Predicting transcriptomes directly from genome sequences is a significant challenge in microbial genomics, particularly for the numerous sequenced microbes that remain unculturable or require complex experimental protocols like RNA-seq. The gap between genomic information and functional understanding leaves us without knowledge of the microbial adaptive processes, survival mechanisms, and gene regulation functions. This must be… →
When it comes to AI tools, chatbots are often the first thing that comes to mind —conversation-based interfaces for users to write queries and receive responses. These dialogue interfaces are certainly useful, but they aren’t always the best fit for handling our everyday work. Often tacked on to the side of our workflows, chatbots supplement… →
Disaggregated systems are a new type of architecture designed to meet the high resource demands of modern applications like social networking, search, and in-memory databases. The systems intend to overcome the physical restrictions of the traditional servers by pooling and managing resources like memory and CPUs among multiple machines. Flexibility, better utilization of resources, and… →
Using LLMs in clinical diagnostics offers a promising way to improve doctor-patient interactions. Patient history-taking is central to medical diagnosis. However, factors such as increasing patient loads, limited access to care, brief consultations, and the rapid adoption of telemedicine—accelerated by the COVID-19 pandemic—have strained this traditional practice. These challenges threaten diagnostic accuracy, underscoring the need… →
The development of multimodal large language models (MLLMs) has brought new opportunities in artificial intelligence. However, significant challenges persist in integrating visual, linguistic, and speech modalities. While many MLLMs perform well with vision and text, incorporating speech remains a hurdle. Speech, a natural medium for human interaction, plays an essential role in dialogue systems, yet… →
Enhancing user experiences and boosting retention using recommendation systems is an effective and ever-evolving strategy used by many industries, such as e-commerce, streaming services, social media, etc. These systems must analyze complex relationships between users, items, and contextual factors to suggest precisely what the user might want. However, the existing recommendation systems are static, relying… →
Conversational AI has come a long way, but one challenge persists: getting systems to engage proactively in a way that feels natural. Many AI tools either wait passively for direct prompts or overwhelm users by jumping into conversations unnecessarily. This is especially tricky in multi-party settings, where timing and relevance are everything. Striking the right… →
Artificial intelligence has come a long way, transforming the way we work, live, and interact. Yet, challenges remain. Many AI systems rely heavily on cloud-based infrastructure, which raises valid privacy concerns. Others offer limited user control, making customization a difficult task. On top of that, aligning AI behavior with specific needs is often more complicated… →
Graph generation is an important task across various fields, including molecular design and social network analysis, due to its ability to model complex relationships and structured data. Despite recent advancements, many graph generative models still rely heavily on adjacency matrix representations. While effective, these methods can be computationally demanding and often lack flexibility. This can… →