The hottest Data Substack posts right now

And their main takeaways
Category
Top Literature Topics

Joe's Nerdy Rants #2

Joe Reis • 294 implied HN points • 27 May 23
🕹 Technology Data AI Business Startups Learning
  1. Identify your motivation to learn in a rapidly changing industry by finding your ultimate goal or purpose.
  2. Focus on mastering the fundamentals of a topic by understanding it from end to end and learning from first principles.
  3. Be patient, read widely, and connect various ideas together to grow your knowledge over time.

AI Isn’t Good Enough

Irregular Ideas with Paul Kedrosky & Eric Norlin of SKV • 172 HN points • 23 Aug 23
🕹 Technology AI Automation Productivity Tools Data
  1. There is a significant shortage of workers in the U.S. across various industries, leading to the need for automation.
  2. Current AI technology has limitations and is not yet capable of addressing the workforce shortage effectively.
  3. To avoid economic disruptions, future automation needs to focus on delivering high productivity gains that outweigh worker displacement.
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Update #68: Whispering Indigenous Languages and Neural Net Training Dynamics

The Gradient • 27 implied HN points • 13 Feb 24
🕹 Technology AI Language Data Research Ethics
  1. Papa Reo raised concerns about Whisper's ability to transcribe the Māori language, highlighting challenges faced by indigenous languages in technology.
  2. Neural networks learn statistics of increasing complexity throughout training, with a focus on low-order moments first before higher-order correlations.
  3. Including native speakers in language corpora and model evaluation processes can substantially improve the performance of natural language processing systems for languages like Māori.

Quo vadis, Data Open source

timo's substack • 157 implied HN points • 03 Sep 23
🕹 Technology Open Source Data Community Business strategy Software Development
  1. Snowplow, dbt, Rudderstack, and Iceberg are examples of open-source data tools each with unique characteristics.
  2. Open-source data tools face challenges in transitioning to successful go-to-market strategies.
  3. Companies need to focus on identifying customer pain points and developing experience-changing solutions in their GTM strategy.

The Politics of Data

Joe Reis • 196 implied HN points • 29 Jul 23
🕹 Technology Data AI Business Ethics Tech
  1. The politics of data often involves using data to push pre-determined agendas.
  2. In organizations, decisions are often driven by politics rather than technical excellence or data.
  3. Understanding the political dynamics within an organization can help navigate potential impacts on one's career.

Extremely Open Science

Rabbit Thoughts • 39 implied HN points • 17 Jan 24
🔬 Science Research Experiment Data
  1. The author will work on a scientific project completely in the open in 2024, streaming and recording sessions for an hour per week.
  2. The project aims to show the process from scratch to help junior researchers understand and learn from the experience of dealing with minor issues.
  3. The author is choosing a question for the project that can be followed along at home with just a personal laptop or desktop computer.

Joe's Nerdy Rants #7

Joe Reis • 216 implied HN points • 01 Jul 23
🕹 Technology Data AI Business Startups Events
  1. The data community deserves better events free of vendor influence.
  2. The major data platforms are in an intense competition and push to capture attention.
  3. Attending big-vendor conferences often involves dealing with aggressive selling tactics.

Models

Sriram Krishnan’s Newsletter • 216 implied HN points • 20 Jun 23
🕹 Technology AI Data Models
  1. Large-language models are open-sourced and ranked based on benchmarks like ChatGPT and Google Bard.
  2. Model performance improves with each iteration, leading to better models rising and lesser ones fading out.
  3. Different types of data sources contribute to the creation of unique models, with more gated data leading to more variety.

Joe's Nerdy Rants #8

Joe Reis • 196 implied HN points • 08 Jul 23
💼 Business Tech AI Data Startups Journalism
  1. People skills are becoming increasingly important in the tech industry.
  2. Technical skills are essential, but communication and empathy separate individuals for career success.
  3. Businesses are shifting towards paying tech vendors based on outcomes, emphasizing accountability.

