The hottest Modeling Substack posts right now

And their main takeaways
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Everyone Loves The Idea Of AI, But Not The Reality

High ROI Data Science • 615 implied HN points • 06 Oct 24
🕹 Technology AI Data Business Modeling Training Platforms
  1. Many businesses love the idea of AI but find it hard to put into practice. It often looks easy on paper, but the reality is very different when trying to make it work.
  2. Data is really important for AI to work well. Companies need good data to build effective AI products, and often, they realize this too late after facing challenges.
  3. AI projects often fail because businesses don’t fully understand what they need to achieve. Companies should focus on solving real problems rather than just using the latest technology.

Entrevista a Grady Booch

Érase una vez un algoritmo... • 39 implied HN points • 27 Oct 24
🕹 Technology Software Engineering AI Development Ethics Modeling Computer Science
  1. Grady Booch is a key figure in software engineering, known for creating UML, which helps developers visualize software systems. His work has changed how we think about software design.
  2. He emphasizes the ongoing evolution in software engineering due to changes like AI and mobile technology. Adaptation and continuous learning are essential for success in this field.
  3. Booch advocates for ethics in technology development, stressing the need for education and accountability among tech leaders to ensure responsible use of AI and other emerging technologies.

N-1 Block A

Soviet Space Substack • 178 implied HN points • 12 Oct 24
🕹 Technology Aerospace Engineering Modeling
  1. The N1-3L rocket has a complex engine system, with different engines numbered for clarity. Understanding these details is crucial for analyzing the rocket's design and performance.
  2. Grid fins are an important feature of the N1 rocket, providing enhanced control during high-speed flights. Their design has evolved over time to improve stability and effectiveness.
  3. There were various design changes made to the Block A of the N1 rocket to improve its function and control. These updates were likely based on lessons learned from previous flight tests.

No, Sora has not “learned physics”

Marcus on AI • 3596 implied HN points • 02 Mar 24
🕹 Technology AI Physics Modeling
  1. Sora is not a reliable source for understanding how the world works, as it focuses more on how things look visually.
  2. Sora's videos often depict objects behaving in ways that defy physics or biology, indicating a lack of understanding of physical entities.
  3. The inconsistencies in Sora's videos highlight the difference between image sequence prediction and actual physics, emphasizing that Sora is more about predicting images than modeling real-world objects.

Tang Shiping predicts Trump "highly likely" to win - probability more than 60%

Pekingnology • 56 implied HN points • 03 Nov 24
🇺🇸 U.S. Politics Elections Forecasting Political Analysis Modeling Public Opinion
  1. A professor predicts that Donald Trump has a greater than 60% chance of winning the 2024 U.S. presidential election. This prediction is based on computer simulations rather than traditional polling.
  2. The simulations suggest Trump will likely win key states like Michigan, Ohio, and Florida, while Harris is expected to win states like Georgia and Arizona.
  3. The forecasting method used is known as Agent-Based Modeling, which combines real data about voters and economic conditions to make predictions rather than relying on expert opinions.
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From Theory to Practice: Inductive Biases in Machine Learning

Mindful Modeler • 639 implied HN points • 23 Apr 24
🕹 Technology Machine Learning Algorithms Data Bias Modeling
  1. Different machine learning models exhibit varying behaviors when extrapolating features, influenced by their inductive biases.
  2. Inductive biases in machine learning influence the learning algorithm's direction, excluding certain functions or preferring specific forms.
  3. Understanding inductive biases can lead to more creative and data-friendly modeling practices in machine learning.

Statistical modeling seen through inductive biases

Mindful Modeler • 419 implied HN points • 28 May 24
🔬 Science Statistics Modeling Machine Learning
  1. Statistical modeling involves modeling distributions and assuming relationships between features and the target with a few interpretable parameters.
  2. Distributions shape the hypothesis space by restricting the range of models compatible with specific distributions like a zero-inflated Poisson distribution.
  3. Parameterization in statistical modeling simplifies estimation, interpretation, and inference of model parameters by making them more interpretable and allowing for confidence intervals.

