Innovation in science is essential to human progress because it drives developments in a wide range of industries, including technology, healthcare, and environmental sustainability. Large Language Models (LLMs) have lately demonstrated potential in expediting scientific discovery by generating research ideas due to their extensive text-processing capabilities. However, because of their limitations in terms of gathering… →
The world of technology is constantly evolving, and programming languages are at the heart. With countless options available, it can be overwhelming to choose the correct programming language for your project or career. Even though most programming languages can do almost anything, they usually have tools and libraries built for specific jobs. We present an… →
Generative artificial intelligence (AI) models are designed to create realistic, high-quality data, such as images, audio, and video, based on patterns in large datasets. These models can imitate complex data distributions, producing synthetic content resembling samples. One widely recognized class of generative models is the diffusion model. It has succeeded in image and video generation… →
Formal theorem proving has emerged as a critical benchmark for assessing the reasoning capabilities of large language models (LLMs), with significant implications for mathematical automation. While these models show promise in assisting mathematicians through proof completion and formalization tools, a substantial challenge persists in bridging the gap between current evaluation methods and real-world theorem proving… →
Machine learning has considerably improved in evaluating large language models (LLMs) for their mathematical reasoning abilities, especially in handling complex arithmetic and deductive reasoning tasks. The field focuses on testing LLMs’ capacity to generalize and solve new types of problems, especially as arithmetic problems increase in complexity. Evaluations that explore reasoning capabilities in LLMs use… →
In the rapidly evolving world of finance, the demand for models that provide robust insights has never been greater. Traditional financial analysis requires an understanding of complex relationships, macroeconomic indicators, and financial nuances. Despite progress in AI, most language models struggle with the intricate aspects of financial data. They often lack the ability to fully… →
CONCLUSIONS: Since 2022, the introduction of OTC HAs has revolutionized access to these devices. Researchers, clinicians, and the general public are keen to evaluate the clinical effectiveness of OTC HAs, as more individuals will likely use them for HL. This increased usage will provide valuable real-world data to understand the benefits and limitations of OTC… →
Despite rapid advancements in language technology, significant gaps in representation persist for many languages. Most progress in natural language processing (NLP) has focused on well-resourced languages like English, leaving many others underrepresented. This imbalance means that only a small portion of the world’s population can fully benefit from AI tools. The absence of robust language… →
Artificial intelligence (AI) is making significant strides in natural language processing (NLP), focusing on enhancing models that can accurately interpret and generate human language. Researchers are working to develop models that grasp complex linguistic structures and generate coherent, contextually relevant responses over extended dialogues. Advancements in this area are vital for applications such as automated… →
Large language models (LLMs) sometimes learn the things that we don’t want them to learn and understand knowledge. It’s important to find ways to remove or adjust this knowledge to keep AI accurate, precise, and in control. However, editing or “unlearning” specific knowledge in these models is very tough. The usual methods to do this… →