The hottest Data Substack posts right now

And their main takeaways
Category
Top Literature Topics

Upcoming Chances to Influence NIH

The Good Science Project • 1 HN point • 01 Feb 24
🔬 Science Data
  1. NIH is seeking input on reducing publication bias against null studies to improve science integrity and innovation
  2. NIH is inviting comments on their strategic plan for data science to ensure data is findable, accessible, and leads to tangible health improvements
  3. Prioritizing null results and meaningful data metrics can advance science and human health

Data in the Age of AI

Pivotal • 1 HN point • 20 May 23
🕹 Technology Data
  1. Data and compute values have changed, affecting software and data business models.
  2. The data explosion in the decade led to new successful business models downstream.
  3. AI impact on data and compute leads to increased data value and the need for new tools and ecosystem in the AI-first world.
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Compliance and Transparency of Immutable Functions in Diamonds

EIP-2535 Diamonds • 1 implied HN point • 07 Apr 23
🕹 Technology Data
  1. The EIP-2535 Diamond standard emphasizes the importance of emitting and returning immutable functions for transparency.
  2. Transparency is crucial to prevent confusion and incorrect data about immutable functions in diamonds.
  3. Ensuring compliance with EIP-2535 Diamond standards avoids situations where functions are unintentionally duplicated or incorrectly referenced.

Scaling and Orchestrating Large Language Models: In Conversation With Databricks CTO Matei Zaharia

Unsupervised Learning • 1 implied HN point • 20 Mar 23
🕹 Technology Data
  1. Decoupling semantic understanding and facts in large language models is challenging and using external indexes for knowledge retrieval can be powerful.
  2. Pulling work out of large language models and into code can give engineers more control and help with complex workflows.
  3. The need for scale in training large language models poses challenges as few can reproduce the largest models, impacting research and innovation.

First principles on AI scaling

DYNOMIGHT INTERNET NEWSLETTER • 1 HN point • 06 Mar 23
🕹 Technology Data
  1. Using scaling laws can help predict how much better language models will get with more computational power or data.
  2. The majority of the error in language models comes from limited data, rather than limited model size.
  3. To improve language models significantly, more data and compute are needed, but there may be a limit to how much more can be added with current technology.

A story about validating language models

Simplicity is SOTA • 0 implied HN points • 14 Aug 23
🕹 Technology Data
  1. Validating language models for inappropriate content is crucial to maintain trustworthiness.
  2. Building confidence in a model's performance through rigorous testing can prevent potential issues.
  3. Structuring data outputs for human review can significantly improve efficiency in evaluating model responses.

Where does the mean squared error come from?

The Palindrome • 0 implied HN points • 21 Dec 23
🔬 Science Data
  1. Mean squared error is a common loss function for machine learning models due to its mathematical simplicity and alignment with statistical principles.
  2. Absolute value functions are not commonly chosen for loss function in machine learning due to issues with differentiability at zero.
  3. The linear model and mean squared error naturally arise when approaching machine learning with a statistical mindset.

Weekly Update Jan 8-12

Business Breakdowns • 0 implied HN points • 12 Jan 24
🕹 Technology Data
  1. Snowflake acquired Samooha to enhance data clean rooms for targeted marketing.
  2. Clean rooms store anonymized data for precise user targeting while maintaining privacy.
  3. Paid subscribers can access the full post for more updates and insights.

A Time When Trusting AI Matters

The Grey Matter • 0 implied HN points • 10 Oct 23
🕹 Technology Data
  1. The Flint water crisis demonstrates the importance of trusting AI to address critical issues like identifying lead pipes.
  2. AI can significantly improve efficiency in tasks like predicting hazardous pipes, but it requires trust and acceptance from both authorities and the public.
  3. The decision to not fully utilize AI in the Flint water crisis led to inefficiencies, showing the balance needed between skepticism and the potential benefits of AI.

How to Measure Anything

TeamCraft • 0 implied HN points • 21 Aug 23
🚌 Education Data
  1. The ability to measure anything can greatly increase your ability to estimate ROI on data initiatives and reduce uncertainty for informed decision-making.
  2. Rethink measurement by understanding that you only need to reduce uncertainty to a manageable level, not eliminate it completely.
  3. Techniques like the Rule of Five, decomposition, and challenging false assumptions about data can help in measuring intangible aspects effectively.

In Case You Missed It: November 2023 Recap

Three Data Point Thursday • 0 implied HN points • 30 Nov 23
🕹 Technology Data
  1. Data and algorithms can evoke fear in humans, so building empathy into business practices is essential.
  2. Time series models like TimeGPT offer significant advancements in machine learning that should not be overlooked.
  3. Successfully monetizing data is a challenge similar to achieving success as a YouTuber - it's rare and difficult to accomplish.

