Cardinality estimation (CE) is crucial in optimizing query performance in relational databases. It involves predicting the number of intermediate results a database query will return, directly influencing the choice of execution plans by query optimizers. Accurate cardinality estimates are essential for selecting efficient join orders, determining whether to use an index and choosing the best…
Information retrieval (IR) is a fundamental aspect of computer science, focusing on efficiently locating relevant information within large datasets. As data grows exponentially, the need for advanced retrieval systems becomes increasingly critical. These systems use sophisticated algorithms to match user queries with relevant documents or passages. Recent developments in machine learning, particularly in natural language…
Integrating No-Code AI in Non-Technical Higher Education: Recent developments in ML underscore its ability to drive value across diverse sectors. Nevertheless, incorporating ML into non-technical academic programs, such as those in social sciences, presents challenges due to its usual ties with technical fields like computer science. To overcome this barrier, a case-based approach utilizing no-code…
Generative AI, an area of artificial intelligence, focuses on creating systems capable of producing human-like text and solving complex reasoning tasks. These models are essential in various applications, including natural language processing. Their primary function is to predict subsequent words in a sequence, generate coherent text, and even solve logical and mathematical problems. However, despite…
IBM releases a new version of Qiskit SDK to address the challenge of optimizing the performance and functionality of the existing version. Qiskit SDK is a leading quantum computing software development kit. As quantum computing evolves, the need for more efficient tools to handle complex quantum workloads becomes increasingly critical. The latest version, Qiskit SDK…
LLMs have advanced significantly in recent years, demonstrating impressive capabilities in various tasks. However, LLMs’ performance often deteriorates when dealing with long input sequences. This limitation can hinder their applicability in domains requiring extensive information processing, such as document summarization, question answering, and machine translation. Current models are limited by short context windows, which restrict…
The field of information retrieval (IR) has rapidly evolved, especially with the integration of neural networks, which have transformed how data is retrieved and processed. Neural retrieval systems have become increasingly important, particularly those using dense and multi-vector models. These models encode queries and documents as high-dimensional vectors and capture relevance signals beyond keyword matching,…
Large Language Models (LLMs) have revolutionized natural language processing but face significant challenges in handling very long sequences. The primary issue stems from the Transformer architecture’s quadratic complexity relative to sequence length and its substantial key-value (KV) cache requirements. These limitations severely impact the models’ efficiency, particularly during inference, making them prohibitively slow for generating…
The digital age has led to a massive increase in the amount of text-based content available online, from research papers and articles to social media posts and corporate documents. Traditional search engines often fall short, providing only a list of relevant documents without delivering comprehensive and contextually accurate answers to specific queries. Manually searching and…
Cohere For AI unveiled two significant advancements in AI models with the release of the C4AI Command R+ 08-2024 and C4AI Command R 08-2024 models. These state-of-the-art language models are designed to push what’s achievable with AI, especially in terms of text generation, reasoning, and tool use. They offer profound implications for both research and…