The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 557

Data Science Weekly Newsletter • 159 implied HN points • 25 Jul 24
🕹 Technology Data science AI Machine Learning Data Visualization Engineering
  1. AI models can break down when trained on data that is generated by other models. This can cause problems in how well they work.
  2. There is scientific research about the history of Italian filled pasta. It shows that most types likely came from a single area in northern Italy.
  3. There are new resources and guides available for improving predictive modeling with tabular data. These can help you build better models by focusing on how data is represented.

Code Clinic | Orchestrating Transformers Agents 2.0 for Internet Search

Encyclopedia Autonomica • 19 implied HN points • 09 Oct 24
🕹 Technology AI Software Machine Learning Data science Programming
  1. Using Transformer Agents 2.0 is a step up from traditional methods. They can handle multi-step tasks better and have memory to store information as they work.
  2. Setting up and building a basic ReAct Agent is straightforward. You only need to install some packages and create the agent using selected models and tools.
  3. You can orchestrate multiple agents together for more complex tasks. By combining different agents, you can enhance their capabilities and improve the results of your searches or queries.

Data Science Weekly - Issue 530

Data Science Weekly Newsletter • 1418 implied HN points • 19 Jan 24
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Software Development
  1. Good data visualization is important. Some types of graphs can be misleading, and it's better to avoid them.
  2. In healthcare, it's not just about having advanced technology like AI. The real focus should be on getting effective results from these technologies.
  3. Netflix released a lot of data about what people watched in 2023. Analyzing this can help us understand trends in streaming better.

Scalable Embedding based retrieval for target side value

Recommender systems • 23 implied HN points • 17 May 25
🕹 Technology Machine Learning Data science Algorithms Software Development Social Networks
  1. Scalability is key for embedding-based recommendation systems, especially when dealing with billions of users. Finding effective ways to limit the search can help manage this challenge.
  2. It’s important to deliver value not just to viewers but also to the recommended targets, as this can improve user retention. Balancing recommendations for both sides can create a better experience.
  3. Using advanced algorithms can help ensure viewers don’t get overwhelmed with too many recommendations while also making sure that every target gets the attention they need. This balance is crucial for effective recommendations.

The Super Weight in Large Language Models

Gonzo ML • 189 implied HN points • 29 Nov 24
🕹 Technology AI Research Machine Learning Data science Computational Models Tech Innovation
  1. There's a special weight in large language models called the 'super weight.' If you remove it, the model's performance crashes dramatically, showing just how crucial it is.
  2. Super weights are linked to what's called 'super activations,' meaning they help generate better text. Without them, the model struggles to create coherent sentences.
  3. Finally, researchers found ways to identify and protect these super weights during the model training and quantization processes. This makes the model more efficient and retains its quality.
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A Compendium on Synthetic Data Projects

Encyclopedia Autonomica • 19 implied HN points • 06 Oct 24
🕹 Technology Data science Artificial Intelligence Software Development Machine Learning Open Source
  1. Synthetic data is crucial for AI development. It helps create large amounts of high-quality data without privacy concerns or high costs.
  2. There are various projects focused on generating synthetic data. Tools like AgentInstruct and DataDreamer aim to create diverse datasets for training language models.
  3. Learning methods for synthetic data include using personas to create unique datasets and improving mathematical reasoning skills through specially designed datasets.

What "language" is a language model a model of?

The Counterfactual • 99 implied HN points • 02 Aug 24
🕹 Technology AI Machine Learning Natural Language Processing Computational linguistics Data science
  1. Language models are trained on specific types of language, known as varieties. This includes different dialects, registers, and periods of language use.
  2. Using a representative training data set is crucial for language models. If the training data isn't diverse, the model can perform poorly for certain groups or languages.
  3. It's important for researchers to clearly specify which language and variety their models are based on. This helps everyone better understand what the model can do and where it might struggle.

Small newsletters, big ideas

Artificial Ignorance • 121 implied HN points • 16 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Data science Tech Culture Tech Ethics
  1. There are many small newsletters focusing on AI that offer unique perspectives and insights. They cover topics that go beyond just technical details.
  2. The newsletters featured are all written by humans and aim to provide long-form articles, making them a great choice for those who want to dive deep into AI discussions.
  3. This is a good way to discover hidden gems in the world of AI content, especially from creators with less than 1,000 subscribers.

