The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Weekly Top Picks #67

The Algorithmic Bridge • 116 implied HN points • 18 Mar 24
🕹 Technology AI Tech news Artificial Intelligence Software Engineering Data science
  1. The post discusses Nvidia GTC keynote, BaaS in science, Apple's potential collaboration with Google Gemini, and more key AI topics of the week.
  2. It features conversations between Sam Altman and Lex Friedman, touches on jobs in the AI era, and examines the response from NYT to OpenAI.
  3. There's a question about whether OpenAI's Sora model is trained using YouTube videos, among other intriguing topics.

Do Not Spend too Much Time "Getting Good" at Dealing with Current AML GPT LLMs

Brad DeLong's Grasping Reality • 169 implied HN points • 04 Mar 24
🕹 Technology Machine Learning Artificial Intelligence Data science Internet Software
  1. It's uncertain how current AML GPT LLMs will be most useful in the future, so spending too much time trying to master them may not be the best approach.
  2. Proper prompting is crucial when working with AML GPT LLMs as they can be capable of more than initially apparent. Good prompts can make tasks that seem impossible into achievable ones.
  3. Understanding the thought processes and effective way to prompt AML GPT LLMs is essential, as their responses can vary based on subtle changes or inadequate prompting.
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DBRX: Revolutionizing Language Models for the Open Community

Data Plumbers • 19 implied HN points • 04 Apr 24
🕹 Technology Artificial Intelligence Machine Learning Open Source API Integration Data science
  1. Language models like DBRX are crucial in AI, changing how we use technology from chatbots to code generation.
  2. DBRX is an open-source alternative to closed models, providing high performance and accessibility to developers.
  3. DBRX stands out for its top performance, versatility in specialized domains, efficiency in training, and integration capabilities.

Edge 372: Learn About CALM, Google DeepMind's Method to Augment LLMs with Other LLMs

TheSequence • 98 implied HN points • 22 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Data science Research
  1. Knowledge augmentation is crucial in LLM-based applications with new techniques constantly evolving to enhance LLMs by providing access to external tools or data.
  2. Exploring the concept of augmenting LLMs with other LLMs involves merging general-purpose anchor models with specialized ones to unlock new capabilities, such as combining code understanding with language generation.
  3. The process of combining different LLMs might require additional training or fine-tuning of the models, but can be hindered by computational costs and data privacy concerns.

Google announces AI system for diagnostic medical reasoning and conversation

MLOps Newsletter • 176 implied HN points • 20 Jan 24
🕹 Technology AI Machine Learning Data science Software Development
  1. Google announced an AI system for medical diagnosis and conversation called AMIE.
  2. AMIE's architecture includes multi-turn dialogue management, hierarchical reasoning model, and modular design.
  3. The AI system AMIE showed promising performance in simulated diagnostic conversations, outperforming PCPs and matching specialist physicians.

No sacred masterpieces

Basta’s Notes • 753 HN points • 15 Sep 23
🕹 Technology Software Development Data science Engineering Project management Web Development
  1. Sometimes, valuable projects end abruptly without much recognition or lasting impact.
  2. It's important to focus on creating business value with your work, rather than building impressive but ultimately unnecessary solutions.
  3. Every piece of code you write as an engineer is legacy and may not last forever, so focus on learning from each project's outcome.

Edge 364: About COSP and USP: Two New LLM Reasoning Methods Built by Google Research

TheSequence • 133 implied HN points • 25 Jan 24
🕹 Technology AI Language Models Research Machine Learning Data science
  1. Two new LLM reasoning methods, COSP and USP, have been developed by Google Research to enhance common sense reasoning capabilities in language models.
  2. Prompt generation is crucial for LLM-based applications, and techniques like few-shot setup have reduced the need for large amounts of data to fine-tune models.
  3. Models with robust zero-shot performance can eliminate the need for manual prompt generation, but may have less potent results due to operating without specific guidance.

The Best Skillsets to Learn in 2024 for Generative AI

Rod’s Blog • 238 implied HN points • 15 Dec 23
🕹 Technology AI Programming Machine Learning Data science Resources
  1. Generative AI is a rapidly evolving field creating novel content like images, text, music, etc., with real-world applications from enhancing creativity to helping solve problems.
  2. To succeed in generative AI, you need skills like mathematics and statistics, programming, data science, knowledge of generative AI methods, and creativity in your specific domain.
  3. To learn generative AI in 2024, leverage online courses, books, blogs, tools, and engage in communities and events dedicated to this field.

Saturday morning open tabs

Scott's Substack • 78 implied HN points • 10 Feb 24
🕹 Technology AI Tech news Virtual reality Data science
  1. The post discusses the experience of switching phone carriers and the challenges faced, emphasizing the impact of not having a phone for a few days.
  2. The post touches on upcoming summer plans including workshops in Madrid, Scotland, and potential travel to Vietnam, highlighting the diversity of travel experiences planned.
  3. The author explores the new Apple Vision Pro product, contemplating its potential usage for work, entertainment, and travel, showcasing a mix of curiosity and skepticism.

Why Today Is The Perfect Time to Learn Data | Seattle Data Guy

Data Analysis Journal • 373 implied HN points • 25 Oct 23
🕹 Technology Data science Analytics Data Engineering Learning Resources Career Advice
  1. Learning data is more accessible and better now than in the past years.
  2. For transitioning into data engineering, focus on SQL, programming, data warehouse, and data pipelines.
  3. Analysts should focus on understanding the business problem, building maintainable systems, and following a data analytics process.

