The hottest Machine Learning Substack posts right now

And their main takeaways
Category
Top Business Topics

Governance as the Facilitator in Adopting Technology

Embracing Enigmas • 0 implied HN points • 09 Jul 23
🕹 Technology Machine Learning
  1. Achieving societal acceptance of technology requires safety, reliability, and predictability.
  2. Factors affecting technology adoption include governance of technology outputs and understanding the value of the technology.
  3. Effective AI governance involves defining unwanted outputs, measuring system performance, implementing guardrails, and adjusting outputs when needed.

Interview with CEO of Google DeepMind

Age of AI • 0 implied HN points • 14 Jul 23
🕹 Technology Machine Learning
  1. Large language models (LLMs) are being developed to become universal personal assistants with planning and reasoning capabilities.
  2. LLMs may utilize specialized tools for tasks like folding proteins or playing chess, breaking down the AI system into smaller ones.
  3. LLMs should be equipped with the ability to critique themselves by reasoning and planning, similar to how game programs improve their moves.

A model of everything

Simplicity is SOTA • 0 implied HN points • 17 Jul 23
🕹 Technology Machine Learning
  1. A model of everything predicts final and intermediate goals of a company, is causal, and covers significant inputs.
  2. Foundational choices in building a model of everything include deciding the scope, complexity of relationships, and optimization strategy.
  3. Financial forecasting often involves models of everything, built in spreadsheets, but may not work well for machine learning models.

LLM of the day: LLAMA 2

Age of AI • 0 implied HN points • 20 Jul 23
🕹 Technology Machine Learning
  1. Facebook's LLAMA 2 is an updated LLM comparable to GPT 3.5 and now available for commercial use for up to 700 million users.
  2. LLAMA 2 is not as advanced as GPT 4, but its availability for commercial use is attracting many companies to use it.
  3. There may not be a clear process for external contributions to improving LLAMA 2, but Facebook's decision to open-source it could be for goodwill or competitive reasons.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

🔮 Weekly Dose of AI: “God-like AI could be a force beyond our control or understanding, and one that could usher in the obsolescence or destruction of the human race”

Definite Optimism • 0 implied HN points • 17 Apr 23
🕹 Technology Machine Learning
  1. Elon Musk is starting his own AI company to compete with OpenAI.
  2. AutoGPT and BabyAGI projects integrate recursion into AI, enabling it to perform tasks like ordering coffee and market analysis.
  3. AI-generated Drake and The Weeknd song gains viral popularity, showing the potential of AI in creating music.

Abrupt skill emergence in Large Language Models

Intuitive AI • 0 implied HN points • 31 Aug 23
🕹 Technology Machine Learning
  1. General Large Language Model performance can be predicted based on compute, dataset size, and parameter count.
  2. Task-specific abilities in models show abrupt jumps in proficiency as the parameter count increases.
  3. Abrupt skill emergence is observed in models for tasks like adding numbers or unscrambling words as they reach certain parameter thresholds.

Why on earth are we already lamenting the fall of LLMs?

Deus In Machina • 0 implied HN points • 07 Sep 23
🕹 Technology Machine Learning
  1. Some users expect too much from Large Language Models without putting in additional effort or guidance.
  2. Language models like ChatGPT should be viewed as tools that require ongoing optimization and understanding.
  3. There are various alternatives to ChatGPT, and users should explore and compare different Large Language Models to find the best fit for their needs.

Where does the mean squared error come from?

The Palindrome • 0 implied HN points • 21 Dec 23
🔬 Science Machine Learning
  1. Mean squared error is a common loss function for machine learning models due to its mathematical simplicity and alignment with statistical principles.
  2. Absolute value functions are not commonly chosen for loss function in machine learning due to issues with differentiability at zero.
  3. The linear model and mean squared error naturally arise when approaching machine learning with a statistical mindset.

Linear regression and the least squares problem

The Palindrome • 0 implied HN points • 12 Dec 23
🔬 Science Machine Learning
  1. Linear regression can be optimized by hand, especially for single variable models where the loss function is simple.
  2. Gradient descent for linear regression can be like using a cannonball to shoot a sparrow, due to the simplicity of the loss function.
  3. Premium subscribers of The Palindrome can access exclusive content and chapters of 'Mathematics of Machine Learning' for an in-depth education.

