Diffusion Model
A generative AI architecture that creates data (typically images) by learning to reverse a gradual noising process, starting from pure noise and iteratively denoising to produce coherent outputs.
Diffusion models power leading image generators like DALL-E 3, Stable Diffusion, and Midjourney. Introduced in 2015 and refined in 2020, they overtook GANs as the dominant image generation approach by 2022. Diffusion models have been extended to video (Sora), audio, and 3D generation. The architecture generates higher-quality and more diverse outputs than GANs, though at the cost of slower inference — typically requiring 20-50 denoising steps per image.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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