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Multi-agent planning for mixed human-robot environments faces significant challenges. Current methodologies, often relying on data-driven human motion prediction and hand-tuned costs, struggle with long-term reasoning and complex interactions. Researchers aim to solve two primary issues: developing human-compatible strategies without clear equilibrium concepts and generating sufficient samples for learning algorithms. Existing approaches, while effective in scaling…
In computer science, code efficiency and correctness are paramount. Software engineering and artificial intelligence heavily rely on developing algorithms and tools that optimize program performance while ensuring they function correctly. This involves creating functionally accurate code and ensuring it runs efficiently, using minimal computational resources. A key issue in generating efficient code is that while…
It is a hassle to spin up AI workloads on the cloud. The lengthy training process involves installing several low-level dependencies, which might lead to infamous CUDA failures. It also consists of attaching persistent storage, waiting for the system to boot up for 20 minutes, and much more. Machine learning (ML) support for GPUs that…
LLMs have shown impressive abilities, generating contextually accurate responses across different fields. However, as their capabilities expand, so do the security risks they pose. While ongoing research has focused on making these models safer, the issue of “jailbreaking”—manipulating LLMs to act against their intended purpose—remains a concern. Most studies on jailbreaking have concentrated on the…
As AI models become more integrated into clinical practice, assessing their performance and potential biases towards different demographic groups is crucial. Deep learning has achieved remarkable success in medical imaging tasks, but research shows these models often inherit biases from the data, leading to disparities in performance across various subgroups. For example, chest X-ray classifiers…
In the era of information, data analysis is one of the most powerful tools for any business providing them with insights about market trends, customer behavior, and operational inefficiencies. Despite the large requirements in the field, skilled data analytics are limited, creating a significant gap between the potential value of data and the ability to…
Mathematics is crucial in data science as it underpins algorithms and models used for data analysis and prediction. It helps understand data patterns, optimize solutions, and make informed decisions. Learning math is, therefore, essential for mastering statistical methods, machine learning techniques, and effective problem-solving in data science. This article lists the top courses on mathematics…
Developing efficient language model-based agents is crucial for various applications, from virtual assistants to automated customer service. However, creating these agents can be complex and resource-intensive. One can face challenges in integrating different models, managing actions, and ensuring seamless operation of these intelligent systems. Existing solutions, like some frameworks, are too heavy and lack flexibility,…
Large Language Models (LLMs) have made significant progress in following instructions and responding to user queries. However, the current instruction tuning process faces major challenges. Acquiring human-generated data for training these models is expensive and time-consuming. Moreover, the quality of such data is limited by human capabilities. This limitation is especially evident while addressing the…
Artificial Intelligence (AI) has rapidly advanced, revolutionizing various sectors by performing tasks that require human intelligence, such as learning, reasoning, and problem-solving. Improvements in machine learning algorithms, computational capabilities, and the availability of large datasets drive these advancements. Despite the progress, the field faces significant challenges regarding transparency and reproducibility, which are critical for scientific…