Integration of AI into clinical practices is very challenging, especially in radiology. While AI has proven to enhance the accuracy of diagnosis, its “black-box” nature often erodes clinicians’ confidence and acceptance. Current clinical decision support systems (CDSSs) are either not explainable or use methods like saliency maps and Shapley values, which do not give clinicians… →

LLMs are driving major advances in research and development today. A significant shift has been observed in research objectives and methodologies toward an LLM-centric approach. However, they are associated with high expenses, making LLMs for large-scale utilization inaccessible to many. It is, therefore, a significant challenge to reduce the latency of operations, especially in dynamic… →

The rapid adoption of Large Language Models (LLMs) in various industries calls for a robust framework to ensure their secure, ethical, and reliable deployment. Let’s look at 20 essential guardrails designed to uphold security, privacy, relevance, quality, and functionality in LLM applications. Security and Privacy Guardrails Inappropriate Content Filter: An essential safeguard against disseminating inappropriate… →

The rapid advancement of AI technologies highlights the critical need for Large Language Models (LLMs) that can perform effectively across diverse linguistic and cultural contexts. A key challenge is the lack of evaluation benchmarks for non-English languages, which limits the potential of LLMs in underserved regions. Most existing evaluation frameworks are English-centric, creating barriers to… →

AI4Bharat and Hugging Face have unveiled the Indic-Parler Text-to-Speech (TTS) system, an initiative designed to advance linguistic inclusivity in AI. This development is an effort to bridge the digital divide in a linguistically diverse country like India. Indic Parler-TTS represents a synthesis of cutting-edge technology and cultural preservation to empower users to access digital tools… →

Visual language models (VLMs) have come a long way in integrating visual and textual data. Yet, they come with significant challenges. Many of today’s VLMs demand substantial resources for training, fine-tuning, and deployment. For instance, training a 7-billion-parameter model can take over 400 GPU days, which makes it inaccessible to many researchers. Fine-tuning is equally… →

LMMs have made significant strides in vision-language understanding but still need help reasoning over large-scale image collections, limiting their real-world applications like visual search and querying extensive datasets such as personal photo libraries. Existing benchmarks for multi-image question-answering are constrained, typically involving up to 30 images per question, which needs to address the complexities of… →

Protein design is crucial in biotechnology and pharmaceutical sciences. Google DeepMind, with its patent, WO2024240774A1, unveils a cutting-edge system that harnesses diffusion models operating on full atom representations. This innovative framework redefines the approach to protein design, achieving unprecedented precision and efficiency. DeepMind’s system is a breakthrough in computational biology, combining advanced neural networks with… →

Meta AI just released Llama 3.3, an open-source language model designed to offer better performance and quality for text-based applications, like synthetic data generation, at a much lower cost. Llama 3.3 tackles some of the key challenges in the NLP space by providing a more affordable and easier-to-use solution. The improvements in this version are… →