The FTC authorizes new powers of investigation and compliance for AI

Probabilistic diffusion models are cutting-edge generative models that have gained importance in computer vision. These models use a Markov chain to map the latent space and have impressive generative capabilities. A joint study explores the denoising…

The paper explores Transformers’ capabilities in length generalization on algorithmic tasks and proposes a framework to predict their performance in this area. Accepted at NeurIPS 2023’s MATH workshop, it addresses the paradox of language models’ emergent…

Cardiac Magnetic Resonance Imaging (CMRI) segmentation is critical for diagnosing cardiovascular diseases, with recent advancements focusing on long-axis (LAX) views to visualize atrial structures and diagnose diseases affecting the heart’s apical region. The ENet architecture combined…

Large language models like GPT-3 require substantial energy for training and operational needs, with varying consumption based on factors such as size and task complexity. Researchers at the University of Michigan and the University of Washington…

Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have developed a groundbreaking approach to materials research using neural implicit representations. Unlike previous methods, which relied on image-based data representations, this approach uses coordinates as…

Understanding Vision Transformers and Their Challenges Vision Transformers (ViTs) are crucial in computer vision, known for their strong performance and adaptability. However, their large size and need for high computational power can make them challenging to…

The text discusses the proposal of the asymmetric certified robustness problem for deep learning classifiers, which addresses the vulnerability of these classifiers to adversarial examples. It introduces feature-convex classifiers as a solution to this problem, providing…

Recent advancements in scientific research are being reshaped by the integration of large language models (LLMs). A revolutionary system called Coscientist, detailed in the paper “Autonomous chemical research with large language models,” showcases the capabilities of…

The Challenge LLMs have made significant progress but face limitations in handling long input sequences, hindering their applicability in tasks like document summarization, question answering, and machine translation. The Solution Introducing HashHop Evaluation Tool HashHop uses…

Practical Solutions for Text-to-SQL with LLMs Enhancing Database Accessibility Current methodologies for Text-to-SQL rely on deep learning models, particularly Sequence-to-Sequence (Seq2Seq) models, which directly map natural language input to SQL output. Pre-trained language models (PLMs) and…

The AWS Generative AI Innovation Center, launched in June 2023, has assisted numerous clients in creating custom AI solutions. Starting Q1 2024, the new Custom Model Program will enable customers to fine-tune Anthropic Claude models with…

AI deep fakes, created by advanced technology, blur the line between reality and fiction, making it challenging to distinguish authentic content from manipulated media. This has prompted concerns about their potential impact on democratic processes, as…

Large Language Models (LLMs) require supervised fine-tuning (SFT) to match human instructions, which traditionally caused performance loss. Researchers from Fudan University and Hikvision Inc. propose a solution – LoRAMoE, a plugin version of Mixture of Experts,…