I have an introduction about the challenges of AI and 3B issues in Chapter 1 of the Industrial AI Book. The challenges of AI include data quality, data reproducibility, data reliability, and data safety/security, etc. Please see the book at https://www.springer.com/gp/book/9789811521430.

This is like saying a road grader is poor because it doesn't do well on a concrete pavement. It is like saying the fish tastes red, Red is not a property of fish tasting and machine learning is not a form of data.. WORDS HAVE MEANINGS. Thanks to Prof. Arnold Insel for the fish tasting example. David Booth PS about outliers-how do you know which are bad and which are just part of the say 2.5% of data that may belong in a tail. Outliers are often useful. See the attached.

Thanks for your views. It is an important to develop a systematic perspective to address data technology (DT) issues. Domain knowledge is also critical for data correlation, labeling, and preparation before further data analytics. We also need to address how these data issues can be engineered to deal with multiple prediction criteria including uncertainty, complexity, accuracy, and speed.

Jay Lee Data quality is important. I note that you process data as machine learning does. What is your data quality are you reproducible for example. In any case, reproduciblity is a property of experiments not data sets. Every data set ever collected is reproducible by Xerox technology. Best, David Booth

NASA has developed a Data Repository https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/

These datasets are developed to allow users to reproduce the evaluation process of the data analytics and benchmark with the competing algorithms.