The taxonomy of machine learning paradigms

The Palindrome • 0 implied HN points • 18 Sep 23
🕹 Technology Machine Learning
  1. Machine learning tasks involve three important parameters: the input, the output, and the training data.
  2. The basic machine learning setup consists of a dataset, a true relation function, and a parametric model as an estimation.
  3. Major paradigms of machine learning include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Book Review: Why Machines Will Never Rule the World

The Grey Matter • 0 implied HN points • 17 Jul 23
🕹 Technology Machine Learning
  1. The book emphasizes that machines will never rule the world, as AGI is fundamentally impossible due to computational limitations.
  2. The definitions of intelligence and machine intelligence play a crucial role in the argument against AGI.
  3. Language, context-dependence, and complex systems are central themes analyzed in the book to challenge the possibility of AGI.

ML & AI: From Problem Framing to Integration

CodeLink’s Substack • 0 implied HN points • 20 Sep 23
🕹 Technology Machine Learning
  1. Effective problem framing is crucial in ML engineering to avoid complex solutions that don't deliver results.
  2. For model selection, consider using pre-trained models for common tasks and build custom datasets for niche problems.
  3. During model training, focus on evaluating performance, optimizing latency, and documenting the model for integration into existing systems.

Where Bayes Falls Short

As Clay Awakens • 0 implied HN points • 30 May 23
🔬 Science Machine Learning
  1. Deep learning algorithms are powerful for intelligence and learning, especially in contexts where Bayes' theorem falls short.
  2. Simpson's paradox shows how data separation can change conclusions based on initial beliefs.
  3. Deep learning approaches in regression tasks offer solutions without the need for ad-hoc choices, allowing for better predictions and generalization.

Starting with the Answer

Kiernan • 0 implied HN points • 09 Sep 23
🕹 Technology Machine Learning
  1. Embedding vectors provide numerical representations for different types of content, allowing for easy comparison and search based on similarity.
  2. Starting with the answer in search means casting a broad net by providing an example of what you're looking for, rather than specific keywords.
  3. By utilizing embedding vectors, search results can be tailored to match concepts or sentiments, making searches more efficient and effective.

Example-Driven Development

m3 | music, medicine, machine learning • 0 implied HN points • 17 Aug 23
🕹 Technology Machine Learning
  1. Providing a wider range of examples to ChatGPT helps in generating more natural-sounding outputs.
  2. Using a local plugin for ChatGPT allows for accessing and providing context from local files for better collaboration.
  3. Example-driven development with LLMs is useful for identifying relevant context, mimicking input characteristics, and making connections between different types of files.

How would Deepmind Gemini work?

Yuxi’s Substack • 0 implied HN points • 08 Nov 23
🕹 Technology Machine Learning
  1. Deepmind is working on multimodality, embodiment, and interaction in addition to language models.
  2. Iterative improvements from feedback are crucial for building successful systems and bridging gaps.
  3. Deepmind is exploring deep reinforcement learning in language models, but its deployment in Gemini is uncertain.

Autonomous agent is a BIG bubble

Yuxi’s Substack • 0 implied HN points • 23 Jul 23
🕹 Technology Machine Learning
  1. Autonomous agent is still an open problem in AI, especially with current language models lacking agency and planning
  2. Approximate models like current LMs can cause issues in tasks such as generating legal moves in games
  3. Even games AI like AlphaGo, while strong, can be exploitable before reaching optimal performance

March Newsletter

RSS DS+AI Section • 0 implied HN points • 05 Mar 23
🕹 Technology Machine Learning
  1. Ethical concerns around the use of AI, especially in the military, continue to be a significant issue.
  2. Research in data science is focusing on efficiency, scalability, and the adaptation of large language models.
  3. Generative AI, like ChatGPT, is a hot topic with advancements in business applications and ethical considerations.

AI Reproducibility Crisis

Brain Lenses • 0 implied HN points • 01 Feb 24
🕹 Technology Machine Learning
  1. AI systems can sometimes appear successful based on unintended factors, such as background images, rather than the desired data.
  2. AI reproducibility issues can arise when original research findings cannot be accurately replicated or verified.
  3. The validity and reliability of AI-based techniques require thorough evaluation and validation procedures.

