Opening the Mysteries of Artificial Intelligence
In the 1956 Dartmouth Conference,  the founding event of Artificial Intelligence as a field, eleven mathematicians and scientists held a six-week brainstorming session on “the thinking machine.” Had these eminent experts accepted Allen Newell and Herbert Simon’s suggestion of “complex information processing” for the advanced computer technologies of that period of development, we would not have the stressful struggle of coming to grips with today’s fascination of artificial intelligence. Many are afraid of AI, most don’t understand it, and by-and-large, there is great confusion about it. Many books have attempted to explain artificial intelligence with little success.
However, a recent book seems to have cracked the code: The Digital Mindset: What it Really Means to Thrive in the Age of Data, Algorithms, and AI by Paul Leonardi & Tsedal Neeley. Our authors claim that to understand artificial intelligence, it only takes about 30% fluency to know enough to navigate the science of AI. They use the example of learning a language by a “non-native,” who would need about 12,000 vocabulary words to master English but would use only about 4,000 words to navigate social settings or the workplace. That’s about 30% fluency. They demonstrate after each of their seven chapters what the 30% looks like in their seven topics of AI.
But first they set up definitions of important key words. This is an excellent start of getting a grasp of AI.
Data (a plural word that is pronounced Dayta = long ‘a’) is any information used as
Reference, analysis, composition, including text or images that can be turned into numbers to be processed, stored, and transformed through computing. Data can be digital exhaust (pieces of metadata as any individual online activity) and digital footprint (collection of behaviors or habits as visible in virtual transmissions through phone, internet, etc.)
Technology creates, captures, transforms, transmits, or stores data, which we experience through interconnected devices as sensors, computers, i-phones, software, cloud storage, and so on.
Collaboration: working with AI with teams and machines
Computation: understanding data, sources, analysis, using algorithms, and statistics
Change: It is constant; digital transformation is transitioning, learning by failures and experimentation
Behind the Digital Façade are Algorithms, Scripts, and Code. You don’t need to code but know how it’s done. Very important is this paragraph from page 26:
All digital operations are built on the back of a relationship among three entities: computers, software, and data. Computers do things. Algorithms are implemented in software to tell the computer what to do and how to do it.
After this introduction of the book, you are ready to learn the 30% mastery of artificial intelligence. Enjoy each chapter of Leonardi and Neeley’s easy to read book on your way through the mysteries of AI.
 Heimann, Richard, (2021); Doing AI, a Business-Centric Examination of AI Culture, Goals, and Values, Matt Holt Books, Dallas, TX
 Leonardi, Paul & Neeley, Tsedal (2022); The Digital Mindset: What it Really Means to Thrive in the Age of Data, Algorithms, and AI, Harvard Business School Publishing, Boston, MA