The hottest Modeling Substack posts right now

And their main takeaways
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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.
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Will PostgreSQL switch to a Thread-based model ?

Engineering At Scale • 15 implied HN points • 24 Jun 23
🕹 Technology Database Architecture Modeling Software Development Performance
  1. PostgreSQL currently uses a process-based model for handling client connections and managing data.
  2. The process-based model offers advantages like fault isolation, security guarantees, and efficient resource management.
  3. Although there are advantages to the process-based model, the community is considering a switch to a thread-based model for PostgreSQL in the future.

Modeling p(doom) with TrojanGDP

Data Taboo • 5 implied HN points • 22 Sep 23
🕹 Technology AI Modeling Ethics GDP Automation
  1. There is a lack of mathematical models to assess AI existential risks like p(doom).
  2. The academic community has historically ignored existential risks from AI superintelligence.
  3. The proposed TrojanGDP model aims to estimate the lower bound of AI risk based on factors like GDP contribution and neural Trojan rediscovery.

Deep and obvious points in the gap between your thoughts and your pictures of thought

world spirit sock stack • 2 implied HN points • 16 Feb 24
📖 Philosophy Truth Thoughts Imagination Reality Modeling
  1. Some thoughts can feel profound or obvious, depending on your perspective and understanding. What may seem like a tired cliché to one person can be a significant insight to another.
  2. Our perception of the future is often based on our own imagined versions of it, rather than the actual future itself. Realizing this distinction can be eye-opening.
  3. Sometimes, what seems like common sense can actually challenge our existing thought patterns, leading to moments of revelation and shifts in perspective.

Building the Digital Abacus, an Open-Source Mathematical Modeling App

Human Programming • 3 HN points • 24 Jul 23
🕹 Technology Math Programming UI/UX Open Source Modeling
  1. The Digital Abacus tool allows users to visually understand complex math equations by interactively manipulating values on a flowchart and seeing real-time updates in a plot.
  2. The tool uses a graph data structure called RelGraph to store values and constraints, allowing for easy representation of equations and composite operations.
  3. The system solves for dependent values by updating values iteratively in the graph until equilibrium is reached, showing the math solving process in real-time.

OpenAI Seeding, Model Fingerprints & Log Probabilities

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Nov 23
🕹 Technology AI Machine Learning NLP Data Storage Modeling
  1. The seed parameter helps in reproducing responses from an AI by combining it with the user prompt. This means if you want the same answer again, you need to use the same seed with the same question.
  2. System fingerprints are used to track changes in the AI model or environment. If the fingerprint changes, the responses might also change, so it’s important to keep track of this along with the seed.
  3. Log probabilities will be introduced to help understand which responses the AI is likely to give. This feature can be useful for improving things like search functions and suggestions.

Large Language Models, Foundation Models & Multi-Modal Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Mar 23
🕹 Technology AI NLP Modeling Machine Learning Data science
  1. Large Language Models (LLMs) are being developed into Foundation Models that can handle tasks beyond just language, like images and voice. This shows how technology is evolving to be more versatile.
  2. GPT-4 is now seen as a Multi-Modal Model that combines different types of data, allowing it to work with text, images, and more. This expands the possibilities for AI applications.
  3. As the use of LLMs increases, there will be more focus on creating fine-tuned models. This means turning unstructured data into structured data for better interaction and understanding.

Study 1b: This One Weird Trick does NOT cause incorrectness cascades

From AI to ZI • 0 implied HN points • 20 Apr 23
🔬 Science Research Analysis Statistics Modeling Data Sharing
  1. Study found that changing question format from multiple choice to true/false did not significantly affect GPT-3.5's tendency to prefer factual answers
  2. The study showed mixed results for the hypotheses tested regarding the accuracy of answers based on question format and context
  3. Despite some limitations and deviations from the original plan, the study provided insights on how GPT-3.5 performs in providing factual answers

Numbers and Narrative: Modeling, Story Telling and Investing

Musings on Markets • 0 implied HN points • 24 Jun 14
💼 Business Valuation Investing Finance Narratives Modeling
  1. Valuation is about finding a balance between numbers and narratives. Numbers help provide a foundation, while stories give context to data.
  2. Relying only on numbers can lead to misleading conclusions and shallow analysis. Understanding the story behind the numbers is essential for making informed investment decisions.
  3. Creating a strong narrative can attract investors, but it must be supported by solid numbers. Good storytelling combined with reliable data can improve the chances of investment success.

