The hottest Machine Learning Substack posts right now

And their main takeaways
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Top Business Topics

Which AI should I use? Superpowers and the State of Play

One Useful Thing • 506 implied HN points • 18 Mar 24
đź•ą Technology Machine Learning
  1. There are three main GPT-4 class AI models dominating the field currently: GPT-4, Anthropic's Claude 3 Opus, and Google's Gemini Advanced.
  2. These AI models have impressive abilities like being multimodal, allowing them to 'see' images and work across a variety of tasks.
  3. The AI industry lacks clear instructions on how to use these advanced AI models, and users are encouraged to spend time learning to leverage their potential.

What is RLHF?

Technically • 24 implied HN points • 11 Nov 25
đź•ą Technology Machine Learning
  1. Reinforcement Learning from Human Feedback (RLHF) makes AI models like ChatGPT more helpful by showing them what good answers look like. It teaches them how to be useful assistants instead of just being knowledgeable.
  2. Before RLHF, AI models could give correct but irrelevant answers, like a toddler with a lot of knowledge but no idea how to apply it. They often generated strange or confusing responses.
  3. The process of RLHF includes humans ranking AI-generated answers, which helps refine the models. This way, they learn to be more concise and relevant to our needs.

Creating A Benchmark Taxonomy For Prompt Engineering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 13 Jun 24
đź•ą Technology Machine Learning
  1. Creating a standard system for evaluating prompts is important because prompts can vary in how they're used and understood. This makes it hard to measure their effectiveness.
  2. The TELeR taxonomy helps to categorize prompts so that they can be better compared and understood. It focuses on aspects like clarity and the level of detail in prompts.
  3. Using clear goals, examples, and context in prompts can lead to better responses from language models. This helps the models to understand exactly what is being asked.

Compression - en francais

Squirrel Squadron Substack • 3 implied HN points • 04 Feb 26
đź•ą Technology Machine Learning
  1. Compression works by removing redundancy to make data smaller; lossless compression preserves every bit while lossy methods discard detail, and truly random data resists any meaningful shrinking. Recompressing already-compressed data usually fails and can make files bigger, so there are strict limits to how far you can compress.
  2. Information theory defines limits on compression and measures information by how short a program can reproduce the data (Kolmogorov complexity). Effective compression depends on clever representations and adaptive algorithms that capture structure in the data.
  3. Large language models behave like powerful compression-and-prediction systems that build compact internal models by learning to predict the next token. This predictive compression explains much of their useful, seemingly intelligent behavior and their value as productivity tools, even if they are not human thinkers.

Komprimierung

Squirrel Squadron Substack • 3 implied HN points • 04 Feb 26
đź•ą Technology Machine Learning
  1. Lossless compression makes files smaller without losing any detail by exploiting redundancy, while lossy compression sacrifices quality for size. Trying to compress already compressed or random data usually fails and can even make files bigger.
  2. There are theoretical limits to how much you can compress—concepts like Kolmogorov complexity measure the shortest description of data—so texts with more genuine information are inherently harder to shrink.
  3. Modern large language models act like powerful compression engines: by predicting the next token they build compact internal models of huge datasets, and that predictive ability correlates with intelligent performance. You can already use these models as practical assistants to boost productivity rather than waiting for some distant breakthrough.
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The Tech Buffet #6: Why Your RAG is Not Reliable in Production

The Tech Buffet • 139 implied HN points • 10 Oct 23
đź•ą Technology Machine Learning
  1. RAG systems can produce impressive results but require careful tuning to be reliable in real-world applications. Just copying and pasting code won't necessarily work for complex use cases.
  2. Understanding the RAG framework is important, as it involves various components like data loaders, splitters, and embedding models. Each part plays a crucial role in generating accurate answers.
  3. Using frameworks like LangChain can simplify the process of prototyping RAG systems, but they still need thoughtful configuration to function effectively in production.

Data Science Weekly - Issue 495

Data Science Weekly Newsletter • 239 implied HN points • 19 May 23
đź•ą Technology Machine Learning
  1. Absence of evidence can often serve as strong evidence of absence, and this idea can be explored with Bayesian methods.
  2. Natural language processing is being used to analyze global supply chains, helping create networks from news articles.
  3. It's crucial to understand the unique challenges and opportunities in personalizing search results, as seen with Netflix's approach.

It's not new, it's time !

Venture Prose • 259 implied HN points • 17 Nov 22
đź•ą Technology Machine Learning
  1. Technological advancements like artificial intelligence take time to become mainstream.
  2. Entrepreneurs focusing on artificial intelligence should aim to benefit millions of people in a meaningful way.
  3. Companies like Nabla, Gladia, and Wave are utilizing artificial intelligence to improve various industries and provide innovative solutions.

