AI

  Language model development has historically operated under the premise that the larger the model, the greater its performance capabilities. However, breaking away from this established belief, Microsoft Research’s Machine Learning Foundations team researchers introduced Phi-2, a groundbreaking language model with 2.7 billion parameters. This model defies the traditional scaling laws that have long dictated…

  The employment of numerous deduction rules and the construction of subproofs allows the complexity of proofs to develop infinitely in many deductive reasoning tasks, such as medical diagnosis or theorem proving. It is not practical to find data to cover guarantees of all sizes due to the huge proof space. Consequently, starting with basic…

  Training large transformer models poses significant challenges, especially when aiming for models with billions or even trillions of parameters. The primary hurdle lies in the struggle to efficiently distribute the workload across multiple GPUs while mitigating memory limitations. The current landscape relies on complex Large Language Model (LLM) scaling frameworks, such as Megatron, DeepSpeed,…

  The capabilities of Artificial Intelligence (AI) are stepping into every industry, be it healthcare, finance, or education. In the field of medicine and veterinary medicine, identifying pain is a crucial first step in administering the right treatments. This identification is especially difficult with individuals who are unable to convey their pain, which calls for…

  Natural language processing (NLP) has entered a transformational period with the introduction of Large Language Models (LLMs), like the GPT series, setting new performance standards for various linguistic tasks. Autoregressive pretraining, which teaches models to forecast the most likely tokens in a sequence, is one of the main factors causing this amazing achievement. Because…

  Reinforcement Learning (RL) is a subfield of Machine Learning in which an agent takes suitable actions to maximize its rewards. In reinforcement learning, the model learns from its experiences and identifies the optimal actions that lead to the best rewards. In recent years, RL has improved significantly, and it today finds its applications in…

  How can missing portions of a 3D capture be effectively completed? This research paper from Google Research and UC Berkeley introduces “NeRFiller,” a novel approach for 3D inpainting, which addresses the challenge of reconstructing incomplete 3D scenes or objects often missing due to reconstruction failures or lack of observations. This approach allows precise and…

  The problem of video understanding and generation scenarios has been addressed by researchers of Tencent AI Lab and The University of Sydney by presenting GPT4Video. This unified multi-model framework supports LLMs with the capability of both video understanding and generation. GPT4Video developed an instruction-following-based approach integrated with the stable diffusion generative model, which effectively…

  Large Language Models (LLMs), like GPT, PaLM, LLaMA, etc., have attracted much interest because of their incredible capabilities. Their ability to utilize the strength of Natural Language Processing, Generation, and Understanding by generating content, answering questions, summarizing text, and so on have made LLMs the talk of the town in the last few months….

  A group of researchers from various institutions, including the University of Pittsburgh, Weill Cornell Medicine, Telemedicine & Advanced Technology Research Center, Uniformed Services University, Brooke Army Medical Center, and University of Pittsburgh Medical Center have examined the ethical principles of generative AI in healthcare, particularly focusing on transparency, bias modeling, and ethical decision-making concerns….