From Boom to Bundle: The Great Consolidation of Data Tools

SeattleDataGuy’s Newsletter • 400 implied HN points • 17 Jan 25
🕹 Technology Data Tools Mergers & Acquisitions Analytics Data science Business Intelligence
  1. The data tools market is seeing a lot of consolidation lately, with companies merging or getting acquired. This means there are fewer companies competing, but it can lead to better tools overall.
  2. Acquisitions can be a mixed bag for customers. While some products improve after being bought, others might lose their features or support, making it risky for users.
  3. There's a push for bundled data solutions where customers want fewer, but more comprehensive tools. This could change how data companies operate and how startups survive in the future.

LLM Links, 1/27/2025

In My Tribe • 318 implied HN points • 27 Jan 25
🕹 Technology AI Education Bioengineering Data science Innovation
  1. AI is improving quickly, making it easier for students to answer essay questions by providing high-quality responses from various texts. This change may reduce the value of traditional essay exams.
  2. A World Bank project in Nigeria successfully used AI in education, enhancing learning equivalent to nearly two years in just six weeks. This shows promise for AI to help education in underdeveloped areas.
  3. OpenAI is developing AI models to transform science, including engineering proteins that enhance cellular functions. This could lead to significant advancements in fields like bioengineering.

💥 Tech Talks Weekly #30

Tech Talks Weekly • 39 implied HN points • 19 Sep 24
🕹 Technology Software Development Conferences Programming Languages Data science Tech Talks
  1. Tech Talks Weekly recently reached 2000 subscribers, which shows a growing interest in tech discussions and events.
  2. This issue features talks from 17 different conferences, emphasizing the variety of topics available in tech.
  3. There are special issues highlighting all JavaScript and Java talks of 2024, catering to specific interests among tech enthusiasts.

Agentic AI: Challenges and Opportunities

Gradient Flow • 339 implied HN points • 16 May 24
🕹 Technology Artificial Intelligence Machine Learning Data science Ethics Innovation
  1. AI agents are evolving to be more autonomous than traditional co-pilots, capable of proactive decision-making based on goals and environment understanding.
  2. Enterprise applications of AI agents focus on efficient data collection, integration, and analysis to automate tasks, improve decision-making, and optimize business processes.
  3. The field of AI agents is advancing with new tools like CrewAI, highlighting the importance of MLOps for reliability, traceability, and ensuring ethical and safe deployment.

HN blogs -3/10/24

HackerNews blogs newsletter • 19 implied HN points • 03 Oct 24
🕹 Technology Software Development Machine Learning Programming Web Development Data science
  1. Building a personal ghostwriter can help with productivity and writing tasks. It's about creating a tool that assists you effectively.
  2. Refactoring code is important for improving software. It makes programs easier to understand and maintain, even for those who aren't programmers.
  3. AI and machine learning can benefit from powerful hardware setups. Training models on many GPUs can significantly speed up the process.

💥 Tech Talks Weekly #31

Tech Talks Weekly • 19 implied HN points • 03 Oct 24
🕹 Technology Tech Talks Conferences Software Development Coding Practices Data science
  1. Tech Talks Weekly curates talks from various tech conferences so you can catch up on what you missed. It's a great way to stay updated on industry trends without the hassle of searching multiple platforms.
  2. The newsletter has grown significantly, indicating that many people find the content valuable. Engaging with the audience helps in tailoring future content to better meet their needs.
  3. The latest issue features a lot of new talks, making it a larger edition than usual. This includes recommendations to explore specific talks that have gained a lot of views from various conferences.

The DeepSeek drama, visually explained 🐳

Year 2049 • 22 implied HN points • 28 Jan 25
🕹 Technology AI Machine Learning Open Source Data science Silicon Valley
  1. The actual cost to train DeepSeek R1 is unknown, but it’s likely higher than the reported $5.6 million for its base model, DeepSeek V3.
  2. DeepSeek used a different training method called Reinforcement Learning, which lets the model improve itself based on rewards, unlike OpenAI's supervised learning approach.
  3. DeepSeek R1 is open-source and much cheaper to use for developers and businesses, challenging the idea that expensive hardware is necessary for AI model training.

Data Science Weekly - Issue 529

Data Science Weekly Newsletter • 999 implied HN points • 12 Jan 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Software Development
  1. Using ChatGPT can help you budget better. It can track and categorize your spending easily.
  2. When coding, it's important to find a balance between moving quickly and keeping your code well-structured. This is a real challenge for many developers.
  3. Language models, like GPT-4, are becoming very advanced, but there are big philosophical questions about what that really means for intelligence and understanding.

