The hottest Machine Learning Substack posts right now

And their main takeaways
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AGI is Not Multimodal

The Gradient • 49 implied HN points • 04 Jun 25
🕹 Technology Machine Learning
  1. Recent AI models have shown impressive capabilities, but they don't represent true human-like intelligence. They succeed because of scaled hardware and not because they think like us.
  2. Trying to combine different AI models into a single system won't lead to real understanding or human-level AI. This approach is flawed and unlikely to work.
  3. Instead of mixing models, we should focus on how AI interacts with the world and learns from it. Understanding AI should be about its actions and experiences in the environment.

How could we know if Large Language Models understand language?

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

How to ARCHITECT a search engine like Google Search[System Design Sundays]

Technology Made Simple • 99 implied HN points • 29 Jan 23
🕹 Technology Machine Learning
  1. Design complex systems by layering multiple smaller solutions for better results instead of focusing on individually engineered tasks.
  2. Building a search engine like Google involves accommodating various types of search results like images, text, gifs, and videos while ensuring search quality.
  3. Handling the massive scale of data in Google's search engine system involves using semi-supervised labeling techniques to manage unlabeled data efficiently.

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

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

TechTalks • 39 implied HN points • 29 Jan 24
🕹 Technology Machine Learning
  1. A new technique called Self-Rewarding Language Models helps LLMs improve on instruction-following tasks by creating and evaluating their own training data.
  2. SRLM starts with a base model and seed dataset for fine-tuning instructions, generates new examples and responses, and ranks them using a special prompt.
  3. Experiments show that SRLM enhances model performance in instruction-following and outperforms some existing models on the AlpacaEval benchmark.

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

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

The Race for AI Reasoning is Challenging our Imagination

TheSequence • 112 implied HN points • 22 Dec 24
🕹 Technology Machine Learning
  1. OpenAI and Google are in a fierce competition to improve AI reasoning capabilities. Their advancements could lead to machines that think and solve problems more like humans.
  2. Better reasoning in AI could transform many fields, such as healthcare and law. Imagine AI helping doctors diagnose diseases with high accuracy or assisting lawyers in complex cases.
  3. As AI models become smarter at reasoning, they will change the way we live and work. This could open up many new opportunities and challenges for society.

Weekly Top Picks #91

The Algorithmic Bridge • 116 implied HN points • 09 Dec 24
🕹 Technology Machine Learning
  1. Companies are figuring out how to price AI agents as they become more common. This is important because the cost will affect how businesses use AI technology.
  2. ChatGPT will soon allow users to input videos, which will make interactions even richer and more dynamic.
  3. OpenAI is releasing a new model called o1, which is better for math, coding, and science. It's more accurate and can handle different types of questions more efficiently.

How I Aced Machine Learning Interviews: My Personal Playbook

The ML Engineer Insights • 7 HN points • 03 Jul 24
🕹 Technology Machine Learning
  1. Machine learning interviews often cover four main rounds: breadth, depth, system design, and coding.
  2. Preparing for machine learning interviews requires a balance of understanding fundamental topics and practicing with sample questions.
  3. Machine learning system design interviews focus on problem definition, evaluation metrics, feature and data handling, model development, and deployment strategies.

Q*, Reinforcement Learning and Search

Yuxi’s Substack • 58 implied HN points • 24 Nov 23
🕹 Technology Machine Learning
  1. Q* represents the optimal Q value in reinforcement learning integrating learning and search.
  2. Reinforcement learning helps an agent learn a policy to maximize long-term rewards through interactions with the environment.
  3. RL for LLMs combines learning and search techniques for next-generation language models.

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

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

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

TheSequence • 133 implied HN points • 29 Oct 24
🕹 Technology Machine Learning
  1. State space models (SSMs) are a promising alternative to transformers for processing data. They handle long sequences more efficiently without losing important information.
  2. SSMs are designed to be computationally efficient, scaling linearly with context windows unlike transformers which scale quadratically. This makes them better for tasks needing a lot of information.
  3. Recent models like Mamba show that SSMs can outperform transformers in performance and efficiency, especially for tasks that require understanding long contexts.

Fine-Tuning OpenAI GPT-4o mini

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

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

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

Week #4: Overview Of Conformal Predictors

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

Make softmax great again

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

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

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

What is inner alignment?

Musings on the Alignment Problem • 259 implied HN points • 08 May 22
🕹 Technology Machine Learning
  1. Inner alignment involves the alignment of optimizers learned by a model during training, separate from the optimizer used for training.
  2. In rewardless meta-RL setups, the outer policy must adjust behavior between inner episodes based on observational feedback, which can lead to inner misalignment by learning inaccurate representations of the training-time reward function.
  3. Auto-induced distributional shift can lead to inner alignment problems, where the outer policy may cause its own inner misalignment by changing the distribution of inner RL problems.

Edge 371: Two-Step LLM Reasoning with Skeleton of Thoughts

TheSequence • 266 implied HN points • 20 Feb 24
🕹 Technology Machine Learning
  1. The Skeleton-of-Thoughts (SoT) technique introduces a two-stage process for answer generation in Large Language Models (LLMs) by first creating a basic outline or 'skeleton' of the response and then elaborating on each point simultaneously.
  2. SoT was initially designed to reduce latency in end-to-end inference in LLMs but has significantly impacted the reasoning space by mimicking non-linear human thought patterns.
  3. Microsoft's original SoT paper and the Dify framework for building LLM apps are discussed in Edge 371, providing insights into the innovative techniques used in the field of Large Language Models.

