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Dataset distillation is an innovative approach that addresses the challenges posed by the ever-growing size of datasets in machine learning. This technique focuses on creating a compact, synthetic dataset that encapsulates the essential information of a larger dataset, enabling efficient and effective model training. Despite its promise, the intricacies of how distilled data retains its…
In today’s age, learning AI is crucial as companies increasingly rely on it for efficiency, automation, and personalization, yet not everyone is an expert in the field. Salesforce offers short courses on Trailhead, covering essential AI skills to help you become the AI hero your company needs, positioning you for new opportunities and career advancement.…
Accurately predicting antibody structures is essential for developing monoclonal antibodies, pivotal in immune responses and therapeutic applications. Antibodies have two heavy and two light chains, with the variable regions featuring six CDR loops crucial for binding to antigens. The CDRH3 loop presents the greatest challenge due to its diversity. Traditional experimental methods for determining antibody…
Recent advancements in machine learning have been actively used to improve the domain of healthcare. Despite performing remarkably well on various tasks, these models are often unable to provide a clear understanding of how specific visual changes affect ML decisions. These AI models have shown great promise and even human capabilities in some cases, but…
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…