The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Hallucinations Are Fine, Actually

Artificial Ignorance • 92 implied HN points • 04 Mar 25
🕹 Technology Data science
  1. AI models can often make mistakes or 'hallucinate' by providing wrong information confidently. It's important for humans to check AI output especially for important tasks.
  2. Even though AI hallucinations are a challenge, they're seen as something we can work to improve rather than an insurmountable problem.
  3. Instead of aiming for AI to do everything on its own, we should use it as a tool to help us do our jobs better, understanding that we need to collaborate with it.

MistralAI Reveals the Mystery

Sector 6 | The Newsletter of AIM • 59 implied HN points • 13 Dec 23
🕹 Technology Data science
  1. MistralAI has launched a new model called Mixtral 8x7B that is faster and more efficient than competitors like Llama 2 70B. It can provide great performance while being cost-effective.
  2. Mixtral can handle a lot of information at once, processing up to 32,000 tokens and supporting multiple languages such as English, French, and German.
  3. This model also shows strong abilities in generating code and can be fine-tuned to follow instructions well, which is helpful for various applications.

Weekly Dose of Optimism #120

Not Boring by Packy McCormick • 137 implied HN points • 15 Nov 24
🕹 Technology Data science
  1. The U.S. is planning to triple its nuclear power capacity by 2050, aiming for 200 gigawatts through new reactors and upgrades. This is a big move to meet rising energy demands in a safe and efficient way.
  2. Molecular nanotechnology could revolutionize production, possibly outpacing past technological shifts like the Industrial Revolution. It's an exciting frontier that stands to vastly increase our capabilities in various fields.
  3. Evo, a new AI model, shows promise in predicting and designing genomes, potentially creating new life forms. This technology could push the boundaries of biological science and genetic engineering significantly.

A Must for Indic Language Models

Sector 6 | The Newsletter of AIM • 39 implied HN points • 09 Feb 24
🕹 Technology Data science
  1. There is a big need for benchmarks specifically for Indian languages. This helps assess how well language models perform in those languages.
  2. Upcoming models like Tamil Llama and Odia Llama are pushing for the creation of these benchmarks. They could lead to better evaluations for these Indic language models.
  3. Having a leaderboard for Indic language models is vital. It will spotlight advancements and improvements within India's language technology space.

The Tech Buffet #4: Turn Complex English Instructions Into Executable SQL With LLMs

The Tech Buffet • 79 implied HN points • 16 Sep 23
🕹 Technology Data science
  1. Vanna.AI is a tool that helps turn plain English questions into complex SQL queries quickly. This makes it easier for people who might not be familiar with coding to extract data from databases.
  2. The tool uses a method called Retrieval Augmented Generation (RAG) to understand user queries better. It prepares the right context for the questions by using metadata before generating SQL.
  3. Vanna allows users to continuously improve its performance by incorporating user-feedback into the training process. This feature helps the tool learn and adapt over time, ensuring better results.
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The Rise of the AI Dragon

Sector 6 | The Newsletter of AIM • 59 implied HN points • 04 Dec 23
🕹 Technology Data science
  1. There are new AI models based on LLaMA, like DeepSeek, that are showing great performance. These models are pushing the boundaries of what AI can do.
  2. Chinese companies are making significant progress in open source AI models and many are now leading in popularity and performance.
  3. DeepSeek and other models are being developed with the goal of exploring artificial general intelligence, which aims to create more advanced AI systems.

Small newsletters, big ideas

Artificial Ignorance • 121 implied HN points • 16 Dec 24
🕹 Technology Data science
  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.

Generating Insights from Research with AI

Addition • 78 implied HN points • 28 Jun 23
🕹 Technology 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.

Intents Are Not Going Away…RoNID Is A New Intent Discovery Framework

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 26 Apr 24
🕹 Technology Data science
  1. RoNID helps identify user intents more accurately, allowing chatbots to understand what users really want to talk about. This means better conversations and less frustration.
  2. The framework uses two main steps: generating reliable labels and organizing data into clear groups. This makes it easier to see which intents are similar and which are different.
  3. RoNID outperforms older methods, improving the chatbot’s understanding by creating clearer and more accurate intent classifications. This leads to a smoother user experience.

How could we know if Large Language Models understand language?

