The AI terms that
actually matter.
Clear, technical definitions of 62 key concepts — written by AI engineers, not marketers.
Knowledge Distillation
Knowledge distillation is a model compression technique where a compact 'student' model is trained to replicate the behavior of a larger, more capable 'teacher' model — achieving near-teacher-level accuracy at a fraction of the size, latency, and cost.
Read definitionKokoro
An open-weight TTS model with just 82M parameters that matches the output quality of models ten times its size, running in under 0.3 seconds on CPU — the most efficient high-quality open-source voice model available.
Read definitionLarge Language Diffusion Models
A class of generative AI models that apply diffusion processes, originally popularized in image generation, to text generation. They offer non autoregressive, highly controllable generation by iteratively denoising continuous embeddings or discrete vocabulary spaces.
Read definitionLarge Language Model (LLM)
Large Language Models (LLMs) are advanced AI systems designed to understand, generate, and interact with human language at scale, leveraging vast amounts of text data to perform a wide range of linguistic tasks.
Read definitionLarge Vision Model (LVM)
Large Vision Models (LVMs) are advanced artificial intelligence systems designed to process, analyze, and interpret large volumes of visual data, enabling sophisticated image recognition, object detection, and segmentation tasks.
Read definitionLoRA Fine-Tuning
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that freezes a pre-trained model's weights and injects small trainable low-rank matrices into key layers, enabling effective specialisation at a fraction of the compute and memory cost of full fine-tuning.
Read definitionMamba Model
The Mamba model is a groundbreaking AI architecture that achieves state-of-the-art performance across various modalities by utilizing selective state spaces for efficient, linear-time sequence modeling
Read definitionMiniMax M2.7
MiniMax M2.7 is an open-source, self-evolving Mixture-of-Experts AI model that autonomously participates in 30–50% of its own training workflow, featuring 230 billion total parameters, a 200K-token context window, and top-tier performance on agentic and software engineering benchmarks.
Read definitionMixture of Experts (MoE)
Mixture of Experts is a neural network architecture where a learned routing mechanism activates only a small subset of specialised sub-networks (experts) for each input token — delivering the capacity of a much larger model at a fraction of the per-token compute cost.
Read definitionModel Context Protocol (MCP)
Model Context Protocol (MCP) is an open standard introduced by Anthropic that defines a universal interface for connecting AI models to external data sources, tools, and services — eliminating the need for bespoke integrations for every AI-to-system connection.
Read definitionMoshi
Moshi is a full-duplex, real-time spoken dialogue AI developed by Kyutai that can simultaneously listen and speak — enabling natural, interruption-aware conversations with theoretical latency as low as 160ms.
Read definitionMulti Agent System
A Multi-Agent System (MAS) is an AI architecture where multiple autonomous, specialized agents interact, collaborate, or compete to solve complex workflows that are too difficult or broad for a single, monolithic AI model.
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