Cell segmentation and classification are vital tasks in spatial omics data analysis, which provides unprecedented insights into cellular structures and tissue functions. Recent advancements in spatial omics technologies have enabled high-resolution analysis of intact tissues, supporting initiatives like the Human Tumor Atlas Network and the Human Biomolecular Atlas Program in mapping spatial organizations in healthy…
In AI, a key challenge lies in improving the efficiency of systems that process unstructured datasets to extract valuable insights. This involves enhancing retrieval-augmented generation (RAG) tools, combining traditional search and AI-driven analysis to answer localized and overarching queries. These advancements address diverse questions, from highly specific details to more generalized insights spanning entire datasets.…
Large language models (LLMs) have transformed the development of agent-based systems for good. However, managing memory in these systems remains a complex challenge. Memory mechanisms enable agents to maintain context, recall important information, and interact more naturally over extended periods. While many frameworks assume access to GPT or other proprietary APIs, the potential for local…
In recent years, there has been a growing demand for machine learning models capable of handling visual and language tasks effectively, without relying on large, cumbersome infrastructure. The challenge lies in balancing performance with resource requirements, particularly for devices like laptops, consumer GPUs, or mobile devices. Many vision-language models (VLMs) require significant computational power and…
Anthropic has open-sourced the Model Context Protocol (MCP), a major step toward improving how AI systems connect with real-world data. By providing a universal standard, MCP simplifies the integration of AI with data sources, enabling smarter, more context-aware responses and making AI systems more effective and accessible. Despite remarkable advances in AI’s reasoning capabilities and…
Recommender systems are essential in modern digital platforms, enabling personalized user experiences by predicting preferences based on interaction data. These systems help users navigate the vast online content by suggesting relevant items critical to addressing information overload. By analyzing user-item interactions, they generate recommendations that aim to be accurate and diverse. However, as the digital…
Real-world networks, such as those in biomedical and multi-omics datasets, often present complex structures characterized by multiple types of nodes and edges, making them heterogeneous or multiplex. Most graph-based learning techniques fail to handle such intricate networks because of their intrinsic complexity, even though graph neural networks have been quite in vogue and garnered significant…
Function calling has emerged as a transformative capability in AI systems, enabling language models to interact with external tools through structured JSON object generation. However, current methodologies face critical challenges in comprehensively simulating real-world interaction scenarios. Existing approaches predominantly focus on generating tool-specific call messages, overlooking the nuanced requirements of human-AI conversational interactions. The complexity…
Diffusion models have pulled ahead of others in text-to-image generation. With continuous research in this field over the past year, we can now generate high-resolution, realistic images that are indistinguishable from authentic images. However, with the increasing quality of the hyperrealistic images model, parameters are also escalating, and this trend results in high training and…
Red teaming plays a pivotal role in evaluating the risks associated with AI models and systems. It uncovers novel threats, identifies gaps in current safety measures, and strengthens quantitative safety metrics. By fostering the development of new safety standards, it bolsters public trust and enhances the legitimacy of AI risk assessments. This paper details OpenAI’s…