The hottest Machine Learning Substack posts right now

And their main takeaways
Category
Top Business Topics

A brief history of speech to text + how it actually works

Mythical AI • 19 implied HN points • 08 Mar 23
🕹 Technology Machine Learning
  1. Speech to text technology has a long history of development, evolving from early systems in the 1950s to today's advanced AI models.
  2. The process of converting speech to text involves recording audio, breaking it down into sound chunks, and using algorithms to predict words from those chunks.
  3. Speech to text models are evaluated based on metrics like Word Error Rate (WER), Perplexity, and Word Confusion Networks (WCNs) to measure accuracy and performance.

Monitoring Workflow for Machine Learning Systems

Santiago and the ML Models • 19 implied HN points • 06 Mar 23
🕹 Technology Machine Learning
  1. Machine learning models naturally degrade over time due to changing environments and dynamics.
  2. Traditional ML monitoring methods focus on data drift and realized model performance, which can be limited.
  3. A new ML monitoring workflow emphasizes estimating model performance in real-time and using drift detection for root cause analysis, reducing false alerts.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Market Map and Analysis: Gen AI Business Productivity Companies

The Strategy Deck • 19 implied HN points • 27 Jun 23
🕹 Technology Machine Learning
  1. Generative AI is transforming enterprise productivity by automating tasks and workflows.
  2. Key segments in this field include AI Meeting Assistants, Business Knowledge Base Platforms, and Application Building Tools.
  3. Companies are developing tools like AI assistants for meetings, knowledge base platforms, and app building tools to enhance business productivity.

Lessons from Accelerating Knowledge

Embracing Enigmas • 19 implied HN points • 02 May 23
🕹 Technology Machine Learning
  1. Machine learning progresses quickly due to factors like the leaderboard effect, ease of experimentation, and decreased cost of computation.
  2. Researchers and practitioners in machine learning benefit from sharing knowledge and ideas, leading to rapid improvements in the field.
  3. Machine learning's broad applications across various industries contribute to its growth, attracting investment and fostering cross-pollination of ideas.

job killer or job creator?

aidaily • 19 implied HN points • 24 Apr 23
🕹 Technology Machine Learning
  1. Stability AI introduced a new language model called StableLM which can handle various types of written content.
  2. RedPajama is challenging big tech companies with an open-source messaging app, planning to release fully-trained base models soon.
  3. Microsoft is joining the AI chip wars by developing their own AI chip for machine learning.

The unreasonable effectiveness of LLMs

John’s Contemplations • 19 implied HN points • 08 Mar 23
🕹 Technology Machine Learning
  1. LLMs have displayed surprising reasoning abilities like solving math problems using words.
  2. LLMs can be trained to use tools to address their weaknesses and improve tasks like code generation.
  3. LLMs work well due to the general nature of language, the breakdown of complex tasks into simpler steps, and the efficiency of neural networks like Transformers.

Should Meta AI be Worried?

Sector 6 | The Newsletter of AIM • 19 implied HN points • 03 Oct 23
🕹 Technology Machine Learning
  1. Meta AI faces more competition as other companies are also releasing strong AI models like Stability AI's Stable LM 3B.
  2. There are concerns that Meta might shift from open-source to a closed-source approach, which could limit collaboration.
  3. Mark Zuckerberg is unsure about making their next AI model, Llama 3, open-source, similar to trends seen in other companies.

Ground-truth-in-the-loop

Yuxi’s Substack • 19 implied HN points • 18 Jul 23
🕹 Technology Machine Learning
  1. Ground-truth-in-the-loop is crucial for designing and evaluating systems, especially in AI and machine learning.
  2. For AI systems, having trustworthy training data, evaluation feedback, and a reliable world model is essential.
  3. Researchers should inform non-experts about limitations and potential issues when building systems without ground-truth.

The Last Programming Language?

Maximum Tinkering • 19 implied HN points • 02 May 23
🕹 Technology Machine Learning
  1. Learning to program may become more accessible with the use of large language models (LLMs) that allow anyone who can read and write to code.
  2. Programming languages are gradually being abstracted to be more English-like and user-friendly, potentially leading to the development of a 'last programming language' that simplifies coding for everyone.
  3. While traditional programming languages might still have a place, new tools like LLMs could revolutionize the way people approach learning to code and building software.

