Building Vertex AI Search Applications: A Comprehensive GuideKDnuggets This guide explores the essential components, implementation strategies, and best practices for building production-ready search applications using Vertex AI Search and AI Applications.
This guide explores the essential components, implementation strategies, and best practices for building production-ready search applications using Vertex AI Search and AI Applications. Read More
The Evolving Role of the ML EngineerTowards Data Science Stephanie Kirmer on the $200 billion investment bubble, how AI companies can rebuild trust, and how her day-to-day work changed with the rise of LLMs.
The post The Evolving Role of the ML Engineer appeared first on Towards Data Science.
Stephanie Kirmer on the $200 billion investment bubble, how AI companies can rebuild trust, and how her day-to-day work changed with the rise of LLMs.
The post The Evolving Role of the ML Engineer appeared first on Towards Data Science. Read More
12 Python Libraries You Need to Try in 2026KDnuggets These are 12 Python libraries that made waves in 2025, and that every developer should try in 2026.
These are 12 Python libraries that made waves in 2025, and that every developer should try in 2026. Read More
Building Practical MLOps for a Personal ML ProjectKDnuggets A step-by-step guide to turning a notebook-based analysis into a reproducible, deployable, and portfolio-ready MLOps project
A step-by-step guide to turning a notebook-based analysis into a reproducible, deployable, and portfolio-ready MLOps project Read More
How to Leverage Explainable AI for Better Business DecisionsTowards Data Science Moving beyond the black box to turn complex model outputs into actionable organizational strategies.
The post How to Leverage Explainable AI for Better Business Decisions appeared first on Towards Data Science.
Moving beyond the black box to turn complex model outputs into actionable organizational strategies.
The post How to Leverage Explainable AI for Better Business Decisions appeared first on Towards Data Science. Read More
Introducing GPT-5.3-Codex-SparkOpenAI News Introducing GPT-5.3-Codex-Spark—our first real-time coding model. 15x faster generation, 128k context, now in research preview for ChatGPT Pro users.
Introducing GPT-5.3-Codex-Spark—our first real-time coding model. 15x faster generation, 128k context, now in research preview for ChatGPT Pro users. Read More
My Honest And Candid Review of Abacus AI Deep AgentKDnuggets A glimpse into what might be the early days of artificial general intelligence
A glimpse into what might be the early days of artificial general intelligence Read More
Gemini 3 Deep Think: Advancing science, research and engineeringGoogle DeepMind News Our most specialized reasoning mode is now updated to solve modern science, research and engineering challenges.
Our most specialized reasoning mode is now updated to solve modern science, research and engineering challenges. Read More
Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecturecs.AI updates on arXiv.org arXiv:2503.19339v4 Announce Type: replace-cross
Abstract: The ever-increasing security vulnerabilities in the Internet-of-Things (IoT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while maintaining high precision and recall across various scenarios. The proposed model’s performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen’s kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model’s ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges.
arXiv:2503.19339v4 Announce Type: replace-cross
Abstract: The ever-increasing security vulnerabilities in the Internet-of-Things (IoT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while maintaining high precision and recall across various scenarios. The proposed model’s performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen’s kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model’s ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges. Read More
Is Your LLM Really Mastering the Concept? A Multi-Agent Benchmarkcs.AI updates on arXiv.org arXiv:2505.17512v2 Announce Type: replace
Abstract: Concepts serve as fundamental abstractions that support human reasoning and categorization. However, it remains unclear whether large language models truly capture such conceptual structures or primarily rely on surface-level pattern memorization. Existing benchmarks are largely static and fact oriented, which limits their ability to probe fine-grained semantic understanding and makes them vulnerable to data leakage and overfitting. To address this limitation, we introduce CK-Arena, a dynamic benchmark for conceptual knowledge evaluation based on a multi agent social deduction game, namely the Undercover game. In this setting, LLM based agents are assigned subtly different concept words and must describe, distinguish, and infer conceptual properties from others’ statements. Model performance is evaluated through both game level outcomes and the semantic quality of generated descriptions. Furthermore, CK-Arena leverages the interaction process to automatically construct high quality question answering data for fine grained diagnostic analysis. Experimental results show that conceptual understanding varies substantially across models and categories, and is not strictly aligned with overall model capability. The data and code are available at the project homepage: https://ck-arena.site.
arXiv:2505.17512v2 Announce Type: replace
Abstract: Concepts serve as fundamental abstractions that support human reasoning and categorization. However, it remains unclear whether large language models truly capture such conceptual structures or primarily rely on surface-level pattern memorization. Existing benchmarks are largely static and fact oriented, which limits their ability to probe fine-grained semantic understanding and makes them vulnerable to data leakage and overfitting. To address this limitation, we introduce CK-Arena, a dynamic benchmark for conceptual knowledge evaluation based on a multi agent social deduction game, namely the Undercover game. In this setting, LLM based agents are assigned subtly different concept words and must describe, distinguish, and infer conceptual properties from others’ statements. Model performance is evaluated through both game level outcomes and the semantic quality of generated descriptions. Furthermore, CK-Arena leverages the interaction process to automatically construct high quality question answering data for fine grained diagnostic analysis. Experimental results show that conceptual understanding varies substantially across models and categories, and is not strictly aligned with overall model capability. The data and code are available at the project homepage: https://ck-arena.site. Read More