How to make use of inductive biases

Mindful Modeler • 219 implied HN points • 04 Jun 24
🔬 Science Machine Learning Interpretability Forecasting Modeling
  1. Inductive biases play a crucial role in model robustness, interpretability, and leveraging domain knowledge.
  2. Choosing inherently interpretable models can enhance model understandability by restricting the hypothesis space of the learning algorithm.
  3. By selecting inductive biases that reflect the data-generating process, models can better align with reality and improve performance.

How I made peace with quantile regression

Mindful Modeler • 778 implied HN points • 16 Jan 24
🔬 Science Statistics Machine Learning Estimation Modeling
  1. Quantile regression can be understood through the lens of loss optimization, specifically with the pinball loss function.
  2. In machine learning, quantile regression is essentially regression with the unique pinball loss function that emphasizes absolute differences between actual and predicted values.
  3. The asymmetry of the pinball loss function, controlled by the parameter tau, dictates how models should handle under- and over-predictions, making quantile regression a tool to optimize different quantiles of a distribution.

No Free Dessert in Machine Learning

Mindful Modeler • 399 implied HN points • 20 Feb 24
🔬 Science Machine Learning Generalization Modeling Data
  1. Generalization in machine learning is essential for a model to perform well on unseen data.
  2. There are different types of generalization in machine learning: from training data to unseen data, from training data to application, and from sample data to a larger population.
  3. The No Free Lunch theorem in machine learning highlights that assumptions and effort are always needed for generalization, and there's no free lunch when it comes to achieving further generalization.

GraphRAG Analysis, Part 1: How Indexing Elevates Knowledge Graph Performance in RAG

AI Encoder: Parsing Signal from Hype • 70 HN points • 09 Jul 24
🕹 Technology Analysis Research Evaluation Modeling Metrics
  1. Knowledge graphs do not significantly impact context retrieval in RAG, as all methods showed similar context relevancy scores.
  2. Neo4j with its own index improved answer relevancy and faithfulness compared to Neo4j without indexing and FAISS, showcasing the importance of effective indexing for precise content retrieval in RAG applications.
  3. Developers need to consider the trade-offs between ROI constraints and performance improvements when deciding to use GraphRAG, especially in high-precision applications that require accurate answers.

to kaggle, or not to kaggle

Mindful Modeler • 379 implied HN points • 13 Feb 24
🕹 Technology Machine Learning Modeling Competitions Data science AI
  1. There are conflicting views on Kaggle - some see it as a playground while others believe it produces top machine learning results.
  2. Participating in Kaggle competitions can be beneficial to learn core supervised machine learning concepts.
  3. The decision to focus on Kaggle competitions should depend on how much daily tasks align with Kaggle-style work.

How to sell bread with quantile regression

Mindful Modeler • 339 implied HN points • 23 Jan 24
🕹 Technology Machine Learning Data science Modeling Predictions
  1. Quantile regression can be used for robust modeling to handle outliers and predict tail behavior, helping in scenarios where underestimation or overestimation leads to loss.
  2. It is important to choose quantile regression when predicting specific quantiles, such as upper quantiles, for scenarios like bread sales where under or overestimating can have financial impacts.
  3. Quantile regression can also be utilized for uncertainty quantification, and combining it with conformal prediction can improve coverage, making it useful for understanding and managing uncertainty in predictions.

OpenAI's moat

TechTalks • 334 implied HN points • 15 Jan 24
🕹 Technology AI Open Source Modeling Monetization Innovation
  1. OpenAI is building new protections to safeguard its generative AI business from open-source models
  2. OpenAI is reinforcing network effects around ChatGPT with features like GPT Store and user engagement strategies
  3. Reducing costs and preparing for future innovations like creating their own device are part of OpenAI's strategy to maintain competitiveness

The Future of Prompt Engineering

Gradient Flow • 559 implied HN points • 04 May 23
🕹 Technology AI NLP Modeling Data Analysis Tools
  1. NLP pipelines are shifting to include large language models (LLMs) for accuracy and user-friendliness.
  2. Effective prompt engineering is crucial for crafting useful input prompts tailored to generative AI models.
  3. Future prompt engineering tools need to be interoperable, transparent, and capable of handling diverse data types for collaboration and model sharing.

