Understanding the Theory of Mind (ToM), the ability to grasp the thoughts and intentions of others, is crucial for developing machines with human-like social intelligence. Recent advancements in machine learning, especially with large language models, show some capability in ToM understanding. However, current ToM benchmarks primarily rely on either video or text datasets, neglecting the…
The storage and potential disclosure of sensitive information have become pressing concerns in the development of Large Language Models (LLMs). As LLMs like GPT acquire a growing repository of data, including personal details and harmful content, ensuring their safety and reliability is paramount. Contemporary research has shifted towards devising strategies for effectively erasing sensitive data…
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.
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…
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….
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…
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…
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,…
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…
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…
