This paper explores the domain of uncertainty quantification within large language models (LLMs) to identify scenarios where uncertainty in response to queries is significant. The study encompasses both epistemic and aleatoric uncertainties. Epistemic uncertainty arises from a lack of knowledge or data about the ground truth, whereas aleatoric uncertainty stems from inherent randomness in the…
Recent advances in artificial intelligence, primarily driven by foundation models, have enabled impressive progress. However, achieving artificial general intelligence, which involves reaching human-level performance across various tasks, remains a significant challenge. A critical missing component is a formal description of what it would take for an autonomous system to self-improve towards increasingly creative and diverse…
Sampling from complex, high-dimensional target distributions, such as the Boltzmann distribution, is crucial in many scientific fields. For instance, predicting molecular configurations depends on this type of sampling. Combinatorial Optimization (CO) can be seen as a distribution learning problem where the samples correspond to solutions of CO problems, but it is challenging to achieve unbiased…
Achieving real-time speech recognition directly within a web browser has long been a sought-after milestone. Whisper WebGPU by a Hugging Face Engineer (nickname ‘Xenova’) is a groundbreaking technology that leverages OpenAI’s Whisper model to bring real-time, in-browser speech recognition to fruition. This remarkable development is a monumental shift in interaction with AI-driven web applications. The…
Machine translation, a critical area within natural language processing (NLP), focuses on developing algorithms to automatically translate text from one language to another. This technology is essential for breaking down language barriers and facilitating global communication. Recent advancements in neural machine translation (NMT) have significantly improved translation accuracy and fluency, leveraging deep learning techniques to…
Choosing large language models (LLMs) tailored for specific tasks is crucial for maximizing efficiency and accuracy. With natural language processing (NLP) advancements, different models have emerged, each excelling in unique domains. Here is a comprehensive guide to the most suitable LLMs for various activities in the AI world. Hard Document Understanding: Claude Opus Claude Opus…
Fusion oncoproteins, formed by chromosomal translocations, are key drivers in many cancers, especially pediatric ones. These chimeric proteins are difficult to target with drugs due to their large, disordered structures and lack of distinct binding pockets. Traditional drug design methods, like small molecules, often fail because they need more specificity or bind crucial cellular proteins.…
Microsoft’s AI courses offer comprehensive coverage of AI and machine learning concepts for all skill levels, providing hands-on experience with tools like Azure Machine Learning and Dynamics 365 Commerce. They emphasize practical applications, advanced techniques, and responsible AI practices, equipping learners to develop and deploy AI solutions ethically and effectively. This article lists the top…
Audio classification has evolved significantly with the adoption of deep learning models. Initially dominated by Convolutional Neural Networks (CNNs), this field has shifted towards transformer-based architectures, which offer improved performance and the ability to handle various tasks through a unified approach. Transformers surpass CNNs in performance, creating a paradigm shift in deep learning, especially for…
The ability to discern relevant and essential information from noise is paramount in AI, particularly within large language models (LLMs). With the surge of information and the complexity of tasks, there’s a need for efficient mechanisms to enhance the performance and reliability of these models. Let’s explore the essential tools & techniques for refining LLMs…