Recent advances in segmentation foundation models like the Segment Anything Model (SAM) have shown impressive performance on natural images and videos. Still, their application to medical data remains to be determined. SAM, trained on a vast dataset of natural images, struggles with medical images due to domain differences like lower resolution and unique image challenges.…
Artificial intelligence, particularly AI chatbots like ChatGPT, has ushered in a new era of technological interaction. These intelligent systems, capable of understanding and generating human-like text, are not just prevalent across various applications but are also transforming the way we communicate, work, and learn. The rapid adoption of AI chatbots, particularly ChatGPT, across different domains…
One of the primary challenges in AI research is verifying the correctness of language models (LMs) outputs, especially in contexts requiring complex reasoning. As LMs are increasingly used for intricate queries that demand multiple reasoning steps, domain expertise, and quantitative analysis, ensuring the accuracy and reliability of these models is crucial. This task is particularly…
IncarnaMind is leading the way in Artificial Intelligence by enabling users to engage with their personal papers, whether they are in PDF or TXT format. The necessity of being able to query documents in natural language has increased with the introduction of AI-driven solutions. However, problems still exist, especially when it comes to accuracy and…
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…