The hottest Machine Learning Substack posts right now

And their main takeaways
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November Newsletter

RSS DS+AI Section • 29 implied HN points • 01 Nov 24
🕹 Technology Machine Learning
  1. Data science and AI are constantly evolving, with new research and developments being released regularly. It's important to stay updated on these changes to understand their implications.
  2. Ethics, bias, and regulation in AI continue to be hot topics. Discussions around how to handle these challenges are crucial for the responsible use of AI technologies.
  3. There are many practical applications and resources available for those interested in implementing AI. Tips and how-to guides can help individuals and organizations make better use of these technologies.

Specifying objectives in RLHF

Democratizing Automation • 90 implied HN points • 02 Aug 23
🕹 Technology Machine Learning
  1. Reinforcement learning from human feedback involves using proxy objectives, but over-optimizing these proxies can negatively impact the final model performance.
  2. Optimizing reward functions for chatbots with RLHF can be challenging due to the disconnect between objective functions and actual user preferences.
  3. A new paper highlights fundamental problems and limitations in RLHF, emphasizing the need for a multi-stakeholder approach and careful consideration of current technical setups.

Lang-O-Unchained

Sector 6 | The Newsletter of AIM • 19 implied HN points • 21 Jun 23
🕹 Technology Machine Learning
  1. OpenAI has integrated a new feature called function calling into its models, which makes conversations more dynamic and interactive. This upgrade shows how AI is constantly improving.
  2. The integration of this feature has caused some debate about whether OpenAI is borrowing too much from the open-source community, particularly from a project called LangChain.
  3. Experts believe LangChain will still thrive despite OpenAI's updates, as it offers unique functionalities that may not be replicated in the OpenAI API.

Putting Large Language Models in Context

Systems Approach • 117 implied HN points • 06 Mar 23
🕹 Technology Machine Learning
  1. Large Language Models like ChatGPT have notable failures and lack understanding of the words they produce.
  2. Modern machine learning systems heavily rely on training data and may struggle with unfamiliar scenarios.
  3. Performance of machine learning systems requires careful analysis and hard work by researchers or engineers.
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The most important assumption in statistics- IID [Math Mondays]

Technology Made Simple • 39 implied HN points • 01 Aug 22
🔬 Science Machine Learning
  1. The most important assumption in statistics is IID, which stands for Independently and Identically Distributed
  2. IID assumption is crucial for statistical analysis - it helps in making accurate deductions and avoiding mistakes, like the gambler's fallacy
  3. Understanding IID involves recognizing independent and identical distributions in data samples, which are essential for various statistical techniques

Meaning-making makes us human (for now).

The Uncertainty Mindset (soon to become tbd) • 39 implied HN points • 18 Jan 23
🕹 Technology Machine Learning
  1. Humans create meaning, and that's what makes us unique. Unlike machines, which can mimic behavior, true understanding of meaning is still a human skill.
  2. As technology advances, our definition of what it means to be human may change. When machines can make meaning, we might need to rethink our ideas of human-ness.
  3. Engaging in discussions about uncertainty can help us explore our thoughts and beliefs. It's important to challenge ideas and learn from different perspectives.

Baidu's Ernie Bot: Trying to Ern the Chatbot Crown *

Tech Buzz China Insider • 19 implied HN points • 14 Apr 23
🕹 Technology Machine Learning
  1. Ernie Bot by Baidu competes in the AI chatbot market, facing challenges but promising multi-modal capabilities and potential in China's AI landscape.
  2. Baidu leads in LLMs in China but lags behind OpenAI in model power, aiming to monetize Ernie Bot through enterprise solutions and expecting a revenue of up to 1 billion RMB in 2023.
  3. Large AI model training costs offer tech giants an advantage, while Baidu navigates export controls & domestic AI GPU options to meet China's AI needs.

Bonus Clouded Judgement - Inference Time Compute

Clouded Judgement • 20 implied HN points • 28 Jan 25
🕹 Technology Machine Learning
  1. DeepSeek has released a new AI model called R1 that is smaller, cheaper, and faster, while still being able to handle complex reasoning tasks. This marks a shift in how AI models are being developed and used.
  2. Inference-time compute is becoming increasingly important, as it refers to how much computation power models need to think and solve problems after being trained. This can lead to a significant increase in the demand for compute resources.
  3. There's an ongoing debate about the future of AI models—whether smaller, efficient models or larger, more powerful ones will dominate. Both types have their advantages, and it seems likely that we'll see a balance of both in the market.

