The Black Liszt

Summary: AI Machine Learning Big Data Math Optimization

This is a summary with links to my posts about Big Data and the fancy algorithms and math that are variously called Artificial Intelligence, Cognitive Computing, Machine Learning, Big Data Analytics, math optimization, etc. I have worked as an insider in these fields for over fifty years.

Huge amounts of time and money are spent on these things, but getting practical, real-world results is not so easy. You can make things worse.

https://www.blackliszt.com/2023/03/making-things-worse-with-ai.html

How do you make things better? It starts with data.

http://www.blackliszt.com/2018/03/getting-results-from-ml-and-ai-1.html

Everyone knows that data is important. There’s even a field called Big Data.

What people call Big Data often isn’t nearly as big as people think.

https://www.blackliszt.com/2013/02/big-data-some-little-observations.html

Do the arithmetic!

https://www.blackliszt.com/2012/04/im-tired-of-hearing-about-big-data.html

For all the talk about Big Data, it’s clear that making sure that the data is accurate and complete is too much to ask.

https://www.blackliszt.com/2019/03/nobody-cares-about-data.html

Among other things, the status hierarchy in Data Science makes it clear that anyone with ambition needs to get as far away from the actual data as possible.

https://www.blackliszt.com/2019/03/the-hierarchy-of-software-skills-data-science.html

Without data integrity, your analytics is screwed. It’s worse when the data is about your health. The integrity of data in healthcare electronic medical records is a major issue.

https://www.blackliszt.com/2016/06/healthcare-innovation-emrs-and-data-quality.html

Even simple insurance provider network data is too often wrong, putting yet more obstacles between patients and the services they need.

https://www.blackliszt.com/2017/01/my-cat-taught-me-about-the-state-of-healthcare-provider-data.html

Even if all the data were perfect, no one seems to ask why exactly is big data better?

https://www.blackliszt.com/2015/07/fatal-flaws-of-big-data.html

Is Big Data delivering results? Here’s what happened with Big data and Hadoop at Yahoo.

https://www.blackliszt.com/2019/01/using-advanced-software-techniques-in-business.html

With all the money going to Big Data, people are ignoring the massive benefits of leveraging little data.

https://www.blackliszt.com/2016/08/little-data-vs-big-data.html

The few people who study computer history have noticed that Big Data is remarkably similar to what used to be called EDW, Enterprise Data Warehouse.

https://www.blackliszt.com/2015/10/big-data-and-data-warehouses.html

Same thing with a new name. Hmm. Could it possibly be that Big Data is just a fashion trend?

https://www.blackliszt.com/2013/02/the-big-data-technology-fashion.html

https://www.blackliszt.com/2015/03/big-data-the-driving-force-in-computing.html

At least for insiders, there's lots of humor to be found in data. There's even a book of the fun highlights.

https://www.blackliszt.com/2022/01/data-humor-book-by-rupa-mahanti.html

After data, you move to the amazing variety of techniques available, each of which has specific areas of applicability.

http://www.blackliszt.com/2018/04/getting-results-from-ml-and-ai-2.html

Even a narrow-sounding field like Machine Learning has incredible variety and lots of things you need to do until the algorithm can be effective.

https://www.blackliszt.com/2017/02/learning-machine-and-human.html

The simple concept of closed loop is essential to getting and keeping good results, but isn’t applied as often as it should be.

http://www.blackliszt.com/2018/04/getting-results-from-ml-and-ai-3-closed-loop.html

The application of the principles to healthcare has proven to be challenging, with high-profile failures. But there are successes.

https://www.blackliszt.com/2018/08/getting-results-from-ml-and-ai-4-healthcare-examples.html

I've discussed the application of the principles to fintech, with a focus on anti-fraud. The current leader in the field displaced the incumbent who was using neural network technology, showing that the algorithm chosen and how it’s applied makes all the difference.

https://www.blackliszt.com/2019/2/getting-results-from-ml-and-ai-5-fintech-fraud.html

Natural language AI has proved notoriously hard to make practical. Here’s an example of how it’s been done in fintech customer support.

https://www.blackliszt.com/2019/12/getting-results-from-ml-and-ai-6-fintech-chatbot.html

All too often, AI research is conducted in isolation. There is a great deal to do to assure that any results that are achieved can be integrated into production systems.

https://www.blackliszt.com/2022/06/how-to-integrate-ai-and-ml-with-production-software.html

As is true in most fields, in healthcare the money and attention tends to go to expensive, fancy methods requiring PhD’s instead of simple things that actually work.

https://www.blackliszt.com/2015/07/cognitive-computing-and-healthcare.html

https://www.blackliszt.com/2016/09/healthcare-innovation-from-washing-hands-to-ai.html

https://www.blackliszt.com/2016/05/healthcare-innovation-can-big-data-and-cognitive-computing-deliver-it.html

You don’t need AI or cognitive computing to discover or promulgate the new discoveries that humans make.

https://www.blackliszt.com/2015/08/human-implemented-cognitive-computing-healthcare.html

There are many ways to spend lots of time and not get practical results.

https://www.blackliszt.com/2019/03/the-hierarchy-of-software-skills-data-science.html

Impressive analytics performed by recognized experts are subject to getting the results the Expert wants.

https://www.blackliszt.com/2017/04/big-datas-big-face-plant.html

Corruption of data or process can lead to bad results anywhere.

https://www.blackliszt.com/2019/10/surprising-bugs-at-amazon-shows-how-ai-can-lead-to-disaster.html

For cynical definitions in the tradition of Ambrose Bierce of Big Data, Machine Learning, Deep Learning, Cognitive Computing and AI, see these.

https://www.blackliszt.com/2017/01/devils-dictionary-for-21st-century-computing.html

https://www.blackliszt.com/2017/01/devils-dictionary-for-21st-century-computing-2.html

https://www.blackliszt.com/2017/03/devils-dictionary-for-21st-century-computing-3.html

 

Data Humor Book by Rupa Mahanti

There's a new book out about Data Humor.

