machinelearning - CybAI news

07 Nov

How First-Order Logic is Shaping AI Development in 2025

stp2y0 CommentsNewsai, automaton, coding, community, development, engineering, inclusive, machinelearning, software

As Artificial Intelligence (AI) continues to evolve, the foundational concepts of logic, particularly First-Order Logic (FOL), remain at the core of AI development. First-order logic has provided a framework that helps AI systems reason, make inferences, and interpret complex data. In 2025, FOL is more relevant than ever, driving advancements in natural language processing, knowledge representation, robotics, and more. For a deeper dive into First-Order Logic in AI, explore this comprehensive guide. 1. What is First-Order Logic (FOL) and Why Is It Important? First-order logic (FOL), also known as Predicate Logic, is a form of symbolic logic that extends propositional…

05 Nov

Top 10 MLOps Tools for 2025

stp2y0 CommentsNewscoding, community, development, devops, engineering, inclusive, machinelearning, MLOps, software

With the rapid growth of AI, MLOps tools are becoming a must-use for research and development teams. These tools simplify the development, deployment, and management of machine learning models, making complex processes more manageable. There's a huge demand for ML support. 86% of organizations needed help generating business value from their machine learning (ML) investments in 2023. Hence, MLOps tools address these issues by automating recurring tasks, ensuring reproducibility, and freeing up teams to focus on innovation. What are MLOps Tools? MLOps, which stands for Machine Learning Operations, is a set of practices that weave machine learning into software and data engineering. It…

27 Oct

Rethinking Self-Attention: Polynomial Activations for Capturing Long-Range Dependencies

stp2y0 CommentsNewsai, beginners, coding, community, datascience, development, engineering, inclusive, machinelearning, software

This is a Plain English Papers summary of a research paper called Rethinking Self-Attention: Polynomial Activations for Capturing Long-Range Dependencies. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview The paper proposes an alternative to the softmax activation function for self-attention layers in transformer models. It introduces a new "Self-Attention with Polynomial Activations" (SAPA) approach that uses polynomial functions instead of softmax. The authors provide a theoretical analysis of SAPA and compare it to softmax-based self-attention. Plain English Explanation The softmax function is commonly used in transformer models to calculate the attention…

14 Oct

Wi-Fi Reliability Boost with “It’s Your Turn” Channel Contention Mechanism

stp2y0 CommentsNewsai, beginners, coding, community, datascience, development, engineering, inclusive, machinelearning, software

This is a Plain English Papers summary of a research paper called Wi-Fi Reliability Boost with "It's Your Turn" Channel Contention Mechanism. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview A novel channel contention mechanism called "It's Your Turn" is proposed to improve the reliability of Wi-Fi networks. The mechanism aims to address the limitations of the current Listen-Before-Talk (LBT) approach used in the IEEE 802.11 standard. The proposed solution introduces a new channel access method that provides more coordinated access to the wireless medium. Plain English Explanation The paper presents…

11 Oct

Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base

stp2y0 CommentsNewscoding, community, development, engineering, gpt3, inclusive, machinelearning, rag, software

選定理由 Paper: https://arxiv.org/abs/2408.00798 Code: N/A Blog: https://zenn.dev/knowledgesense/articles/90ac35eedf8b7c 内容詳細は上記ブログを参照。　概要【社会課題】あらゆる産業分野で社内外の大規模な知識データベースを効率的に活用することが求められているが、特定の業界用語や文脈を正確に解釈し、関連情報を迅速に取得できる検索・応答生成手法が必要である。【技術課題】従来の技術(RAG, self-RAG, CRAGなど)では業界特有の用語や文脈を正確に理解し、それに基づいて適切な情報を取得することが困難であった。これはその単語の意味をLLMが正確に把握できないことに起因している。このため、知識ベースから正確かつ効率的に情報を活用することができていなかった。【提案】質問の前処理段階で業界特有の用語や略語を認識し、事前に作成されたDBを参照することでその文脈に基づいて意味を明確にする。その後、明確化された質問に基づいて最も関連性の高い文書を取得するためのフレームワーク Golden-Retriever を提案した。【効果】 Golden-Retrieverは、業界特有のデータセットを用いた評価で、従来のLLMやRAGフレームワークと比較して優れたパフォーマンスを示した。 Source link lol

10 Oct

BIG UPDATE

stp2y0 CommentsNewsai, coding, community, development, engineering, inclusive, machinelearning, programming, python, software

Hey everyone! It's been a while since my last update. I'm back in college for my final year, and I've just started my senior design class. I’m partnered with three amazing teammates, and we've presented our project to the class—it’s focused on optimizing car intersection flow. Unfortunately, I’ll have to put the turret project on hold for now, but I’ll be posting updates about this new project every Friday after our team meetings. Stay tuned! I will be having much more details on this next post. (We are going to simulate this plan by using pygames) Source link lol

08 Oct

LoRA and QLoRA: Simple Fine-Tuning Techniques Explained

stp2y0 CommentsNewsai, coding, community, datascience, development, engineering, inclusive, machinelearning, qlora, software

Fine-tuning large language models (LLMs) can be resource-intensive, requiring immense computational power. LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation) offer efficient alternatives for training these models while using fewer resources. In this post, we’ll explain what LoRA and QLoRA are, how they differ from full-parameter fine-tuning, and why QLoRA takes it a step further. What is fine-tuning? Fine-tuning refers to the process of taking a pre-trained model and adapting it to a specific task. Traditional full-parameter fine-tuning requires adjusting all the parameters of the model, which can be computationally expensive and memory-heavy. This is where LoRA and QLoRA come…

08 Oct

Frontier AI Developers Need Internal Audit Function to Address Key Governance Challenges

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This is a Plain English Papers summary of a research paper called Frontier AI Developers Need Internal Audit Function to Address Key Governance Challenges. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview Internal audit evaluates a company's risk management, control, and governance processes. It is independent from senior management and reports to the board of directors. Internal audit serves as the third line of defense in the Three Lines Model. The article highlights key governance challenges in frontier AI development. It argues that frontier AI developers need an internal audit function…

28 Sep

Thoughts on AI Beating CAPTHCA

stp2y0 CommentsNewsai, coding, community, development, engineering, inclusive, machinelearning, software

AI Outsmarting CAPTCHA: A Double Edged Sword? CAPTCHA, those annoying puzzles we all face to prove we’re human—has long been the gatekeeper against bots. But with AI's rapid evolution, CAPTCHA may no longer be as effective as we think. Recently, AI systems have demonstrated the ability to bypass these security measures with alarming accuracy. This raises questions about internet security. CAPTCHA, designed to separate humans from bots, might soon become obsolete as AI gets better at mimicking human behavior. The broader challenge lies not just in CAPTCHA failing but in how we can stay ahead of increasingly sophisticated AI-powered attacks.…

25 Sep

New defenses still fall short against adversarial attacks on Go AIs

stp2y0 CommentsNewsai, beginners, coding, community, datascience, development, engineering, inclusive, machinelearning, software

This is a Plain English Papers summary of a research paper called New defenses still fall short against adversarial attacks on Go AIs. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview Previous research has shown that superhuman Go AI systems like KataGo can be defeated by simple adversarial strategies. This paper examines whether simple defenses can improve KataGo's performance against the worst-case scenarios. The paper tests three natural defenses: adversarial training on hand-constructed positions, iterated adversarial training, and changing the network architecture. Plain English Explanation The researchers wanted to see if…