A Few Thoughts about O1

AI Research & Strategy • 2 HN points • 12 Sep 24
🕹 Technology Machine Learning
  1. The new O1 models from OpenAI show impressive results, but they can't be fairly compared to earlier models because they use a different reasoning process.
  2. OpenAI's O1 models are not meant to replace older models entirely and require a system to decide when to use them, which could complicate things.
  3. OpenAI has a controversial pricing strategy, where users might pay for features they can't fully see or understand, raising concerns about transparency.

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

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

Top 10 Events 2024

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

🔥 LLMs on Fire 🔥

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

Metaphors And Analogies

Teaching computers how to talk • 115 implied HN points • 24 Nov 24
🕹 Technology Machine Learning
  1. Metaphors and analogies are a big part of how we talk about AI. They can help us understand things but sometimes make it harder to see what's really going on.
  2. Many people see AI as having human-like qualities, which can lead to overestimating its abilities. We should remember that AI is just a tool and not something with a mind.
  3. It's important to rethink how we view AI and use better descriptions. AI should help us improve our thinking and creativity, not replace them.

Faster computers afford dumber solutions

Wednesday Wisdom • 104 implied HN points • 18 Dec 24
🕹 Technology 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.

Must Learn AI Security Compendium 14: Securing On-prem LLMs

Rod’s Blog • 59 implied HN points • 09 Nov 23
🕹 Technology Machine Learning
  1. On-prem LLMs offer privacy benefits by keeping data and texts secure from unauthorized access or leaks.
  2. On-prem LLMs enhance security by reducing cyber attack risks due to not relying on external components or services.
  3. On-prem LLMs improve performance by utilizing an organization's own hardware and software resources for efficient language generation.

The Tech Buffet #12: Improve RAG Pipelines With These 3 Indexing Methods

The Tech Buffet • 59 implied HN points • 06 Nov 23
🕹 Technology Machine Learning
  1. You can index data in different ways to improve how retrieval works. This means you don't always have to use the same data for both indexing and retrieving.
  2. One method is to break chunks of data into smaller parts. This helps ensure that the information retrieved is more relevant to what the user is looking for.
  3. Another approach is to index data by the questions they answer or their summaries. This makes it easier to find the right content, even if a user isn't very clear in their queries.

Edge 452: The AI Magic Behind Google's NotebookLM Audio Features

TheSequence • 112 implied HN points • 28 Nov 24
🕹 Technology Machine Learning
  1. NotebookLM is a popular AI tool for generating podcasts, using clever techniques that combine humor and realistic dialogue. People are starting to recognize the voices in these generated podcasts.
  2. The audio features of NotebookLM are powered by technologies from Google DeepMind, notably SoundStorm and AudioLM, which focus on creating realistic sounds and speech.
  3. Research in audio generation is advancing quickly, aiming to develop systems that can produce coherent and realistic speech and music. Google DeepMind is leading the way in this exciting field.

How to Fine-Tune Your Own Mistral-7B

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

Qualcomm’s Cloud AI 100 PCIe: Now For All

More Than Moore • 93 implied HN points • 06 Jan 25
🕹 Technology Machine Learning
  1. Qualcomm's Cloud AI 100 PCIe card is now available for the wider embedded market, making it easier to use for edge AI applications. This means businesses can run AI locally without relying heavily on cloud services.
  2. There are different models of the Cloud AI 100, offering various compute powers and memory capacities to suit different business needs. This flexibility helps businesses select the right fit based on how much AI processing they require.
  3. Qualcomm is keen to support partnerships with OEMs to build appliances that use their AI technology, but they are not actively marketing it widely. Interested users are encouraged to reach out directly for collaboration opportunities.

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

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

Synthetic Data In A Nutshell

Three Data Point Thursday • 39 implied HN points • 11 Jan 24
🕹 Technology Machine Learning
  1. Synthetic data is fake data that is becoming increasingly practical and valuable.
  2. Generative AI and the growing gap between data demand and availability are driving forces for the usefulness of synthetic data.
  3. Synthetic data is beneficial in various fields beyond just machine learning, offering opportunities for innovation and improvement.

Evaluating LLM Agents and Applications

LLMs for Engineers • 79 implied HN points • 11 Jul 23
🕹 Technology Machine Learning
  1. Evaluating large language models (LLMs) is important because existing test suites don’t always fit real-world needs. So, developers often create their own tools to measure accuracy in specific applications.
  2. There are four main types of evaluations for LLM applications: metric-based, tools-based, model-based, and involving human experts. Each method has its strengths and weaknesses depending on the context.
  3. Understanding how well LLM applications are performing is essential for improving their quality. This allows for better fine-tuning, compiling smaller models, and creating systems that work efficiently together.

Machine Learning at a Pegacorn

Gradient Flow • 199 implied HN points • 04 Aug 22
🕹 Technology Machine Learning
  1. Major tech companies are investing in the Metaverse along with AI and cloud computing, based on 2022 coverage.
  2. In the podcast 'Data Exchange', topics like data infrastructure for computer vision and machine learning at Gong are discussed.
  3. Tree-based learners outperform neural network-based learners on tabular data, and Transformers are used to cluster papers from ICML 2022.

Reconsidering OpenAI

Dana Blankenhorn: Facing the Future • 59 implied HN points • 21 Nov 23
🕹 Technology Machine Learning
  1. OpenAI faced issues due to the contradiction of being a non-profit owning a for-profit entity.
  2. Microsoft's investment in OpenAI through Azure services raised questions about the true value and motives of the partnership.
  3. Generative AI, like ChatGPT, is not true Artificial General Intelligence, but a tool with limitations.

Alibaba QwQ Really Impresses at GPT-o1 Levels

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