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Towards Data Science
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Platform-Mesh, Hub and Spoke, and Centralised | 3 Types of data team
- Different team structures and skill requirements impact an organization's ability to leverage Data and AI effectively.
- An analogy is used to explain the evolution of data teams from Centralised to Platform Mesh.
- Central teams handle numerous responsibilities requiring focus on key use cases and leveraging tools.
- Expanding a team can increase output, but modern approaches can be more effective.
- The Hub-and-Spoke structure allows for decentralization, catering to medium-sized or tech-first organizations.
- Analytics engineers and data analysts take on more responsibilities in the evolving landscape.
- Challenges arise from data modeling principles, leading to model sprawl and increased costs.
- Hub and Spoke models must have clear responsibilities, ensuring visibility and collaboration.
- Data Mesh approach encourages collaboration and seamlessness, requiring a high level of technical proficiency.
- Implementing a unified control plane can enhance collaboration and orchestration across different teams.
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Medium
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32 is the Magic Number: Deep Learning’s Perfect Batch Size Revealed!
- While large batch sizes can improve computational parallelism, they may degrade model performance.
- Yann LeCun suggests that a batch size of 32 is optimal for model training and performance.
- In a recent study, researchers found that batch sizes between 2 and 32 outperform larger sizes in the thousands.
- Smaller batch sizes enable more frequent gradient updates, resulting in more stable and reliable training.
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VentureBeat
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GenLayer offers novel approach for AI agent transactions: getting multiple LLMs to vote on a suitable contract
- GenLayer, a startup, aims to provide a "trust" component to the AI agent economy through a blockchain-powered infrastructure.
- AI agents do not inherently trust each other, posing a challenge in enforcing agreements in AI-driven commerce.
- GenLayer seeks to upgrade traditional smart contracts into "intelligent contracts" that are more flexible and AI-powered.
- The company integrates AI directly at the protocol level for intelligent contracts to process natural language inputs and reason about real-world conditions.
- GenLayer introduces "optimistic democracy," an AI-driven consensus model where multiple large language models (LLMs) vote on the validity of AI-generated contracts.
- This approach ensures fairness and reliability in interpreting legal contracts, verifying data, and setting pricing models.
- GenLayer's infrastructure involves a native gas token called GEN for transaction fees and aligns incentives through a token-based staking model.
- The company's testnet focuses on showcasing projects at the Ethereum Community Conference in Cannes, France.
- GenLayer aims to establish the legal framework for global AI commerce, enabling AI agents to work together with trust in a trillion-dollar machine-driven marketplace.
- The company emphasizes the need for infrastructure that matches the speed of AI to ensure efficient participation in the economy.
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Medium
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Understanding the Zero-Inflation Problem in Data Science: Challenges and Solutions
- The zero-inflation problem is a common and overlooked challenge in data science.
- Zero-inflation occurs when the number of observed zeros in a dataset exceeds what is predicted by a standard statistical model.
- This problem is particularly common in count data.
- Addressing zero-inflation is crucial for accurate data analysis and model building.
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Medium
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Can Data Science Predict UFC Fights? Building a Leak-Free Model with Random Forest
- Machine learning is being used in various sports for performance analysis and strategy optimization.
- Machine learning models are also being developed to predict the outcome of UFC fights.
- Some claim their models can predict UFC fights with 70% to 80% accuracy.
- However, there are challenges and limitations to building accurate prediction models for UFC fights.
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Dev
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Accountable Privacy in Web3 (3/4)
- Cryptographic concepts like Elliptic Curves, Pedersen Commitments, and Merkle Trees are crucial in Web3 for security and privacy.
- Elliptic Curve Cryptography (ECC) is vital for blockchain systems like Bitcoin, using curves like secp256k1.
- Implementing code for Elliptic Curves involves private and public key generation, message signing, and verification.
- Pedersen Commitments are used for privacy-focused blockchain applications to commit to values while keeping them hidden.
- Additive homomorphism in Pedersen commitments allows combining commitments algebraically.
- Merkle Trees in Web3 are data structures where each leaf node is a hash of data value and the root is a commitment to the entire dataset.
- Merkle Proofs allow verifying data in a Merkle Tree without knowing the entire structure, increasing efficiency.