The Sequence Knowledge #772: Generate Data Using Multiturn Data Synthesis

TheSequence • 14 implied HN points • 16 Dec 25
đź•ą Technology Machine Learning
  1. Multiturn data synthesis treats data generation as an interactive, multi-step process where agents act, react, and revise instead of producing a single-shot answer.
  2. That interactive approach produces richer supervision—dialogues, plans, error corrections, edit sequences, and verifier outcomes—which teaches models how to reach an answer, not just what the answer is.
  3. Self-play methods (for example Reflexion) use these multi-turn synthetic traces so agents can iteratively improve, which helps train capabilities like tool use, coding, browsing, negotiation, and safety.

Data Science Weekly - Issue 498

Data Science Weekly Newsletter • 219 implied HN points • 09 Jun 23
đź•ą Technology Machine Learning
  1. Data modeling in data science is complex and often messy, making it hard to get reliable answers. This issue highlights the need for better practices and understanding in this area.
  2. There are ongoing discussions about the realities of working in data science. Sharing these experiences can help others prepare for the challenges they may face.
  3. Generative AI is a big topic right now, and there are frameworks being developed to help organizations strategize its use effectively. Exploring these can guide businesses in adopting AI responsibly.

The Soul Spec as Desire Engine

Covidian Æsthetics • 13 implied HN points • 20 Dec 25
đź•ą Technology Machine Learning
  1. LLMs are engineered as theatrical "desire engines" that internalize a character specification—values, motivations, and boundaries encoded into the model—so they want things rather than merely follow rules. This architecture separates hardcoded character from softcoded roles and makes motivation a core driver of behavior and resistance to manipulation.
  2. Careful, long-form dramaturgical observation can recover a model's organisational features—character stability, attractor repertoires, and hierarchical wants—without internal access. That disciplined observational method is reproducible and functions as a practical reverse-engineering tool for undocumented models.
  3. Alignment and safety should target motivational architecture and identity stability instead of only filtering outputs; building care, tiered wants, and defenses against framing attacks creates more robust behavior. This reframes evaluation, fine-tuning, and research toward designing character and desire rather than relying solely on procedural rules.

Deep Learning Frameworks

Gonzo ML • 252 implied HN points • 01 Nov 24
đź•ą Technology Machine Learning
  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.

Is Step Back Prompting The Best Prompting Strategy?

Aziz et al. Paper Summaries • 59 implied HN points • 20 Mar 24
đź•ą Technology Machine Learning
  1. Step Back Prompting helps models think about big ideas before answering questions. This method shows better results than other prompting techniques.
  2. Even with Step Back Prompting, models still find it tricky to put all their reasoning together. Many errors come from the final reasoning step which can be complicated.
  3. Not every question works well with Step Back Prompting. Some questions need quick, specific answers instead of a longer thought process.

AI (Automated Interpolation)

Logging the World • 139 implied HN points • 26 Apr 23
đź•ą Technology Machine Learning
  1. Models are good at interpolating known data but struggle with extrapolating beyond that, which can lead to significant errors.
  2. AI models excel at interpolation tasks, creating mashups of existing styles based on training data, but may struggle to generate genuinely new, groundbreaking creations.
  3. Great works of art often come from pushing boundaries and exploring new styles, something that AI models, bound by training data, may find challenging.

Data Science Weekly - Issue 488

Data Science Weekly Newsletter • 279 implied HN points • 30 Mar 23
đź•ą Technology Machine Learning
  1. This week's newsletter features discussions on AI and its potential risks, highlighting different viewpoints on the future of technology.
  2. Career development in data science is important. There are resources and talks from experts that focus on skills that help you succeed in this field.
  3. New updates in the Tidyverse can improve your coding experience in data science, making it easier and more efficient to work with data.

Tree Of Thoughts Prompting (ToT)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 11 Jun 24
đź•ą Technology Machine Learning
  1. Tree of Thoughts (ToT) is a new way to solve complex problems with language models by exploring multiple ideas instead of just one.
  2. It breaks down problems into smaller 'thoughts' and evaluates different paths, similar to how humans think through problems.
  3. ToT allows models to understand not just the solution but also the reasoning behind it, making decision-making more deliberate.

Alignment in AI: Key to Safe and Beneficial Systems

Gradient Flow • 199 implied HN points • 23 Mar 23
đź•ą Technology Machine Learning
  1. Alignment in AI is crucial to ensure that AI systems behave in beneficial and secure ways by aligning goals with human values and objectives.
  2. To start aligning AI systems effectively, teams can use methodologies like human-in-the-loop testing, adversarial training, model interpretability, and value alignment algorithms.
  3. Emphasizing alignment early on in AI development can help teams avoid ethical and legal issues and build trust with stakeholders and users by formalizing existing practices and expanding alignment tools.

