The hottest Big Data Substack posts right now

And their main takeaways
Category
Top Technology Topics

Sam Altman Wants $7 Trillion

Astral Codex Ten • 16656 implied HN points • 13 Feb 24
🕹 Technology AI Machine Learning Artificial Intelligence Big Data Computing
  1. Sam Altman aims for $7 trillion for AI development, highlighting the drastic increase in costs and resources needed for each new generation of AI models.
  2. The cost of AI models like GPT-6 could potentially be a hindrance to their creation, but the promise of significant innovation and industry revolution may justify the investments.
  3. The approach to funding and scaling AI development can impact the pace of progress and the safety considerations surrounding the advancement of artificial intelligence.

Gods, slaves and playmates

imperfect offerings • 139 implied HN points • 26 Feb 24
🕹 Technology AI Big Data Automation Ethics
  1. The essay/post explores AI fantasies and their significance in education.
  2. People tend to relate to synthetic models as if they have agency, even though they don't.
  3. Big tech industry creates a narrative around AI as gods or monsters, while in reality, these AI systems are often designed to serve in subservient roles.

A Guide to Optimising your Spark Application Performance (Part 2)

SwirlAI Newsletter • 314 implied HN points • 06 Aug 23
🕹 Technology Programming Big Data Optimization Data Storage
  1. Choose the right file format for your data storage in Spark like Parquet or ORC for OLAP use cases.
  2. Understand and utilize encoding techniques like Run Length Encoding and Dictionary Encoding in Parquet for efficient data storage.
  3. Optimize Spark Executor Memory allocation and maximize the number of executors for improved application performance.

SAI Notes #07: What is a Vector Database?

SwirlAI Newsletter • 412 implied HN points • 18 Jun 23
🕹 Technology Data Engineering Machine Learning Big Data Database Management Data Storage
  1. Vector Databases are essential for working with Vector Embeddings in Machine Learning applications.
  2. Partitioning and Bucketing are important concepts in Spark for efficient data storage and processing.
  3. Vector Databases have various real-life applications, from natural language processing to recommendation systems.
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SAI #26: Partitioning and Bucketing in Spark (Part 1)

SwirlAI Newsletter • 373 implied HN points • 15 Apr 23
🕹 Technology Data Engineering Big Data Performance optimization Data Storage Data processing
  1. Partitioning and bucketing are two key data distribution techniques in Spark.
  2. Partitioning helps improve performance by allowing skipping reading the entire dataset when only a part is needed.
  3. Bucketing is beneficial for collocating data and avoiding shuffling in operations like joins and groupBys.

Meta's New AI model, Generating websites and 3D textures using natural language, economic potential of Generative AI and more

AI Brews • 20 implied HN points • 16 Jun 23
🕹 Technology AI Generative AI Machine Learning Artificial Intelligence Big Data
  1. Meta AI introduces a new Image Joint Embedding Predictive Architecture model that excels in computer vision tasks and is open-sourced.
  2. McKinsey's report highlights the economic potential of generative AI, estimating it could add trillions annually across various use cases.
  3. EU lawmakers pass regulations for AI systems, requiring review of generative AI like ChatGPT before commercial release and banning real-time facial recognition.

Log Transformations for efficient multiplication between lots of positive numbers [Technique Tuesdays]

Technology Made Simple • 39 implied HN points • 02 Nov 22
🔬 Science Mathematics Computing Big Data Machine Learning Algorithms
  1. Log transformations can be used for efficient multiplication between large numbers by converting the problem into addition of logs, making it more manageable.
  2. Logs have interesting properties that make them useful for handling computations with very large or very small numbers.
  3. Using log transformations is a clever math technique that is commonly used in fields like AI, Big Data, and Machine Learning to handle large computations.

The unreasonable effectiveness of LLMs

John’s Contemplations • 19 implied HN points • 08 Mar 23
🕹 Technology AI Machine Learning Neural Networks Big Data Efficiency
  1. LLMs have displayed surprising reasoning abilities like solving math problems using words.
  2. LLMs can be trained to use tools to address their weaknesses and improve tasks like code generation.
  3. LLMs work well due to the general nature of language, the breakdown of complex tasks into simpler steps, and the efficiency of neural networks like Transformers.

Data Week: How to manage your data

Joshua Gans' Newsletter • 19 implied HN points • 12 Oct 20
🕹 Technology Data Management Big Data Software Data Analysis
  1. Management of mission-critical data should ensure robust systems to avoid errors like the UK Excel scandal.
  2. Having a unified data infrastructure for COVID-19 reporting across various testing venues is crucial for accurate data collection.
  3. Lessons from data management failures, such as the UK Excel error, underline the importance of investing in advanced data systems for efficient pandemic handling.

Two-tower models for ranking problems

Simplicity is SOTA • 0 implied HN points • 22 May 23
🕹 Technology Big Data Machine Learning Research Neural Networks Algorithm
  1. Two-tower models are a technique being used in academia to improve ranking systems by looking into how position and user behavior affects clicks.
  2. Critiques have been raised against the two-tower models, questioning if they effectively separate biases and relevance in ranking.
  3. A new method called GradRev is emerging as a potential improvement over the previous two-tower models, applying a different approach to address bias in learning-to-rank systems.

Five thoughts for 2022

The Intersection • 0 implied HN points • 09 Jan 22
🕹 Technology Design Data Tech NFT Big Data
  1. 2022 is predicted to have ups and downs like 1999, followed by unexpected changes in the next 15-20 years.
  2. Creativity is now decentralized, open to anyone with determination to create, and technology plays a crucial role in democratizing creative work.
  3. The power is shifting from social media platforms to individual creators, making individual creators the focus rather than the platforms themselves.

Asking the right questions on Apple

Joshua Gans' Newsletter • 0 implied HN points • 22 May 16
🕹 Technology Artificial Intelligence Big Data Natural Language Processing
  1. Apple's potential risk with AI: The article discusses how Google's advancements in AI could pose a threat to Apple, especially in big-data services and AI where Apple lags behind.
  2. The importance of in-house AI development: The importance of Apple investing in in-house AI talent and assets is highlighted to remain competitive, rather than relying on partnerships or acquisitions.
  3. Need for innovation and adaptation: The article emphasizes the need for Apple to adapt to potential industry shifts in AI interfaces, stay aware of dominant design trends, and align their capabilities accordingly.

Separate, but forever plugged in

Links I Would Gchat You If We Were Friends • 0 implied HN points • 14 Jul 16
🕹 Technology Social media Mental health Big Data Wearable Technology Online Dating
  1. Technology has disrupted the truth by prioritizing clicks over accuracy, causing misinformation to spread rapidly.
  2. Apps on our phones may not change our lives dramatically, but they can contribute positively to our mental health.
  3. Big data meeting the porn industry can lead to subtle shaping of views on sexuality by companies targeting advertisements.

Chances are your date will never call you again!

Links I Would Gchat You If We Were Friends • 0 implied HN points • 09 Oct 14
🎭️ Culture Social media Big Data Sexuality Food Technology
  1. Social media serves as a modern autobiography for many individuals, showcasing personal journeys and struggles.
  2. Big data originated as an experimental concept with socialist intentions in 1970s Chile.
  3. There is a significant presence of couples documenting and sharing their sexual experiences on public platforms like Tumblr.