The hottest Machine Learning Substack posts right now

And their main takeaways
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Are LLMs less sophisticated versions of human brains?

ailogblog • 39 implied HN points • 05 Jan 24
🕹 Technology Machine Learning
  1. Language is only meaningful in a social context. Large Language Models (LLMs) do not understand context, so they do not reason or think in ways similar to humans.
  2. Human brains are embodied, while LLMs are not. This difference is crucial because it affects how language and information processing occur.
  3. The complexity of the human brain far surpasses that of LLMs in terms of size and dimensionality, making direct comparison between the two a category error.

Building a Working Recommendation Engine from Scratch

Building a Recommendation Engine • 3 HN points • 04 Aug 24
🕹 Technology Machine Learning
  1. A recommendation engine can work without complex machine learning. Instead, it can be built using straightforward connections between content to suggest things users might like.
  2. Using an API from a platform like Are.na allows easy access to user content and helps find connections between different channels, making recommendations more relevant.
  3. It's important to filter out content that users already know or follow to give them fresh and exciting recommendations. Regular updates to the recommendations can also help keep things interesting.

Edge 441: SSMs Beyond Language

TheSequence • 119 implied HN points • 22 Oct 24
🕹 Technology Machine Learning
  1. SSMs can be used in areas beyond just language, like audio processing. This makes them very useful for handling complex and irregular data.
  2. Meta AI is researching how SSMs can improve speech recognition, showing their potential in understanding spoken language better.
  3. The Llama-Factory framework helps in pretraining large language models, making them more efficient and powerful.

8 Pitfalls To Avoid When Interpreting Machine Learning Models

Mindful Modeler • 159 implied HN points • 18 Oct 22
🕹 Technology Machine Learning
  1. Different interpretation methods have different goals, so define your interpretation goal first and then choose the appropriate method.
  2. Ensure your model generalizes well by using proper out-of-sample evaluation like cross-validation.
  3. Consider using simpler models for better interpretability and always analyze and correct for dependencies and uncertainties in your interpretation.

In cautious defense of LLM-ology

The Counterfactual • 119 implied HN points • 02 Mar 23
🕹 Technology Machine Learning
  1. Studying large language models (LLMs) can help us understand how they work and their limitations. It's important to know what goes on inside these 'black boxes' to use them effectively.
  2. Even though LLMs are man-made tools, they can reflect complex behaviors that are worth studying. Understanding these systems might reveal insights about language and cognition.
  3. Research on LLMs, known as LLM-ology, can provide valuable information about human mind processes. It helps us explore questions about language comprehension and cognitive abilities.
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DBRX: Revolutionizing Language Models for the Open Community

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

eBook: Mastering AI Agents

TheSequence • 77 implied HN points • 07 Feb 25
🕹 Technology Machine Learning
  1. You can learn to create effective AI agents with the right guidance. There's a helpful eBook that covers how these agents work and when to use them.
  2. The book reviews three frameworks for developing AI agents, helping you choose what's best for your needs. It also shares case studies to show real-life applications.
  3. It addresses common reasons AI agents fail and provides solutions to avoid these problems. This can help ensure your AI projects succeed.

A new kind of coding

Sunday Letters • 139 implied HN points • 06 Feb 23
🕹 Technology Machine Learning
  1. Coding with LLMs combines precise programming with flexible models. It's about using the strengths of both to build effective programs.
  2. When creating complex documents, breaking down tasks into smaller pieces is key. This helps models manage and generate content smoothly.
  3. As AI technology grows, we need to be open and experiment. Learning new patterns will help us understand how to best use these models in the future.

o3 is important, but not because of benchmarks

Artificial Ignorance • 92 implied HN points • 23 Dec 24
🕹 Technology Machine Learning
  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.

The Sequence Chat: The End of Data. Or Maybe Not

TheSequence • 105 implied HN points • 20 Nov 24
🕹 Technology Machine Learning
  1. There's a big debate about whether we're running out of data for AI. Some people believe that as AI keeps growing, we might hit a point where there's just not enough new data to use.
  2. Many AI models have already used a lot of data from the internet. This raises concerns that without fresh and vast data sources, these models might not improve much anymore.
  3. To tackle the data issue, some suggest focusing on getting better quality data or even creating new, artificial datasets. This could help keep AI development moving forward.

