Tom Libertiny, Libertiny, Thomas Libertiny, Applied Artificial Intelligence

Applied Artificial Intelligence for Data Analysis and Content

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My family has a history of being involved in science and engineering.  This goes back to fairly ancient history when our family was knighted in Europe for a scientific prediction that proved to be true.

As you can imagine, there was a lot of expectation that I would also embrace a technical career.  To help me along in this direction, my parents, both engineers, started by teaching me how to code while I was in elementary school–their philosophy being that I was too young to know that computer coding was considered difficult.  Starting with versions of Fortran and Basic for database development and data analysis I later taught myself Assembly Language and C so that I could control robots and make things with CNC equipment using very outdated, inexpensive computers.

My parent’s same teaching philosophy brought about my early education in trigonometry, geometry, and calculus, although there was a financial benefit embedded into those lessons for my family:  our house needed a new roof.

I remember my dad telling me one summer about the roof project:

I’ve taught you all the math you need to know so you can calculate the amount of wood, nails, and shingles required to redo the roof on our house.  I’ll purchase whatever amount of materials you say you need and your job is to rebuild the roof.

It took me a month to determine how to remove the existing roof, ensure that the roof was covered in case of inclement weather, and then teach myself to roof the house.  I did get the math right for the materials and my dad ensured that the rafters were sound underneath all the old materials I had removed.  But until I discovered the Chalk Line while at a hardware store, the aesthetics of my shingle work was less than pleasing.  So, I gave myself the “opportunity” to redo the shingles again and the second time I was successful at ensuring that they were straight.

In the end, I was hired by several home owners nearby to redo their roofs and my education about Business Strategy continued.

Applied Artificial Intelligence (AI)

AI as we know it has been around for a long time.  What changed was the introduction of affordable Artificial Neural Networks in 2017 and Large Language Models (LLM) a few years later.  These two technologies plus quantum computing, where areas in which I kept a close watch because some combination of them had the ability to substantially move the world forward for data collection and analysis.

Secrets to Success

In 2022 two of these technologies converged to the point that commercially available AI was available that could both write computer code and then do data collection and analysis with simple cues from people.  Just like my experience learning to code in Assembly Language and C, I’m a self taught in AI.

  1. Teaming with the large number of commercially available AIs is similar to what I learned in Business Strategy:  the diversity of “thought processes” between human and computer makes for a powerful combination.
  2. Education: At this point, ongoing education in AI isn’t a choice because there are substantial weekly updates to many AIs.  Like my experience with learning to code and building roofs, planning coupled with trial-and-error are currently the best ways to keep on top of what AI can and can’t do.


I’ve always believed in giving back to the community, that’s why I’ve started a teaching series (podcast and one-on-one) to help individuals and teams learn how to best use AI in a business setting.

Learn more about me HERE