Are complex models the answer?

Musings on Markets • 0 implied HN points • 01 Dec 10
💼 Business Economics Finance Investment Modeling Forecasting
  1. Complex models can struggle when predicting unpredictable human behavior. Simple models might work better in uncertain situations.
  2. Small changes in a complex model can lead to large unexpected outcomes, a phenomenon known as the butterfly effect.
  3. When faced with uncertainty, it's better to simplify models by focusing on key variables and reducing complexity.

Yet More On Decoupling, Part 4 – Don’t Get Into A Macromuddle!

filterwizard • 0 implied HN points • 08 Sep 24
🕹 Technology Electronics Simulation Circuit Design Modeling Amplifiers
  1. Many op-amp simulation models don’t accurately show how they react to power supply changes. This makes it hard to trust the results from these models.
  2. Just because an op-amp is high quality doesn't mean its simulation model will behave well in tests. It's important to check each model individually.
  3. Finding a reliable op-amp model for testing took a lot of effort. Even after trying many, only a few performed as expected.

Object-Oriented Programming in LM

Andrew’s Substack • 0 implied HN points • 17 Oct 24
🕹 Technology Programming Software Data Structures Abstraction Modeling
  1. LM does not have a traditional object model, class model, or inheritance model, but it can represent some object-oriented features.
  2. The 'Diamond Problem' in inheritance can be avoided in LM by using plural type notation, which clearly shows type relationships.
  3. LM supports features like object subtyping, runtime types, and aspect-oriented programming, making it versatile despite its assembly-like nature.

Smarter Reasoning, Efficient Fine-Tuning, and Learning with LLMs

HackerPulse Dispatch • 0 implied HN points • 27 Dec 24
🕹 Technology AI Modeling Optimization Learning Education
  1. OREO uses offline reinforcement learning to help language models improve multi-step reasoning for tasks like math and control, making them smarter and less data-hungry.
  2. Memory layers make models more efficient by using key-value lookups, which can cut computational costs in half while maintaining performance even at a large scale.
  3. LoHan allows fine-tuning of huge models on regular GPUs, making the process cheaper and more effective, while LearnLM enhances teaching capabilities of AI, making it a preferred choice among educational tools.

SQLCoder-70b Becomes the Leading AI SQL Model

AI Disruption • 0 implied HN points • 27 Apr 24
🕹 Technology AI Modeling Data processing Artificial Intelligence
  1. SQLCoder-70b is a leading AI SQL model that outperforms GPT-4 in text-to-SQL generation, showing potential to surpass it.
  2. SQLCoder-70b achieved remarkable breakthroughs in data processing speed and accuracy, making it a significant development in the AI field.
  3. The model was shockingly released on Hugging Face during the peak of the AI wave, demonstrating its competitiveness in the industry.

Weekly Questions - Domain-Driven Design

🔮 Crafting Tech Teams • 0 implied HN points • 07 May 23
🕹 Technology Modeling
  1. Domain-Driven Design focuses on modeling the business language used to define your domain, not the models directly.
  2. DDD concepts do not replace E-R diagrams and planning; they serve to provide a different approach to understanding and representing the domain.
  3. Crafting Tech Teams offers a 7-day free trial for those interested in exploring more about Domain-Driven Design and tech team development.

A model for computing goals as signals

Space chimp life • 0 implied HN points • 30 May 23
🕹 Technology Computing Physics Mathematics Modeling
  1. Detecting the position of a particle is crucial, as it helps decide if action is needed or not. A good detection system can distinguish between being inside or outside a boundary.
  2. The effectiveness of an actuator is important too. It should reliably apply force when needed, helping to keep the particle within the desired area.
  3. Adding more detectors and actuators can enhance the chances of success, but they still can't guarantee it. Each added component improves the probability but only approaches success asymptotically.

Managing Your OpenAI Token Use

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 28 Nov 23
🕹 Technology AI NLP Modeling Cost management Implementation
  1. Managing OpenAI token usage is important for understanding costs. Each interaction you have with the model uses a certain number of tokens, which can add up quickly.
  2. Tokens are calculated differently depending on the model you use. It's essential to know how to convert text to tokens to estimate the cost for your specific needs.
  3. Most current implementations of LLMs focus on experimentation rather than real-time use. This means many users are not fully aware of the cost implications associated with extensive token use in their applications.