The hottest Machine Learning Substack posts right now

And their main takeaways
Category
Top Business Topics

The Sequence Research #553: Self-Evaluating LLMs Are Here: Inside Meta AI’s J1 Framework

TheSequence • 63 implied HN points • 30 May 25
🕹 Technology AI Machine Learning Software Innovation Research
  1. LLMs are now used as judges, which is an exciting new trend in AI. This can help improve how we evaluate AI outputs.
  2. Meta AI's J1 framework is a significant development that makes LLMs more like active thinkers rather than just content creators. This means they can make better evaluations.
  3. Using reinforcement learning, J1 allows AI models to learn effective ways to judge tasks. This helps ensure that their evaluations are both reliable and understandable.

DeepSeek-V3: Training

Gonzo ML • 126 implied HN points • 08 Feb 25
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Computer Science
  1. DeepSeek-V3 uses a lot of training data, with 14.8 trillion tokens, which helps it learn better and understand more languages. It's been improved with more math and programming examples for better performance.
  2. The training process has two main parts: pre-training and post-training. After learning the basics, it gets fine-tuned to enhance its ability to follow instructions and improve its reasoning skills.
  3. DeepSeek-V3 has shown impressive results in benchmarks, often performing better than other models despite having fewer parameters, making it a strong competitor in the AI field.

We need to do something about AI now

Philosophy bear • 486 implied HN points • 05 Jan 25
🕹 Technology AI Ethics Digital economy Data Privacy Machine Learning
  1. AI is rapidly advancing and could soon take over many jobs, which might lead to massive unemployment. We need to pay attention and prepare for these changes.
  2. There's a real fear that AI could create a huge gap between a rich elite and the rest of society. We shouldn't just accept this as a given; instead, we should work towards solutions.
  3. To protect our rights and livelihoods, we need to build movements that unite people concerned about AI's impact on jobs and society. It's important to act before it’s too late.

The Weekly Kaitchup #61

The Kaitchup – AI on a Budget • 139 implied HN points • 04 Oct 24
🕹 Technology AI Models Machine Learning Computational efficiency Software Development Tech industry
  1. NVIDIA's new NVLM-D-72B model is a large language model that works well with both text and images. It has special features that make it good at understanding and processing high-quality visuals.
  2. OpenAI's new Whisper Large V3 Turbo model is significantly faster than its previous versions. While it has fewer parameters, it maintains good accuracy for most languages.
  3. Liquid AI introduced new models called Liquid Foundation Models, which are very efficient and can handle complex tasks. They use a unique setup to save memory and improve performance.

Quick reflection on AI in 2024

Gonzo ML • 504 implied HN points • 02 Jan 25
🕹 Technology AI Development Machine Learning Data science Software Engineering Innovations
  1. In 2024, AI is focusing on test-time compute, which is helping models perform better by using new techniques. This is changing how AI works and interacts with data.
  2. State Space Models are becoming more common in AI, showing improvements in processing complex tasks. People are excited about new tools like Bamba and Falcon3-Mamba that use these models.
  3. There's a growing competition among different AI models now, with many companies like OpenAI, Anthropic, and Google joining in. This means more choices for users and developers.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 564

Data Science Weekly Newsletter • 119 implied HN points • 12 Sep 24
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Engineering
  1. Understanding AI interpretability is important for building resilient systems. We need to focus on why interpretability matters and how it relates to AI's resilience.
  2. Testing machine learning systems can be challenging, but starting with basic best practices like CI pipelines and E2E testing can help. This ensures the models work well in real-world scenarios.
  3. Visualizing machine learning models is crucial for better understanding and analysis. Tools like Mycelium can help create clear visual representations of complex data structures.

✨🎄 Some AGI optimism: an early Xmas present

Faster, Please! • 639 implied HN points • 23 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Tech Innovation
  1. OpenAI has released a new AI model called o3, which is designed to improve skills in math, science, and programming. This could help advance research in various scientific fields.
  2. The o3 model performs much better than the previous model, o1, and other AI systems on important tests. This shows significant progress in AI performance.
  3. There's a feeling of optimism about AGI technology as these advancements might bring us closer to achieving more intelligent and capable AI systems.

How does Notion handle 200 billion data entities?