Playing Chess - LLMs and Actual Chess AIs

Age of AI • 19 implied HN points • 04 Jul 23
🕹 Technology Machine Learning
  1. Large Language Models like ChatGPT can learn strategy games but won't reach top chess AI levels.
  2. True Chess AI like AlphaZero and MuZero outperform traditional chess programs by learning through reinforcement.
  3. Human-level chess AI like Maia Chess is designed to play like humans, predicting moves without looking ahead.

Can LLMs Improve Like AlphaZero?

Age of AI • 19 implied HN points • 06 Jul 23
🕹 Technology Machine Learning
  1. Human feedback is crucial for AI learning, but automatic methods are more scalable.
  2. AI companies are exploring ways for LLMs to determine text quality automatically.
  3. In specific domains like programming and math, LLMs could surpass human output by learning from feedback and evaluation.

The Tech Buffet #5: Build and Deploy a Voice Assistant with LangChain

The Tech Buffet • 19 implied HN points • 01 Oct 23
🕹 Technology Machine Learning
  1. You can build a voice assistant using LangChain by combining speech-to-text, a language model, and text-to-speech. It's a fun project that teaches you about machine learning.
  2. The tutorial breaks down the process into separate parts, making it easier to follow along step by step. You'll learn not just how to code, but also about app development and deployment.
  3. To deploy your assistant, you can use BentoML for serving your models and BentoCloud for cloud deployment. This setup allows for a smooth transition from local development to a live application.

How can Artificial Intelligence provide insights to modern strategic thought? Using wargames as a bridge between machines and strategists

Baptiste’s Substack • 19 implied HN points • 24 Jul 23
🕹 Technology Machine Learning
  1. AIs can be strategic agents on their own, producing effective solutions to complex problems.
  2. Wargaming is a key method to unlock AI's strategic potential by providing empirical models.
  3. Biases in the process and the need for proper organization are critical factors in the integrated use of AI and wargaming.

DALL·E Ho!

Sector 6 | The Newsletter of AIM • 19 implied HN points • 02 Aug 23
🕹 Technology Machine Learning
  1. DALL·E is being revived and the new version, DALL·E 3, is set to be much more advanced than its competitors. It's exciting to see how it can improve image generation technology.
  2. DALL·E 3 can create images with more detail, like better hair and lighting, which is a big step forward. This could help artists and creators in many ways.
  3. When compared to other tools like Midjourney and Stability Diffusion, DALL·E 3 is showing better results so far. This competition can push all technologies to improve even more.

The Camel Principle: Why Adding Zero is the Most Powerful Trick in Mathematics

The Palindrome • 1 implied HN point • 12 Jan 26
🚌 Education Machine Learning
  1. The camel principle is the idea that you can add zero in clever ways to transform problems, and that tiny trick can unlock big simplifications.
  2. Adding zero is essential because it helps rewrite expressions, simplify derivations, and connect different methods across mathematics and machine learning.
  3. A practical workshop can teach these foundations by building linear regression from scratch, covering vectors, vectorized code, optimization, and gradient descent with notebooks and recordings for practice.

I am your father, NO!

Sector 6 | The Newsletter of AIM • 79 implied HN points • 09 May 22
🕹 Technology Machine Learning
  1. Meta has released a new AI language model called OPT-175B, which is part of a series of recent AI advancements.
  2. There is some curiosity and speculation about another model named OPT-175A, suggesting it might be hidden or not yet revealed.
  3. This excitement highlights how fast technology is changing, especially in the field of artificial intelligence.

Math for Computer Science [Math Mondays]

Technology Made Simple • 59 implied HN points • 26 Apr 22
🕹 Technology Machine Learning
  1. Focus on Calculus for software development: Understand precalc topics like functions, transformation, and algebra well.
  2. Importance of Probs and Stats: Learn to think in a Bayesian context, focus on probabilistic thinking.
  3. Value of Linear Algebra: Grasp foundational concepts, computational side less important for traditional software development.

Giving GPT "Infinite" Knowledge

Sudo Apps • 121 HN points • 06 May 23
🕹 Technology Machine Learning
  1. Training Large Language Models (LLMs) with new data constantly is impractical due to the vast amount of information and privacy concerns.
  2. OpenAI's focus on improving LLMs in other ways instead of just increasing model size indicates the end of giant model era.
  3. Using tokens, embeddings, vector storage, and prompting can help provide LLMs with large amounts of data for better interpretation and understanding.

What's a vector database?