There's no silver bullet in AI

The AI Frontier • 99 implied HN points • 25 Jul 24
🕹 Technology AI Machine Learning Data science Software Development Tech Innovations
  1. In AI, there's no single fix that will solve all problems. Success comes from making lots of small improvements over time.
  2. Data quality is very important. If you don't start with good data, the results won't be good either.
  3. It's essential to measure changes carefully when building AI applications. Understanding what works and what doesn't can save you from costly mistakes.

Shedding light on "Impossibility Theorems for Feature Attribution"

Mindful Modeler • 199 implied HN points • 18 Jun 24
🕹 Technology Data science Interpretation
  1. The limitations of feature attribution methods like SHAP and Integrated Gradients have been studied, particularly focusing on their reliability for explaining predictions as a sum of attributions.
  2. Tasks such as algorithmic recourse, characterizing model behavior, and identifying spurious feature identification all revolve around how predictions change with slight feature alterations, making SHAP unsuitable for these specific tasks.
  3. It's important to avoid using SHAP for questions related to minor changes in feature values or counterfactual analysis, as it may yield unreliable results in such scenarios.

Weekly Top Picks #91

The Algorithmic Bridge • 116 implied HN points • 09 Dec 24
🕹 Technology AI Machine Learning Software Digital Media Data science
  1. Companies are figuring out how to price AI agents as they become more common. This is important because the cost will affect how businesses use AI technology.
  2. ChatGPT will soon allow users to input videos, which will make interactions even richer and more dynamic.
  3. OpenAI is releasing a new model called o1, which is better for math, coding, and science. It's more accurate and can handle different types of questions more efficiently.

Data Science Weekly - Issue 527

Data Science Weekly Newsletter • 959 implied HN points • 29 Dec 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Analytics
  1. This week, there's a focus on using data science techniques for practical decision-making, highlighted by an interview with Steven Levitt, who discusses making tough choices using data.
  2. There's a roundup of AI developments from 2023, showing how the field has evolved over the past year, which can help professionals stay updated.
  3. Understanding data quality is essential, as it directly impacts how useful data is for decision-making and analysis in any organization.

Grok's Secret Weapon: Prioritizing Truth Over Political Correctness

The Dossier • 212 implied HN points • 18 Feb 25
🕹 Technology Artificial Intelligence Innovation Computer Science Software Development Data science
  1. Grok stands out in AI by focusing on truth instead of political correctness. This helps it learn faster and respond better.
  2. Unlike other AI models, Grok gives detailed and nuanced answers, even on tough topics. This makes it smarter in reasoning and understanding complex issues.
  3. By embracing all kinds of information, Grok is set to become a major player in AI. Its approach could change how AI helps people across various industries.

The AI Conversations That Shaped 2023

Gradient Flow • 878 implied HN points • 28 Dec 23
🕹 Technology AI Machine Learning Data science Podcasts Books
  1. AI and machine learning advancements in 2023 sparked vibrant discussions among developers, focusing on topics like large language models, infrastructure, and business applications.
  2. Technology media shifted its focus to highlight rapid AI advancements, covering diverse AI applications across industries while also addressing concerns about deepfakes and biases in AI systems.
  3. The book 'Mixed Signals' by Uri Gneezy was named the 2023 Book of the Year, offering insights on how incentives shape behavior in AI, technology, and business, with a focus on aligning incentives with ethical values.

OpenAI's Reinforcement Finetuning and RL for the masses

Democratizing Automation • 427 implied HN points • 11 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Deep Learning Data science API Development
  1. Reinforcement Finetuning (RFT) allows developers to fine-tune AI models using their own data, improving performance with just a few training samples. This can help the models learn to give correct answers more effectively.
  2. RFT aims to solve the stability issues that have limited the use of reinforcement learning in AI. With a reliable API, users can now train models without the fear of them crashing or behaving unpredictively.
  3. This new method could change how AI models are trained, making it easier for anyone to use reinforcement learning techniques, not just experts. This means more engineers will need to become familiar with these concepts in their work.

The Hallucination Problem

Teaching computers how to talk • 178 implied HN points • 04 Nov 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Computing
  1. Hallucinations in AI mean the models can give wrong answers and still seem confident. This overconfidence is a big problem, making it hard to trust what they say.
  2. OpenAI's SimpleQA helps check how often AI gets facts right. The results show that many times the AI doesn't know when it’s wrong.
  3. The way AI is built makes it hard for them to understand their own errors. Improvements are needed, but current technology has limitations in recognizing when they're unsure.