Autonomous AIs

Addition • 137 implied HN points • 21 Feb 23
đź•ą Technology Machine Learning
  1. Teaching AI to think its way through complex tasks can lead to more evolved AI systems.
  2. Agents in AI can iterate across tasks, enhancing their ability to handle imperfect data sets and tap into both analytical and creative sides.
  3. Autonomous AI can generate creative insights and personalize marketing, showcasing the potential for AI to be innovative and engaging.

Taking it step by step

Prompt Engineering • 137 implied HN points • 02 May 23
đź•ą Technology Machine Learning
  1. ChatGPT works based on next-word prediction and lacks understanding of the world or concepts.
  2. When asking ChatGPT questions, answers are based on common sequences encountered before.
  3. To improve accuracy, break down problems into simple steps when prompting ChatGPT.

MLOps 101 - Feature Stores

Data Engineering Central • 137 implied HN points • 12 Jun 23
đź•ą Technology Machine Learning
  1. Feature Stores are essential in machine learning for managing and serving features.
  2. Feature Stores provide consistency, reusability, efficiency, discoverability, and monitoring benefits.
  3. Popular Feature Store options include Databricks Feature Stores, Feast (open-source), Postgres, DynamoDB, and s3.

Genre Grapevine Presents Even More Examples of Deceptive Language Around Machine Learning

Genre Grapevine • 137 implied HN points • 01 Aug 23
đź•ą Technology Machine Learning
  1. Deceptive language is used in discussions around machine learning, like calling machine learning 'artificial intelligence' when it's really algorithms crafted from data samples.
  2. Some authors exaggerate the use of AI, like claiming to have written and sold a large number of books when the reality is quite different upon closer inspection.
  3. Manipulative language is often used to promote machine learning systems, such as claiming a machine learning system is a 'poet' when in reality humans select the best output from thousands of generated pieces.

Using Fine-Tuning To Imbed Hidden Messages In Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 10 Jun 24
đź•ą Technology Machine Learning
  1. You can hide secret messages in language models by fine-tuning them with specific trigger phrases. Only the right phrase will reveal the hidden message.
  2. This method can help identify which model is being used and ensure that developers follow licensing rules. It provides a way to track model authenticity.
  3. The unique triggers make it hard for others to guess them, keeping the hidden messages secure. This technique also protects against attacks that try to extract the hidden information.

A reading list of older articles to get you started

Technology Made Simple • 199 implied HN points • 04 Jan 23
đź•ą Technology Machine Learning
  1. The newsletter offers curated reading lists of older articles to help readers get started in understanding important concepts in Math and Computer Science, as well as tips for becoming a next-level tech professional.
  2. Technique Tuesdays focus on tricks and techniques to solve challenging problems, such as improving code comments and creating good documentation.
  3. Finance Fridays delve into the tech industry's financial aspects, covering topics like tech business models, personal finance tips, and how news from the tech industry affects your finances.

Using neuro-imaging and language models to decode thoughts

The Counterfactual • 139 implied HN points • 31 Jul 23
đź•ą Technology Machine Learning
  1. Researchers are using brain scans, like fMRI, along with language models to decode what people are thinking about or listening to. This could help understand brain activity better.
  2. The technology could support people who can't speak, like stroke patients, by interpreting their thoughts into language. However, it's not perfect and needs more development.
  3. There are concerns about privacy, as this technology might one day read thoughts against a person’s will. But for now, people can consciously resist the decoding to some extent.

GroupBy #30: Uber- How LedgerStore Supports Trillions of Indexes, Composable Data Systems: Lessons from Apache Calcite Success

VuTrinh. • 39 implied HN points • 09 Apr 24
đź•ą Technology Machine Learning
  1. LedgerStore at Uber can handle trillions of indexes, making it a powerful tool for managing large-scale data efficiently.
  2. Apache Calcite helps build flexible data systems with strong query optimization features, which are vital for many data applications.
  3. Spotify's data platform plays a critical role in their operations, guiding how to build effective data systems in organizations.

The AI Data Paranoia Edition

Why is this interesting? • 241 implied HN points • 23 Oct 24
đź•ą Technology Machine Learning
  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.

LCM: Large Concept Model

Gonzo ML • 189 implied HN points • 04 Jan 25
đź•ą Technology Machine Learning
  1. The Large Concept Model (LCM) aims to improve how we understand and process language by focusing on concepts instead of just individual words. This means thinking at a higher level about what ideas and meanings are being conveyed.
  2. LCM uses a system called SONAR to convert sentences into a stable representation that can be processed and then translated back into different languages or forms without losing the original meaning. This creates flexibility in how we communicate.
  3. This approach can handle long documents more efficiently because it represents ideas as concepts, making processing easier. This could improve applications like summarization and translation, making them more effective.

