​[[{“value”:” Large Language Models (LLMs) based on Transformer architectures have revolutionized AI development. However, the complexity of their training process remains poorly understood. A significant challenge in this domain is the inconsistency in optimizer performance. While the Adam optimizer has become the standard for training… Read More »Unraveling Transformer Optimization: A Hessian-Based Explanation for Adam’s Superiority over SGD Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Recent research highlights that Transformers, though successful in tasks like arithmetic and algorithms, need help with length generalization, where models handle inputs of unseen lengths. This is crucial for algorithmic tasks such as coding or reasoning, where input length often correlates with problem difficulty.… Read More »Improving Length Generalization in Algorithmic Tasks with Looped Transformers: A Study on n-RASP-L Problems Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” One of the critical challenges in the development and deployment of Large Language Models (LLMs) is ensuring that these models are aligned with human values. As LLMs are applied across diverse fields and tasks, the risk of these models operating in ways that may… Read More »Are Language Models Culturally Aware? This AI Paper Unveils UniVaR: a Novel AI Approach to High-Dimension Human Value Representation Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Cardiotocography (CTG) is a non-invasive method used to monitor fetal heart rate and uterine contractions during pregnancy. This data can help identify potential complications early on, such as fetal distress, preeclampsia, or preterm labor. However, interpreting CTG recordings can be subjective and prone to… Read More »Google AI Researchers Investigate Temporal Distribution Shifts in Deep Learning Models for CTG Analysis Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Retrieval Augmented Generation (RAG) is an AI framework that optimizes the output of a Large Language Model (LLM) by referencing a credible knowledge base outside of its training sources. RAG combines the capabilities of LLMs with the strengths of traditional information retrieval systems such… Read More »Enhancing Language Models with Retrieval-Augmented Generation: A Comprehensive Guide Shobha Kakkar Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Cardinality estimation (CE) is essential to many database-related tasks, such as query generation, cost estimation, and query optimization. Accurate CE is necessary to ensure optimal query planning and execution within a database system. Adopting machine learning (ML) techniques has introduced new possibilities for CE,… Read More »AutoCE: An Intelligent Model Advisor Revolutionizing Cardinality Estimation for Databases through Advanced Deep Metric Learning and Incremental Learning Techniques Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have gained significant attention in machine learning, shifting the focus from optimizing generalization on small datasets to reducing approximation error on massive text corpora. This paradigm shift presents researchers with new challenges in model development and training methodologies. The primary… Read More »Scaling Laws and Model Comparison: New Frontiers in Large-Scale Machine Learning Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​UI prototyping often involves iterating and blending elements from examples such as screenshots and sketches, but current tools offer limited support for incorporating these examples. Inspired by the cognitive process of conceptual blending, we introduce a novel UI workflow that allows developers to rapidly incorporate… Read More »Misty: UI Prototyping Through Interactive Conceptual Blending Apple Machine Learning Research

​Machine learning (ML) and artificial intelligence (AI) systems rely heavily on human-annotated data for training and evaluation. A major challenge in this context is the occurrence of annotation errors, as their effects can degrade model performance. This paper presents a predictive error model trained to… Read More »Generalizable Error Modeling for Human Data Annotation: Evidence from an Industry-Scale Search Data Annotation Program Apple Machine Learning Research

​[[{“value”:” Artificial intelligence (AI) is transforming rapidly, particularly in multimodal learning. Multimodal models aim to combine visual and textual information to enable machines to understand and generate content that requires inputs from both sources. This capability is vital for tasks such as image captioning, visual… Read More »Ovis-1.6: An Open-Source Multimodal Large Language Model (MLLM) Architecture Designed to Structurally Align Visual and Textual Embeddings Asif Razzaq Artificial Intelligence Category – MarkTechPost