Holy Grails of Data: Self-Service, Single Truths, and the Role of AI

SeattleDataGuy’s Newsletter • 365 implied HN points • 27 Dec 24
🕹 Technology Data science AI Analytics Business Intelligence Machine Learning
  1. Self-service analytics is still a goal for many companies, but it often falls short. Users might struggle with the tools or want different formats for the data, leading to more questions instead of fewer.
  2. Becoming truly data-driven is a challenge for many organizations. Trust issues with data, preference for gut feelings, and poor communication often get in the way of making informed decisions.
  3. People need to be data literate for businesses to succeed with data. The data team must present insights clearly, while business teams should understand and trust the data they work with.

Humanoid Robots: China’s Grind Toward Embodied Intelligence

ChinaTalk • 400 implied HN points • 16 Dec 24
🕹 Technology AI Robotics Manufacturing Data science Innovation
  1. China aims to become a top producer of humanoid robots by 2027, planning to use them in various industries like manufacturing and services. This is partly because they face labor shortages and believe humanoids can do many tough jobs.
  2. Humanoid robots need advanced technology in hardware and AI to work well. This includes making them mimic human movements and learning from real-world experiences, which is still a big challenge.
  3. The automotive industry could be key for testing and improving humanoid robots. Car factories have structured environments that help robots learn new tasks safely while addressing labor shortages in that sector.

Data Science Weekly - Issue 528

Data Science Weekly Newsletter • 799 implied HN points • 05 Jan 24
🕹 Technology Data science AI Machine Learning Software Engineering Research
  1. Data Science Weekly shares curated news and articles each week related to data science, AI, and machine learning. This helps readers stay updated on important trends and topics.
  2. Deepnote emphasizes using its own platform for building data infrastructure, showcasing how versatile tools can simplify data tasks. It highlights the importance of a universal computational medium.
  3. A reliable A/B testing system is essential for businesses to make informed decisions and optimize performance. Companies that use effective experimentation platforms can significantly improve their outcomes and reduce manual work.

Olympics, AI, and some BI

HyperArc • 59 implied HN points • 05 Aug 24
🕹 Technology AI Data science Analytics Machine Learning Software Development
  1. AI can help us learn about the Olympics and analyze different aspects, like who won medals and their physical attributes. It starts with basic questions and gets more complicated over time.
  2. While AI is good at remembering information and summarizing it, it struggles with reasoning about things it hasn't seen before. This means it can't always come up with new insights without the right data.
  3. For businesses, using AI with their private data can lead to smarter insights and faster decisions. It's important to combine human knowledge with AI to make the best use of available information.

OpenAI's o1 using "search" was a PSYOP

Democratizing Automation • 435 implied HN points • 04 Dec 24
🕹 Technology AI Research Machine Learning Data science Computer Science Software Development
  1. OpenAI's o1 models may not actually use traditional search methods as people think. Instead, they might rely more on reinforcement learning, which is a different way of optimizing their performance.
  2. The success of OpenAI's models seems to come from using clear, measurable outcomes for training. This includes learning from mistakes and refining their approach based on feedback.
  3. OpenAI's approach focuses on scaling up the computation and training process without needing complex external search strategies. This can lead to better results by simply using the model's internal methods effectively.

Data Science Weekly - Issue 554

Data Science Weekly Newsletter • 119 implied HN points • 04 Jul 24
🕹 Technology Data science AI Machine Learning Data Engineering Visualization
  1. Staying updated in data science, AI, and machine learning is essential for improving skills and knowledge. Weekly newsletters provide curated articles and resources that help you keep up with the latest trends.
  2. Effective structuring of data science teams can greatly enhance productivity. Learning from past experiences on team reorganizations can help in clarifying roles and increasing effectiveness.
  3. Building interactive dashboards in Python can make data more accessible. Using tools like PostgreSQL and specific libraries can simplify the process and enhance data visualization.

Issue #10 - The Data Lifecycle

The Data Ecosystem • 159 implied HN points • 16 Jun 24
🕹 Technology Data science Data Management Data security Data Analysis Data Engineering Data Visualization
  1. The data lifecycle includes all the steps from when data is created until it is no longer needed. This helps organizations understand how to manage and use their data effectively.
  2. Different people and companies might describe the data lifecycle in slightly different ways, which can be confusing. It's important to have a clear understanding of what each term means in context.
  3. Properly managing data involves stages like storage, analysis, and even disposal or archiving. This ensures data remains useful and complies with regulations.