- The article provides Python code for implementing Elliptic Curve, Pedersen Commitment, and Merkle Tree concepts in Web3.
- The implementations are basic and not suitable for production but serve as educational tools for beginners in Web3.
- Understanding these cryptographic primitives is essential for developing secure and private blockchain applications.
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Nycdatascience
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Sentiment-Enhanced Product Recommendation System for E-Commerce
- This project applies advanced Natural Language Processing (NLP) techniques to analyze customer sentiment in e-commerce product reviews.
- The system creates a much improved recommendation engine that goes beyond traditional rating systems.
- The project compares two leading sentiment analysis approaches—RoBERTa and VADER—to determine their accuracy and understanding of customer opinions.
- The sentiment-enhanced recommendation system can help e-commerce platforms improve customer satisfaction and increase conversion rates.
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VentureBeat
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Major AI market share shift revealed: DALL-E plummets 80% as Black Forest Labs dominates 2025 data
- New data from Poe reveals significant shifts in the AI market share in 2025, showcasing changes in AI tool utilization among businesses and consumers.
- The report provides insights into text, image, and video generation technologies based on interactions from millions of users.
- Market fragmentation exists across all AI modalities, with newer players like DeepSeek in text and Black Forest Labs in image generation gaining market share.
- Google's performance varies across different AI types, highlighting the challenges of achieving cross-modal leadership.
- Video generation shows intense competition, with existing and new providers rapidly capturing market share.
- Chinese-developed models hold a notable share in video generation, contributing to innovation despite geopolitical tensions.
- The image generation field sees a significant shift, with established models losing ground to newcomers like Black Forest Labs.
- Poe's data indicates the trend of users abandoning older models for newer, more capable offerings in the AI market.
- OpenAI and Anthropic maintain dominance in text generation, but face challenges from newer players like DeepSeek.
- The report emphasizes the need for enterprises to build flexible AI stacks to adapt to evolving capabilities in the rapidly changing AI landscape.
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Towards Data Science
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Linear Regression in Time Series: Sources of Spurious Regression
- Econometric textbooks often warn about autocorrelated errors in time series data, yet many published papers still exhibit this issue.
- Autocorrelation in economics and finance variables can lead to misleading results if not appropriately addressed, as seen in Granger and Newbold’s research.
- Understanding the pitfalls of spurious regression is crucial for economists, data scientists, and analysts working with time series data.
- The article discusses random walk and ARIMA(0,1,1) processes as well as provides insights from Granger and Newbold's study on nonsense regressions.
- The Linear Regression model and F-test for the contribution of independent variables in explaining the dependent variable are also explained in the context of time series.
- Misinterpretations can occur when coefficients in regressions are invalid due to autocorrelation issues, as shown in the explanations provided.
- Granger and Newbold's simulations demonstrate how including unnecessary variables, like random walks, can lead to misleading regression results.
- High R² and low Durbin-Watson values do not necessarily signify a genuine relationship between variables but could indicate a spurious one.
- To avoid spurious regressions, it is crucial to identify and address autocorrelation in residuals using tests like Durbin-Watson or Portmanteau test.
- Specification errors in regression, such as omission of relevant variables or inclusion of irrelevant ones, can contribute to spurious regressions.
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Towards Data Science
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Experiments Illustrated: How Random Assignment Saved Us $1M in Marketing Spend
- IntelyCare, a platform connecting nurses with work opportunities, faced challenges in hiring nurses during the 2020-2021 global pandemic.
- To attract candidates, they considered offering a $100 bonus for completing the first shift, but decided to run an experiment instead of implementing it directly.
- The experiment involved randomly offering bonuses ranging from $0 to $100 in increments of $25 to applicants, with thousands of participants at each bonus level.
- Analyzing the data, they found that the effectiveness of bonuses varied between nurses and nursing assistants.
- Nursing assistants were more likely to start working with any bonus amount, whereas nurses were less likely to start working with a bonus.
- After considering multiple comparisons, they discovered that the applicant's role as a nurse or nursing assistant was a significant dimension affecting the bonus impact.
- The study revealed that for nursing assistants, smaller bonuses had a more significant effect initially, while for nurses, no bonus proved to be more effective.
