Florence (computer vision model)
Florence is a computer vision model developed for advanced image recognition and analysis, leveraging deep learning techniques.
Technology & Innovation
RoBERTa
RoBERTa is a transformer-based language model developed by Facebook AI Research as an optimized variant of BERT. It improves language understanding by training with larger datasets and modified training strategies.
Meta AI
Meta AI is the artificial intelligence research division of Meta Platforms, focusing on advancing AI technologies through research and development. It aims to develop AI models and systems that enhance various applications across Meta’s platforms and the broader AI community.
AI capability control
AI capability control refers to methods and strategies designed to regulate, limit, and guide the abilities of artificial intelligence systems to ensure their safe and ethical operation. This field addresses concerns about AI systems acting unpredictably or autonomously beyond intended boundaries.
Loss function
A loss function is a mathematical function used in optimization and machine learning to measure the difference between predicted values and actual values. It guides algorithms in minimizing errors during model training.
RT-1 (robotics transformer)
RT-1 is an advanced robotics transformer designed for versatile applications in various fields, integrating robotics and AI technologies.
OPT (language model)
OPT (Open Pre-trained Transformer) is a large-scale language model developed to provide an open alternative to proprietary models. It is designed to facilitate research and transparency in natural language processing by offering publicly accessible weights and code.
VQ-VAE
VQ-VAE (Vector Quantized Variational Autoencoder) is a generative model architecture that combines discrete latent representations with variational autoencoders, enabling efficient learning and synthesis of complex data like images and audio.
Libratus
Libratus is an artificial intelligence program developed to play and excel at no-limit Texas hold ’em poker. It demonstrated significant advances in AI by defeating top human professional players in 2017, showcasing new techniques in game theory and decision-making under uncertainty.
Prompt tuning
Prompt tuning is a technique in natural language processing that adapts large language models to specific tasks by optimizing a small set of prompt parameters instead of fine-tuning the entire model. It offers a parameter-efficient alternative to traditional model fine-tuning, enabling task adaptation with reduced computational resources.