A Pragmatic View of Uncertainty in Machine Learning

Mindful Modeler • 359 implied HN points • 06 Jun 23
🕹 Technology Machine Learning Uncertainty Modeling Prediction Calibration
  1. Machine learning models have uncertainty in predictions, categorized into aleatoric and epistemic uncertainty.
  2. Defining and distinguishing between aleatoric and epistemic uncertainty is a complex task influenced by deterministic and random factors.
  3. Conformal prediction methods capture both aleatoric and epistemic uncertainty, providing prediction intervals reflecting model uncertainty.

Defending machine learning in a room full of old-school statisticians

Mindful Modeler • 299 implied HN points • 27 Jun 23
🕹 Technology Machine Learning Statistics Modeling
  1. Be mindful of your modeling mindset and be open to exploring other modeling cultures beyond your current beliefs.
  2. Recognize that differences in modeling mindsets are deeply rooted in culture and background, influencing how individuals approach statistical modeling.
  3. Interpretability remains a significant concern for modelers, especially in the context of machine learning advancements, although progress has been made in providing tools for better understanding models.

The Social Network (Part 2)

Logging the World • 279 implied HN points • 13 Apr 23
🔬 Science Networks Statistics Social media Modeling
  1. Real social networks exhibit more complex behaviors than simple mathematical models can capture.
  2. The structure of social media follower counts differs significantly from the Erdős–Rényi network model, with some users having exponentially more followers than others.
  3. Recent network models like the Barabási-Albert model better represent the dynamics of online social networks like Twitter, where heavy-tailed distributions of follower counts emerge.

The Statistician Who Loved Machine Learning

Mindful Modeler • 279 implied HN points • 23 May 23
🕹 Technology Machine Learning Statistics Modeling Algorithms
  1. Leo Breiman emphasized the importance of both data modeling culture and algorithmic modeling culture in statistical modeling.
  2. Breiman advocated for being problem-focused over solution-focused, encouraging modelers to choose the appropriate mindset based on the task at hand.
  3. Understanding various modeling mindsets, such as statistical inference and machine learning, is crucial for effective modeling.

Should we stop interpreting ML models because XAI methods are imperfect?

Mindful Modeler • 199 implied HN points • 31 Oct 23
🕹 Technology Machine Learning Interpretability Neural Networks Modeling
  1. Don't let a pursuit of perfection in interpreting ML models hinder progress. It's important to be pragmatic and make decisions even in the face of imperfect methods.
  2. Consider the balance of benefits and risks when interpreting ML models. Imperfect methods can still provide valuable insights despite their limitations.
  3. While aiming for improvements in interpretability methods, it's practical to use the existing imperfect methods that offer a net benefit in practice.

Explore Your Modeling Mindset With A Quiz

Mindful Modeler • 179 implied HN points • 20 Jun 23
🕹 Technology Modeling Web Development Machine Learning Programming
  1. Modeling assumptions affect how the model can be used. For instance, causal considerations lead to causal claims.
  2. Revisiting and understanding our modeling assumptions can help us tackle problems more effectively, beyond our usual mindset.
  3. Creating simple static websites can be made easier with tools like GPT-4, especially if you have some understanding of HTML, CSS, and JavaScript.

Getting Started with LoRAs + Vodka Photorealism

followfox.ai’s Newsletter • 176 implied HN points • 15 Jun 23
🕹 Technology AI Training Modeling
  1. The post discusses getting started with LoRAs and creating a photorealistic LoRA for Vodka models.
  2. It includes steps like downloading and using a LoRA, training the first LoRA, and finally fine-tuning a custom LoRA for photorealistic results.
  3. The process involves using specific tools, datasets, and parameters to train LoRAs, and explores possibilities for creating high-quality, realistic images.

Can you explain GPT with ... GPT?

Mindful Modeler • 199 implied HN points • 16 May 23
🕹 Technology Neural Networks Interpretability Modeling Language Models AI Ethics
  1. OpenAI experimented with using GPT-4 to interpret the functionality of neurons in GPT-2, showcasing a unique approach to understanding neural networks.
  2. The process involved analyzing activations for various input texts, selecting specific texts to explain neuron activations, and evaluating the accuracy of these explanations.
  3. Interpreting complex models like LLMs with other complex models, such as using GPT-4 to understand GPT-2, presents challenges but offers a method to evaluate and improve interpretability.