The 4 Problems of Airdrops

DeFi Weekly • 235 implied HN points • 26 Apr 23
🔮 Crypto Liquidity Valuations Data
  1. Decentralisation theatrics don't necessarily protect against legal issues with airdrops.
  2. Airdrops can lead to early liquidity for team members and investors, impacting valuations.
  3. Inflated user counts from airdrops may not reflect genuine user ownership or value creation.

Pinterest improves their Closeup Recommendation System through foundational changes

MLOps Newsletter • 98 implied HN points • 07 Oct 23
🕹 Technology AI Data ML Models Libraries
  1. Pinterest improved their Closeup Recommendation System with foundational changes like hybrid data logging and sampling.
  2. Pinterest uses a model refreshing framework to keep their Closeup Recommendation model up-to-date and adaptable.
  3. Distilling step-by-step can help train smaller, more efficient, and interpretable language models like LLMs.

Is AI a Silver Bullet for our Climate Problems?

This Week in MCJ (My Climate Journey) • 216 implied HN points • 07 Mar 23
🔬 Science Climate AI Data Education Energy
  1. AI solutions in climate problems can be biased towards easily accessible data, encouraging broader solution development is crucial.
  2. AI must quantify its confidence in recommendations for climate problem-solving due to the high cost of mistakes.
  3. Encouraging new datasets and AI methods with confidence measurement can lead to more successful projects in addressing climate challenges.

Cloud, Data, AI, GenAI

Laszlo’s Newsletter • 37 implied HN points • 03 Jan 24
🕹 Technology Cloud Data AI GenAI
  1. Cloud computing provides flexibility in resources and enables experimentation without high upfront costs.
  2. Establishing a strong data stack is crucial before implementing AI/GenAI to ensure data quality and reliable insights.
  3. Traditional AI involves well-defined tools for extracting business-relevant information from data, while generative AI like Prompt Engineering and Finetuning require sophisticated infrastructures and specific business goals.

The Value of Stability in Scientific Funding, and Why We Need Better Data

The Good Science Project • 18 implied HN points • 17 Feb 24
🔬 Science Funding Data Research Policy Employment
  1. Scientific funding instability negatively impacts researchers' ability to plan and conduct research effectively, leading to swings in funding and unnecessary time spent on grant proposals.
  2. Improved data tracking is crucial to understanding the impact of funding gaps on researchers' employment outcomes, highlighting the need for long-term empirical studies in science policy.
  3. Addressing funding stability issues and utilizing detailed longitudinal data can help prevent obstacles in scientific progress and support the longevity of researchers' careers.

Llama 2 follow-up: too much RLHF, GPU sizing, technical details

Democratizing Automation • 146 implied HN points • 21 Jul 23
🕹 Technology AI Data Programming Hardware Research
  1. The Llama 2 model may be exhibiting trigger-happy behaviors due to excessive use of RLHF during training.
  2. There are challenges with GPU sizing for different model variants, with considerations for inference and fine-tuning.
  3. Meta's evaluation of the chat models reveals potential issues with model refusal rates and ensemble techniques.

GPT-4’s Placeholder Problem: A Case Study of GPT-4 Fake Data Presented as Fact

Data at Depth • 39 implied HN points • 26 Dec 23
🕹 Technology AI Data
  1. GPT-4 can find and present information in various formats based on how you ask it to, whether as a paragraph, a chart, or even a poem.
  2. The issue highlighted is GPT-4 presenting data as facts, raising concerns about the accuracy and authenticity of information generated by AI models.
  3. The post emphasizes the importance of being vigilant and critical when consuming information generated by AI like GPT-4.

On a Personal Note

Sarah's Newsletter • 319 implied HN points • 20 Dec 22
💼 Business Career Consulting Startups Marketing Data
  1. The author is relocating to Vermont, excited about being closer to snow for ski season and connecting with local communities.
  2. The author's startup, Versionable, is currently taking a back seat as they focus on settling into new changes and exploring different angles to address marketing challenges.
  3. The author is embarking on a new role as the Growth Lead at Prefect, highlighting their interest in ambitious team goals and a UI-first experience in data tooling space.