Where do we go from here

Normcore Tech • 1145 implied HN points • 28 Feb 23
🕹 Technology Social media Artificial Intelligence Data science Tech Trends Personal Projects
  1. The landscape of social media is changing with platforms like Twitter and Facebook losing users to newer platforms like TikTok
  2. Users are moving to private, fragmented social media landscapes with platforms like Discord and Mastodon
  3. Creators are facing challenges in standing out in the mass-creation of art facilitated by tools like ChatGPT and StableDiffusion

📌 Exciting news! The speaker lineup for apply() 2024 is now live

TheSequence • 21 implied HN points • 15 Mar 24
🕹 Technology AI ML Data science Events
  1. The speaker lineup for apply() 2024 event is now live, featuring industry leaders from companies like LangChain, Meta, Visa, and more.
  2. The event offers actionable insights to master AI and ML in production, with sessions on topics like LangChain Keynote, Semi-Supervised Learning, and Uplift Modeling.
  3. Attendees can register for free to join the event live on April 3rd, with the option to receive on-demand videos as well.

Celebrating An Anniversary: Three Years of Writing About Analytics - Issue 154

Data Analysis Journal • 452 implied HN points • 26 Jul 23
🕹 Technology Analytics Data science Newsletter Writing Career development
  1. The author reflects on three years of writing a newsletter about analytics, thanking supporters and subscribers.
  2. The author's newsletter aims to document their journey, bridge the gap between academics and industry, and encourage classic data analysis.
  3. The author shares insights on their writing strategy, the power of being small and independent, and future plans for the newsletter.

How RLHF actually works

Democratizing Automation • 306 implied HN points • 21 Jun 23
🕹 Technology AI Machine Learning Data science Open Source Scaling
  1. RLHF works when there is a signal that vanilla supervised learning alone doesn't work, like pairwise preference data.
  2. Having a capable base model is crucial for successful RLHF implementation, as imitating models or using imperfect datasets can greatly affect performance.
  3. Preferences play a key role in the RLHF process, and collecting preference data for harmful prompts is essential for model optimization.

2023 Wrap up

RSS DS+AI Section • 53 implied HN points • 31 Dec 23
🕹 Technology Data science Artificial Intelligence Events Newsletter
  1. The focus for the year was 'Effective and Efficient Data Science' to highlight the critical aspects of the field beyond hype.
  2. Various events and discussions were held throughout the year to promote best practices in Data Science.
  3. Engagement with the community through events, surveys, and articles was emphasized to ensure diverse voices are heard in influencing policy.

Synthetic Data In A Nutshell

Three Data Point Thursday • 39 implied HN points • 11 Jan 24
🕹 Technology Data science AI Data Engineering Machine Learning Software Development
  1. Synthetic data is fake data that is becoming increasingly practical and valuable.
  2. Generative AI and the growing gap between data demand and availability are driving forces for the usefulness of synthetic data.
  3. Synthetic data is beneficial in various fields beyond just machine learning, offering opportunities for innovation and improvement.

What To Do When You're Stuck At A Business That Doesn't Care About Data Science

High ROI Data Science • 353 implied HN points • 27 Feb 23
💼 Business Data science Leadership Strategic Planning
  1. Many data scientists in companies that don't prioritize data science end up doing basic reporting and analytics.
  2. Technical management in such companies often lack the understanding and incentives to support data initiatives.
  3. Navigating a lack of data culture and strategy in a company requires significant effort but can lead to valuable career opportunities.

Week 78 - E6 - 🏰 Building a Moat for Your AI Startup 🏰

The Product Channel By Sid Saladi • 20 implied HN points • 11 Feb 24
🕹 Technology AI Startups Product Management Data science Innovation AI Generative AI Product Management Data Training AI Startups
  1. Building a competitive moat in AI involves strategic navigation of the generative AI value chain to create unique advantages.
  2. For AI startups, it's crucial to focus on acquiring proprietary data, integrating AI into comprehensive workflows, and specializing models through incremental training techniques.
  3. Companies like Anthropic, Landing AI, and Stability AI showcase effective moat-building strategies in AI by emphasizing ethical development, democratizing technology, and niche specialization.

Embracing the New Era of Accelerated Testing - Issue 150

Data Analysis Journal • 235 implied HN points • 28 Jun 23
🕹 Technology Data science Analytics A/B Testing Experimentation Tooling
  1. Embracing accelerated testing in the modern data analysis landscape is essential for success.
  2. The current traditional academic workflow for A/B testing may not be suitable for the evolving landscape of experimentation.
  3. To thrive in the era of rapid feature flagging and A/B testing, teams need to adapt by automating statistical checks, simplifying documentation, and eliminating bias.

The Roundup of Blogs and Newsletters About Analytics - Issue 133

Data Analysis Journal • 314 implied HN points • 22 Feb 23
🕹 Technology Data Analysis Data science Product Analytics SaaS
  1. The post discusses a roundup of blogs and newsletters about analytics.
  2. It highlights key articles on adjacent users measurement, ML in product analytics, and SQL case statements.
  3. Various expert blogs and newsletters are recommended for analysts, data practitioners, and anyone interested in data and analytics.

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.

SAI #22: Decomposing the Data System.

SwirlAI Newsletter • 294 implied HN points • 18 Mar 23
🕹 Technology Data science Data Engineering MLOps Machine Learning Data Systems
  1. Learning to decompose a data system is crucial for better reasoning and understanding of large infrastructure
  2. Decomposing a data system allows for scalability, identification of bottlenecks, and total event processing latency optimization
  3. The different layers in a data system include data ingestion, transformation, and serving layers, each with specific functions and technologies

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.