If you like data, you will be amused by this book. If you feel tortured by data, join the crowd -- and read this book, it will relieve some of the stress. If you were wondering what nerd humor was all about, read this book -- better to learn about nerd humor by getting the giggles.

The author searched far and wide for data humor. She stumbled upon a blog that had some bits she thought were funny -- this blog, yes, the one you're reading now!

She contacted me to ask permission to quote me. After thinking hard about whether I should grant permission -- for about a microsecond -- I gave it. She asked me to check out a draft of the book. I guess she liked what I said because my quote went on the back cover and was the first of the quotes on Amazon.

"This is a brilliant book. The title says it's humorous. It's hilarious! But even more valuable is the sad-but-true insights it conveys about humans, lost and wandering in uncharted forests of data, anxious to escape."—David B. Black, Technology Partner, Oak HC/FT Partners

It's quite amazing how widely she searched for quotes, from places I never would have thought to look:

... book containing a collection of more than 400 funny and quirky quotes, puns, and punchlines related to data, big data, statistics, and data science, from different sources and a wide array of cultural figures, thought leaders and key influencers across the world- William Edwards Deming, Charles Wheelan, Brené Brown, David B. Black, Tim O’Reilly, Jill Dyché, Evan Levy, Gwen Thomas, George Mount, David Shenk, James Gleick, Jim Barksdale, Vincent Granville, Cathy O’Neill, Dale Carnegie, Martyn Richard Jones, Timo Elliott, Mark Twain, Phil Simon, Lewis Carroll, Oscar Wilde, Thomas H. Davenport, DJ Patil, Damian Mingle, Thomas C. Redman, Cassie Korykorv, Brent Dykes, Guy Bradshaw, Scott Taylor, Susan Walsh, Winston Churchill, Ronald Reagan, Arthur Conan Doyle, and many more.

Here's just one:

Data isn't information, any more than fifty tons of cement is a skyscraper.
Clifford Stoll (Stoll 1996)

Don't you need some light in your life? I promise, it's lighter in every way than fifty tons of concrete...

The Hierarchy of Software Skills and Status in Data Science

There is a striking hierarchy of skills in software, as I've explained here. When you dive into any particular aspect of software, you usually find that it's got a hierarchy all its own. Data science is a subject of intense interest these days, so in this post I'll explain some of the basics of the data science skills hierarchy.

A skills hierarchy is very much an insider's game. What most people care about is status. I talk about the basics of software status here. Remember, the skills hierarchy is a whole world away from the status hierarchy that most people care about. Don't confuse the two!

Data Science skills

First and foremost, it's important to understand the incredibly broad range of subjects covered  by the term "data science." I attempt to explain the basics of the range in this blog post. You can be just amazing in one of those subjects, while being a neophyte in one that the outside world may consider to be "related," but which in practice is not just down the hall, but in a different building on a different campus.The general understanding of this range is SO pathetic that "data science" is typically managed as a completely independent, free-standing group. Which makes about as much sense as believing that a sous-chef belongs in something that isn't a kitchen. Or that everything that everyone does in a kitchen is basically the same thing.

Here's one cut at the hierarchy in data science, starting from the base:

1. Tool users.These are people who have learned how to use some software tool, or maybe a couple. Most "data scientists" fall into this category.
   • They don’t understand how the tools are built or any of the underlying software
   • They may know their tool, but aren't real clear on what the other tools are about, much less when you might consider using one.
2. Some have broader knowledge of tools
3. Very few have the sophistication to understand real data analysis, per my series of AI/ML; see this and the links in it: http://www.blackliszt.com/2018/04/getting-results-from-ml-and-ai-3-closed-loop.html
4. Of those, even fewer can understand the underlying algorithms and follow the latest research literature
5. Of those, even fewer can make real algorithmic advances and implement them as tools and deliver practical value.
6. Of anyone who can do all of the above, it is rare to meet anyone who can address and solve deep tool-level problems in other domains needed to make their code practical in the real world.
7. Finally, it is extremely rare to meet people who, in addition to their deep and broad prowess in data science and relevant skills needed to make it real-world practical, have similarly deep skills in an associated domain needed to make the data science fully effective for a business.

The issues of this hierarchy are compounded by the usual over-selling by people who are good promoters but little else, and corporate/government big-wigs who don't want to be bothered with details, but are keen to be seen as "doing something" on such a high-visibility topic. Getting results that make a difference is pretty low on the typical priority list.

And then of course there are the "data scientists" themselves, who most often are sincere people who are trying to do a good job as they've been taught to do it -- mostly by professors and others who have no idea what real-world success looks like, much less how to bring it about.

Finally, there is the usual "manage something that's invisible to you" phenomenon I have often discussed in this blog, which leads to so much dysfunction and so many wonderful Dilbert cartoons.

Conclusion

People talk as though "data science" were a thing, with the usual kind of hierarchy based on level of management and/or "experience." Those typical patterns of hierarchy just don't cut it for understanding what's going on in data science, just like they don't cut it for understanding software development. We will continue to see waste and dead-end efforts until we at least make a start at making our understanding of data science more sophisticated, and aligned with the facts on the ground.