI applications.	Microsoft Learn AI Fundamentals
3	Natural Language Processing & LLMs	Techniques for understanding and generating human language using models like GPT and LLaMA.	NVIDIA Blog - Open Reasoning Models
4	AI Model Training & Evaluation	Methods for training models, tuning hyperparameters, and measuring model accuracy and robustness.	OpenAI & NVIDIA Open Models
5	AI Agents & Automation	Building goal-oriented autonomous agents for tasks like software testing automation and decision making.	Reddit AI Agents
6	Responsible AI & Ethics	Principles ensuring AI fairness, bias mitigation, transparency, and accountability in AI systems.	Microsoft AI Responsible Use
7	Data Infrastructure for AI	Data collection, cleansing, and pipeline management fundamental for high-quality AI inputs.	NVIDIA Blog - AI Systems Building Blocks
8	Generative AI	Models capable of creating content such as text, images, and videos, powering creative applications.	NVIDIA Blog - GPT and Generative AI
9	AI Development Ecosystems & Tools	Platforms like Hugging Face, CUDA, and frameworks facilitating AI model development & deployment.	Hugging Face Blog
10	Industry Collaboration & AI Research Trends	Recent AI breakthroughs and collaborations driving innovation in hardware and software ecosystems.	Perplexity AI Blog Deep Dive