The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

March Newsletter

RSS DS+AI Section • 11 implied HN points • 01 Mar 24
🕹 Technology Data science AI Ethics Research Applications
  1. The newsletter discussed various updates and activities in the field of data science and AI, including committee activities, advancements in research, and real-world applications.
  2. Ethical considerations, bias, diversity, regulation, and safety in AI and data science were highlighted as hot topics in the newsletter, with examples of AI-related consequences and efforts to improve safety.
  3. The newsletter also featured practical tips, how-to guides, and bigger picture ideas in the field, providing a broad range of information for data science practitioners.

BizML: a framework for success in applied machine learning

TechTalks • 19 implied HN points • 05 Feb 24
🕹 Technology AI Machine Learning Data science Business Framework
  1. Most machine learning projects fail due to a gap in understanding between data scientists and business professionals.
  2. Eric Siegel introduces bizML, a six-step framework for successful machine learning projects that emphasizes starting with the end business goal.
  3. Improving human understanding and leadership is crucial for the success of advanced technologies like machine learning.

Artificial intelligence and "big data" cannot replace public opinion polls | #213 - April 10, 2023

G. Elliott Morris's Newsletter • 117 implied HN points • 10 Apr 23
🇺🇸 U.S. Politics Polling Public Opinion Data science Political Analysis Social Trends
  1. Artificial intelligence and big data cannot fully replace public opinion polls, as they rely on polls for calibration and may not be as reliable for all groups.
  2. Changes in polling methods, like switching from phone to online surveys, can impact results, highlighting the importance of consistency over time.
  3. Studies show genuine change in attitudes, like increasing racial liberalism, but also caution against biases affecting survey responses.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

How Data Scientists can sell "Tech Debt" to their managers?

Laszlo’s Newsletter • 5 implied HN points • 04 Mar 24
🕹 Technology Data science Code Quality
  1. Bad code wastes company resources by making the team spend more time on fixes. Refactoring can free up time for working on features.
  2. Mildly bad code slows down new feature delivery. Refactoring can make the team faster and deliver more features in less time.
  3. In data science projects, data quality issues can lead to excessive time spent on bug fixing, emphasizing the need for refactoring to increase efficiency.

Should Your Data Science Team be Centralized or Decentralized? Why Not Intentionally Rotate?

Mike Talks AI • 98 implied HN points • 19 May 23
🕹 Technology Data science Team Management Organizational Design
  1. Consider a hybrid approach for data science teams to balance the strengths of both centralized and decentralized setups.
  2. Some companies are experimenting with intentionally rotating between centralized and decentralized structures every few years.
  3. Switching between centralization and decentralization periodically allows for exploration and scalability of diverse ideas within data science teams.

Generating Insights from Research with AI

Addition • 78 implied HN points • 28 Jun 23
🕹 Technology AI Research Insights Machine Learning Data science
  1. AI can synthesize vast amounts of information to generate insights faster than humans.
  2. AI can complement human strategists, giving them superpowers to transform the art of strategy.
  3. The tool shared in the post helps improve human strategists' AI superpowers by synthesizing research, generating insights, and providing creative interpretations.

3 Techniques to Make your Machine Learning more efficient[Technique Tuesdays]

Technology Made Simple • 99 implied HN points • 04 Apr 23
🕹 Technology Machine Learning AI Software Engineering Tech industry Data science
  1. Reducing the number of features in your data can improve performance and keep costs down in machine learning processes.
  2. Active learning focuses on prioritizing data points for efficient machine learning model training.
  3. Using filters and simpler models for specific tasks can lead to better performance and cost savings compared to always using large, powerful models in AI.

Tidal Waves in Venture Capital

East Wind • 17 implied HN points • 11 Dec 23
💼 Business Venture Capital Data science Investment Trends Market Dynamics Future Predictions
  1. Venture capital is facing challenges like the curse of scale and lower returns, making the industry more competitive.
  2. Data science and AI are reshaping VC investment processes, improving deal sourcing and evaluation.
  3. VC is becoming higher frequency, with firms leveraging AI to move faster and secure deals in a more competitive landscape.

AI Explained: Retrieval-Augmented Generation (RAG)

Year 2049 • 8 implied HN points • 26 Jan 24
🕹 Technology AI Machine Learning Data science Applications Education
  1. RAG solves problems with AI like hallucinations, outdated knowledge, being too general, and privacy concerns
  2. RAG allows for retrieving specific knowledge, adding new updated documents easily, and not training the AI on your data
  3. RAG can be used to create assistants for tasks like onboarding new employees, customer service, coding, and design, improving productivity through knowledge access

November Newsletter

RSS DS+AI Section • 23 implied HN points • 04 Nov 23
🕹 Technology Data science AI Research Generative AI Applications
  1. The newsletter covers various topics in Data Science and AI including ethics, research, and practical applications.
  2. Committee activities include calls for new members, updates on AI Safety Summit, and announcements for events like the Christmas social.
  3. The newsletter also highlights significant developments in AI research, such as GenAI, robotics, and Large Language Models.

