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Towards Data Science

Research

📄 Towards Data Science

Dec 24, 2025

The Machine Learning Advent Calendar Day 23: CNN in Excel

A novel implementation of a one-dimensional convolutional neural network (1D CNN) for text analysis has been developed entirely within Microsoft Excel, providing full transparency of its internal components. This approach allows users to visualize and understand each filter, weight, and decision-making process step-by-step, making complex deep learning operations accessible without specialized software.

Microsoft Machine Learning +1

MarkTechPost

General

How to Build a Proactive Pre-Emptive Churn Prevention Agent with Intelligent Observation and Strategy Formation - AI news coverage from MarkTechPost in General

General

📄 MarkTechPost

Dec 23, 2025

How to Build a Proactive Pre-Emptive Churn Prevention Agent with Intelligent Observation and Strategy Formation

A new proactive Pre-Emptive Churn Agent has been developed to identify at-risk users and automatically generate personalized re-engagement emails before users cancel their subscriptions. This system leverages behavioral analysis, strategic incentive formulation, and the Google Gemini language model to create human-ready email drafts, forming an agentic loop that continuously observes user inactivity, analyzes patterns, and initiates targeted outreach. By orchestrating the entire processfrom data simulation to email drafting and managerial approvalthe approach aims to reduce churn rates through timely, personalized engagement. This innovation integrates advanced AI components, notably Google Gemini, to automate and personalize customer

Google AI

Towards Data Science

Research

📄 Towards Data Science

Dec 22, 2025

The Machine Learning Advent Calendar Day 22: Embeddings in Excel

A recent development demonstrates how text embeddings, typically complex in nature, can be understood and explored using simple models and Microsoft Excel. This approach aims to make the concept of embeddings more accessible by leveraging familiar tools, enabling users to analyze and visualize text representations without advanced programming skills.

Microsoft Machine Learning

Towards Data Science

Research

📄 Towards Data Science

Dec 22, 2025

The Machine Learning Advent Calendar Day 21: Gradient Boosted Decision Tree Regressor in Excel

The article discusses a novel approach to gradient descent in function space using decision trees, specifically focusing on gradient boosting techniques. This method enhances the training process of decision tree ensembles by applying gradient descent principles directly within the function space, leading to more efficient and accurate predictive models.

Machine Learning

Towards Data Science

Research

📄 Towards Data Science

Dec 22, 2025

The Machine Learning Advent Calendar Day 20: Gradient Boosted Linear Regression in Excel

The article explains the core concept of gradient boosting, a powerful ensemble learning technique that sequentially combines weak learners to improve predictive accuracy. It highlights how gradient boosting optimizes models by iteratively minimizing residual errors through gradient descent, with practical demonstrations such as implementing gradient-boosted linear regression in Excel, illustrating its accessibility and versatility for data scientists.

Machine Learning

Towards Data Science

Research

📄 Towards Data Science

Dec 22, 2025

ChatLLM Presents a Streamlined Solution to Addressing the Real Bottleneck in AI

Recent discussions in AI have shifted from identifying the "best" model to focusing on specialized capabilities such as reasoning, writing, coding, or processing images, audio, and video. A significant development highlighted is a streamlined solution that addresses the core bottleneck in AI performance, moving beyond model comparison to optimize the underlying infrastructure and processes that enable more efficient and effective AI applications. This approach aims to enhance AI's practical utility across diverse domains by tackling fundamental technical limitations rather than solely refining model architectures.

Towards Data Science

Research

📄 Towards Data Science

Dec 22, 2025

Understanding Vibe Proving

The article discusses advancements in enabling large language models (LLMs) to perform reasoning through verifiable, step-by-step logic, addressing a key challenge in AI interpretability and reliability. It introduces the concept of "Vibe Proving," a methodology designed to enhance LLMs' capacity for structured reasoning, thereby improving their ability to generate logically consistent and verifiable outputs. This development represents a significant step toward more transparent and trustworthy AI systems capable of complex problem-solving and reasoning tasks.

Towards Data Science

Research

📄 Towards Data Science

Dec 22, 2025

What Happens When You Build an LLM Using Only 1s and 0s

A new large language model (LLM) has been developed that achieves a 41-fold increase in efficiency and is nine times faster than existing standard models. This breakthrough is attributed to constructing the LLM using only binary data1s and 0shighlighting a novel approach that significantly enhances computational performance and resource utilization in AI systems.

VentureBeat AI

Ethics

📈 VentureBeat AI

Dec 21, 2025

Agent autonomy without guardrails is an SRE nightmare

AI agents are increasingly being adopted by large organizations, with over half already deploying them and more planning to follow within two years. However, many early adopters are now recognizing the importance of establishing governance frameworks and policies to ensure responsible, ethical, and secure use of AI, especially as autonomous AI agents introduce new security risks such as shadow AI and accountability gaps. The key technical challenge lies in balancing rapid AI deployment with the implementation of guardrails to mitigate risks, including unauthorized AI tool usage and potential incidents stemming from AI autonomy. Organizations need to develop processes for controlled experimentation and clear ownership structures to manage AI

Autonomous Systems

Towards Data Science

Research

📄 Towards Data Science

Dec 21, 2025

How to Do Evals on a Bloated RAGPipeline

The article discusses the challenges of evaluating retrieval-augmented generation (RAG) pipelines, particularly when dealing with complex, multi-component systems. It emphasizes the importance of standardized metrics and methodologies for comparing performance across different datasets and models, highlighting recent efforts to streamline evaluation processes and improve reproducibility in RAG workflows.

Towards Data Science

Research

📄 Towards Data Science

Dec 21, 2025

Running Evals on a Bloated RAGPipeline

The article discusses the challenges of evaluating retrieval-augmented generation (RAG) pipelines, particularly when comparing metrics across diverse datasets and models. It highlights the importance of standardized evaluation frameworks to accurately assess performance, especially in complex, multi-component systems that integrate retrieval and generation modules, ensuring more reliable comparisons and improvements in RAG architectures.

Towards Data Science

Research

📄 Towards Data Science

Dec 21, 2025

Tools for Your LLM: a Deep Dive into MCP

MCP (Model Control Protocol) is a crucial technology that transforms large language models (LLMs) into autonomous agents by enabling them to access real-time data and execute actions through specialized tools. This development enhances the capabilities of LLMs, allowing for dynamic interactions and more practical applications in real-world scenarios. The deep dive into MCP details its operational mechanisms, optimal use cases, and potential pitfalls, providing a comprehensive understanding of how to effectively integrate this protocol into AI systems. By leveraging MCP, developers can significantly expand the functional scope of LLMs, making them more adaptable and responsive to complex tasks

Autonomous Systems

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