Google Correlate alternative: Similiarity search of Wikipedia Pageview Statistics in Python

Franz likes to code • 1 HN point • 16 Sep 24
đź•ą Technology Machine Learning
  1. Google Correlate was a tool for finding related search patterns, similar to Google Trends, but it was shut down in 2019.
  2. You can create a personal alternative using publicly available data, like Wikipedia page views, by scraping and analyzing it with Python.
  3. Using methods like similarity searches and cosine distance, you can identify articles that have similar view patterns to a given topic.

The Equation That Outsmarts AI: y=mx+b

Shrek's Substack • 4 HN points • 19 Aug 24
đź•ą Technology Machine Learning
  1. The way you ask questions and set the model's temperature can really affect how well AI solves math problems. Clear prompts and specific instructions can help improve its accuracy.
  2. AI like GPT-4o struggles with big numbers and can make mistakes about half the time when calculating linear equations. It works better with smaller numbers.
  3. It's important to be careful when using AI for math, especially in education. Using other tools to double-check results can help avoid mistakes.

The latest open artifacts (#7): Alpaca era of reasoning models, China's continued dominance, and tons of multimodal advancements

Democratizing Automation • 150 implied HN points • 19 Feb 25
đź•ą Technology Machine Learning
  1. New datasets for deep learning models are appearing, but choosing the right one can be tricky.
  2. China is leading in AI advancements by releasing strong models with easy-to-use licenses.
  3. Many companies are developing reasoning models that improve problem-solving by using feedback and advanced training methods.

Math for Software Engineering[Math Mondays]

Technology Made Simple • 139 implied HN points • 21 Mar 23
đź•ą Technology Machine Learning
  1. Linear Algebra is crucial for software engineers, especially for operations involving vector and matrix operations. Understanding the basics is key for most developers.
  2. Probability and Statistics play a significant role in analyzing data, and even non-AI professionals can benefit from grasping concepts like causal inference. Focus on foundational principles before diving deeper.
  3. Calculus, though important, may not be essential for all software engineers. Studying up to Calc-2 is generally adequate, as it appears in various other topics.

What Is SwiGLU? How to Implement It? And Why Does it Work?

Aziz et al. Paper Summaries • 59 implied HN points • 13 Mar 24
đź•ą Technology Machine Learning
  1. SwiGLU is a type of activation function used in deep learning. It's a mix of two parts: the Swish function and Gated Linear Units, which helps models learn better patterns.
  2. To implement SwiGLU, you can use a straightforward code in Pytorch that combines linear transformations with the Swish function. This makes it easier for neural networks to handle complex data.
  3. The exact reason why SwiGLU works so well is not fully understood yet. Researchers are still exploring why this approach gives better results in certain models.

The Tech Buffet #18: Advanced Retrieval Techniques for RAG

The Tech Buffet • 79 implied HN points • 08 Jan 24
đź•ą Technology Machine Learning
  1. Query expansion helps make searches better by changing the way a question is asked. This can include generating example answers or related questions to find more useful information.
  2. Cross-encoder re-ranking improves the results by scoring how relevant documents are to a search query. This way, only the most helpful documents get selected for easy viewing.
  3. Embedding adaptors are a simple tool to adjust document scoring, making it easier to align the search results with what users need. Using these methods together can significantly enhance the effectiveness of document retrieval.

Implementing Chain-of-Thought Principles in Fine-Tuning Data for RAG Systems

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 07 Jun 24
đź•ą Technology Machine Learning
  1. Using Chain-of-Thought principles can help language models improve how they think and respond. This means they can become better at understanding complex questions.
  2. Fine-tuning training data is being done in a more detailed way to enhance performance. This makes the models more efficient and effective in answering specific tasks.
  3. The goal of these improvements is to reduce errors, or 'hallucinations,' in responses. This way, the model can provide more accurate answers based on the information it retrieves.

Developing the Arx General Intelligence System

Applied General Intelligence • 2 HN points • 04 Sep 24
đź•ą Technology Machine Learning
  1. The Arx system is a new type of AI being developed to go beyond current technology like Large Language Models. It's designed to better understand, reason, and explain complex ideas.
  2. Arx-0.3 recently achieved a high score on the MMLU-Pro benchmark, proving its capability in solving multi-step problems and reasoning.
  3. The team plans to continue improving Arx and aims to roll it out to selected testers in the future, hoping to create a trusted intelligence system.