I Finally Listened to You and I’m So Glad I Did

The Palindrome • 3 implied HN points • 15 Jan 26
🚌 Education Machine Learning
  1. A YouTube channel now hosts video versions of fan-favorite educational posts, with three "greatest hits" videos already uploaded.
  2. Subscribing is a quick, zero-cost way to support growth and help the channel reach more machine learning practitioners.
  3. The project aims to teach the fundamentals of math and machine learning clearly and steadily, avoiding hype and short-lived trends, with big plans for 2026.

The Tech Buffet #8: Flowise - An Interface To Build and Test LLMs Apps Easily

The Tech Buffet • 59 implied HN points • 18 Oct 23
🕹 Technology Machine Learning
  1. Flowise is a no-code tool that helps you build and test applications using LLMs right from your web browser. It makes creating complex workflows easier by allowing you to choose and connect components visually.
  2. You can easily set up Flowise either from source code or using Docker. Once it's running, you can create ChatFlows, which are workflows for LLM applications, by simply dragging and dropping elements in the interface.
  3. Flowise is great for prototyping applications quickly, but it still has room for improvement, like better error handling and documentation. Overall, it's a handy tool for developers experimenting with language models.

Must Learn AI Security Compendium 12: Red Teaming Strategies for Safeguarding Large Language Models and Their Applications

Rod’s Blog • 59 implied HN points • 17 Oct 23
🕹 Technology Machine Learning
  1. Red teaming is crucial for identifying vulnerabilities and strengthening the defenses of AI systems like large language models.
  2. Large language models, while powerful, are not immune to vulnerabilities such as manipulation by malicious actors or amplification of biases.
  3. Effective red teaming involves systematic approaches like threat modeling and penetration testing, and collaboration between red and blue teams is key for a comprehensive defense strategy in AI security.

The Sequence Knowledge #482: An Introduction to Corrective RAG

TheSequence • 77 implied HN points • 04 Feb 25
🕹 Technology Machine Learning
  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.

Must Learn AI Security Compendium 11: Threat Modeling AI/ML Systems

Rod’s Blog • 59 implied HN points • 16 Oct 23
🕹 Technology Machine Learning
  1. Threat modeling is crucial for identifying and mitigating security threats in AI/ML systems by adopting the perspective of an attacker and uncovering vulnerabilities.
  2. Key considerations in threat modeling for AI/ML systems include data poisoning, adversarial perturbation, model extraction, and membership inference attacks.
  3. To protect AI/ML systems, organizations should implement mitigation strategies like robust data validation, adversarial training, access controls, and privacy-preserving techniques.

The Sequence Knowledge #468: A New Series About RAG

TheSequence • 84 implied HN points • 13 Jan 25
🕹 Technology Machine Learning
  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.

Taking time series modeling and stream processing mainstream

Gradient Flow • 139 implied HN points • 10 Nov 22
🕹 Technology Machine Learning
  1. The global market for time series analysis software is growing significantly, presenting opportunities for companies and startups
  2. There is a need to focus on stream processing to gain competitive advantages in making quick decisions and leveraging incoming data
  3. Open source tools and collaborations play a key role in advancing fields like time series modeling and stream processing

AI Eats the World

The Product Channel By Sid Saladi • 6 implied HN points • 15 Dec 25
🕹 Technology Machine Learning
  1. AI is the next major platform shift with huge, uncertain upside and massive infrastructure spending that reshapes who can compete.
  2. Models are converging into commodities, so the real value will come from products, distribution, and embedding AI into workflows that users actually trust.
  3. Treat AI as “infinite interns”: focus on tasks that tolerate errors, add verification or supervision, and pursue vertical unbundling where automation replaces tedious human work.

Evolution of LLM Agents

LLMs for Engineers • 79 implied HN points • 21 Jun 23
🕹 Technology Machine Learning
  1. Large Language Models (LLMs) are becoming more powerful and can now perform complex tasks with the help of internet data and tools. This could significantly boost productivity for both individuals and corporations.
  2. The evolution of LLMs has progressed through several levels, starting from simple API calls to advanced agents that understand tasks better and can even interact without much human guidance.
  3. While these advancements are exciting, there are still challenges to overcome, such as reliability, cost, and the potential for errors in the output of LLMs.

