The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Nerds are better investors

Klement on Investing • 2 implied HN points • 05 Jun 25
💰 Finance Investment Data science Market Analysis Quantitative finance Portfolio Management
  1. More companies are hiring data scientists to help with investment decisions. This often leads to better returns for those companies.
  2. Hiring data scientists can help firms focus more on specific investments, which improves their insight and portfolio performance.
  3. However, too much reliance on data scientists can make the stock market less efficient, leaving room for traditional analysts to find good investment opportunities.

Edge 443: EVERYTHING you Need to Know About State Space Models

TheSequence • 133 implied HN points • 29 Oct 24
🕹 Technology AI Machine Learning Neural Networks Computational efficiency Data science
  1. State space models (SSMs) are a promising alternative to transformers for processing data. They handle long sequences more efficiently without losing important information.
  2. SSMs are designed to be computationally efficient, scaling linearly with context windows unlike transformers which scale quadratically. This makes them better for tasks needing a lot of information.
  3. Recent models like Mamba show that SSMs can outperform transformers in performance and efficiency, especially for tasks that require understanding long contexts.

Data Science Weekly - Issue 516

Data Science Weekly Newsletter • 299 implied HN points • 13 Oct 23
🕹 Technology Data science AI Machine Learning Data Engineering Analytics
  1. The newsletter is deciding whether to publish twice a week, but will stick to one issue for now to review feedback from readers.
  2. There's a focus on providing useful resources for data science, including articles and job opportunities in the field.
  3. New tools and methods in AI and data engineering are highlighted, addressing challenges like data integration and AI model training.

Data Science Weekly - Issue 511

Data Science Weekly Newsletter • 319 implied HN points • 07 Sep 23
🕹 Technology Data science AI Machine Learning Analytics Software Development
  1. AI startups can receive significant support through programs like AI Grant, offering up to $250,000 for development.
  2. Recent studies have shown that large language models can learn from just one example, which challenges previous beliefs about their efficiency.
  3. Using advanced tools like the Semantic Layer and LLMs can greatly improve data accuracy and speed for businesses, making analytics much easier.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 515

Data Science Weekly Newsletter • 299 implied HN points • 06 Oct 23
🕹 Technology Data science Artificial Intelligence Machine Learning Analytics Software Development
  1. There's a lot happening in data science right now. The team is considering adding a second newsletter each week to cover more exciting content.
  2. High-performing data scientists have specific traits that set them apart from others. Companies are researching these traits to help improve their teams.
  3. Art institutions can greatly benefit from data and analytics. Collaborating with leaders can help them use data to improve their operations and strategies.

Edge 459: Quantization Plus Distillation

TheSequence • 77 implied HN points • 24 Dec 24
🕹 Technology Machine Learning AI Models Data science Model optimization Deep Learning
  1. Quantized distillation helps make deep neural networks smaller and faster by combining two techniques: knowledge distillation and quantization.
  2. This method transfers knowledge from a high-precision model (teacher) to a low-precision model (student) without losing much accuracy.
  3. Using soft targets from the teacher model can reduce problems that often come with using simpler models, keeping performance strong.

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

Building reliability from unreliable parts

Sunday Letters • 59 implied HN points • 12 May 24
🕹 Technology AI Cloud Software Data science Systems Engineering
  1. Modern AI systems have a random element, making them sometimes unpredictable or unreliable. This means they can give different answers even to the same question, which is a challenge for creating consistent outputs.
  2. Just like the early cloud systems, we need to use smart software solutions to make our current AI technologies more reliable. Instead of relying solely on the AI itself, we should layer software to handle and fix errors.
  3. To build better AI systems, it’s important to explore structured approaches, like guided conversations or iterative processes. This way, we can combine the strengths of AI with reliable system design.

Data Science Weekly - Issue 512

Data Science Weekly Newsletter • 299 implied HN points • 14 Sep 23
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Programming
  1. Nvidia has been a leader in AI technology, but its dominance might not last. Changes in the market and technology could shift the competitive landscape soon.
  2. For those who know R and want to learn Python, there are resources available to help make the transition easier. These resources provide advice and tips catered to R users.
  3. Reinforcement Learning with Human Feedback (RLHF) is an important part of training large language models. It's essential for improving how these models understand and respond to human preferences.

