Human-Timescale Adaptation in an Open-Ended Task Space
Shows that training an RL agent at scale leads to a general in-context learning algo that can adapt to open-ended novel embodied 3D problems as quickly as humans.
Can help improve business operations that involve open-ended problem-solving through RL.
Towards Models that Can See and Read
Presents UniTNT, which leads to the first single model that successfully handles both "seeing" and "reading" images to solve VQA problems.
Can help businesses that require image and text analysis to solve problems such as customer service and product recommendation.
OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation
Proposes OmniObject3D, a large vocabulary 3D object dataset with high-quality real-scanned 3D objects, to improve 3D perception, reconstruction and generation in the real world.
Can help businesses in industries such as manufacturing and architecture that require 3D perception, reconstruction, and generation.
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
Proposes the first Human vs. ChatGPT comparison corpus, named HC3.
Provides insights into the characteristics of ChatGPT's responses, the differences and gaps from human experts, and future directions for LLMs. Also presents effective detection systems to distinguish between ChatGPT-generated text and human-generated text.