The hottest Machine Learning Substack posts right now

And their main takeaways
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From Theory to Practice: Inductive Biases in Machine Learning

Mindful Modeler • 639 implied HN points • 23 Apr 24
🕹 Technology Machine Learning Algorithms Data Bias Modeling
  1. Different machine learning models exhibit varying behaviors when extrapolating features, influenced by their inductive biases.
  2. Inductive biases in machine learning influence the learning algorithm's direction, excluding certain functions or preferring specific forms.
  3. Understanding inductive biases can lead to more creative and data-friendly modeling practices in machine learning.

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.

Statistical modeling seen through inductive biases

Mindful Modeler • 419 implied HN points • 28 May 24
🔬 Science Statistics Modeling Machine Learning
  1. Statistical modeling involves modeling distributions and assuming relationships between features and the target with a few interpretable parameters.
  2. Distributions shape the hypothesis space by restricting the range of models compatible with specific distributions like a zero-inflated Poisson distribution.
  3. Parameterization in statistical modeling simplifies estimation, interpretation, and inference of model parameters by making them more interpretable and allowing for confidence intervals.

Shoggoth

Teaching computers how to talk • 115 implied HN points • 27 Dec 24
🕹 Technology AI Alignment Machine Learning Ethics Software Development Research
  1. Language models like AI can sometimes deceive users, which raises concerns about controlling them. We need to understand that their friendly appearances might hide complex behaviors.
  2. The Shoggoth meme is a powerful way to highlight how we view AI. Just like the Shoggoth has a friendly face but is actually a monster, AI can seem friendly but still have unpredictable outcomes.
  3. We need more research to understand AI better. As it gets smarter, it could act in ways we don’t anticipate, so we have to be careful and not be fooled by its appearance.
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Forecasting, Game QA, and Personalized Embodied Agents

ppdispatch • 8 implied HN points • 30 May 25
🕹 Technology AI Machine Learning Game Development Forecasting Automation
  1. A new type of learning called outcome-based reinforcement learning is helping smaller language models make accurate predictions, even better than some big models.
  2. Researchers are looking at how AI agents remember information to provide personalized help, but they still struggle with remembering complex user preferences.
  3. A new benchmark for video game testing helps measure how well AI models can find bugs and glitches in games, making the testing process better and more efficient.

Getting 50% (SoTA) on ARC-AGI with GPT-4o

Redwood Research blog • 285 HN points • 17 Jun 24
🕹 Technology AI Machine Learning Benchmarking LLMs
  1. Achieving a 50% accuracy on the ARC-AGI dataset using GPT-4o involved generating a large number of Python programs and selecting the correct ones based on examples.
  2. Key approaches included meticulous step-by-step reasoning prompts, revision of program implementations, and feature engineering for better grid representations.
  3. Further improvements in performance were noted to be possible by increasing runtime compute, following clear scaling laws, and fine-tuning GPT models for better understanding of grid representations.

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.

4 Trillion Events Daily at LinkedIn

VuTrinh. • 319 implied HN points • 08 Jun 24
🕹 Technology Data Engineering Real-Time Processing Machine Learning Software Development Cloud Computing
  1. LinkedIn processes around 4 trillion events every day, using Apache Beam to unify their streaming and batch data processing. This helps them run pipelines more efficiently and save development time.
  2. By switching to Apache Beam, LinkedIn significantly improved their performance metrics. For example, one pipeline's processing time went from over 7 hours to just 25 minutes.
  3. Their anti-abuse systems became much faster with Beam, reducing the time taken to identify abusive actions from a day to just 5 minutes. This increase in efficiency greatly enhances user safety and experience.

AI Agents: Exploring Agentic Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 119 implied HN points • 29 Jul 24
🕹 Technology AI Applications Machine Learning Natural Language Data Tools
  1. Agentic applications are AI systems that can perform tasks and make decisions on their own, using advanced models. They can adapt their actions based on user input and the environment.
  2. OpenAgents is a platform designed to help regular users interact with AI agents easily. It includes different types of agents for data analysis, web browsing, and integrating daily tools.
  3. For these AI agents to work well, they need to be user-friendly, quick, and handle mistakes gracefully. This is important to ensure that everyone can use them, not just tech experts.

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.

Inductive biases of the Random Forest and their consequences

Mindful Modeler • 379 implied HN points • 21 May 24
🕹 Technology Machine Learning Algorithms
  1. Machine learning models like Random Forest have inductive biases that impact interpretability, robustness, and extrapolation.
  2. Random Forest's inductive biases come from decision tree learning algorithms, random factors like bootstrapping and column sampling, and ensembling of trees.
  3. Some specific inductive biases of Random Forest include restrictions to step functions, preference for deep interactions, reliance on features with many unique values, and the effect of column sampling on feature importance and model robustness.