VuTrinh. • 519 implied HN points • 06 Aug 24
🕹 Technology Data Engineering Database Management Analytics Machine Learning
  1. Notion uses a flexible block system, letting users customize how they organize their notes and projects. Each block can be changed and moved around, making it easy to create what you need.
  2. To manage the huge amount of data, Notion shifted from a single database to a more complex setup with multiple shards and instances. This change helps them handle stronger user demands and analytics needs more efficiently.
  3. By creating an in-house data lake, Notion saved a lot of money and improved data processing speed. This new system allows them to quickly get data from their main database for analytics and support new features like AI.

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

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

The Sequence Research #543: The Leaderboard Illusion Challenges Chatbot Arena Type Benchmarks

TheSequence • 119 implied HN points • 16 May 25
🕹 Technology AI Machine Learning Benchmarks Data science Research
  1. Leaderboards in AI help direct research by showing who is doing well, but they can also create problems. They might not show the whole picture of how models really perform.
  2. The Chatbot Arena is a way to judge AI models based on user choices, but it has issues that make it unfair. Some big labs can take advantage of the system more than smaller ones.
  3. To make AI evaluations better, there need to be rules that ensure fairness and transparency. This way, everyone gets a fair chance in the AI race.

Sam Altman Wants $7 Trillion

Astral Codex Ten • 16656 implied HN points • 13 Feb 24
🕹 Technology AI Machine Learning Artificial Intelligence Big Data Computing
  1. Sam Altman aims for $7 trillion for AI development, highlighting the drastic increase in costs and resources needed for each new generation of AI models.
  2. The cost of AI models like GPT-6 could potentially be a hindrance to their creation, but the promise of significant innovation and industry revolution may justify the investments.
  3. The approach to funding and scaling AI development can impact the pace of progress and the safety considerations surrounding the advancement of artificial intelligence.

Data Science Weekly - Issue 563

Data Science Weekly Newsletter • 139 implied HN points • 05 Sep 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. AI prompt engineering is becoming more important, and experts share helpful tips on how to improve your skill in this area.
  2. Researchers in AI should focus on making an impact through their work by creating open-source resources and better benchmarks.
  3. Data quality is a common concern in many organizations, yet many leaders struggle to prioritize it properly and invest in solutions.

Data Science Weekly - Issue 562

Data Science Weekly Newsletter • 179 implied HN points • 29 Aug 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Statistics
  1. Distributed systems are changing a lot. This affects how we operate and program these systems, making them more secure and easier to manage.
  2. Statistics are really important in everyday life, even if we don't see it. Talks this year aim to inspire students to understand and appreciate statistics better.
  3. Understanding how AI models work internally is a growing field. Many AI systems are complex, and researchers want to learn how they make decisions and produce outputs.

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

TheSequence • 42 implied HN points • 27 May 25
🕹 Technology AI safety Machine Learning Benchmarks Evaluation Risk Assessment
  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.

OpenAI's Reinforcement Finetuning and RL for the masses

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

OK, I can partly explain the LLM chess weirdness now

DYNOMIGHT INTERNET NEWSLETTER • 796 implied HN points • 21 Nov 24
🕹 Technology AI LLMs Machine Learning Data science Chess
  1. LLMs like `gpt-3.5-turbo-instruct` can play chess well, but most other models struggle. Using specific prompts can improve their performance.
  2. Providing legal moves to LLMs can actually confuse them. Instead, repeating the game before making a move helps them make better decisions.
  3. Fine-tuning and giving examples both improve chess performance for LLMs, but combining them may not always yield the best results.

The AI Nobels

Dana Blankenhorn: Facing the Future • 59 implied HN points • 09 Oct 24
🕹 Technology AI Machine Learning Research Innovation Computing
  1. Two major Nobel prizes were awarded to individuals working in AI, highlighting its importance and growth in science. Geoffrey Hinton won a physics prize for his work in machine learning.
  2. Current AI technology is still in the early stages and relies on brute force data processing instead of true creativity. The systems we have are not yet capable of real thinking like humans do.
  3. Exciting future developments in AI could come from modeling simpler brains, like that of a fruit fly. This may lead to more efficient AI software without requiring as much power.

