Artificial intelligence is a vast and complex field, often filled with technical jargon that can feel intimidating. To help make sense of this rapidly evolving landscape, we’ve put together a friendly, SEO-optimized glossary of some of the most important AI terms and concepts. We’ll keep updating this guide as researchers continue to innovate and address emerging safety challenges in AI.
**Artificial General Intelligence (AGI)**
AGI refers to AI systems that surpass the capabilities of the average human across many tasks. Think of it as a “median human” co-worker—highly autonomous and outperforming humans in most economically valuable work. Definitions vary: OpenAI describes AGI as systems outperforming humans at most tasks, while others see it as AI at least as capable as humans at key cognitive functions. Even experts are still debating what truly qualifies as AGI.
**AI Agent**
An AI agent is a tool that leverages AI technologies to perform complex tasks on your behalf—more than simple chatbots. These can include filing expenses, booking reservations, or even writing and maintaining code. While the concept is promising, infrastructure is still being built to fully realize these autonomous systems, which often draw from multiple AI models to complete multi-step tasks.
**Chain-of-Thought Reasoning**
When you solve a problem, your brain often breaks it down into smaller steps. In AI, chain-of-thought reasoning mimics this process to improve accuracy—especially in language and logic tasks. Large language models (LLMs) optimized for this approach analyze problems step-by-step, leading to more correct and reliable answers, though it may take longer to get there.
**Deep Learning**
Deep learning is a subset of machine learning that uses neural networks with multiple layers—drawing inspiration from the human brain’s interconnected neurons. These models automatically identify important data features without human guidance and learn from mistakes through iterative adjustments. They require vast amounts of data and computational power but excel at tasks like image recognition, language understanding, and more.
**Diffusion Models**
Diffusion is a technique behind many AI-generated art, music, and text models. Inspired by physics, diffusion gradually adds noise to data until it’s destroyed, then learns to reverse this process to recreate the original information. This approach enables AI to generate realistic images or audio by learning how to denoise corrupted data.
**Knowledge Distillation**
Distillation involves transferring knowledge from a large, complex AI model (the “teacher”) to a smaller, more efficient one (the “student”). Developers use the teacher’s outputs to train the student, creating a streamlined model that performs similarly but faster—like how OpenAI developed GPT-4 Turbo, a quicker version of GPT-4.
**Fine-Tuning**
Fine-tuning is the process of further training an AI model on specialized data to improve its performance in a specific domain or task. Many startups take general-purpose models and refine them with domain-specific information to boost utility in sectors like healthcare, finance, or legal services.
**Generative Adversarial Networks (GANs)**
GANs are a powerful framework where two neural networks compete: one generates data (like images or videos), and the other evaluates its realism. Over time, this “adversarial” process produces highly realistic outputs—used for deepfakes, realistic art, and synthetic data—though they’re typically suited for narrow applications rather than general AI.
**Hallucination in AI**
In AI, hallucination describes when models invent or confidently generate false or misleading information. This is a major challenge because it can lead to inaccurate or dangerous outputs—like medical advice or critical data—highlighting the importance of verification and domain-specific models to reduce these errors.
**Inference**
Inference is the process of running an AI model to make predictions or draw conclusions based on previous training. It requires suitable hardware—ranging from smartphones to powerful cloud servers—and the speed of inference depends on the model size and computational resources.
**Large Language Models (LLMs)**
LLMs power many AI assistants, such as ChatGPT, Google’s Bard, or Meta’s Llama. These deep neural networks, made up of billions of parameters, learn language patterns from vast datasets like books and articles. When you ask a question, they generate the most probable response based on learned relationships between words.
**Neural Networks**
Neural networks are the backbone of deep learning, mimicking brain-like connections to process complex data. The rise of GPUs—originally designed for gaming—has significantly advanced neural network capabilities, enabling AI to excel in areas like speech recognition, autonomous driving, and drug discovery.
**Training AI Models**
Training is the process where AI models learn from data by identifying patterns. Starting from random parameters, models adapt based on feedback from data inputs, enabling them to perform tasks like image recognition or language generation. Training can be resource-intensive and expensive, but it’s essential for developing capable AI systems.
**Transfer Learning**
Transfer learning reuses a pre-trained model as a starting point for related tasks, saving time and resources. It’s especially useful when data is limited for the specific application. However, models relying on transfer learning may still need additional training to perform optimally in new domains.
**Weights**
Weights are numerical values within neural networks that determine the importance of different features in data. During training, these weights are adjusted to improve the model’s accuracy—much like tuning a recipe for the perfect outcome based on taste tests.
This glossary aims to demystify AI’s most critical concepts, helping you better understand the innovations shaping our future. Stay tuned for updates as the field continues to evolve!