The Counterfactual • 219 implied HN points • 18 Oct 22
🕹 Technology Data science
  1. There's a big debate about whether large language models truly understand language or if they're just mimicking patterns from the data they were trained on. Some people think they can repeat words without really grasping their meaning.
  2. Two main views exist: One says LLMs can't understand language because they lack deeper meaning and intent, while the other argues that if they behave like they understand, then they might actually understand.
  3. As LLMs become more advanced, we need to create better ways to test their understanding. This will help us figure out what it really means for a machine to 'understand' language.

UniMS-RAG: Unified Multi-Source RAG for Personalised Dialogue

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 30 Jan 24
🕹 Technology Data science
  1. UniMS-RAG is a new system that helps improve conversations by breaking tasks into three parts: choosing the right information source, retrieving information, and generating a response.
  2. It uses a self-refinement method that makes responses better over time by checking if the answers match the information found.
  3. The system aims to make interactions feel more personalized and helpful, leading to smarter and more relevant conversations.

The Sequence Knowledge #560: The Amazing World of Agentic Benchmarks

TheSequence • 49 implied HN points • 10 Jun 25
🕹 Technology Data science
  1. Agentic benchmarks are new ways to evaluate AI that focus on decision-making rather than just answering questions. They look at how well AI can plan and adapt to different tasks.
  2. Traditional evaluation methods aren't enough for AI that acts like agents. We need tests that measure how AI can handle complex situations and multi-step processes.
  3. One exciting example of these benchmarks is the Web Arena, which helps assess AI's ability to perform tasks on the web. This includes how well they interact with online tools and environments.

Weekly Top Picks #91

The Algorithmic Bridge • 116 implied HN points • 09 Dec 24
🕹 Technology 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.

The Sequence Knowledge #473: Not All RAGs are Created Equal

TheSequence • 98 implied HN points • 21 Jan 25
🕹 Technology Data science
  1. RAG stands for Retrieval Augmented Generation. It's a way for machines to pull in outside information, helping them give better and more accurate answers.
  2. There are many kinds of RAG, like Standard RAG and Fusion RAG. Each type helps machines deal with different problems and has its special strengths.
  3. Understanding these RAG types is important for anyone working in AI. It helps them choose the right approach for different challenges.

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

TheSequence • 133 implied HN points • 29 Oct 24
🕹 Technology 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.

Fine-Tuning OpenAI GPT-4o mini

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 2 HN points • 21 Aug 24
🕹 Technology Data science
  1. OpenAI's GPT-4o Mini allows for fine-tuning, which can help customize the model to better suit specific tasks or questions. Even with just 10 examples, users can see changes in the model's responses.
  2. Small Language Models (SLMs) are advantageous because they are cost-effective, can run locally for better privacy, and support a range of tasks like advanced reasoning and data processing. Open-sourced options provide users more control.
  3. GPT-4o Mini stands out because it supports multiple input types like text and images, has a large context window, and offers multilingual support. It's ideal for applications that need fast responses at a low cost.

The Sequence Opinion #557: Millions of GPUs, Zero Understanding: The Cost of AI Interpretability

TheSequence • 49 implied HN points • 05 Jun 25
🕹 Technology Data science
  1. AI models are becoming super powerful, but we don't fully understand how they work. Their complexity makes it hard to see how they make decisions.
  2. There are new methods being explored to make these AI systems more understandable, including using other AI to explain them. This is a fresh approach to tackle AI interpretability.
  3. The debate continues about whether investing a lot of resources into understanding AI is worth it compared to other safety measures. We need to think carefully about what we risk if we don't understand these machines better.

Week #4: Overview Of Conformal Predictors

Mindful Modeler • 139 implied HN points • 10 Jan 23
🕹 Technology Data science
  1. Conformal prediction is a versatile approach applicable to various machine learning tasks beyond just regression and classification.
  2. When learning about a new conformal prediction method, it's important to consider the machine learning task, non-conformity score used, and how the method deviates from the standard recipe.
  3. Staying up to date with new research in conformal prediction can be facilitated by resources like the 'Awesome Conformal Prediction' repository and following experts in the field on platforms like Twitter.

Make softmax great again

Gonzo ML • 126 implied HN points • 06 Nov 24
🕹 Technology Data science
  1. Softmax is widely used in machine learning, especially in transformers, to turn numbers into probabilities. However, it struggles when dealing with new kinds of data that the model hasn't seen before.
  2. The sharpness of softmax can fade when there's a lot of input data. This means it sometimes can't make clear predictions about which option is best in bigger datasets.
  3. To improve softmax, researchers suggest using 'adaptive temperature.' This idea helps make the predictions sharper based on the data being processed, leading to better performance in some tasks.

