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Unit testing aims to identify and resolve bugs at the earliest stages by testing individual components or units of code. This process ensures software reliability and quality before the final product is delivered. Traditional methods of unit test generation, such as search-based, constraint-based, and random-based techniques, have been utilized to automate the creation of unit…
The European Artificial Intelligence Act came into force on August 1, 2024. It is a significant milestone in the global regulation of artificial intelligence all over the world. It is the world’s first comprehensive milestone in terms of regulation of AI and reflects EU’s ambitions to establish itself as a leader in safe and trustworthy…
Multimodal generative models represent an exciting frontier in artificial intelligence, focusing on integrating visual and textual data to create systems capable of various tasks. These tasks range from generating highly detailed images from textual descriptions to understanding and reasoning across different data types. The advancements in this field are opening new possibilities for more interactive…
The development of autonomous agents capable of performing complex tasks across various environments has gained significant traction in artificial intelligence research. These agents are designed to interpret and execute natural language instructions within graphical user interface (GUI) environments, such as websites, desktop operating systems, and mobile devices. The ability of these agents to seamlessly navigate…
Parler-TTS has emerged as a robust text-to-speech (TTS) library, offering two powerful models: Parler-TTS Large v1 and Parler-TTS Mini v1. Both models are trained on an impressive 45,000 hours of audio data, enabling them to generate high-quality, natural-sounding speech with remarkable control over various features. Users can manipulate aspects such as gender, background noise, speaking…
Human reward-guided learning is often modeled using simple RL algorithms that summarize past experiences into key variables like Q-values, representing expected rewards. However, recent findings suggest that these models oversimplify the complexity of human memory and decision-making. For instance, individual events and global reward statistics can significantly influence behavior, indicating that memory involves more than…
The Qwen Team has recently released the Qwen 2-Math series. This release, encompassing several model variants tailored for distinct applications, demonstrates the team’s commitment to enhancing AI’s proficiency in handling complex mathematical tasks. The Qwen 2-Math series is a comprehensive set of models, each designed to cater to different computational needs. The lineup includes: Qwen…
Introduction: Code Large Language Models (CodeLLMs) have demonstrated remarkable proficiency in generating code. However, they struggle with complex software engineering tasks, such as developing an entire software system based on intricate specifications. Recent works, including ChatDev and MetaGPT, have introduced multi-agent frameworks for software development, where agents collaborate to achieve complex goals. These works follow…
Automated information extraction from radiology notes presents significant challenges in the field of medical informatics. Researchers are trying to develop systems that can accurately extract and interpret complex medical data from radiological reports, particularly focusing on tracking disease progression over time. The primary challenge lies in the limited availability of suitably labeled data that can…
Large Language Models (LLMs) have significantly impacted software engineering, primarily in code generation and bug fixing. These models leverage vast training data to understand and complete code based on user input. However, their application in requirement engineering, a crucial aspect of software development, remains underexplored. Software engineers have shown reluctance to use LLMs for higher-level…