8 Insights to Make Sense of OpenAI o3

The Algorithmic Bridge • 424 implied HN points • 23 Dec 24
🕹 Technology AI Machine Learning Computing Innovation Data science
  1. OpenAI's new model, o3, has demonstrated impressive abilities in math, coding, and science, surpassing even specialists. This is a rare and significant leap in AI capability.
  2. There are many questions about the implications of o3, including its impact on jobs and AI accessibility. Understanding these questions is crucial for navigating the future of AI.
  3. The landscape of AI is shifting, with some competitors likely to catch up, while many will struggle. It's important to stay informed to see where things are headed.

A Visual Guide to Mamba and State Space Models

Exploring Language Models • 3942 implied HN points • 19 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Algorithm Design
  1. Mamba is a new modeling technique that aims to improve language processing by using state space models instead of the traditional transformer approach. It focuses on keeping essential information while being efficient in handling sequences.
  2. Unlike transformers, Mamba allows for selective attention, meaning it can choose which parts of the input to focus on. This makes it potentially better at understanding context and relevant information.
  3. The architecture of Mamba is designed to be hardware-friendly, helping it to perform well without excessive resource use. It uses techniques like kernel fusion and recomputation to optimize speed and memory use.

Evaluating Consciousness and Reasoning in Abstract Strategic Games (I)

Encyclopedia Autonomica • 19 implied HN points • 20 Oct 24
🕹 Technology AI Game Theory Machine Learning Computational Models
  1. Tic Tac Toe is a simple game that can be played on bigger boards. The larger boards lead to more complex strategies and reduce the first-move advantage that smaller boards often have.
  2. Different player types can be implemented in the game, such as random players and those using reinforcement learning. These players can have various strengths and weaknesses based on their strategies.
  3. As players compete, the performance of agents like the Cognitive ReAct agent is evaluated. Analyzing how these agents think and make moves helps understand their reasoning and decision-making processes.

Deconstructing the Transformers ReAct JSON System Prompt

Encyclopedia Autonomica • 39 implied HN points • 13 Oct 24
🕹 Technology AI Software Machine Learning Data Analysis Development
  1. Transformers use a specific structure for commands called JSON. This makes it easier to describe actions clearly and effectively.
  2. The system prompt includes rules that the agent must follow, like focusing on one action at a time and using the correct values for inputs.
  3. The design also emphasizes iterative reasoning, where the agent can build on previous observations to make better decisions in tasks.

Juicy Research Ideas and How to Find them?

AI Research & Strategy • 297 implied HN points • 01 Sep 24
🕹 Technology AI Research Idea Generation Machine Learning Data science Academic Publishing
  1. People often find AI research ideas by reading papers, talking to experts, or browsing online platforms like Twitter and GitHub. These are effective ways to spark inspiration.
  2. There are various strategies for generating AI research ideas, such as inventing new tasks, improving existing methods, or exploring gaps in current research. Each approach can lead to publishing valuable findings.
  3. Building better AI research assistants can involve encoding these idea-generation strategies into their programming. This could make them more effective in supporting researchers.

Navigating the AI Jungle - Chat Bots

Erik Explores • 61 implied HN points • 02 Feb 25
🕹 Technology AI Tools User Experience Applications Machine Learning
  1. There are many AI tools available, and it can be confusing to choose the right one. It's helpful to rely on personal experiences to see which tools work well.
  2. OpenAI's ChatGPT is popular for its good interface and features, like voice chat, which makes learning interactive and fun.
  3. DeepSeek allows for using AI models directly on your computer, giving flexibility, but it's important to choose the right model for your specific task.

OpenAI's o1 using "search" was a PSYOP

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

AI Roundup 104: Deep Research

Artificial Ignorance • 63 implied HN points • 07 Feb 25
🕹 Technology AI Development Machine Learning Software updates Tech regulation Data Privacy
  1. OpenAI has launched new models like o3-mini, which is cheaper and faster than previous versions. There's also a new tool called Deep Research that helps with complex online research.
  2. GitHub Copilot has introduced 'Agent mode', allowing it to fix its own code and work more independently. This upgrade makes it a powerful tool for many developers.
  3. The EU has started enforcing the AI Act, which bans harmful AI uses like emotion tracking at work. They are imposing hefty fines for violations, showing they take AI regulation seriously.