How AI generates text, visually explained 📝

Year 2049 • 6 implied HN points • 18 Jan 25
🕹 Technology AI Machine Learning Automation Data science Software Development
  1. AI generates text by analyzing patterns in data, similar to how a DJ mixes music. This means it learns from examples to create new content.
  2. Understanding how AI learns helps us see its strengths and weaknesses, like how it can sometimes be biased.
  3. The next episode will focus on how AI creates images, which is another interesting aspect of how AI works.

Has AI Progress Stalled?

The Future of Life • 19 implied HN points • 21 Jul 24
🕹 Technology AI Machine Learning Software Development Computing Data science
  1. AI improvement has slowed down in terms of new abilities since GPT-4 came out, but other factors like cost and speed have gotten much better.
  2. The focus now is on practical changes and making AI more valuable, which will help set the stage for bigger breakthroughs in the future.
  3. Reaching human-level skills in tests doesn't mean AI will be truly intelligent. Future development will need to incorporate more complex abilities like planning and learning from experiences.

Data Science Weekly - Issue 520

Data Science Weekly Newsletter • 239 implied HN points • 10 Nov 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Analytics
  1. Data scientists share interesting links and news weekly about AI, machine learning, and data visualization. It's a great way to stay updated on trends and tools in the field.
  2. Learning about the basics of deep learning and mathematical foundations is important for anyone starting in machine learning. Understanding key concepts helps you tackle complex problems more effectively.
  3. There are many job opportunities in data science and related fields. Keeping an eye on openings can lead to exciting career advancements and collaborations.

There's no such thing as "machine learning."

Top Carbon Chauvinist • 19 implied HN points • 20 Jul 24
🕹 Technology AI Machine Learning Philosophy Computing Data science
  1. Machines don't really learn like humans do. They can take in data and improve performance, but they don't understand or experience learning in the same way we do.
  2. The term 'machine learning' can be misleading. It's more about machines mimicking learning processes rather than actually experiencing them.
  3. Understanding how machines operate helps clarify their limitations. They can process large amounts of information but lack conscious experience or true comprehension.

Edge 457: Can we Distill Specific Knowledge in LLMs? An Intro to Attention-Based Distillation

TheSequence • 77 implied HN points • 17 Dec 24
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Natural Language Processing
  1. Attention-based distillation (ABD) is a method that helps smaller models learn from larger models by mimicking their attention patterns. This can make the smaller models perform better with fewer resources.
  2. Unlike traditional methods that just look at output predictions, ABD focuses on the reasoning process of the larger model. This leads to a deeper understanding and better results for the smaller model.
  3. Using ABD can produce student models that perform well even when they have less complexity. This is useful for applications where efficiency is key.

World Models are Coming and They are Awesome

TheSequence • 84 implied HN points • 08 Dec 24
🕹 Technology AI Machine Learning Generative AI 3D Modeling Data science
  1. This week saw the release of two exciting world models that can create 3D environments from simple prompts. These models are important for advancing AI's abilities in various fields.
  2. DeepMind's Genie 2 can generate interactive 3D worlds and simulate realistic object interactions, making it very useful for AI training and game development.
  3. World Labs has introduced a user-friendly system for designing 3D spaces, allowing artists to create and manipulate environments easily, which can help in game prototyping and creative workflows.

GPT-4o mini

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 18 Jul 24
🕹 Technology AI NLP Machine Learning Software Development Data science
  1. GPT-4o mini is a new language model that's cheaper and faster than older models. It handles text and images and is great for tasks requiring quick responses.
  2. Small Language Models (SLMs) like GPT-4o mini can run efficiently on devices without relying on the cloud. This helps with costs, privacy, and gives users more control over the technology.
  3. SLMs are designed to be flexible and customizable. They can learn from various types of inputs and can adapt more easily to specific needs.

Issue #4 - The Five Minute History of Data

The Data Ecosystem • 59 implied HN points • 05 May 24
🕹 Technology Data science AI Analytics Data processing Cloud Computing
  1. Data is generated and used everywhere now, thanks to smart devices and cheaper storage. This means businesses can use data for many purposes, but not all those uses are helpful.
  2. Processing data has become much easier over the years. Small companies can now use tools to analyze data without needing a team of experts, although some guidance is still necessary.
  3. Analytics has shifted from just looking at past data to predicting future trends. This helps companies make better decisions, and AI is starting to take over some of these tasks.

Phi-3, your Pocket LLM!