In Case You Missed It, July Recap

Three Data Point Thursday • 0 implied HN points • 27 Jul 23
🕹 Technology Data
  1. Surgical fine-tuning improves ML models for business contexts through precision changes.
  2. LLM architectures are important for building with language models, with a recommended architecture to start out.
  3. Every business should strive to become a data business to survive in the current market.

Don’t be a ducker

Three Data Point Thursday • 0 implied HN points • 08 Jun 23
🕹 Technology Data
  1. Big data vs. small data debate isn't the main focus in data orchestration.
  2. Data orchestration companies are raising significant amounts of funding.
  3. New orchestrator, Orchestra, aims to combine observability and data assets without code.

Dark Data; Thoughtful Friday #27

Three Data Point Thursday • 0 implied HN points • 17 Mar 23
🕹 Technology Data
  1. Dark data is information collected but not utilized, similar to dark matter in the universe.
  2. There are 6 categories of data, including what is used, not used but should be, and should be collected but isn't.
  3. Having unique data, especially dark data, can provide a competitive advantage and is valuable for a company.

PlacesGPT

Expand Mapping with Mike Morrow • 0 implied HN points • 12 Dec 23
🕹 Technology Data
  1. PlacesGPT brings point of interest data into ChatGPT.
  2. Using Google's Places Text Search API helps with ambiguous address queries.
  3. The Google Places API usage for PlacesGPT will be limited due to cost until the GPT marketplace launches.

Will Model Weights Be Patented?

Embracing Enigmas • 0 implied HN points • 07 Mar 23
🕹 Technology Data
  1. Model weights in AI may become a subject of patenting, similar to chemical molecules.
  2. Current AI models are approximations that may converge to similar results, leading to a race for patenting to gain advantage.
  3. Enforcing patents on model weights in AI may face challenges due to the complexity of the weights and the rapidly evolving nature of the field.

The AI War: Open-Source vs. Closed-Source Models

Embracing Enigmas • 0 implied HN points • 03 Apr 23
🕹 Technology Data
  1. The battle for AI dominance is ongoing between open-source and closed-source models.
  2. Open-source models may excel in general areas while closed-source models have an edge in specialized fields.
  3. The ability to fine-tune models through interactions creates a dynamic landscape in the AI industry.

Data Set Match is moving on

Data Set Match • 0 implied HN points • 06 Apr 23
🕹 Technology Data
  1. Data Set Match is transitioning to open source software and a new newsletter called Once a Maintainer.
  2. They encourage readers to find them at www.infield.ai and subscribe to Once a Maintainer to learn about open source maintainers.
  3. Their focus is now on supporting the data community and highlighting individuals in the field.

Data Imperialism

Amadeus Pagel's Newsletter • 0 implied HN points • 11 Apr 23
🕹 Technology Data
  1. Data can be used in limitless ways, leading to limitless expansion in technology.
  2. Programs tend to expand their functionalities over time, following Zawinski's law.
  3. Questions about fair competition arise when companies expand their services and features.

Smart and stupid: The combination that makes AI so dangerous

Augmented • 0 implied HN points • 07 May 23
🕹 Technology Data
  1. AI can be dangerous due to its combination of intelligence and occasional stupidity.
  2. The concern with AI lies in its lack of grounded understanding in the world, not just its intelligence level.
  3. Large language models are intriguing and dangerous because they exhibit a mix of extreme intelligence and notable gaps in logic.

On Rails in Mt. View California 🚂

Thinking Through • 0 implied HN points • 03 Jul 23
🕹 Technology Data
  1. The public data on rails in the Bay Area provides interesting insights, like weight, manufacturing details, and design specifications.
  2. Rail dimensions like height and width play crucial roles in supporting the track and preventing rail rolling.
  3. Many intriguing questions arise about rails during train rides, from spacing between rails to the forces rails experience.

Knowledge, Plugins and Understanding

Age of AI • 0 implied HN points • 03 Aug 23
🕹 Technology Data
  1. AI tools like ChatGPT can benefit from plugins like 'Tasty Recipes' to enhance performance.
  2. Having background knowledge can help AI tools better understand and summarize texts.
  3. Different plugins and tools, like 'PDF summary' plugins and NotebookLM, are being used to improve AI's ability to process and summarize information.

What's Next for Malloy in 2024

Making Things • 0 implied HN points • 09 Jan 24
🕹 Technology Data
  1. The Malloy community is expanding globally and working on enhancing language capabilities like SQL features.
  2. Efforts are being made to improve analytical completeness by implementing partition clauses and percentile functions.
  3. The team aims to enable users to call arbitrary aggregate or window functions in the underlying database.

Malloy's 10x

Making Things • 0 implied HN points • 23 Nov 23
🕹 Technology Data
  1. If you can make something 10x more efficient, you have a winner.
  2. Malloy aims to replace SQL for asking questions of data.
  3. Malloy's efficiency shines when multiple queries are involved, offering reusability and speed.