Variational Autoencoder (VAE)
A generative model that learns a compressed, probabilistic representation of data and can generate new samples by decoding points from the learned latent space.
VAEs combine neural networks with probabilistic inference, learning a continuous latent space where similar data points are grouped together. They are used in drug discovery (generating novel molecular structures), image synthesis, and anomaly detection. Stable Diffusion uses a VAE as part of its architecture to compress images into a latent space before applying diffusion. While VAEs produce less sharp images than GANs or diffusion models, they provide a mathematically principled framework for learning data distributions and offer smooth interpolation between generated samples.
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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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