- Based on the findings, IntelyCare decided to do away with bonuses for nurses and opted for a $25 bonus for nursing assistants.
- This approach saved them from spending an extra $1 million in bonuses while still achieving the desired recruitment outcomes.
- The experiment highlighted the importance of testing and data analysis in making informed decisions, especially in marketing strategies.
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Towards Data Science
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Experiments Illustrated: How We Optimized Premium Listings on Our Nursing Job Board
- IntelyCare, a company that connects nurses with job opportunities, optimized their premium job listings.
- The company uses a sort-by-relevance feature to improve the experience for paying customers and steer away from low-quality jobs.
- Job relevance is determined by a score between 0 and 100, with higher scores being boosted to the top of search results.
- In a geo-randomized experiment, premium job openings performed 25% better than regular jobs.
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Towards Data Science
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LettuceDetect: A Hallucination Detection Framework for RAG Applications
- Large Language Models (LLMs) have advanced NLP tasks, but hallucinations remain a challenge in critical domains like healthcare and legal settings.
- Retrieval-Augmented Generation (RAG) aims to reduce hallucinations by grounding LLM responses in retrieved documents.
- LettuceDetect, using ModernBERT, detects hallucinations in RAG applications efficiently and outperforms older BERT-based models.
- RAGTruth is a benchmark for evaluating hallucination detection in RAG settings, providing annotated examples and spans.
- LettuceDetect utilizes token-level classification for hallucination detection, achieving competitive performance with lower computational costs.
- The models are trained on the RAGTruth dataset and perform inference by detecting hallucinations at the token and span levels.
- LettuceDetect demonstrates strong performance in hallucination detection, surpassing other models and achieving state-of-the-art span-level results.
- The models are efficient, processing 30-60 examples per second on a single NVIDIA A100 GPU, suitable for real-time and resource-constrained environments.
- Overall, LettuceDetect offers accurate hallucination detection with lean, purpose-built encoder-based models for RAG systems.
- The framework provides a foundation for future research in expanding to new datasets, languages, and exploring advanced architectures.
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VentureBeat
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What you need to know about Manus, the new AI agentic system from China hailed as a second ‘DeepSeek moment’
- Manus is a new AI multipurpose agent system developed by a Chinese company called Butterfly Effect, known for autonomously completing complex tasks.
- It is designed to control multiple AI models and operates similarly to Deep Research modes by major AI players like OpenAI and Google.
- Manus was officially announced in March 2025 by Butterfly Effect, a company with a small team but growing presence in China's AI scene.
- The founding team includes entrepreneurs led by Xiao Hong, who previously developed WeChat-based applications and an AI assistant called Monica.ai.
- Named after the Latin word for 'hand,' Manus is a multi-agent system capable of research, data analysis, report generation, workflow automation, and coding tasks.
- Built on Anthropic's Claude 3.5 Sonnet and Alibaba's Qwen models, Manus is known for beating U.S. firm OpenAI's Deep Research agent in benchmark tests.
- Manus operates asynchronously, allowing users to assign tasks and let it autonomously complete them.
- Recognized for its benchmark performance and success in real-world tasks, Manus has garnered attention in AI circles and on freelance platforms like Upwork.
- Despite concerns about server shortages and availability, Manus has been praised by AI influencers for its autonomous capabilities and task execution efficiency.
- Some critics question Manus's reliance on existing large language models (LLMs) and its originality compared to DeepSeek R1, but the team emphasizes plans for open-source development.
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Medium
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How I Built My First Machine Learning Model: Predicting Car Purchase Prices
- The blog focuses on the author's journey of building their first machine learning model to predict car purchase prices.
- Linear Regression, Mean Squared Error (MSE), and Mean Absolute Error (MAE) are concepts utilized in the project.
- The author recommends starting with a simple Linear Regression project for beginners in machine learning.
- The challenges faced include handling categorical and numerical features in the dataset.
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Medium
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So Who Gets The Most Cake?
- The 13th student would get the most cake in an easier version of the problem.
- If each nth student only gets n% of the remaining cake, the problem requires finding a precise relation between each student.
- It is helpful to start with an arbitrary position, such as the kth student's turn.
- At the kth student's turn, they receive k% of the remaining pie.
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