Artificial intelligence (AI) is transforming the world while simultaneously creating a new vocabulary. Within minutes of reading about AI, one might encounter terms like LLMs, RAG, RLHF, and many others that can make even tech-savvy individuals feel out of their depth. This glossary aims to clarify these terms and is updated regularly as the field progresses, forming a living document akin to the AI systems it explains.
Artificial general intelligence (AGI) is broadly defined as AI that exceeds the capabilities of an average human in most tasks. OpenAI CEO Sam Altman described AGI as akin to a “median human” one might hire. OpenAI’s charter refers to AGI as autonomous systems surpassing humans in economically valuable work. Google DeepMind suggests AGI encompasses AI at least on par with humans in cognitive tasks, differing slightly from other definitions. Experts at the AI research forefront are also unsure of its exact nature.
An AI agent performs tasks autonomously beyond basic AI chatbots, such as filing expenses, booking reservations, or coding. However, the term “AI agent” varies between interpretations, and its infrastructure is developing. The core idea suggests an autonomous system capable of executing multi-step tasks using various AI methods.
API endpoints function as “buttons” within software applications, enabling direct functionalities like data retrieval or third-party service control by AI agents. These endpoints are crucial for integrations, and as AI agents become more advanced, they increasingly leverage these endpoints, unlocking automation possibilities.
Human brains can effortlessly answer simple questions without intensive processing. In contrast, AI systems require breaking problems into intermediate steps, enhancing the accuracy of outcomes. This chain-of-thought reasoning involves dissecting problems for better results, particularly in logical or coding scenarios. Although it takes more time, it yields more accurate answers by employing reinforcement learning.
AI agents capable of coding handle programming tasks independently, from writing to debugging, similar to an efficient, focused intern. Although they automate much of the iterative work, human oversight is still needed to review outcomes.
Compute power is essential across AI industries, driving training and deployment of AI models. It refers to computational resources such as GPUs, CPUs, and TPUs underlying AI functions.
Deep learning uses multi-layered artificial neural networks to forge complex correlations. Unlike simple machine learning systems, deep learning models autonomously identify data features, improving outputs through repetitive learning. They require extensive data and typically involve prolonged training.
Diffusion technology underlies many AI systems, including art and text generation, using a process inspired by physics to restore data from noise.
Distillation extracts information from large AI models, training smaller, efficient models. It’s instrumental for AI development but can breach API terms when utilized to emulate competitor models.
Transferring knowledge from a trained AI model assists in developing new models, optimizing tasks with transfer learning techniques. This approach reuses existing training knowledge, although models might need additional data for specific domains.
Large language models (LLMs) power AI assistants like ChatGPT, processing user requests using deep neural networks built on linguistic representations. During interactions, LLMs generate responses by identifying relational patterns across vast datasets.
Memory caching optimizes inference, reducing the computational load required during response generation by storing calculations for reuse.
Neural networks underpin deep learning and generative AI tools, deriving inspiration from the neuron pathways in human brains. Their efficiency improved markedly with GPU advancements, aiding developments across diverse fields.
Open source in AI means sharing software code publicly, enabling collective progress and safety audits, contrasting with closed-source models like OpenAI’s GPTs.
Parallelization involves multitasking in AI computations, vital to efficiently building and deploying complex AI models. This method enhances the performance of training and inference processes, influenced by GPU capacities.
RAMageddon describes the scarcity of RAM chips due to AI’s expansion, affecting sectors like gaming and consumer electronics as demand escalates.
Reinforcement learning teaches AI through trial-based rewards, optimizing models for accuracy and safety. It’s a prominent method for enhancing large language models with real-time feedback mechanisms.
Tokens are units of human-AI interaction, essential for transforming text into model-readable formats during tasks. They influence LLM operational costs and processing capabilities, with token throughput reflecting system capacity.
Training AI involves data-driven learning, recognizing patterns to generate desired outputs. It requires significant resources, prompting hybrid designs to economize costs through specialized training methods.
Weights in AI models signify feature importance during training, adjusting dynamically to fine-tune outputs based on influential input variables. They shape how AI systems prioritize factors for predictive accuracy.
Validation loss measures model learning efficiency throughout training, ensuring generalization rather than memorization. Lower validation loss indicates successful pattern recognition without overfitting.
This article continuously incorporates new information.
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