The Unreasonable Impact of Gradient Checkpointing for Fine-tuning LLMs

The Kaitchup – AI on a Budget • 79 implied HN points • 03 Oct 24
🕹 Technology AI Machine Learning Data science Programming Computing
  1. Gradient checkpointing helps to reduce memory usage during fine-tuning of large language models by up to 70%. This is really important because managing large amounts of memory can be tough with big models.
  2. Activations, which are crucial for training models, can take up over 90% of the memory needed. Keeping track of these is essential for successfully updating the model's weights.
  3. Even though gradient checkpointing helps save memory, it might slow down training a bit since some activations need to be recalculated. It's a trade-off to consider when choosing methods for model training.

LLMs Fight With Both Hands Tied Behind Their Back

Am I Stronger Yet? • 313 implied HN points • 27 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Programming Data science Software Development
  1. Large Language Models (LLMs) like o3 are becoming better at solving complex math and coding problems, showing impressive performance compared to human competitors. They can tackle hard tasks with many attempts, which is different from how humans might solve them.
  2. Despite their advances, LLMs struggle with tasks that require visual reasoning or creativity. They often fail to understand spatial relationships in images because they process information in a linear way, making it hard to work with visual puzzles.
  3. LLMs rely heavily on knowledge in their 'heads' and do not have access to real-world knowledge. When they gain access to more external tools, their performance could improve significantly, potentially changing how they solve various problems.

Did OpenAI Just Solve Abstract Reasoning?

AI: A Guide for Thinking Humans • 344 implied HN points • 23 Dec 24
🕹 Technology AI Machine Learning Computing Data science Research
  1. OpenAI's new model, o3, showed impressive results on tough reasoning tasks, achieving accuracy levels that could compete with human performance. This signals significant advancements in AI's ability to reason and adapt.
  2. The ARC benchmark tests how well machines can recognize and apply abstract rules, but recent results suggest some solutions may rely more on extensive compute than true understanding. This raises questions about whether AI is genuinely learning abstract reasoning.
  3. As AI continues to improve, the ARC benchmark may need updates to push its limits further. New features could include more complex tasks and better ways to measure how well AI can generalize its learning to new situations.

Can LLMs earn $1M freelancing?

HackerPulse Dispatch • 5 implied HN points • 21 Feb 25
🕹 Technology AI Machine Learning Software Development Multimodal models Freelancing
  1. AI models are being tested to see if they can earn a million dollars through freelancing. But it turns out many of them struggle with real-world tasks.
  2. A new video model can create high-quality videos from text descriptions. It uses advanced techniques to improve video quality and generation.
  3. Small AI models can perform better when they are trained on easier tasks instead of trying to learn from more complex ones.

The Sequence Knowledge # 555: Not All Benchmark are that Simple: An Intro to Multiturn Benchmarks

TheSequence • 14 implied HN points • 03 Jun 25
🕹 Technology AI Machine Learning Evaluation Natural Language Benchmarks
  1. Multi-turn benchmarks are important for testing AI because they make AIs more like real conversation partners. They help AIs keep track of what has already been said, making the chat more natural.
  2. These benchmarks are different from regular tests because they don’t just check if the AI can answer a question; they see if it can handle ongoing dialogue and adapt to new information.
  3. One big challenge for AIs is remembering details from previous chats. It's tough for them to keep everything consistent, but it's necessary for good performance in conversations.

The Sequence Radar #544: The Amazing DeepMind's AlphaEvolve

TheSequence • 63 implied HN points • 18 May 25
🕹 Technology AI Machine Learning Computing Research Innovation
  1. AlphaEvolve is a new AI model from DeepMind that helps discover new algorithms by combining language models with evolutionary techniques. This allows it to create and improve entire codebases instead of just single functions.
  2. One of its big achievements is finding a faster way to multiply certain types of matrices, which has been a problem for over 50 years. It shows how AI can not only generate code but also make important mathematical discoveries.
  3. AlphaEvolve is also useful in real-world applications, like optimizing Google's systems, proving it's not just good in theory but has practical benefits that improve efficiency and performance.

⤴⤵ Up Wing/Down Wing #29

Faster, Please! • 365 implied HN points • 21 Dec 24
🕹 Technology AI Machine Learning Organizational Design Innovation Workplace culture
  1. OpenAI has introduced a new AI called o3, which is really good at solving math and science problems. It even did better than its previous version in many tasks.
  2. Companies will start changing how they work by using AI more in their structure. This can help teams work better together and boost productivity in the workplace.
  3. AI is becoming an important part of how organizations will operate in the future. Successful companies will mix human skills with AI to improve their processes and create more value.

