[[{“value”:” Deep learning is a subset of machine learning that has become a cornerstone in many technological breakthroughs. At the core of deep learning, it’s a model inspired by the human brain, which we call a neural network. Contrary to the traditional machine learning model,… Read More »5 Tips for Getting Started with Deep Learning Cornellius Yudha Wijaya MachineLearningMastery.com
[[{“value”:” Controllable Learning (CL) is emerging as a crucial component of trustworthy machine learning. It emphasizes ensuring that learning models meet predefined targets and adapt to changing requirements without retraining. Let’s delve into the methods and applications of CL, particularly focusing on its implementation within… Read More »A Survey of Controllable Learning: Methods, Applications, and Challenges in Information Retrieval Aswin Ak Artificial Intelligence Category – MarkTechPost
[[{“value”:” Adversarial attacks are attempts to trick a machine learning model into making a wrong prediction. They work by creating slightly modified versions of real-world data (like images) that a human wouldn’t notice as different but that cause the model to misclassify them. Neural networks… Read More »MALT (Mesoscopic Almost Linearity Targeting): A Novel Adversarial Targeting Method based on Medium-Scale Almost Linearity Assumptions Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost
[[{“value”:” It’s clear that generative AI is already being used by a majority—a large majority—of programmers. That’s good. Even if the productivity gains are smaller than many think, 15% to 20% is significant. Making it easier to learn programming and begin a productive career is… Read More »Programming, Fluency, and AI Mike Loukides AI & ML – Radar
[[{“value”:” Microsoft has unveiled an extensive AI learning journey designed to deal with the diverse needs of various personas within a business, ranging from business leaders to citizen developers. This initiative is structured into four stages: Understanding AI, Preparing for AI, Using AI, and Building… Read More »Microsoft’s Comprehensive Four-Stage AI Learning Journey: Empowering Businesses with Skills for Effective AI Integration and Innovation Sana Hassan Artificial Intelligence Category – MarkTechPost
[[{“value”:” The research on vision-language models (VLMs) has gained significant momentum, driven by their potential to revolutionize various applications, including visual assistance for visually impaired individuals. However, current evaluations of these models often need to pay more attention to the complexities introduced by multi-object scenarios… Read More »Enhancing Vision-Language Models: Addressing Multi-Object Hallucination and Cultural Inclusivity for Improved Visual Assistance in Diverse Contexts Sana Hassan Artificial Intelligence Category – MarkTechPost
[[{“value”:” Graph comprehension and complex reasoning in artificial intelligence involve developing and evaluating the abilities of Large Language Models (LLMs) to understand and reason about graph-structured data. This field is critical for various applications, including social network analysis, drug discovery, recommendation systems, and spatiotemporal predictions.… Read More »GraCoRe: A New AI Benchmark for Unveiling Strengths and Weaknesses in LLM Graph Comprehension and Reasoning Nikhil Artificial Intelligence Category – MarkTechPost
[[{“value”:” Accurately modeling magnetic hysteresis is a significant challenge in the field of AI, especially for optimizing the performance of magnetic devices such as electric machines and actuators. Traditional methods often struggle to generalize to novel magnetic fields, limiting their effectiveness in real-world applications. Addressing… Read More »This Paper Addresses the Generalization Challenge by Proposing Neural Operators for Modeling Constitutive Laws Aswin Ak Artificial Intelligence Category – MarkTechPost
[[{“value”:” Large Language Models (LLMs) with parametric memory of rules and knowledge have shown limitations in implicit reasoning. Research has shown that these models, even more complex ones like GPT-4, have trouble applying and integrating internalized facts reliably. For instance, even when they are aware… Read More »This AI Research from Ohio State University and CMU Discusses Implicit Reasoning in Transformers And Achieving Generalization Through Grokking Tanya Malhotra Artificial Intelligence Category – MarkTechPost
[[{“value”:” Complex Human Activity Recognition (CHAR) in ubiquitous computing, particularly in smart environments, presents significant challenges due to the labor-intensive and error-prone process of labeling datasets with precise temporal information of atomic activities. This task becomes impractical in real-world scenarios where accurate and detailed labeling… Read More »VCHAR: A Novel Artificial Intelligence AI Framework that Treats the Outputs of Atomic Activities as a Distribution Over Specified Intervals Shreya Maji Artificial Intelligence Category – MarkTechPost