September/October 2023 safety news: Sparse autoencoders, A is B is not B is A, Image hijacks

AI safety takes • 58 implied HN points • 17 Oct 23
🕹 Technology Machine Learning
  1. Research shows that sparse autoencoders are being used to find interpretable features in neural networks.
  2. Language models have shown a struggle in learning reversals like 'A is B' vs 'B is A', highlighting challenges in their training.
  3. There are concerns and efforts to tackle AI deception, with studies on lie detection in black-box language models.

Databricks just dropped an LLM bomb – DBRX 💣

Sector 6 | The Newsletter of AIM • 19 implied HN points • 31 Mar 24
🕹 Technology Machine Learning
  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.

Must Learn AI Security Compendium 10: Challenges of Enhancing AI Language Models with External Knowledge

Rod’s Blog • 59 implied HN points • 12 Oct 23
🕹 Technology Machine Learning
  1. Retrieval-Augmented Generation (RAG) enhances AI language models by combining them with external knowledge sources, improving the quality and accuracy of generated responses.
  2. RAG offers benefits such as access to current information, increased contextual understanding, and reduced risk of incorrect data, but it also comes with challenges like data integration and semantic relevance.
  3. The future of RAG includes developments like fine-grained relevance ranking, domain-specific knowledge bases, real-time updates, and ethical considerations to ensure responsible use.

Edge 458: From Pre-training to Post-training. Inside the Amazing Tülu 3 Framework

TheSequence • 91 implied HN points • 19 Dec 24
🕹 Technology Machine Learning
  1. There is a new focus in AI from pre-training models to post-training methods. This change is happening because it's now easier to train models with data from the internet.
  2. The Tülu 3 framework is designed to improve existing language models after their initial training. It highlights how important the post-training process is for making models work better.
  3. By making post-training techniques more open and accessible, Tülu 3 aims to help the open-source community compete with top-performing private models.

Must Learn AI Security Part 19: Deepfake Attacks Against AI

Rod’s Blog • 59 implied HN points • 02 Oct 23
🕹 Technology Machine Learning
  1. Deepfake attacks against AI involve using fake videos or audios created by AI to deceive AI systems into making harmful decisions.
  2. Types of deepfake attacks include adversarial attacks, poisoning attacks, and data injection attacks, each with different strategies to compromise AI systems.
  3. To mitigate AI-generated deepfake attacks, organizations should focus on data validation, anomaly detection, AI model monitoring, and ongoing training to protect against potential financial, political, or personal gains by attackers.

Microsoft Sentinel SOC 101: How to Detect and Mitigate Man/Adversary-in-the-Middle (MitM/AitM) Attacks with Microsoft Sentinel

Rod’s Blog • 59 implied HN points • 29 Sep 23
🕹 Technology Machine Learning
  1. Man-in-the-Middle attacks are serious cyber threats that can lead to data breaches and financial loss for organizations.
  2. Microsoft Sentinel is a powerful tool that leverages AI, machine learning, and integration with Microsoft Defender for Endpoint to detect and mitigate Man-in-the-Middle attacks effectively.
  3. Implementing best practices such as using secure communication protocols, regular system updates, multi-factor authentication, and employee training can further enhance network security against Man-in-the-Middle attacks.

Must Learn AI Security Part 13: Generative Attacks Against AI

Rod’s Blog • 59 implied HN points • 15 Sep 23
🕹 Technology Machine Learning
  1. Generative attacks against AI involve creating or manipulating data to deceive AI systems, compromising their performance and trustworthiness.
  2. Defending against generative attacks requires understanding the target AI system, identifying vulnerabilities, and developing robust AI models and defense mechanisms.
  3. Types of generative attacks include adversarial examples, data poisoning, model inversion, trojan attacks, and GANs based attacks, each with unique approaches and potential negative effects on AI systems.

Must Learn AI Security Compendium 2: Generative AI vs. Machine Learning

Rod’s Blog • 59 implied HN points • 11 Sep 23
🕹 Technology Machine Learning
  1. Machine learning empowers computers to learn from data without explicit programming, helping them make predictions and decisions.
  2. Generative AI focuses on creating new data based on training data, emphasizing creativity and innovation.
  3. Both machine learning and generative AI have unique applications - from fraud detection and image recognition in machine learning to image generation and music composition in generative AI.