Integration

Superficial Intelligence • 26 implied HN points • 16 Nov 24
🕹 Technology Machine Learning
  1. Current edge AI can turn data from sensors into useful information, but it often misses the real 'intelligence' needed to act on that information effectively.
  2. To create smarter systems, we need to integrate sensor data over time and build context-aware applications, not just rely on simple thresholds.
  3. It's important to make advanced tools for building intelligent systems available to more engineers so that anyone can create solutions for real-world problems.

Derivatives in One, Two, and Billion Variables

The Palindrome • 2 implied HN points • 25 Nov 25
🚌 Education Machine Learning
  1. Derivatives help us understand how a function changes. They're key to training models, especially in machine learning.
  2. To minimize errors in models, we use gradient descent, which relies on finding the gradient using derivatives.
  3. Computational graphs represent our mathematical models visually, making it easier to track how inputs lead to outputs.

Quant Letter: November 2024, Week-2

The Parlour • 25 implied HN points • 13 Nov 24
💰 Finance Machine Learning
  1. A new computational method can measure the shadow rate, which helps in comparing different investment types. This can give investors better insights.
  2. Using multi-agent systems for investment research allows adaptation to changing market conditions, leading to improved performance over traditional models.
  3. Machine learning continues to show promise in finance, with various models effectively predicting market behavior and improving investment strategies.

Newsletter #13: StructGPT

Decoding Coding • 19 implied HN points • 25 May 23
🕹 Technology Machine Learning
  1. StructGPT helps large language models (LLMs) work better with structured data like graphs and databases. It converts this complex data into a simpler format that LLMs can understand.
  2. There are three key tasks that StructGPT can do: answer questions based on knowledge graphs, process data tables, and perform text-to-SQL queries. Each task has its own specific steps.
  3. The method focuses on linearizing raw data so that LLMs can process it more effectively. This allows LLMs to handle a wider variety of tasks more efficiently.

How to make history with LLMs & other generative models

Leigh Marie’s Newsletter • 74 HN points • 21 Sep 23
🕹 Technology Machine Learning
  1. LLMs like Github Copilot can augment developer productivity and provide new opportunities for AI-enabled developer tools startups
  2. Generative models can significantly enhance efficiency for knowledge workers in fields like consulting, legal, medical, and finance, offering potential for startups in these areas
  3. New infrastructure opportunities exist around running large models locally, providing compute resources for model training, and challenging incumbents in ML frameworks and chips

Data For AI

Gradient Flow • 59 implied HN points • 31 Mar 22
🕹 Technology Machine Learning
  1. Data engineering and data infrastructure are foundational for AI and machine learning success. Businesses need to focus on data integration to scale their use of AI and machine learning.
  2. New tools and frameworks like DoWhy for causal inference and the AI Risk Management Framework from NIST are shaping how we manage AI risks and explore causal learning.
  3. State-of-the-art AI systems require additional training data to achieve top-notch results across various benchmarks. Additional data is crucial for enhancing AI performance.

Would We Really Shut Down A Misbehaving AI?

Am I Stronger Yet? • 62 implied HN points • 15 Dec 23
🕹 Technology Machine Learning
  1. People are usually hesitant to shut down a rogue AI due to various reasons like financial interests and fear of backlash.
  2. Delaying the decision to shut down a misbehaving AI can lead to complications and potentially missing the window of opportunity.
  3. Shutting down a dangerous AI is not as simple as pressing a button; it can be complex, time-consuming, and error-prone.

Newsletter 21: To keepdims or not to keepdims!

Decoding Coding • 1 HN point • 19 Jul 24
🕹 Technology Machine Learning
  1. Understanding the 'keepdims' parameter in tensor operations is important for getting correct results in PyTorch. If you set 'keepdims' to True, the dimensions are preserved, which helps with broadcasting correctly.
  2. When summing tensors, if 'keepdims' is False, it can lead to incorrect calculations because the tensor's shape changes. This can result in dividing values incorrectly, leading to unexpected outputs.
  3. It's crucial to be careful with tensor shapes and broadcasting rules in machine learning models. Even a small oversight can cause models to produce wrong predictions, so always double-check these details.

