Author: Only AI Stuff

The development of large language models (LLMs) that can understand and interpret the subtleties of human language is a complex challenge in natural language processing (NLP). Even then, a significant gap remains, especially in the models’ capacity to understand and use context-specific linguistic features. Researchers from Georgetown University and Apple have made strides in this

Artificial intelligence, particularly large language models (LLMs), has advanced significantly due to reinforcement learning from human feedback (RLHF). However, there are still challenges associated with creating original content purely based on this feedback. The development of LLMs has always grappled with optimizing learning from human feedback. Ideally, machine-generated responses are refined to closely mimic what

Nomic AI unveils the Nomic Embed, an open-source, auditable, and high-performing text embedding model with an extended context length. The release addresses the restricted openness and auditability of pre-existing models such as the OpenAI’s text-embedding-ada-002. Nomic Embed incorporates a multi-stage training pipeline based on contrastive learning and provides an 8192 context length, ensuring reproducibility and

Researchers from Pinterest have developed a reinforcement learning framework to enhance diffusion models – a set of generative models in Machine Learning that add noise to training data and then learn to recover it. This exciting advancement allows the models to accomplish top-tier image quality. These models’ performance, however, largely relies on the training data’s

Artificial Intelligence aims to automate everyday computer operations through autonomous agents capable of reasoning, planning, and acting independently. A significant challenge in this field is devising agents that can easily operate computers, handle textual and visual inputs, grasp complex language commands and execute tasks to meet predefined objectives. Existing research and benchmarks have mainly focused