AI

Large language models (LLMs) are integrated into modern technology, driving agent systems that interact dynamically with external environments. Despite their impressive abilities, LLMs are very vulnerable to rapid injection attacks. These attacks occur when opponents inject malicious instructions through non -procedure data sources aimed at compromising the system by extracting sensitive data or performing harmful … Read more

In the evolving field of artificial intelligence, a significant challenge has developed models that can effectively resonate through complex problems, generate accurate code and process multiple types of data. Traditional AI systems often stand out in specific tasks, but struggle to generalize across different domains, limiting their practical uses. This fragmentation emphasizes the need for … Read more

Deep learning architectures such as CNNs and transformers have significantly advanced biological sequence modeling by capturing local and long -term dependencies. However, their use in biological contexts is limited by high calculation requirements and the need for large data sets. CNNs effectively detect local sequence patterns with subquadratic scaling, whereas transformers utilize self -storage to … Read more

Software maintenance is an integral part of the software development life cycle, where developers often visit existing code bases to resolve errors, implement new features and optimize the performance. A critical task in this phase is code localization that clarifies specific locations in a code base that needs to be changed. This process has been … Read more

LLMs quickly go across multiple domains, but their effectiveness in tackling complex financial problems is still an area of ​​active study. The iterative development of LLMs has significantly driven the development of artificial intelligence to artificial general intelligence (AGI). Openai’s O1 series and similar models such as QWQ and Marco-O1 have improved complex reasoning features … Read more

Artificial intelligence has made significant progress in recent years, yet it is a complex challenge to integrate real -time speech interaction with visual content. Traditional systems are often dependent on separate components of voice activity detection, speech recognition, text dialogue and text-to-speech synthesis. This segmented approach can introduce delays and may not be able to … Read more

In the field of artificial intelligence, multilingual speech recognition and translation have become important tools to facilitate global communication. Development of models, which, however, can precisely transcribe and translate multiple languages ​​in real time, constitute significant challenges. These challenges include managing different linguistic shades, maintaining high accuracy, ensuring low latency and implementation of models effectively … Read more

In today’s information -rich world, it is crucial to find relevant documents quickly. Traditional keyword -based search systems often fall short when dealing with semantic significance. This tutorial demonstrates how to build a powerful document search engine using: Hugging Face’s embedded models to convert text to rich vector representations Chroma DB as our Vektordatabase for … Read more

Fetch-Augmented Generation (RAG) has emerged as a strong paradigm to improve the capabilities of large language models (LLMS). By combining LLMS ‘creative generational skills with the actual accuracy of the retrieval systems, RAG offers a solution to one of LLMS’ most sustained challenges: hallucination. In this tutorial we build a complete clothing system using: FAISS … Read more

Machine learning has been expanded beyond traditional Euclidean spaces in recent years and examined representations in more complex geometric structures. Non-Euklidic representation learning is a growing field that seeks to capture the underlying geometric properties of data by embedding it in hyperbolic, spherical or mixed curvature product spaces. These approaches have been particularly useful for … Read more