By Rana Gujral
Artificial intelligence as a discipline consists of hundreds of individual technologies, concepts, and applications. These terms have become increasingly important as STEM education expands and there is a boom in practical household and...
The European General Data Protection Regulation (GDPR) goes into effect on 25th May. What is it and how does it affect your business?
Deep reinforcement learning is one of the most interesting branches of AI, responsible for achievements such as mastering complex games, self-driving cars, and robotics.
The fourth industrial revolution is well underway — and it's thanks to a confluence of technologies we wrote off as science fiction just a few short years ago.
Low-rank adaptation (LoRA) is a technique that cuts the costs of fine-tuning large language models (LLM) to a fraction of its actual figure.
The quest for creating thinking machines has split artificial intelligence into two fields: Narrow AI, what we have, and general AI, what we wish to achieve
NLP and NLG, the two branches of AI that parse and generate human language, have removed many of the barriers between humans and computers.
Graph neural networks (GNN) are a type of machine learning algorithm that can extract important information from graphs and make useful predictions.
From game-playing bots to robotic hands that dexterously handle objects, reinforcement learning creates AI models that requires little training data.
Everything to know about LLM fine-tuning, supervised fine-tuning, reinforcement learning from human feedback (RLHF), and parameter-efficient fine-tuning (PEFT)