In artificial intelligence (AI), utilizing monolithic large language models (LLMs) such as GPT-4 has been pivotal in advancing modern generative AI applications. However, the maintenance, training, and deployment of these LLMs at scale are fraught with challenges, primarily due to the high costs and complexities involved. These challenges are exacerbated by a growing disproportion in… →

Workplace creativity, analysis, and decision-making are all being revolutionized by AI. Today, artificial intelligence capabilities present a tremendous opportunity for businesses to hasten expansion and better control internal processes. Artificial intelligence applications are vast, ranging from automation and predictive analytics to personalization and content development. Here is a rundown of the best artificial intelligence tools… →

Pre-training large models on time series data faces several challenges: the lack of a comprehensive public time series repository, the complexity of diverse time series characteristics, and the infancy of experimental benchmarks for model evaluation, especially under resource-constrained and minimally supervised scenarios. Despite these hurdles, time series analysis remains vital across applications like weather forecasting,… →

Artificial intelligence hinges on using broad datasets, drawing from global internet resources like social media, news outlets, and more, to power algorithms that shape many facets of modern life. The training of generative models, such as GPT-4, Gemini, Cluade, and others, relies on often insufficiently documented and vetted data. This unstructured and obscure data collection… →

In computational linguistics, the interface between human language and machine understanding of databases is a critical research area. The core challenge lies in enabling machines to interpret natural language and convert these inputs into SQL queries executable by database systems. This translation process is vital for making database interaction accessible to users without deep technical… →

Methods like Molecular Dynamics simulations, Quantitative Structure-Property Relationships (QSPR), and First-Principles calculations are based on scientific principles and complex mathematical models. They require expensive computational resources, have limited accuracy with complex models, and heavily depend on the quality and quantity of available data. These methods for material development rely on physical synthesis and testing, which… →

Transformers are at the forefront of modern artificial intelligence, powering systems that understand and generate human language. They form the backbone of several influential AI models, such as Gemini, Claude, Llama, GPT-4, and Codex, which have been instrumental in various technological advances. However, as these models grow in size & complexity, they often exhibit unexpected… →