The Tech Buffet #2: How To Use LangChain to Perform Question Answering Over Documents

The Tech Buffet • 59 implied HN points • 06 Sep 23
🕹 Technology Machine Learning
  1. You can use LangChain to build a question-answering system that works with documents. It helps you fetch answers from documents effortlessly.
  2. The process involves loading a document, splitting it into manageable chunks, and then using these chunks to find answers. This way, you have context to support the answers generated.
  3. It's important to keep experimenting and refining your system for better answers. Check out more details in the LangChain documentation for tips and improvements.

Must Learn AI Security Part 9: Hyperparameter Attacks Against AI

Rod’s Blog • 59 implied HN points • 07 Sep 23
🕹 Technology Machine Learning
  1. A hyperparameter attack against AI manipulates crucial adjustable settings of an algorithm to influence the machine learning model's performance and behavior
  2. Different types of hyperparameter attacks can target aspects like performance, biases, vulnerability to adversarial examples, transferability, and resource consumption
  3. Mitigating hyperparameter attacks involves securing data access, monitoring hyperparameter changes, testing robustness, updating models, and following responsible AI practices

🕵️🗺️ Where do I deploy Llama-2? 🦙🦙

LLMs for Engineers • 59 implied HN points • 22 Aug 23
🕹 Technology Machine Learning
  1. There are many options for hosting Llama-2, including big names like AWS, GCP, and Azure, as well as newer providers like Lambda Labs and CoreWeave. Each has its own pricing and GPU options.
  2. Understanding how much you plan to use Llama-2 is important. This helps you decide whether to use a cloud service provider or a function-based option like Replicate.
  3. Cost-effectiveness varies with different providers. For low usage, function providers can be cheaper, but for higher usage, CSPs might save you money in the long run.

Is ChatGPT Getting worse? A Case Study on Confirmation Bias

The Data Score • 59 implied HN points • 20 Jul 23
🕹 Technology Machine Learning
  1. Testing and improving AI models, like ChatGPT, is crucial as our reliance on AI grows. Ensuring model performance and explainability is key for professionals in the field.
  2. Machine learning and AI models face challenges with explainability, especially in the context of large language models like ChatGPT. Specific wording and temperature settings can greatly impact model outputs.
  3. Confirmation bias is a common human tendency to search for and interpret information that aligns with existing beliefs. It's important to recognize and manage biases when assessing AI model performance.

Writing Noise into Noise

Cybernetic Forests • 79 implied HN points • 12 Feb 23
🎨 Art & Illustration Machine Learning
  1. Diffusion models start by generating random noise and work backward to create images based on prompts.
  2. The model aims to remove noise based on the prompt, creating a recursive process of noise refinement.
  3. Diffusion models struggle with abstract details like Gaussian noise, leading to errors in representation.

The Sequence Knowledge #550: Let's Talk About Safety Benchmarks

TheSequence • 42 implied HN points • 27 May 25
🕹 Technology Machine Learning
  1. Safety benchmarks are important tools that help evaluate AI systems. They make sure these systems are safe as they become more advanced.
  2. Different organizations have created their own frameworks to assess AI safety. Each framework focuses on different aspects of how AI systems can be safe.
  3. Understanding and using safety benchmarks is essential for responsible AI development. This helps manage risks and ensure that AI helps, rather than harms.

LLMs are machines. Are you one too?

70 Years Old. WTF! • 58 implied HN points • 19 Feb 23
🕹 Technology Machine Learning
  1. LLMs are Large Language Models, which are computer systems trained to generate language based on patterns.
  2. LLMs can write better than most humans, but they lack the freedom of expression that humans have.
  3. The difference between how a human writes and how a machine like ChatGPT generates text is the ability to freely use explicit language.

Apposition Turns Two

Apposition • 58 implied HN points • 22 Apr 23
🕹 Technology Machine Learning
  1. Apposition turned two without the author noticing.
  2. AI like Chat GPT can do impressive things but may not be as intelligent as it seems.
  3. Chat GTE is fast media but we still need slow processing for better understanding.

Unintended Data Poisoning

Embracing Enigmas • 58 implied HN points • 21 Mar 23
🕹 Technology Machine Learning
  1. AI systems might lose the ability to create novel content if the rate of true signal decreases.
  2. Data poisoning in AI systems poses a serious cybersecurity threat and may reduce the effectiveness of AI models.
  3. Implementing validation systems early is crucial to prevent disruptions caused by AI system vulnerabilities.