From bare-bones to holistic machine learning

Mindful Modeler • 159 implied HN points • 08 Aug 23
🕹 Technology Machine Learning Modeling Interpretability Data Tools
  1. Machine learning can range from simple, bare-bones tasks to more complex, holistic approaches.
  2. In bare-bones machine learning, the modeling choices are defined, making it about the model's performance and tuning.
  3. Holistic machine learning involves designing the model to connect with the larger context, considering factors like uncertainty, interpretability, and shifts in distribution.

Week #3: Conformal Prediction For Regression

Mindful Modeler • 279 implied HN points • 03 Jan 23
🔬 Science Regression Modeling
  1. In regression, conformal prediction can turn point predictions into prediction intervals with guarantees of future observation coverage.
  2. Starting from point predictions or non-conformal intervals from quantile regression are two common approaches to creating prediction intervals.
  3. Conformalized mean regression and conformalized quantile regression are two techniques to generate prediction intervals in regression models.

Novaxia vs Bigpharmia

Logging the World • 199 implied HN points • 04 Nov 22
🔬 Science Health Graphs Vaccines Modeling Data Analysis
  1. Understand the impact of vaccines on disease spread: Novaxia and Bigpharmia are examples of two scenarios showing how vaccines can affect the spread of a disease differently.
  2. Graphs help visualize data trends: Using different types of graphs can show how disease spread changes over time and the effectiveness of interventions like vaccines.
  3. Consider the importance of logarithmic scales: Logarithmic scales can provide a different perspective on data trends, allowing for better understanding of the impact of interventions like vaccines.

Understanding Different Uncertainty Mindsets

Mindful Modeler • 179 implied HN points • 24 Jan 23
🔬 Science Probability Modeling Uncertainty Machine Learning Statistics
  1. Understanding the fundamental difference between Bayesian and frequentist interpretations of probability is crucial for grasping uncertainty quantification techniques.
  2. Conformal prediction offers prediction regions with a frequentist interpretation, similar to confidence intervals in linear regression models.
  3. Conformal prediction shares similarities with the evaluation requirements and mindset of supervised machine learning, emphasizing the importance of separate calibration and ground truth data.

Preventing model exfiltration with upload limits

Redwood Research blog • 19 implied HN points • 08 May 24
🕹 Technology Security Compression Modeling Encryption
  1. Preventing model exfiltration can be crucial for security; setting upload limits can be a simple yet effective way to protect large model weights from being stolen.
  2. Implementing compression schemes for model generations can significantly reduce the amount of data that needs to be uploaded, providing an additional layer of protection against exfiltration.
  3. Limiting uploads, tracking and controlling data flow from data centers, and restricting access to model data are practical approaches to making exfiltration of model weights harder for attackers.

Why you should analyze the distribution of your Data [Math Mondays]

Technology Made Simple • 59 implied HN points • 14 Mar 23
🕹 Technology Data Analysis Math Software Engineering Modeling Statistics
  1. Analyzing the distribution of your data is crucial for accurate analysis results, helps in choosing the right statistical tests, identifying outliers, and confirming data collection systems.
  2. Common techniques to analyze data distribution include histograms, boxplots, quantile-quantile plots, descriptive statistics, and statistical tests like Shapiro-Wilk or Kolmogorov-Smirnov.
  3. Common mistakes in analyzing data distribution include ignoring or dropping outliers, using the wrong statistical test, and not visualizing data to identify patterns and trends.

Be a centaur

Sunday Letters • 39 implied HN points • 04 Dec 23
🕹 Technology AI Innovation Modeling Interfaces User Experience
  1. Technology is changing fast, and it's important to keep learning and adapting. It's easy to think things have settled down, but we're still on an upward curve.
  2. As AI models improve, they will be more useful in specific areas. It's crucial to understand how to use these models effectively to stay competitive.
  3. To stay relevant, we need to focus on asking the right questions instead of just knowing the answers. Learning how to work with AI tools can give you an edge.