OpenAI’s biggest worry isn’t DeepSeek

Enterprise AI Trends • 253 implied HN points • 31 Jan 25
🕹 Technology AI Machine Learning Data science Computing Innovation
  1. DeepSeek's release showed that simple reinforcement learning can create smart models. This means you don't always need complicated methods to achieve good results.
  2. Using more computing power can lead to better outcomes when it comes to AI results. DeepSeek's approach hints at cost-saving methods for training large models.
  3. OpenAI is still a major player in the AI field, even though some people think DeepSeek and others will take over. OpenAI's early work has helped it stay ahead despite new competition.

Data Science Weekly - Issue 550

Data Science Weekly Newsletter • 179 implied HN points • 07 Jun 24
🕹 Technology Data science AI Machine Learning Computing Data Engineering
  1. Curiosity in data science is important. It's essential to critically assess the quality and reliability of the data and models we use, especially when making claims about complex issues like COVID-19.
  2. New fields, like neural systems understanding, are blending different disciplines to explore complex questions. This approach can help unravel how understanding works in both humans and machines.
  3. Understanding AI advancements requires keeping track of evolving resources. It’s helpful to have a well-organized guide to the latest in AI learning resources as the field grows rapidly.

Issue #11 - Dispelling the AI Hype Train

The Data Ecosystem • 139 implied HN points • 23 Jun 24
🕹 Technology AI Data science Business strategy Digital Transformation Organizational Culture
  1. AI needs a proper plan and strategy to work well. Companies shouldn't think they can just jump in without understanding how it will fit into their overall goals and data.
  2. Many AI projects fail because organizations overlook the importance of data quality and proper infrastructure. Good data practices are essential for AI to be effective.
  3. It's important to get everyone in the company on board with AI. This means training employees and creating a culture that embraces the technology, rather than fearing it.

Data Science Weekly - Issue 555

Data Science Weekly Newsletter • 99 implied HN points • 11 Jul 24
🕹 Technology Data science AI Machine Learning Data Engineering Data Visualization
  1. Large language models can sometimes create false or confusing information, a problem known as hallucination. Understanding the cause of these mistakes can help improve their accuracy.
  2. Good data visualizations are important to effectively communicate patterns and insights. Poorly designed visuals can lead to misunderstandings, especially among those not familiar with graphics.
  3. There's an ongoing debate about copyright in the context of generative AI. Many believe it would be better to focus on finding compromises rather than pursuing strict legal battles.

Data Science Weekly - Issue 551

Data Science Weekly Newsletter • 159 implied HN points • 13 Jun 24
🕹 Technology Data science AI Machine Learning Software Development Computer Science
  1. Data Science Weekly shares curated articles and resources related to Data Science, AI, and Machine Learning each week. It's a helpful way to stay updated in the field.
  2. There are various interesting projects mentioned, such as the exploration of Bayesian education and improving code completion for languages like Rust. These projects can help in learning and improving skills.
  3. Free passes to an upcoming AI conference in Las Vegas are available, offering a chance to network and learn from industry leaders. It's a great opportunity for anyone interested in AI.

In Data Science for Non-STEM Majors, Is Learning-by-Watching Live Calculating Possible? Likely? Reasonable to Expect?

Brad DeLong's Grasping Reality • 238 implied HN points • 28 Jan 25
🚌 Education Data science Pedagogy Economics Analysis Learning methods
  1. Students today need basic data science skills to succeed after graduation. It's like letting them leave school without knowing how to read or write.
  2. Teaching data science can be tricky because students have different backgrounds. Some find it confusing, while others think it's too basic.
  3. It's important to keep trying to teach data science. Finding the right way to do it is necessary for better education and understanding.

Data Science Weekly - Issue 552

Data Science Weekly Newsletter • 139 implied HN points • 20 Jun 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. Notebooks can be easy to use, but they might make you lazy in coding. It's important to follow good practices even when using them.
  2. When handling large datasets, it's crucial to learn how to scale effectively. Knowing how to use resources wisely can help you reach your goals faster.
  3. Retrieval Augmented Generation (RAG) can improve how models generate information. It's complex, but understanding it can boost the performance of your projects.