Quant Letter: February 2024, Week-1

The Parlour • 0 implied HN points • 07 Feb 24
💰 Finance Machine Learning
  1. The piece discusses a multi-agent framework for portfolio management using reinforcement learning.
  2. The framework aims to balance returns and risks while outperforming other approaches.
  3. Readers can access the full post archives with a 7-day free trial subscription.

Exclusive: Dr. Karl Friston Unveils Cutting-Edge Active Inference AI Research at IWAI

Spatial Web AI by Denise Holt • 0 implied HN points • 17 Dec 23
🕹 Technology Machine Learning
  1. Active Inference AI research by Dr. Karl Friston is being recognized for its potential in Artificial General Intelligence, showcasing breakthroughs like mimicking biological intelligence and developing 'smart' data models.
  2. The focus on state spaces within generative models and understanding their dynamics is crucial in comprehending how intelligent systems predict and react to stimuli.
  3. Research around emergent communication systems among intelligent agents demonstrates how active learning can lead to the development of common communication methods and predictive structures.

VERSES AI compared to Open AI

Spatial Web AI by Denise Holt • 0 implied HN points • 23 May 23
🕹 Technology Machine Learning
  1. Active Inference AI has advantages over LLMs like ChatGPT, including better alignment with human values, real-time data access, reduced cost, and ability to handle novel situations.
  2. Active Inference AI combined with the Spatial Web can act as a nervous system for companies and cities by perceiving real-time data, processing it in a generative model, and making informed decisions to optimize operations.
  3. VERSES AI offers unique capabilities with its Active Inference AI methodology that integrates with the Spatial Web, providing a sophisticated system for monitoring and managing complex systems efficiently.

AI — The Maker vs. The Operator

Spatial Web AI by Denise Holt • 0 implied HN points • 09 May 23
🕹 Technology Machine Learning
  1. Active Inference AI is a new type of networked AI designed for real-time operations in managing functions like hospitals, airports, and smart cities, streamlining and automating real-world activities.
  2. Predictive machine learning models rely on historical data and lack real-time decision-making abilities, unlike Active Inference AI which continuously updates its understanding of the world through real-life data.
  3. The Spatial Web Protocol and HSML enable Active Inference AI to interact with the world in real-time, mimicking human decision-making abilities like perceiving and taking action based on changing contexts.

From the Impossible to the Inevitable: AGI is Coming Faster Than You Think

Spatial Web AI by Denise Holt • 0 implied HN points • 16 Jan 23
🕹 Technology Machine Learning
  1. Active Inference and the Free Energy Principle are key concepts developed by Dr. Karl Friston for explaining how agents can maintain their internal states and behavior based on minimizing the difference between their beliefs and reality, paving the way for Artificial General Intelligence.
  2. The proposed stages of development suggest a timeline for achieving different levels of artificial intelligence, from Systemic Intelligence to Artificial Super Intelligence, showing a path towards creating more advanced AI.
  3. Active Inference AI within the Spatial Web has the potential to transform artificial intelligence into a self-evolving system that learns from real-time data, considers context, and optimizes behavior, which could lead to the realization of Artificial General Intelligence.

The Spatial Web and the Era of AI — Part 1

Spatial Web AI by Denise Holt • 0 implied HN points • 30 Dec 22
🕹 Technology Machine Learning
  1. Deep Learning AI lacks consciousness and reasoning abilities, focusing on pattern recognition. The desire for Artificial General Intelligence requires models with 'awareness' abilities.
  2. Machine Learning AI, like GANs and Transformers, excel in specific tasks but are limited. They may lack comprehension and struggle with dynamic, real-time data.
  3. The emergence of Active Inference AI within the Spatial Web Protocol offers a roadmap to Artificial General Intelligence by enabling adaptive intelligence in a context-rich environment.

Zoo of RAGs

Shchegrikovich’s Newsletter • 0 implied HN points • 11 Feb 24
🕹 Technology Machine Learning
  1. Retrieval Augmented Generation (RAG) improves LLM-based apps by providing accurate, up-to-date information through external documents and embeddings.
  2. RAPTOR enhances RAG by creating clusters from document chunks and generating text summaries, ultimately outperforming current methods.
  3. HiQA introduces a new RAG perspective with its Hierarchical Contextual Augmentation approach, utilizing Markdown formatting, metadata enrichment, and Multi-Route Retrieval for document grounding.