New embedding models and API updates
We are launching a new generation of embedding models, new GPT-4 Turbo and moderation models, new API usage management tools, and soon, lower pricing on GPT-3.5 Turbo.
Researchers at the University of Waterloo Developed GraphNovo: A Machine Learning-based Algorithm that Provides a More Accurate Understanding of the Peptide Sequences in Cells
In medicine, scientists face a challenge in treating serious diseases like cancer. The problem lies in understanding the unique composition of cells, particularly the sequences of peptides within them. Peptides are like the building blocks of cells, playing a crucial role in our bodies. Identifying these peptide sequences is essential for developing personalized treatments, especially…
NousResearch Released Nous-Hermes-2-Mixtral-8x7B: An Open-Source LLM with SFT and DPO Versions
In artificial intelligence and language models, users often face challenges in training and utilizing models for various tasks. The need for a versatile, high-performing model to understand and generate content across different domains is apparent. Existing solutions may provide some level of performance, but they need to catch up in achieving state-of-the-art results and adaptability….
This AI Paper from the University of Washington, CMU, and Allen Institute for AI Unveils FAVA: The Next Leap in Detecting and Editing Hallucinations in Language Models
Large Language Models (LLMs), which are the latest and most incredible developments in the field of Artificial Intelligence (AI), have gained massive popularity. Due to their human-imitating skills of answering questions like humans, completing codes, summarizing long textual paragraphs, etc, these models have utilized the potential of Natural Language Processing (NLP) and Natural Language Generation…
This AI Paper from UCLA Revolutionizes Uncertainty Quantification in Deep Neural Networks Using Cycle Consistency
With the growth of Deep learning, it is used in many fields, including data mining and natural language processing. It is also widely used in solving inverse imaging problems, such as image denoising and super-resolution imaging. The image denoising techniques are used to generate high-quality images from raw data. However, deep neural networks are inaccurate…
Researchers from ByteDance and Sun Yat-Sen University Introduce DiffusionGPT: LLM-Driven Text-to-Image Generation System
In image generation, diffusion models have significantly advanced, leading to the widespread availability of top-tier models on open-source platforms. Despite these strides, challenges in text-to-image systems persist, particularly in managing diverse inputs and being confined to single-model outcomes. Unified efforts commonly address two distinct facets: first, the parsing of various prompts during the input stage,…
Fireworks AI Open Sources FireLLaVA: A Commercially-Usable Version of the LLaVA Model Leveraging Only OSS Models for Data Generation and Training
A variety of Large Language Models (LLMs) have demonstrated their capabilities in recent times. With the constantly advancing fields of Artificial Intelligence (AI), Natural Language Processing (NLP), and Natural Language Generation (NLG), these models have evolved and have stepped into almost every industry. In the growing field of AI, it has become essential to have…
Google DeepMind Researchers Propose a Novel AI Method Called Sparse Fine-grained Contrastive Alignment (SPARC) for Fine-Grained Vision-Language Pretraining
Contrastive pre-training using large, noisy image-text datasets has become popular for building general vision representations. These models align global image and text features in a shared space through similar and dissimilar pairs, excelling in tasks like image classification and retrieval. However, they need help with fine-grained tasks such as localization and spatial relationships. Recent efforts…
Meet einx: A Python Library that Allows Formulating Many Tensor Operations as Concise Expressions Using Einstein Notation
Meet einx, a novel Python library developed in the tensor operations landscape, offers a streamlined approach to formulating complex tensor operations using Einstein notation. Inspired by einops, einx distinguishes itself through a fully composable and powerful design, incorporating []-notation for expressive tensor expressions. Developed by researchers, this library is a versatile tool for efficient tensor…
This 200-Page AI Report Covers Vector Retrieval: Unveiling the Secrets of Deep Learning and Neural Networks in Multimodal Data Management
Artificial Intelligence has witnessed a revolution, largely due to advancements in deep learning. This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. These developments have not just incrementally advanced fields like machine translation, natural language understanding, information retrieval, recommender systems, and computer vision but have caused a quantum leap…
