Calendars, specifically Google Calendars, have both positive and negative aspects. For example, they can help plan gatherings, track time spent on individual tasks, and even keep in touch with pals. However, our schedule has the potential to balloon out of control quickly. Having nothing to go on but a sea of blue checkboxes on your…
Large Language Models (LLMs) are advancing rapidly resulting in more complex architecture. The high cost of LLMs has been a major barrier to their widespread adoption in various industries. Businesses and developers have been hesitant to invest in these models due to the substantial operational expenses. A significant portion of these costs arises from the…
Visual representation learning using large models and self-supervised techniques has shown remarkable success in various visual tasks. However, deploying these models in real-world applications is challenging due to multiple resource constraints such as computation, storage, and power consumption. Adapting large pre-trained models for different scenarios with varying resource limitations involves weight pruning, knowledge distillation, or…
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…