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The AI Terms You Need to Know in 2025
By
Moments Lab Content Team
February 4, 2025
AI's rise has introduced a myriad of new concepts and terms into the public sphere—many of which can feel daunting for professionals and enthusiasts alike. These terms are keys to understanding how AI systems function and how they work to reshape industries. Our guide to essential AI terms is here to support your learning curve and help you better understand the evolving world of artificial intelligence.
As technology becomes increasingly integrated into everyday language, terms like AI, generative AI (GenAI), and machine learning are often used interchangeably, which can lead to confusion. It’s important to start by defining AI and its broader scope, which extends far beyond large language models (LLMs) and generative AI (GenAI). At its core, AI refers to machines or computer systems designed to mimic human-like capabilities, such as decision-making, problem-solving, and language understanding. Its applications span a wide range of fields, including machine learning, natural language processing, robotics, and more.
Machine learning is a subset of AI that focuses on building algorithms that help machines learn through data. Models based on machine learning are capable of identifying patterns, predicting trends and future behaviours. There are different types of ML: supervised, unsupervised, and reinforcement learning.
Multimodal AI mimics human understanding by analyzing multiple data sources like objects, text, facial recognition, geo-location, and translations. It creates detailed metadata to pinpoint exact moments and provide precise context, continuously improving through feedback learning. This technology can organize media assets efficiently, with ongoing enhancements in areas like speaker diarization and multi-language transcription. Learn about how Moments Lab uses multimodal AI.
Deep learning draws inspiration from the human brain’s structure, to process information and structure decisions in a similar manner. It learns from unstructured data without supervision, and lays the foundation for computer vision, speech recognition and natural language processing.
This machine learning type means an algorithm trains and learns from interacting with its environment, and is rewarded and penalized based on its behaviour. It’s an incremental component of robotics, gaming, and autonomous systems. The DeepSeek AI model for instance, is based on reinforcement learning.
This type of AI subset can create content, such as text, images, and code by finding patterns in large amounts of data. It has been largely democratized thanks to tools like ChatGPT and DALL-E.
Video understanding is a field of AI that involves analyzing and interpreting video data to extract meaningful information. This includes tasks like object detection, activity recognition, scene understanding, and temporal analysis. Video understanding aims to infer the context, actions, and relationships within video content. Learn about our research work to help shape the future of AI video understanding.
Large Language Models (LLMs) are advanced AI models trained on vast amounts of text data to understand and generate human-like language. These models, such as GPT (Generative Pre-trained Transformer), use deep learning techniques to perform tasks like text completion, translation, summarization, and question-answering. LLMs are characterized by their large scale, often involving billions of parameters.
Mixture of Experts is a machine learning architecture where multiple specialized models (experts) are combined to solve a problem. Each expert is trained to handle specific subsets of the input data, and a gating network determines which expert(s) to use for a given input. We chose this approach at Moments Lab with our MXT technology, because it allows for more efficient and scalable models, as different parts of the network can focus on different tasks or data types.
Frugal AI refers to the development and deployment of artificial intelligence systems that prioritize efficiency, cost-effectiveness, and resource optimization. This includes techniques to reduce computational requirements, energy consumption, and data needs while maintaining or improving performance.
Finetuning adapts a pretrained AI model to a specific task by training it on a smaller dataset. LoRA (Low-Rank Adaptation) finetuning is more a resource-efficient method, because it does not have to modify all of the model’s parameters–enabling faster customization.
Quantification is an optimization process which compresses AI models by reducing precision (e.g., using 8-bit integers instead of 32-bit), improving speed and efficiency. Pruning can go even further, by removing redundant parts or parameters of a model.
Knowledge distillation is a machine learning technique that involves transferring knowledge from a large, complex "teacher" model to a smaller "student" model. This process reduces the computational burden of deep learning while maintaining performance.
Also called a semantic network, a knowledge graph organizes information into entities and relationships, enabling machines to understand context and meaning. They power systems like search and recommendation engines.
RAG combines AI models with external databases, allowing models to retrieve factual information to improve responses. GraphRAG extends this by incorporating knowledge graphs, adding a richer, relational structure. Take a look at how Moments Lab uses the RAG approach for enhancing video content creation.
Vector representation refers to the numerical representation of data, such as words or images. Embeddings are data representations that are meaningful and structured and capture the data’s semantic meaning in a compact format. They are the backbone of search engines, recommendation systems, and natural language processing (NLP).
Agents are AI entities that can perform tasks on behalf of a user or a system autonomously. They can comprise a wide range of functionalities, and examples include chatbots, virtual assistants, and autonomous drones.
World models, sometimes called world simulators, are a simulated representation of an environment–our world, enabling AI agents to predict outcomes of actions and improve decision-making. This concept is key in robotics and reinforcement learning and shows potential in generative video.
Transformers are deep learning models designed for sequential data, like text. They use attention mechanisms to process information efficiently. Tokens are the building blocks of transformers–the smallest units of data (e.g., words, subwords, or characters) that models process.
Joint-Embedding Predictive Architecture (JEPA) predicts relationships between input and output data. V-JEPA extends this to video data, enabling advanced understanding of sequences and temporal patterns. Teaching machines to learn by watching videos has been a large part of Yann Le Cun’s work on advanced machine intelligence for AI at Meta.
Diffusion models generate data by simulating a gradual process of denoising (a task intended to remove “noise” from an image). Since 2020, they have been gaining popularity and are now widely used in image generation, for example in Dall-E or Stable Diffusion.
Embodied AI focuses on creating AI systems that interact with and learn from the physical world. By using machine learning, robot learning, language understanding and computer vision, embodied AI represents an autonomous system capable of sensory input and motor action. The most common examples of embodied AI are robots or drones.
Mastering AI’s terminology is not just technical knowledge—it’s a necessary skill for thriving in a rapidly changing world. We hope this guide to essential AI terms has helped you understand how these concepts are already shaping our economies and the opportunities they unlock for the future. For a deeper dive into these ideas, check out the top 10 AI conferences to attend in 2025.
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