Join 📚 Kevin's Highlights
A batch of the best highlights from what Kevin's read, .
I delineate them by the range of changes considered:
**responsible AI** seeks to *make the AI less harmful*,
**ethical AI** *challenges if AI should even be used* in certain applications, and
**just AI** argues that *AI applications must actively challenge oppression* (if this is even possible).
Classification for AI Ethics
@willie_agnew on Twitter
Starting Hand Standards
A reasonable set of opening requirements at most tables would be as follows:
**Early position**: Ten high or better badugis; Smooth 7 high tris or better
**Hijack**: Jack high or better badugis, Smooth 8 high tris or better
**Cut-off**: All badugis, 8 high tris or better, A2 and A3
**Button**: All badugis, 9 high tris or better, A2, A3, 23, A4
Badugi Rules and Basic Strategy
countingouts.com
When we unpack the common threads of how various people define data engineering, an obvious pattern emerges:
a **data engineer**
*gets data, stores it, and prepares it for consumption*
by **data scientists**, **analysts**, and others.
We define data engineering and data engineer as follows:
**Data engineering** is
the *development*, *implementation*, and *maintenance*
of **systems** and **processes** that take in raw data
and produce high-quality, consistent information
that supports downstream use cases,
such as analysis and machine learning.
**Data engineering** is
the intersection of
*security*,
*data management*,
*DataOps*,
*data architecture*,
*orchestration*, and
*software engineering*.
A **data engineer**
*manages the data engineering lifecycle*,
beginning with getting data from source systems and
ending with serving data for use cases,
such as analysis or machine learning.
Fundamentals of Data Engineering
Reis, Joe;Housley, Matt;
...catch up on these, and many more highlights
Readwise