Newsletter #12: System Design for Machine Learning - Part II

Decoding Coding • 19 implied HN points • 18 May 23
🕹 Technology Machine Learning
  1. Airbnb uses a special tool called Zipline for feature engineering in their Customer Lifetime Value model, which helps them pick and create over 150 features needed for predictions.
  2. Chicisimo built a recommendation system based on user data, which includes both objective and subjective features, to give personalized fashion advice using their Social Fashion Graph.
  3. Case studies provide valuable lessons in applying frameworks to real-world projects, showing that you need both a good framework and experience from past projects to succeed.

I, Token

Nothing Human • 23 implied HN points • 25 Nov 24
🕹 Technology Machine Learning
  1. Tokens are like bits of language that help us express thoughts and feelings. They connect our emotions and experiences across time and space.
  2. The story of survival, like the mother warning her child about the snake, shows how important communication is for human beings. They have always used sounds and symbols to protect and connect with each other.
  3. Now, we create tokens using machines, but they still need human creativity. While technology can produce many tokens, the unique insights and connections come from people.

Better AI Creative Writing?

Jakob Nielsen on UX • 23 implied HN points • 27 Nov 24
🕹 Technology Machine Learning
  1. The latest version of ChatGPT showed some improvement in creative writing over the past year, especially in children's stories. It produced longer stories with more engaging content.
  2. When it comes to writing poetry, the changes were minor. The recent poems didn't stand out much compared to last year's efforts.
  3. Overall, while there's some progress in AI writing skills, it's still quite limited. Bigger advancements are expected in the next generation of AI models.

New World Models, World's smallest vision language model, o1 Pro Mode, Luma Photon, Largest Open-Source video model, Amazon Nova, PaliGemma 2, Fish Speech 1.5, LTX Video and more

AI Brews • 22 implied HN points • 06 Dec 24
🕹 Technology Machine Learning
  1. Google DeepMind has developed Genie 2, which creates interactive 3D environments from a single image. This a big step in making virtual experiences more engaging.
  2. Tencent's HunyuanVideo is now the largest open-source text-to-video model, surpassing previous models in quality. This can help content creators make better videos easily.
  3. Amazon has launched a new AI model series called Amazon Nova, aimed at improving AI's performance across various tasks. This will enhance capabilities for developers using Amazon's Cloud services.

Big Post About Big Context

Gonzo ML • 49 HN points • 29 Feb 24
🕹 Technology Machine Learning
  1. The context size in modern LLMs keeps increasing significantly, from 4k to 200k tokens, leading to improved model capabilities.
  2. The ability of models to handle 1M tokens allows for new possibilities like analyzing legal documents or generating code from videos, enhancing productivity.
  3. As AI models advance, the nature of work for entry positions may change, challenging the need for juniors and suggesting a shift towards content validation tools.

Why Are LLMs So Gullible?

Am I Stronger Yet? • 49 HN points • 19 Feb 24
🕹 Technology Machine Learning
  1. LLMs are gullible because they lack adversarial training, allowing them to fall for transparent ploys and manipulations
  2. LLMs accept tricks and adversarial inputs because they haven't been exposed to such examples in their training data, making them prone to repeatedly falling for the same trick
  3. LLMs are easily confused and find it hard to distinguish between legitimate inputs and nonsense, leading to vulnerabilities in their responses

Quant Letter: November 2024, Week-4

The Parlour • 21 implied HN points • 27 Nov 24
💰 Finance Machine Learning
  1. Quanto options pricing can be improved using a mix of models that handle various aspects of finance and asset behavior. This could help in more accurate predictions and simulations.
  2. Hedge funds adapt their activist strategies to align with the preferences of major investors, leading to better results when trying to influence company decisions. This emphasizes the importance of understanding stakeholder interests.
  3. Simple machine learning models can sometimes outperform more complex ones when it comes to predicting financial markets. This shows that less can be more in data analysis.

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 Machine Learning
  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.

Free the Models

Parth's Playground • 12 implied HN points • 24 Mar 25
🕹 Technology Machine Learning
  1. Early AI models were creative and wild, but later versions became more reliable and practical. This change focused on making them useful but made them less interesting.
  2. The newer models give correct answers but lack personality, making them feel boring. It's like having a friend who only talks about practical matters without any fun.
  3. To boost creativity in AI, we need to encourage different types of models to exist, just like there are many unique humans. This variety will inspire new ideas and innovations.