Technically • 34 implied HN points • 21 Oct 24
🕹 Technology Machine Learning
  1. A vector database is a special storage for data used in AI. It helps store numbers that represent different types of information like text or images.
  2. To make AI models smarter, they need to use unique data from companies. This helps tailor responses and improve accuracy.
  3. There are ways to enhance AI models with unique data, like fine-tuning them or using a method called Retrieval Augmented Generation (RAG) to include important information in prompts.

Gradient Flow #46: Smarter Language Models; Data Engineering Trends

Gradient Flow • 99 implied HN points • 04 Nov 21
🕹 Technology Machine Learning
  1. Data scientists should transition into social scientists in addition to being computer scientists.
  2. The report presents insights from a global online survey of 372 respondents on data engineering trends and challenges.
  3. Information on improvements in large language models, modernizing data integration, and the importance of data quality is shared in the podcast.

The Prompt Engineering Layer

From the New World • 134 implied HN points • 15 Feb 23
🕹 Technology Machine Learning
  1. Prompt engineering is the process of designing specific inputs for machine learning models.
  2. Creativity in prompt engineering can lead to novel results and opportunities beyond bypassing censorship.
  3. Artificial intelligence, like OpenAI, presents both benefits and challenges, particularly in terms of legal considerations and activism.

Llama-2 and the open source LLM 🌊

LLMs for Engineers • 19 implied HN points • 03 Aug 23
🕹 Technology Machine Learning
  1. Llama-2 makes it easier for anyone to run and own their LLM applications. This means people can create their own models at home while keeping their data private.
  2. Self-hosting Llama-2 helps improve performance and reduces delays. This makes the model more efficient for specific tasks and can even reach higher accuracy levels.
  3. There are guides and tools available to help users set up Llama-2 quickly. Users can try it out or integrate it with other platforms, making it more accessible for everyone.

Can I Solve Science?

TheSequence • 63 implied HN points • 10 Mar 24
🕹 Technology Machine Learning
  1. AI can advance scientific workflows but will always be limited by computational irreducibility.
  2. Stephen Wolfram's theory explores the potential of AI in discovering new science.
  3. The combination of AI and computational languages could open doors to advancing science.

Edge 453: Distillation Across Different Modalities

TheSequence • 28 implied HN points • 03 Dec 24
🕹 Technology Machine Learning
  1. Cross-modal distillation allows one model to teach another model that works with a different type of data. This means you can share knowledge even if the models are processing images, text, or something else entirely.
  2. This method can be really helpful when there's not much paired data available. It helps improve the learning process in situations where gathering data might be difficult.
  3. Hugging Face’s Gradio lets developers create AI applications for the web easily. It's a neat tool that helps bring AI to everyday use in a user-friendly way.

The DeepSeek drama, visually explained 🐳

Year 2049 • 22 implied HN points • 28 Jan 25
🕹 Technology Machine Learning
  1. The actual cost to train DeepSeek R1 is unknown, but it’s likely higher than the reported $5.6 million for its base model, DeepSeek V3.
  2. DeepSeek used a different training method called Reinforcement Learning, which lets the model improve itself based on rewards, unlike OpenAI's supervised learning approach.
  3. DeepSeek R1 is open-source and much cheaper to use for developers and businesses, challenging the idea that expensive hardware is necessary for AI model training.

Confidential AI: The Dog That Didn't Bark In The Night

State of the Future • 29 implied HN points • 05 Nov 24
🕹 Technology Machine Learning
  1. We need to prioritize data privacy as AI gets more personal. New technologies could help us protect our information while still allowing AI to learn.
  2. Building fair and unbiased AI models is crucial, as biased models can worsen social inequalities. We have tools to help create better AI that considers everyone fairly.
  3. There's a big opportunity to use decentralized systems for AI training and inference. This could make AI more accessible and less dependent on a few large companies.

AI Roundup 093: Diminishing returns

Artificial Ignorance • 29 implied HN points • 15 Nov 24
🕹 Technology Machine Learning
  1. Big AI companies are realizing that just making their models bigger doesn't always improve their performance. They're facing challenges because the quality of training data is more important than simply using more computing power.
  2. AI companies need to create new ways to measure performance since the old benchmarks are outdated. This lack of standard testing makes it hard for people to compare how different AI models stack up against each other.
  3. AI-generated art is becoming more popular and accepted in the market. A recent artwork sold for a lot of money, showing that people are starting to appreciate creations made by AI, even though it raises questions about what creativity really means.