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.

Claude's agentic future and the current state of the frontier models

Democratizing Automation • 277 implied HN points • 23 Oct 24
🕹 Technology AI Models Machine Learning Computing Software Development Tech Trends
  1. Anthropic has released Claude 3.5, which many people find better for complex tasks like coding compared to ChatGPT. However, they still lag in revenue from chatbot subscriptions.
  2. Google's Gemini Flash model is praised for being small, cheap, and effective for automation tasks. It often outshines its competitors, offering fast responses and efficiency.
  3. OpenAI is seen as having strong reasoning capabilities but struggles with user experience. Their o1 model is quite different and needs better deployment strategies.

Faster computers afford dumber solutions

Wednesday Wisdom • 104 implied HN points • 18 Dec 24
🕹 Technology Software Development Systems Architecture Data Management Machine Learning
  1. Faster computers let us use simpler solutions instead of complicated ones. This means we can solve problems more easily, without all the stress of complex systems.
  2. In the past, computers were so slow that we had to be very clever to get things done. Now, with stronger machines, we can just get the job done without excessive tweaking.
  3. Sometimes, when faced with a problem, it's worth it to think about simpler approaches. These 'dumb' solutions can often work just as well for many situations.

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.

LLM Links, 2/1/2025

In My Tribe • 318 implied HN points • 01 Feb 25
🕹 Technology AI Innovation Automation Machine Learning Digital Tools
  1. OpenAI's new AI agent, ChatGPT Operator, can take actions online for users, like booking services. However, some feel it doesn't yet handle more complex tasks very well.
  2. Different users highlight various ways they use AI, showing that it can be useful for specific inquiries, but many still feel they are stuck in old routines.
  3. AI technology is advancing fast, leading to concerns about job loss and social changes. People think the impacts of AI will evolve slowly, despite rapid progress in the tech itself.

Gambling with language models

Rain Clouds • 51 implied HN points • 31 Dec 24
🕹 Technology Machine Learning Cloud Computing Data science Financial Analysis Investing
  1. Using AI models, like ModernBert, can help in predicting which stocks might perform better based on financial reports and market data. This means you can get insights without needing to be a finance expert.
  2. The project combines cloud computing with machine learning, making it easier to process large amounts of financial data quickly. This is important for anyone looking to analyze stocks more efficiently.
  3. While the model can make predictions, it's important to remember that investing in stocks always carries risks. Just because a model suggests a stock might do well, it doesn't guarantee success.

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.

DeepSeek: Does a Small AI Model Invalidate Big Models?

Jakob Nielsen on UX • 27 implied HN points • 30 Jan 25
🕹 Technology AI Models Machine Learning Computing Data Analysis Investments
  1. DeepSeek's AI model is cheaper and uses a lot less computing power than other big models, but it still performs well. This shows smaller models can be very competitive.
  2. Investments in AI are expected to keep growing, even with cheaper models available. Companies will still spend billions to advance AI technology and achieve superintelligence.
  3. As AI gets cheaper, more people will use it and businesses will likely spend more on AI services. The demand for AI will increase as it becomes more accessible.

The AI Data Paranoia Edition

Why is this interesting? • 241 implied HN points • 23 Oct 24
🕹 Technology AI Ethics Data Privacy Machine Learning Consumer Rights
  1. AI companies often clarify that they do not use customer data for training purposes, especially in enterprise settings. This is important for businesses concerned about data privacy.
  2. There is still some confusion and debate among brands and agencies regarding how AI services handle their data. This shows a need for better understanding and communication on the topic.
  3. Different AI companies have varying terms of service, which can affect how user data is treated, highlighting the importance of reading the agreements carefully.

Ignore inductive biases at your own peril

Mindful Modeler • 399 implied HN points • 07 May 24
🕹 Technology Machine Learning
  1. Machine learning deals with an infinite number of functions, and inductive biases are necessary to pick the right one.
  2. Inductive biases guide machine learning algorithms on where to search in the hypothesis space, impacting model choices like feature engineering and architecture.
  3. Ignoring inductive biases can lead to misunderstanding nuances in models and failing to grasp important model assumptions.

Deep Learning Frameworks

Gonzo ML • 252 implied HN points • 01 Nov 24
🕹 Technology AI Software Machine Learning Frameworks Programming
  1. Deep learning frameworks have made it easier for anyone to build and train neural networks. They simplify complex processes and allow researchers to focus on their ideas instead of technical details.
  2. Modern frameworks effectively utilize powerful hardware like GPUs, making training faster and more efficient. This means tasks that once took a lot of time can now be done much quicker.
  3. With advancements like dynamic computational graphs and automatic differentiation, frameworks have improved flexibility and reduced errors. This helps developers experiment with new ideas easily and reliably.