Aziz et al. Paper Summaries • 79 implied HN points • 29 Apr 24
🕹 Technology AI Machine Learning Hardware Software Data science
  1. Microsoft's Phi-3 is a new AI model that is small enough to run on your phone, yet still performs well. This is a big deal because most AI models are too large for personal devices.
  2. The model uses high-quality, filtered data for training, focusing on reasoning and educational materials. This approach makes Phi-3 better at understanding rather than just memorizing facts.
  3. Even though Phi-3 is powerful, it has some limitations, like not being multilingual. There are also tasks it struggles with, like those needing lots of factual knowledge.

The Importance Of Granular Data Design For Fine-Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 02 May 24
🕹 Technology AI Data science Machine Learning Natural Language Processing Software Development
  1. Granular data design helps improve the behavior and abilities of language models. This means making training data more specific so the models can reason better.
  2. New methods like Partial Answer Masking allow models to learn self-correction. This helps them improve their responses without needing perfect answers in the training data.
  3. Training models with a focus on long context helps them retrieve information more effectively. This approach tackles issues where models can lose important information in lengthy input.

Where do we go from here

Normcore Tech • 1155 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

Data Science Weekly - Issue 510

Data Science Weekly Newsletter • 279 implied HN points • 31 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Cloud Computing
  1. Autonomous drones can now race at human champion levels using deep reinforcement learning. This shows how advanced technology can mimic skilled human behavior in competitive sports.
  2. Google is rapidly developing its AI capabilities and plans to surpass GPT-4 by a significant margin soon. This could lead to more powerful AI tools for various applications.
  3. Reinforced Self-Training (ReST) is a new method for improving language models by aligning their outputs with human preferences. It offers better translation quality and can be done efficiently with less data.

AI Roundup 093: Diminishing returns

Artificial Ignorance • 29 implied HN points • 15 Nov 24
🕹 Technology AI Machine Learning Software Data science Innovation
  1. Big AI companies are realizing that just making their models bigger doesn't always improve their performance. They're facing challenges because the quality of training data is more important than simply using more computing power.
  2. AI companies need to create new ways to measure performance since the old benchmarks are outdated. This lack of standard testing makes it hard for people to compare how different AI models stack up against each other.
  3. AI-generated art is becoming more popular and accepted in the market. A recent artwork sold for a lot of money, showing that people are starting to appreciate creations made by AI, even though it raises questions about what creativity really means.

📽 Webinar: How To Maximize Model Accuracy

TheSequence • 70 implied HN points • 16 Dec 24
🕹 Technology Machine Learning Webinars Data science Software Development
  1. Models can lose accuracy over time in real use. It's important to know why this happens so you can fix it.
  2. Just because a model works well during training doesn't mean it will perform the same way in the real world. There are often differences that can affect results.
  3. Smart feature engineering is crucial for maintaining model accuracy without spending too much money. There are ways to improve performance that don't break the bank.

The Sequence Chat: Thinking About Transformers as Computers

TheSequence • 105 implied HN points • 30 Oct 24
🕹 Technology Artificial Intelligence Computing Machine Learning Natural Language Data science
  1. Transformers are changing AI, especially in how we understand and use language. They're not just tools; they act more like computers in some ways.
  2. The way transformers can adapt and scale is really impressive. It's like they can learn and adjust in ways traditional computers can't.
  3. Thinking of transformers as computers opens up new ideas about how we approach AI. This perspective can help us find new applications and improve our understanding of tech.

Speculative RAG By Google Research

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 12 Jul 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Computing
  1. Retrieval Augmented Generation (RAG) is a way to improve answers by using a mix of information from language models and external sources. By doing this, it gives more accurate and timely responses.
  2. The new Speculative RAG method uses a smaller model to quickly create drafts from different pieces of information, letting a larger model check those drafts. This makes the whole process faster and more effective.
  3. Using smaller, specialized language models for drafting helps save on costs and reduces wait times. It can also improve the accuracy of answers without needing extensive training.

Maximizing the Potential of Large Language Models

Gradient Flow • 359 implied HN points • 09 Mar 23
🕹 Technology Artificial Intelligence Data Management Data science Natural Language Processing
  1. Language models need a three-pronged strategy of tuning, prompting, and rewarding to unlock their full potential.
  2. Fine-tuning pre-trained models is a common practice to tailor models for specific tasks and domains.
  3. Teams require simple and versatile tools to create custom models efficiently and effectively.

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.

