AI Glossary (2024 Updated)
This glossary will be regularly updated. A B C D E F G H L M N O P R S T W Z
How Generative AI Works: A Non-Technical Explanation
Generative AI is one of the most groundbreaking advancements in artificial intelligence, capable of creating entirely new content based on patterns learned from training data. Unlike traditional AI systems designed…
Generative Adversarial Networks (GANs) Are Why AI Looks Awesome
Generative Adversarial Networks (GANs) have emerged as one of the most fascinating innovations in artificial intelligence. First introduced by Ian Goodfellow et al. in 2014, GANs leverage the competition between…
FM (Foundational Model): The Backbone of AI Innovation
Artificial intelligence has evolved rapidly, and much of its progress is thanks to Foundational Models (FMs). These large-scale, pre-trained models act as versatile frameworks for various AI applications. By learning…
Feature Engineering: Creating Features to Improve Machine Learning Models
Machine learning models are often praised for their sophistication, but even the most advanced algorithms are only as good as the data fed into them. That’s where feature engineering comes…
The Runaway Risks of AGI (FOOM)
Imagine building an intelligent machine that, instead of improving gradually, suddenly takes off, advancing so quickly that humans can no longer control it. That’s the essence of FOOM—a theory around…
Expert System: Emulating Human Expertise with AI
Imagine having access to expert-level knowledge at any time, no matter the field. Expert systems bring this vision to life by emulating human decision-making within highly specialized domains. Designed to…
Ethical Considerations in AI: Balancing Privacy, Fairness, and Safety
As artificial intelligence (AI) continues to transform industries and everyday life, ethical concerns surrounding its use are growing. From privacy issues in data collection to fairness in automated decision-making, the…
End-to-End Learning (E2E): Streamlining AI to Master Complex Tasks
Traditional AI models often rely on several stages to complete a task—each requiring separate training, fine-tuning, and optimization. But what if a single model could handle everything from start to…
Dimensionality Reduction: Simplifying Data for Superior AI Model Performance
As machine learning models become more complex and datasets grow larger, the importance of dimensionality reduction has skyrocketed. It’s no longer just a technical buzzword but a crucial step that…