Data Design For Fine-Tuning To Improve Small Language Model Behaviour

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 17 Apr 24
🕹 Technology Data science
  1. Small Language Models can be improved by designing their training data to help them reason and self-correct. This means creating special ways to present information that guide the model in making better decisions.
  2. Two methods, Prompt Erasure and Partial Answer Masking (PAM), help models learn how to think critically and correct mistakes on their own. They get trained in a way that shows them how to approach problems without providing the exact questions.
  3. The focus is shifting from just updating a model's knowledge to enhancing its behavior and reasoning skills. This means training models not just to recall information, but to understand and apply it effectively.

The Day My Project Went to Space

The Palindrome • 2 implied HN points • 02 Feb 26
🔬 Science Data science
  1. Space experiments demand massive behind-the-scenes work: detailed proposals, strict approvals, extensive documentation, and coordination with agencies.
  2. Consumer mobile IMUs can be used in microgravity but pose real challenges—orientation tracking, gravity removal, sensor bias, and noise make trajectory reconstruction hard and require careful calibration and advanced integration methods.
  3. Leading a flight experiment often means becoming a full‑stack engineer: build a simple, robust flight-ready app, pass platform and agency reviews, run thorough tests, and use quick prototypes or ML demos to validate and showcase the work.

Retrieval-Augmented Generation (RAG) vs LLM Fine-Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 19 Jan 24
🕹 Technology Data science
  1. Retrieval-Augmented Generation (RAG) is great for adding specific context and making models easier to use. It's a good first step if you're starting with language models.
  2. Fine-tuning a model provides more accurate and concise answers, but it requires more upfront work and data preparation. It can handle large datasets efficiently once set up.
  3. Using RAG and fine-tuning together can boost accuracy even more. You can gather information with RAG and then fine-tune the models for better performance.

Top 10 Events 2024

Bytewax • 39 implied HN points • 18 Jan 24
🕹 Technology Data science
  1. Top tech conferences in 2024 focus on AI, data science, ML, and Python.
  2. Events offer opportunities to learn, connect with peers, and expand skills.
  3. Attendees benefit from valuable insights, networking, and community engagement.

🔥 LLMs on Fire 🔥

Sector 6 | The Newsletter of AIM • 19 implied HN points • 15 Apr 24
🕹 Technology Data science
  1. OpenAI's GPT-4 Turbo is currently leading the chatbot rankings, but there are strong competitors like Anthropic's Claude 3 Opus and Gemini Pro from Google.
  2. Cohere's Command R+ has also made its mark among the top models, showing that it can compete with big-name AI.
  3. Exciting new models like Llama 3 and GPT-5 are set to launch soon, which could shake things up even more in the AI race.

Genesis Mission challenges for biotechnology

A Biologist's Guide to Life • 8 implied HN points • 03 Dec 25
🔬 Science Data science
  1. Automating research in high-security labs can make work safer and more efficient. This will help scientists handle dangerous pathogens without direct human contact, which is crucial for preventing accidents.
  2. There is a need for better tools in genetics, specifically for aligning and annotating DNA sequences. Modernizing these tools can lead to faster results and more discoveries in biology.
  3. Improving how quickly patients receive medical care is essential. By using AI to streamline processes and reduce paperwork, we can make healthcare more efficient and improve patient experiences.

How to Fine-Tune Your Own Mistral-7B

The Beep • 39 implied HN points • 14 Jan 24
🕹 Technology Data science
  1. You can fine-tune the Mistral-7B model using the Alpaca dataset, which helps the model understand and follow instructions better.
  2. The tutorial shows you how to set up your environment with Google Colab and install necessary libraries for training and tracking the model's performance.
  3. Once you prepare your data and configure the model, training it involves monitoring progress and adjusting settings to get the best results.

Edge 455: Building Smaller Foundation Models Using Graph-Based Distillation

TheSequence • 105 implied HN points • 10 Dec 24
🕹 Technology Data science
  1. Graph-based distillation helps smaller models learn better by using the connections between data points. Instead of just focusing on individual data, it looks at how they relate to one another.
  2. This technique uses attention networks to improve how student models understand data, making them more effective in learning.
  3. There’s a new framework called Hugging Face Autotrain that allows for easier training of foundation models without needing too much coding knowledge.