Measuring the "readability" of texts with Large Language Models

The Counterfactual • 119 implied HN points • 02 Feb 24
🕹 Technology AI Education Data science Human-computer interaction Machine Learning
  1. Readability is how easy it is to understand a text. It matters in many areas like education, manuals, and legal documents.
  2. Traditional readability formulas like Flesch-Kincaid are simple but not enough. New methods that consider more linguistic features are being developed for better accuracy.
  3. Using large language models like GPT-4 can give good estimates of text readability. In one study, GPT-4's scores were better than traditional methods in predicting human readability judgments.

Edge 439: SSMs with Attention, Understanding Zamba

TheSequence • 112 implied HN points • 15 Oct 24
🕹 Technology AI Machine Learning Data science Software Engineering Computer Science
  1. Combining state space models (SSMs) with attention layers can create better hybrid architectures. This fusion allows for improved learning capabilities and efficiency.
  2. Zamba is an innovative model that enhances learning by using a mix of Mamba blocks and a shared attention layer. This approach helps it manage long-range dependencies more effectively.
  3. The new architecture reduces the computational load during training and inference compared to traditional transformers, making it more efficient for AI tasks.

Edge 461: The Many Challenges of Kowledge Distillation

TheSequence • 56 implied HN points • 31 Dec 24
🕹 Technology AI Machine Learning Data science Algorithms Software Development
  1. Knowledge distillation can be tricky because there’s a big size difference between the teacher model and the student model. The teacher model usually has a lot more parameters, making it hard to share all the useful information with the smaller student model.
  2. Transferring the complex knowledge from a large model to a smaller one isn't straightforward. The smaller model might not be able to capture all the details that the larger model has learned.
  3. Despite the benefits, there are significant challenges that need to be tackled when using knowledge distillation in machine learning. These challenges stem from the complexity and scale of the models involved.

Data Science Weekly - Issue 507

Data Science Weekly Newsletter • 279 implied HN points • 11 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Big Data
  1. Large Language Models (LLMs) can take over some data tasks, but they won't replace all data jobs. Many tasks still need human insight and specialized skills.
  2. Understanding machine learning theory takes a long time, but in the industry, practical implementation is often more important. It's crucial to balance theory and hands-on skills.
  3. The new field of mechanistic interpretability is growing. Researchers are looking at how models learn and generalize, aiming to make sense of how AI works.

The Sequence Chat: Why are Foundation Models so Hard to Explain and What are we Doing About it?

TheSequence • 77 implied HN points • 27 Nov 24
🕹 Technology AI Models Machine Learning Data science Interpretability Natural Language
  1. Foundation models are really complex and hard to understand. They act like black boxes, which makes it tough to know how they make decisions.
  2. Unlike older machine learning models, these large models have much more advanced capabilities but also come with bigger interpretability challenges.
  3. New fields like mechanistic interpretability and behavioral probing are trying to help us figure out how these complex models work.

Data Science Weekly - Issue 502

Data Science Weekly Newsletter • 319 implied HN points • 07 Jul 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Computing
  1. Generative design is making strides in drug discovery, but there are still challenges to address for better outcomes.
  2. The UK government is investing in a Foundation Model Taskforce to harness AI for societal benefits and safety.
  3. Keeping updated with developments in data science, such as new models and applications, is essential for professionals in the field.

Data Science Weekly - Issue 535

Data Science Weekly Newsletter • 99 implied HN points • 23 Feb 24
🕹 Technology Data science Artificial Intelligence Machine Learning Software Engineering Data Engineering Statistical Analysis
  1. Scaling AI tools like ChatGPT involves overcoming many engineering challenges to handle large user demands. It's important to manage growth effectively to keep users satisfied.
  2. There's a lot of information out there about generative AI, making it hard to keep up. A guidebook can help condense this information and provide practical insights.
  3. Linear regression is still a valuable tool in data science. Sometimes going back to basics can yield better results than relying on complex models.

E-Mail Course On Conformal Prediction

Mindful Modeler • 479 implied HN points • 13 Dec 22
🚌 Education Predictive Modeling Data science Machine Learning Research
  1. Conformal prediction turns point predictions into prediction sets with a probability guarantee of covering the true outcome, working for any model without requiring a distribution assumption.
  2. The 5-week email course on conformal prediction offers a free, convenient way to learn about this uncertainty quantification method.
  3. Resources like Valeriy's list on conformal prediction and an academic introduction paper can be helpful for diving into and understanding conformal prediction.