The development of effective AI models is crucial in deep learning research, but finding optimal model architectures remains challenging and costly. Traditional manual and automated approaches often fail to expand design possibilities beyond basic architectures like Transformers or hybrids, and the high cost of exploring a comprehensive search space limits model improvement. Manual optimization demands… →

In today’s world, large language models have shown great performance on various tasks and demonstrated different reasoning capabilities. This is important for advancing Artificial General Intelligence (AGI) and its use in robotics and navigation. Spatial reasoning includes quantitative aspects (e.g., distances, angles) and qualitative aspects (e.g., relative positions like “near” or “inside”). While humans excel… →

Artificial intelligence has been progressively transforming with domain-specific models that excel in handling tasks within specialized fields such as mathematics, healthcare, and coding. These models are designed to enhance task performance and resource efficiency. However, integrating such specialized models into a cohesive and versatile framework remains a substantial challenge. Researchers are actively seeking innovative solutions… →

Universities face intense global competition in the contemporary academic landscape, with institutional rankings increasingly tied to the United Nations’ Sustainable Development Goals (SDGs) as a critical social impact assessment benchmark. These rankings significantly influence crucial institutional parameters such as funding opportunities, international reputation, and student recruitment strategies. The current methodological approach to tracking SDG-related research… →

Building large language model (LLM)-powered applications for real-world production scenarios is challenging. Developers often face issues such as inconsistent responses from models, difficulties in ensuring robustness, and a lack of strong type safety. When building applications that leverage LLMs, the goal is to provide reliable, accurate, and contextually appropriate outputs to users, which requires consistency,… →

The static knowledge base and hallucination-creating inaccuracy or fabrication of information are two common issues with large language models (LLMs). The parametric knowledge within LLMs is inherently static, making it challenging to provide up-to-date information in real-time scenarios. Retrieval-augmented generation (RAG) addresses the problem of integrating external, real-time information to enhance accuracy and relevance. However,… →

The development of machine learning (ML) models for scientific applications has long been hindered by the lack of suitable datasets that capture the complexity and diversity of physical systems. Many existing datasets are limited, often covering only small classes of physical behaviors. This lack of comprehensive data makes it challenging to develop effective surrogate models… →

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent process by clipping individual gradients to a fixed norm and adding noise to the aggregated gradients of each mini-batch. This approach enables privacy by preventing sensitive information in… →

Cohere is a prominent company specializing in enterprise-focused artificial intelligence (AI) solutions. Located in Toronto, Canada, it has contributed significantly throughout 2024 with groundbreaking advancements. These developments span generative AI, multilingual processing, and enterprise-ready AI applications, reflecting Cohere’s focus on driving innovation and enhancing accessibility. Cohere Toolkit in April 2024: This open-source repository is designed… →