Artificial intelligence has stopped working alone. Multi-Agent Systems (MAS) create networks of specialized agents that negotiate, compete, and cooperate — reshaping the AI paradigm from the ground up.
The Transformer is a neural network architecture that in 2017 replaced recurrent models and launched the era of large language models. Understanding how it works is the key to grasping where ChatGPT, BERT, GPT-4, and Vision Transformers came from.
Embeddings are mathematical representations of words, sentences, and documents in a multidimensional vector space — the foundation of modern NLP, semantic search, and RAG architectures. Discover how Word2Vec, GloVe, BERT and cosine similarity became the common language of machines.
A token is the fundamental unit used by every large language model. Understanding how tokens are generated, counted, and priced is key to using AI systems effectively and cost-efficiently.
An AI agent (also called agentic AI) is a category of systems capable of autonomously planning and executing multi-step tasks without continuous human supervision. It is not a chatbot — it is a technology layer that turns language models into independently operating digital collaborators.
Large language models (LLMs) are a class of artificial intelligence systems built on neural networks and trained on massive text corpora to generate and understand natural language. Understanding their architecture and limitations is essential for anyone using AI tools or making decisions about depl
Physical AI is the branch of artificial intelligence development that moves AI capabilities from purely digital environments into the physical world — enabling machines to perceive their surroundings, reason about them, and act in real time. Understanding this paradigm is essential for anyone following where robotics, industrial automation, and autonomous systems are heading in the coming decade.