The Three Tenets for AI Security and How to Audit Activity Logs

Rod’s Blog • 59 implied HN points • 10 Nov 23
🕹 Technology AI Security Data Access
  1. AI security involves three main tenets: secure code, secure data, and secure access. It is crucial for security professionals to ensure AI systems are designed, developed, and maintained following these principles.
  2. To achieve secure code, monitor and update AI systems regularly, validate and verify their performance, and adhere to secure development practices and tools.
  3. When auditing activity logs, focus on detecting cyberthreats, troubleshooting and resolving issues, and optimizing performance. It involves collecting, analyzing, visualizing, and reporting on the activities within the AI system.

A 3-Part Series: Thriving Across the Worlds of Finance, Business, Data, and Technology

The Data Score • 118 implied HN points • 09 Aug 23
💼 Business Finance Technology Data Decision-making Integration
  1. Problems in the fields of finance, business, data, and technology are becoming more interconnected and complex.
  2. There is a need to break down silos and create alignment among stakeholders to make more impactful decisions.
  3. Increasing overlap between business, data, and technology requires expertise from multiple domains to navigate high-risk environments.

The Impact of AI on the Environment: A Critical Analysis

Rod’s Blog • 19 implied HN points • 05 Feb 24
🔬 Science Environment AI Data Research Education
  1. AI has both direct and indirect impacts on the environment. It can lead to high energy consumption and carbon emissions due to the computational complexity and rapid innovation cycle of AI systems.
  2. The way AI is used can either help or harm the environment. It can optimize energy efficiency and support sustainable development, but it can also increase resource demand, pollution, and disrupt ecosystems.
  3. To lessen the negative environmental effects of AI, collaborative efforts are essential. This includes implementing ethical guidelines, promoting green AI research, educating about AI's environmental impact, and incentivizing energy-efficient AI solutions.

Battery Ventures' Max Schireson: To Build Value in Tech, Build Different

Condensing the Cloud • 98 implied HN points • 31 Aug 23
🕹 Technology Innovations Startups Data Computing Tech Companies
  1. To build value in the tech industry, aim to do things differently, not just better or faster.
  2. Doing something different can polarize users, with some finding it better and others not.
  3. Success in tech often comes from being unique and offering something new, not just improving existing technologies.

Datetime vs Timestamp datatype in databases - Which one is better?

Arpit’s Newsletter • 176 implied HN points • 26 Apr 23
🕹 Technology Data Database Storage
  1. In databases, you can use DATE, DATETIME, or TIMESTAMP data types to store date and time information, each with its own range of values.
  2. DATETIME is best for storing static timestamps like appointment schedules, while TIMESTAMP is ideal for recording event timestamps with efficient storage and automatic timezone handling.
  3. Consider factors like range, storage requirements, and use cases when choosing between DATETIME and TIMESTAMP for accurate and efficient temporal data storage.

Unfortunately, OpenAI and Google have moats

Democratizing Automation • 174 implied HN points • 17 May 23
🕹 Technology AI Data Open Source Innovation Research
  1. Companies like OpenAI and Google have competitive advantages known as 'moats' through data and user habits.
  2. Creating and fine-tuning chatbots based on large language models require extensive data and resources, posing challenges for open-source development.
  3. Consumer behavior and association biases often prevent users from switching to alternative platforms, reinforcing the dominance of tech giants like Google.

The Modern ML Stack is broken, but it won't be for long

Entry Level Investing • 184 implied HN points • 20 Feb 23
🕹 Technology AI Data Tech startups
  1. AI infrastructure is essential for organizations to participate in the AI revolution.
  2. The current ML infrastructure landscape is messy, and there is a need for consolidated solutions.
  3. Entrepreneurs have a huge opportunity to build enduring businesses by focusing on end-to-end ML application offerings and addressing the challenges in the AI infrastructure space.

Data Materialization is a Convergence Problem

Data People Etc. • 159 implied HN points • 10 Apr 23
🕹 Technology Data Workflow Operations
  1. Data materialization is not just a workflow orchestration problem but also a convergence problem.
  2. In a convergence-based approach to data materialization, a materialization controller could continuously compare the state of the warehouse with the desired state of models to automate the materialization process.
  3. Challenges in implementing a materialization controller include explainability, managing over-eagerness, and dealing with drift in the system.