A benchmark designed to test AI models' capabilities in making accurate 3-month future predictions.

A 30-task benchmark for evaluating long-horizon planning capabilities across 16 different AI models.

AlexNet paper that sparked the modern deep learning revolution through convolutional neural networks.

Practical insights into ML pitfalls and best practices for machine learning practitioners.

Introduces an object-centric sim-to-real policy that enables zero-shot dexterous tool use on physical robots without task-specific fine-tuning. Leverages simulation data for robust real-world transfer.

Pretrains robot policies on large-scale web video to acquire dynamics and physics understanding instead of static images or VLMs. Yields faster training, better generalization, and superior dexterous manipulation results in real-world tasks.

Introduced residual connections (ResNet) enabling training of very deep networks, still widely used in modern architectures.

Introduced the Transformer architecture based purely on attention mechanisms, becoming the foundation of nearly all modern large language models.

World foundation models for video and physics prediction with SynthID watermarking for responsible AI practices. Developed in collaboration with Google DeepMind.

Introduces a three-stage pipeline that extracts a latent action space from noisy, low-quality human motion capture, then trains a high-level RL policy in simulation to compose and execute dynamic whole-body tennis skills. Achieves volleys at human-level performance on a humanoid robot.