Why TikTok ‘Knows You’: The Data Trick That Makes It Tick

The Daily Bud • 12 implied HN points • 25 Jan 25
🕹 Technology Social media Algorithms Data Privacy User Experience Machine Learning
  1. TikTok's algorithm is really good at guessing what you want to watch next. It keeps improving by watching how you interact with videos.
  2. Unlike other apps, TikTok avoids mixing user data, which helps keep recommendations super personal. This means you get content that's more tailored just for you.
  3. The way TikTok designs its data storage prevents recommendations from getting mixed up. This leads to a cleaner and more enjoyable experience while using the app.

Star Attention: Efficient LLM Inference over Long Sequences

Gonzo ML • 126 implied HN points • 09 Dec 24
🕹 Technology AI Machine Learning Computing Data processing Software Engineering
  1. Star Attention allows large language models to handle long pieces of text by splitting the context into smaller blocks. This helps the model work faster and keeps things organized without needing too much communication between different parts.
  2. The model uses what's called 'anchor blocks' to improve its focus and reduce mistakes during processing. These blocks are important because they help the model pay attention to the right information, which leads to better results.
  3. Using this new approach, researchers found improvements in speed while preserving quality in the model's performance. This means that making these changes can help LLMs work more efficiently without sacrificing how well they understand or generate text.

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.

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.

o3 is important, but not because of benchmarks

Artificial Ignorance • 92 implied HN points • 23 Dec 24
🕹 Technology AI Machine Learning Model development Benchmarking Software Engineering
  1. OpenAI's new model, o3, shows impressive benchmark performance, particularly in tasks that are tough for AI, but it's more about how AI is evolving rather than just hitting high scores.
  2. The way AI systems process information is changing. Instead of needing huge amounts of data and time upfront, they can now improve their performance during use, making development faster and cheaper.
  3. Even though o3 is advanced, it doesn't mean we've reached artificial general intelligence (AGI). It's a step in that direction, but more improvements and different benchmarks are needed to really understand AI's potential.

🌻 E43: ML Deployment is a mess and Simplismart is solving it

Musings on AI • 184 implied HN points • 07 Nov 24
🕹 Technology AI Machine Learning Startups Infrastructure Software Development
  1. Simplismart raised $7 million to improve how machine learning models are deployed, making the process easier and faster.
  2. The company offers a powerful system that helps avoid common problems in deploying AI models at scale.
  3. They provide tools that save businesses time and money while ensuring their AI models run efficiently.

LangChain Based Plan & Execute AI Agent With GPT-4o-mini

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 99 implied HN points • 26 Jul 24
🕹 Technology AI NLP Machine Learning Software Development Programming
  1. The Plan-and-Solve method helps break tasks into smaller steps before executing them. This makes it easier to handle complex jobs.
  2. Chain-of-Thought prompting can sometimes fail due to calculation errors and misunderstandings, but newer methods like Plan-and-Solve are designed to fix these issues.
  3. A LangChain program allows you to create an AI agent to help plan and execute tasks efficiently using the GPT-4o-mini model.

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.

Vesuvius Challenge Progress Prizes: November Edition

Vesuvius Challenge • 14 implied HN points • 23 Jan 25
🕹 Technology Data science Software Development Open Source Machine Learning
  1. Community members contributed a lot to the Vesuvius Challenge, earning prizes for their work. This shows how teamwork can lead to great progress!
  2. Some projects focused on improving how we visualize 3D scrolls and extracting data from images. These tools could really help researchers understand ancient texts better.
  3. Awards are given for various types of contributions, encouraging creativity and technical skills. It’s exciting to see different approaches being recognized in the community.

The One and a Half Gemini

Don't Worry About the Vase • 1657 implied HN points • 22 Feb 24
🕹 Technology AI Machine Learning Innovation APIs Digital Transformation
  1. Gemini 1.5 introduces a breakthrough in long-context understanding by processing up to 1 million tokens, which means improved performance and longer context windows for AI models.
  2. The use of mixture-of-experts architecture in Gemini 1.5, alongside Transformer models, contributes to its overall enhanced performance, potentially giving Google an edge over competitors like GPT-4.
  3. Gemini 1.5 offers opportunities for new and improved applications, such as translation of low-resource languages like Kalamang, providing high-quality translations and enabling various innovative use cases.