Changed "questions" to challenges to refer back to the original wording in the previous paragraph.
Reword sections to clarify the distinctions between traditional software architectures and machine learning systems. Enhance explanations of the evolution of AI paradigms and the implications for systems engineering. This might clear up a potential for confusion as the original seems to be anthropomorphizing AI.
Before submitting your Pull Request, please review and complete the items on this checklist.
The text has been proofread for grammar and spelling errors.
All images, figures, and tables render properly without any glitches.
All images have a source or they are properly linked to external sites.
The chapter's formatting is consistent with the rest of the book.
The chapter has been locally built and tested using Quarto.
Note
Refines introduction wording to replace “questions” with “challenges,” clarify symbolic vs ML approaches, and emphasize systems/infrastructure implications of AI (vision) vs ML (methodology).
Replace phrasing: These questions → These challenges.
Clarify Symbolic AI description (explicitly encoding principles) and contrast with ML’s data-driven learning.
Expand contrast of rule-based vs ML roles (humans curate data, design architectures, set metrics; systems learn operational logic from examples).
Rephrase learning parallels (object recognition, NLP) to avoid anthropomorphism and ground in mathematical foundations.
Highlight implications of AI (vision) vs ML (methodology) for large-scale data and infrastructure requirements.
Written by Cursor Bugbot for commit 9a4ddf2ba7. This will update automatically on new commits. Configure here.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/harvard-edge/cs249r_book/pull/1012
**Author:** [@foundingnimo](https://github.com/foundingnimo)
**Created:** 11/1/2025
**Status:** ✅ Merged
**Merged:** 11/1/2025
**Merged by:** [@profvjreddi](https://github.com/profvjreddi)
**Base:** `dev` ← **Head:** `foundingnimo-patch-3`
---
### 📝 Commits (2)
- [`5fd38b7`](https://github.com/harvard-edge/cs249r_book/commit/5fd38b78cdf4241cdbdccfee774beb618305a15c) Clarify distinctions between ML and traditional software
- [`9a4ddf2`](https://github.com/harvard-edge/cs249r_book/commit/9a4ddf2ba7d3b7a729c29d5a84b3795ecaa7f9dc) Refines introduction to machine learning
### 📊 Changes
**1 file changed** (+5 additions, -5 deletions)
<details>
<summary>View changed files</summary>
📝 `quarto/contents/core/introduction/introduction.qmd` (+5 -5)
</details>
### 📄 Description
1. Changed "questions" to challenges to refer back to the original wording in the previous paragraph.
2. Reword sections to clarify the distinctions between traditional software architectures and machine learning systems. Enhance explanations of the evolution of AI paradigms and the implications for systems engineering. This might clear up a potential for confusion as the original seems to be anthropomorphizing AI.
Before submitting your Pull Request, please review and complete the items on this checklist.
- [x] The text has been proofread for grammar and spelling errors.
- [x] All images, figures, and tables render properly without any glitches.
- [x] All images have a source or they are properly linked to external sites.
- [x] The chapter's formatting is consistent with the rest of the book.
- [x] The chapter has been locally built and tested using Quarto.
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Refines introduction wording to replace “questions” with “challenges,” clarify symbolic vs ML approaches, and emphasize systems/infrastructure implications of AI (vision) vs ML (methodology).
>
> - **Introduction (`quarto/contents/core/introduction/introduction.qmd`)**:
> - Replace phrasing: `These questions` → `These challenges`.
> - Clarify Symbolic AI description (explicitly encoding principles) and contrast with ML’s data-driven learning.
> - Expand contrast of rule-based vs ML roles (humans curate data, design architectures, set metrics; systems learn operational logic from examples).
> - Rephrase learning parallels (object recognition, NLP) to avoid anthropomorphism and ground in mathematical foundations.
> - Highlight implications of AI (vision) vs ML (methodology) for large-scale data and infrastructure requirements.
>
> <sup>Written by [Cursor Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit 9a4ddf2ba7d3b7a729c29d5a84b3795ecaa7f9dc. This will update automatically on new commits. Configure [here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
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📋 Pull Request Information
Original PR: https://github.com/harvard-edge/cs249r_book/pull/1012
Author: @foundingnimo
Created: 11/1/2025
Status: ✅ Merged
Merged: 11/1/2025
Merged by: @profvjreddi
Base:
dev← Head:foundingnimo-patch-3📝 Commits (2)
5fd38b7Clarify distinctions between ML and traditional software9a4ddf2Refines introduction to machine learning📊 Changes
1 file changed (+5 additions, -5 deletions)
View changed files
📝
quarto/contents/core/introduction/introduction.qmd(+5 -5)📄 Description
Before submitting your Pull Request, please review and complete the items on this checklist.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.