Multi-robot collaboration,Grok 3 , smallest video language model, Generative AI Model for Gameplay, AI co-scientist, Mistral Saba, Fiverr Go, Step-Video-T2V and Step-Audio, Pikaswaps & more

AI Brews • 15 implied HN points • 21 Feb 25
🕹 Technology Artificial Intelligence Robotics Machine Learning Software Development Open Source
  1. Grok 3 is a powerful reasoning model that can handle a massive amount of information at once, making it one of the best tools for chatbots right now.
  2. New advancements in AI, like the Vision-Language-Action model Helix and the generative AI model Muse, are making robots smarter and more capable in their tasks.
  3. AI tools are getting more user-friendly, such as Pikaswaps, which allows you to easily replace parts of videos with your own images, making editing simpler for everyone.

BLT: Byte Latent Transformer

Gonzo ML • 315 implied HN points • 23 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Data processing Software Development Innovation
  1. The Byte Latent Transformer (BLT) uses patches instead of tokens, allowing it to adapt based on the complexity of the input. This means it can process simpler inputs more efficiently and allocate more resources to complex ones.
  2. BLT can accurately encode text at a byte level, overcoming issues with traditional tokenization that often lead to mistakes in understanding languages and simple tasks like counting letters.
  3. BLT architecture has shown better performance than older models, handling tasks like translation and sequence manipulation more effectively. This advancement could improve the application of language models across different languages and reduce errors.

A Heuristic Proof of Practical Aligned Superintelligence

Transhuman Axiology • 39 implied HN points • 11 Oct 24
🕹 Technology AI Machine Learning Superintelligence Ethics Philosophy
  1. Aligned superintelligence can be created. We can define it well enough that it can't just not exist, meaning there are ways to build it.
  2. Modern AI can mimic human thinking tasks effectively. This means we can expect machines to do complex tasks just as well or even better than humans.
  3. AI alignment isn't just possible, but it might be easier than we think. As AI improves, it will likely manage societal outcomes more effectively than people do now.

Report: OpenAI Spends Millions a Year Miscounting the R's in 'Strawberry'

The Algorithmic Bridge • 573 implied HN points • 22 Nov 24
🕹 Technology Artificial Intelligence Machine Learning Tech Ethics Data science Software Development
  1. OpenAI has spent a lot of money trying to fix an issue with counting the letter R in the word 'strawberry.' This problem has caused a lot of confusion among users.
  2. The CEO of OpenAI thinks the problem is silly but feels it's important to address because users are concerned. They are also looking into redesigning how their models handle letter counting.
  3. Some employees joked about extreme solutions like eliminating red fruits to avoid the R issue. They are also thinking of patches to improve letter counting, but it's clear they have more work to do.

OpenAI Deep Research Explains Itself

From the New World • 26 implied HN points • 06 Feb 25
🕹 Technology AI Hardware Software Data science Machine Learning
  1. AI hardware has evolved significantly, from early specialized chips to powerful GPUs and TPUs. These advancements make training AI models much faster and more efficient.
  2. The design of algorithms, especially with transformers, has greatly improved AI's ability to understand and generate language. These models can now learn complex patterns that were hard to capture before.
  3. Building and maintaining large AI systems requires careful planning and practices. Companies need efficient workflows and monitoring systems to manage data, hardware, and software effectively.

GPTs Are Maxed Out

The Algorithmic Bridge • 647 implied HN points • 11 Nov 24
🕹 Technology AI Computing Machine Learning Data science Software Development
  1. AI companies are hitting limits with current models. Simply making AI bigger isn't creating better results like it used to.
  2. The upcoming models, like Orion, may not meet the high expectations set by previous versions. Users want more dramatic improvements and are getting frustrated.
  3. A new approach in AI may focus on real-time thinking, allowing models to give better answers by taking a bit more time, though this could test users' patience.

Not All Layers Are Equal

Gonzo ML • 63 implied HN points • 31 Jan 25
🕹 Technology AI Research Machine Learning Data science Neural Networks Computational Theory
  1. Not every layer in a neural network is equally important. Some layers play a bigger role in getting the right results, while others have less impact.
  2. Studying how information travels through different layers can reveal interesting patterns. It turns out layers often work together to make sense of data, rather than just acting alone.
  3. Using methods like mechanistic interpretability can help us understand neural networks better. By looking closely at what's happening inside the model, we can learn which parts are doing what.