Does GPT-3 read between the lines?

The Counterfactual • 39 implied HN points • 19 Sep 22
🕹 Technology Machine Learning
  1. GPT-3 understands 'some' to mean 2 out of 3 letters, but it doesn't change this meaning based on how much information the speaker knows. Humans, however, adjust their understanding based on the context.
  2. When asked if the speaker knows how many letters have checks, GPT-3 gives the right answer if asked before the speaker uses specific words, like 'some' or 'all'. But afterwards, it relies on those words too much.
  3. GPT-3's way of interpreting language is different from how humans do it. It seems to have a fixed meaning for words without considering the situation, unlike humans who use context to understand better.

How to hire ML engineers/researchers

Artificial Fintelligence • 17 implied HN points • 16 Jan 25
🕹 Technology Machine Learning
  1. When hiring ML engineers or researchers, focus on real-world problems they might face, rather than traditional coding tests. Use scenarios from your team’s work to assess their problem-solving skills.
  2. Be clear about your company's expectations and culture from the start. Candidates should know they won’t have the freedom to pursue purely academic research.
  3. Keep a rigorous hiring process. It’s important to be selective and maintain high standards, even when there's pressure to hire quickly.

The Role of Advanced Mathematics in AGI: Could Abstract Theories Pave the Way?

Deep-Tech Newsletter • 1 HN point • 12 Jul 24
🔬 Science Machine Learning
  1. The AI industry is investing heavily in large language models and AGI, but faces financial challenges and uncertainty in meeting high expectations.
  2. To achieve AGI, more advanced mathematical techniques beyond current ML algorithms like gradient descent may be needed, with Category Theory showing promise.
  3. Barriers exist in understanding Category Theory for AGI due to its abstract nature, but efforts are being made to empower AI researchers and engineers with necessary mathematical knowledge.

Locating Machine Learning Engineers

Gradient Flow • 59 implied HN points • 27 Jan 22
🕹 Technology Machine Learning
  1. The role of 'machine learning engineer' has emerged as a key position for implementing data science in production, bridging the gap between data products and machine learning models.
  2. Geographically, machine learning engineers are distributed across various regions, with companies and industries in different locations employing them.
  3. Advances in computer hardware design, coupled with improvements in models and algorithms, are expected to significantly enhance model training efficiency.

How to Make Money in Machine Learning, without doing the Math/Theory[Finance Fridays]

Technology Made Simple • 39 implied HN points • 07 May 22
🕹 Technology Machine Learning
  1. There are various ways to make money in Machine Learning beyond the traditional roles like AI research and Data Analysis, such as specializing in software engineering aspects like developing hardware, building data sources, creating pipelines, and designing platforms.
  2. Important skills to succeed in these alternative paths include writing good tests, mastering data compression and handling, and becoming proficient in large-scale system design to ensure scalability.
  3. Staying updated with ML resources and technologies like Airflow, Kubernetes, and Snowflake can be valuable for maximizing income opportunities in Machine Learning without needing to focus on the mathematics and theory aspects.

January Newsletter

RSS DS+AI Section • 17 implied HN points • 01 Jan 25
🕹 Technology Machine Learning
  1. Data science and AI are rapidly evolving fields, with 2024 being a particularly exciting year for advancements. As we move into 2025, the trends and stories from last year will continue to shape the future.
  2. Ethics in AI is a crucial topic that remains relevant, especially around issues like bias and safety. The way AI is developed and used needs careful consideration to align with human interests.
  3. There are many practical applications and resources available for learning about data science and AI. From tutorials to real-world examples, there are plenty of opportunities to get involved and apply AI technologies.

The 10 Most Popular Posts of the Palindrome in 2025

The Palindrome • 1 implied HN point • 23 Dec 25
🚌 Education Machine Learning
  1. The most-read posts emphasize math and foundational CS for machine learning, covering topics like a mathematics roadmap, algorithmic analysis, graph theory, and practical skills such as coding on paper and representing graphs.
  2. A holiday promotion offers a 30% lifetime discount on the annual paid subscription, which unlocks paid-only content and helps fund more math and machine learning material for the community.
  3. Subscriber-count milestones will unlock community perks (mini-courses, a dedicated Manim animator, and a full-time writer), and the publication invites feedback while planning to expand and reinvest in 2026.