Saturday, October 10, 2026

OpenAI: is math "solved?"

 OpenAI unleashes hundreds more math results upon a field already in shock | Scientific American


Is Claude Conscious? Pope Rejects, Model Welfare Movement, OpenAI's Math Backlash, France Riots - YouTube

(38:28) OpenAI's math breakthroughs and community backlash

Here is a summary of the segment covering OpenAI's mathematical achievements and the subsequent community backlash:

  • Massive Mathematical Breakthroughs: OpenAI revealed that its internal models have successfully tackled massive, long-standing open problems in mathematics—including resolving a major Millennium Prize problem and subsequently outputting hundreds of additional mathematical solutions.

  • Novel Approaches Without Massive Costs: Unlike previous brute-force methods, these models achieved results using novel approaches at a fraction of the cost previously expected, sending shockwaves through the academic community.

  • The Academic and Community Backlash: Despite the technical feat, the math community has pushed back heavily. Critics and mathematicians argue that raw solutions or trillion-line proofs lack human-interpretable insight, context, or intuition.

  • The "Process" vs. "Result" Debate: The hosts and academic critics emphasize that mathematics is fundamentally about human understanding, teaching, and the journey of problem-solving—not just checking a final answer box. Handing over uncontextualized solutions risks short-circuiting how future generations learn to think and explore mathematically.

This video provides a deep dive into OpenAI's recent mathematics announcements and the broader pushback from the scientific community.







Data Science books

 Free Principles of Data Science Book Available for Download - OpenStax

Principles of Data Science is intended to support one- or two-semester courses in data science. It is appropriate for data science majors and minors as well as students concentrating in business, finance, health care, engineering, the sciences, and a number of other fields where data science has become critically important.

Principles of Data Science PDF



An Introduction to Statistical Learning

The chapters cover the following topics:

What is statistical learning?
Regression
Classification
Resampling methods
Linear model selection and regularization
Moving beyond linearity
Tree-based methods
Support vector machines
Deep learning
Survival analysis
Unsupervised learning
Multiple testing

ISLP_DataScience_py.pdf


Python for Data Analysis, 3E

ChaptersPreface
1 Preliminaries
2 Python Language Basics, IPython, and Jupyter Notebooks
3 Built-In Data Structures, Functions, and Files
4 NumPy Basics: Arrays and Vectorized Computation
5 Getting Started with pandas
6 Data Loading, Storage, and File Formats
7 Data Cleaning and Preparation
8 Data Wrangling: Join, Combine, and Reshape
9 Plotting and Visualization
10 Data Aggregation and Group Operations
11 Time Series
12 Introduction to Modeling Libraries in Python
13 Data Analysis Examples
AppendicesA Advanced NumPy
B More on the IPython System

Veridical Data Science

Acknowledgments
Part I: An Introduction to Veridical Data Science1 An Introduction to Veridical Data Science
2 The Data Science Life Cycle
3 Setting Up Your Data Science Project
Part II: Preparing, Exploring, and Describing Data4 Data Preparation
5 Exploratory Data Analysis
6 Principal Component Analysis
7 Clustering
Part III: Prediction8 An Introduction to Prediction Problems
9 Continuous Responses and Least Squares
10 Extending the Least Squares Algorithm
11 Binary Responses and Logistic Regression
12 Decision Trees and the Random Forest Algorithm
13 Producing the Final Prediction Results
14 Conclusion
Answers to True or False Exercises