2023

Enormous large language models (LLMs) can exhibit appealing skills like producing human-like discourse and responding to complicated inquiries because they have been trained at scale on large text corpora. While undoubtedly amazing, most cutting-edge LLMs are trained on text-only data downloaded from the Internet. They frequently cannot absorb concepts based on the actual world because…

Implicit neural representations (INRs) or neural fields are coordinate-based neural networks representing a field, such as a 3D scene, by mapping 3D coordinates to color and density values in 3D space. Recently, neural fields have gained a lot of traction in computer vision as a means of representing signals like pictures, 3D shapes/scenes, movies, music,…

Generative AI has emerged as a transformative force across numerous sectors, including the automobile industry, where its influence is on the rise. With applications from manufacturing advancements to enhanced automation, passenger well-being, and safety, generative AI can revolutionize various aspects of the automotive landscape. This article discusses the various applications of Generative AI for current…

It is widely recognized that artificial intelligence (AI) has made significant strides in recent years, leading to remarkable achievements and breakthrough outcomes. However, it is not true that AI can attain equally impressive results across all tasks. For instance, while AI can surpass human performance in certain visual tasks, such as facial recognition, it can…

The exponentially expanding scale of deep learning models is a major force in advancing the state-of-the-art and a source of growing worry over the energy consumption, speed, and, therefore, feasibility of massive-scale deep learning. Recently, researchers from Cornell talked about Transformer topologies, particularly how they are dramatically better when scaled up to billions or even…