Synthetic Data In A Nutshell

Three Data Point Thursday • 39 implied HN points • 11 Jan 24
🕹 Technology Data science
  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.

My biggest mistakes as a data-science team lead

inexactscience • 59 implied HN points • 27 Oct 23
💼 Business Data science
  1. Leadership style should change based on each team member's skills and motivation. It's important to adjust how you lead as people grow and face new challenges.
  2. Focusing only on problems can lead to neglecting high performers. Instead of constantly putting out fires, you should aim to create overall value in the team.
  3. Using data to measure success in a team is crucial. Setting clear metrics helps you understand progress and ensure your efforts are effective.

Alibaba QwQ Really Impresses at GPT-o1 Levels

TheSequence • 105 implied HN points • 01 Dec 24
🕹 Technology Data science
  1. Alibaba's new AI model called QwQ is doing really well in reasoning tasks, even better than some existing models like GPT-o1. This shows that it's becoming a strong competitor in the AI field.
  2. QwQ is designed to think carefully and explain its reasoning step by step, making it easier for people to understand how it reaches its conclusions. This transparency is a big deal in AI development.
  3. The rise of models like QwQ indicates a shift towards focusing on reasoning abilities, rather than just making models bigger. This could lead to smarter AI that can learn and solve problems more effectively.

Graphing in the blue skies

Conspirador Norteño • 36 implied HN points • 27 Jun 25
🕹 Technology Data science
  1. Repost network graphs are helpful to understand how ideas spread on social media, especially on platforms like Bluesky. You can visualize how hashtags or posts gain popularity and interaction.
  2. You can create these graphs from datasets of reposts and original posts, using Python to handle the data. This allows researchers to analyze which accounts are most influential in sharing content.
  3. Different types of conversations on social media can create unique patterns in these graphs. For instance, debates might lead to clusters of accounts, while friendly interactions could show a more unified graph with fewer divisions.

The Sequence Knowledge #482: An Introduction to Corrective RAG

TheSequence • 77 implied HN points • 04 Feb 25
🕹 Technology Data science
  1. Corrective RAG is a smarter way of using AI that makes it more accurate by checking its work. It helps prevent mistakes or errors in the information it gives.
  2. This method goes beyond basic retrieval-augmented generation (RAG) by adding feedback loops that refine and improve the output as it learns.
  3. The goal of Corrective RAG is to provide answers that are factually accurate and coherent, reducing confusion or incorrect information.

The Sequence Knowledge #468: A New Series About RAG

TheSequence • 84 implied HN points • 13 Jan 25
🕹 Technology Data science
  1. Retrieval Augmented Generation, or RAG, helps AI models use outside information to improve their answers. This makes the responses more accurate and relevant.
  2. RAG works in two steps: first, it finds useful information, and then it uses that information to create better responses. This method is great for applications that need quick and correct answers.
  3. A key paper introduced RAG and showed that combining different types of memory can lead to better results in language tasks, like answering questions or generating text.

Databricks just dropped an LLM bomb – DBRX 💣

Sector 6 | The Newsletter of AIM • 19 implied HN points • 31 Mar 24
🕹 Technology Data science
  1. Databricks has released a new powerful open-source language model called DBRX. It aims to outperform existing models in areas like reasoning, coding, and math.
  2. DBRX has shown better performance than other popular models, including Meta’s LLaMA and Google's Gemini Pro. This showcases Databricks' advancements in AI technology.
  3. The release is generating excitement in the AI community, highlighting the competitive landscape of language models and their capabilities.

The Silent Infrastructure Behind AI: Why Vector Databases Are Booming

philsiarri • 22 implied HN points • 21 Aug 25
🕹 Technology Data science
  1. Vector databases store information in a way that captures meaning, helping AI search for similarities instead of exact matches. This means a sentence or an image can be turned into a special numeric form that AI understands better.
  2. Traditional databases are good for exact searches but struggle with the complex needs of AI. Vector databases are designed for quick and efficient searches involving high-dimensional data, making them much better for AI applications.
  3. Many companies like Pinecone and Weaviate are leading the way in vector databases, which are being used in various areas like e-commerce, fraud detection, and customer support to improve how we find and use information.