ChatGPT and Supply Chain: Podcast Guest, Three Lessons, and Research Opportunities

Mike Talks AI • 58 implied HN points • 13 Jun 23
🕹 Technology AI Supply Chain Podcast Research Data science
  1. Supply chain professionals can use ChatGPT as a 'loss leader' to educate leaders about AI's potential for supply chains.
  2. ChatGPT can help supply chain teams build more AI algorithms by breaking down syntax barriers and expanding team capabilities.
  3. Exploring how ChatGPT can turn vast supply chain data into valuable insights is an important research opportunity.

On TikTok, The Algorithm Optimizes YOU

Never Met a Science • 55 implied HN points • 31 May 23
🕹 Technology Social media Algorithm Data science Simulation Machine Learning
  1. TikTok's algorithm shapes content creators' behavior based on feedback and viral success.
  2. The algorithm aims to keep both creators and consumers engaged, but risks leading to repetitive content.
  3. Data science and algorithms in platforms like TikTok create simplified simulations of reality for optimization, focusing on subjective metrics.

How Hugging Face and Kaggle Bolster the Open Source Machine Learning Community

The Strategy Deck • 39 implied HN points • 26 Jul 23
🕹 Technology Machine Learning Open Source Data science ML models
  1. Open source ML hubs like Hugging Face and Kaggle provide platforms for managing, sharing, and deploying ML models.
  2. Hugging Face focuses on models, datasets, deployment infrastructure, and community engagement.
  3. Kaggle empowers learners, developers, and researchers with educational resources, open source models, and a competitive platform.

Concordium and 2021.ai Team Up to Make AI More Trustworthy

Concordium Monthly Updates • 39 implied HN points • 20 Jul 23
🕹 Technology AI Blockchain Data science Compliance Trust
  1. Partnership between Concordium and 2021.ai enhances trust in AI through data validation and audit trails.
  2. Integration of Concordium's blockchain into 2021.ai's platform enables new use cases like ESG Validation and MiCA compliance.
  3. Collaboration aims to promote responsible and ethical use of AI, driving innovation and building trust in the AI industry.

October Newsletter

RSS DS+AI Section • 23 implied HN points • 02 Oct 23
🕹 Technology AI Data science Ethics Research Innovation
  1. The newsletter discusses various Committee Activities like professional development certification and sessions at the RSS conference.
  2. Ethics, bias, and diversity are hot topics in data science and AI, with ongoing discussions on AI regulation and accountability.
  3. The newsletter covers exciting developments in Data Science and AI research, including generative AI, real-world applications, and practical tips.

You are not a method

Counting Stuff • 54 implied HN points • 02 May 23
🕹 Technology Data science Research Organizational dynamics Career development Networking
  1. Teams are often created to fill niche use cases, leading to specialized roles and organizational politics.
  2. Being type-cast into a specific role can limit opportunities for growth and variety in work tasks.
  3. To break out of being type-cast, showcase your ability to do different kinds of work and actively seek out diverse opportunities.

How does the Air Quality Index work anyways?

Counting Stuff • 43 implied HN points • 13 Jun 23
🔬 Science Data science Air Quality
  1. The Air Quality Index is a single score that combines 6 pollutants into a usable number for people to understand.
  2. Time frames are important in the AQI, as it is based on daily air quality summaries and forecasts are encouraged for planning purposes.
  3. The AQI simplifies complex air quality data by using a linear scaling system, with the max value among pollutants determining the overall index.

December Newsletter

RSS DS+AI Section • 11 implied HN points • 03 Dec 23
🕹 Technology Data science AI Ethics Research
  1. The December newsletter covers a wide range of activities and achievements in the field of Data Science and AI.
  2. There is a focus on topics like ethics, regulations, and bias in the AI industry.
  3. The newsletter also delves into the latest developments in Generative AI and provides practical tips for driving analytics and ML into production.

A Brief History of MLOps in Three Acts

Laszlo’s Newsletter • 54 implied HN points • 20 Feb 23
🕹 Technology MLOps Data science Machine Learning
  1. The evolution of MLOps tools started from handling big data and SQL to deployment, feature stores, model monitoring, and more
  2. The increasing complexity of ML models led to the development of tools like XGBoost, TensorFlow, PyTorch, and the need for distributed computing
  3. Machine Learning Engineers play a crucial role in navigating the ever-changing landscape of MLOps tools and technologies

How I networked with one of the most prominent Data Scientists online[Storytime Saturdays]

Technology Made Simple • 59 implied HN points • 29 Jan 23
💼 Business Networking Data science Career development
  1. Networking is a valuable skill to add to your toolbox for personal growth, career progression, or assisting others.
  2. Even without an established online presence, you can stand out and network effectively with prominent individuals in your field.
  3. Effective networking can lead to opportunities and contracts that showcase your skills and expertise to the right people.

BYOK

Aipreneur • 39 implied HN points • 08 Mar 23
🕹 Technology AI Data science Machine Learning Cloud Computing Mobile technology
  1. BYOD (Bring Your Own Device) became popular in corporates due to iPhone's rise and employee preferences.
  2. BYOD is beneficial for companies in cost-saving, convenience, increased mobility, and changing workforce demographics.
  3. The emerging trend of BYOK (Bring Your Own Keys) is starting in AI platforms, where users need to pay for keys to access and use data responsibly.