The hottest Explainable AI Substack posts right now

And their main takeaways
Category
Top Technology Topics

A short history of SHAP

Mindful Modeler • 359 implied HN points • 30 May 23
🕹 Technology Machine Learning Explainable AI AI Ethics Research Interpretability
  1. Shapley values originated in game theory in 1953 and contributed to fair resource distribution methods.
  2. In 2010, Shapley values were introduced to explain machine learning predictions, but didn't gain traction until the SHAP method in 2017.
  3. SHAP gained popularity for its new estimator for Shapley values, unification of existing methods, and efficient computation, leading to widespread adoption in machine learning interpretation.

The SHAP Book is Available in Print 🥳

Mindful Modeler • 159 implied HN points • 12 Sep 23
🕹 Technology Machine Learning Explainable AI Book
  1. SHAP is an explainable AI technique that computes Shapley values for machine learning predictions, attributing predicted value among features fairly.
  2. SHAP is versatile and model-agnostic, working with any model type from linear regression to deep learning, and handling various data formats like tabular, image, or text.
  3. The SHAP Book offers a comprehensive guide to mastering the theory and application of SHAP, suitable for data scientists, statisticians, machine learners, and those familiar with Python.

Explainable AI : Black Box of Artificial Intelligence

Product Mindset's Newsletter • 5 implied HN points • 10 Mar 24
🕹 Technology AI Explainable AI Interpretability
  1. Explainable AI (XAI) helps provide transparency in AI models so users can understand the logic behind predictions.
  2. Understanding how AI decisions are made is crucial for accountability, identifying biases, and improving model performance.
  3. Principles of Explainable AI include transparency in outputs, user-centric design, accurate explanations, and awareness of system limitations.

Revolutionary AI Whitepaper Validated by New Neuroscience Breakthrough

Spatial Web AI by Denise Holt • 0 implied HN points • 23 Aug 23
🕹 Technology AI Neuroscience Energy Efficiency Artificial General Intelligence Explainable AI
  1. The future of AI is moving towards shared, distributed intelligence where diverse nodes contribute to a collective system.
  2. Active Inference AI, based on the Free Energy Principle, mimics biological intelligence by updating internal models to minimize surprise and uncertainty, enabling more efficient learning.
  3. The VERSES AI whitepaper proposes a revolutionary approach to AI focusing on explainable, energy-efficient, and scalable intelligence, validated by recent neuroscience breakthroughs.
Get a weekly roundup of the best Substack posts, by hacker news affinity: