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AI research has converged around foundation models across various domains.

In previous blog posts, we explored self-supervised visual learning methods that transform images and videos into information-rich embeddings.

A crucial aspect of both human and animal intelligence is our internal model of the world.

The autoencoder is a simple and intuitive machine learning architecture.

While the core transformer architecture introduced in “Attention is All You Need” remains effective and widely used today, numerous architectural improvements have emerged since its inception.

The Transformer architecture has emerged as the cornerstone of deep learning and artificial intelligence.

In a previous blog post, we explored image-based self-supervised learning primarily with contrastive learning.

In recent years, self-supervised learning (SSL) has emerged as a powerful paradigm in computer vision, allowing models to learn meaningful representations from unlabeled data.

[2025-07-04 Added section on V-JEPA 2] In the AI research community, Yann LeCun has a unique and often controversial perspective.

Many of the state-of-the-art results in deep learning are achieved using multiple GPUs.