Predicting the Unknown
The History and Future of Data Science and Artificial Intelligence
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Beschreibung
As a society, we’re in a constant struggle to control uncertainty and predict the unknown. Quite often, we think of scientific fields and theories as being separate from each other. But a more careful investigation can uncover the common thread that ties many of those together. From ChatGPT, to Amazon’s Alexa, to Apple’s Siri, data science, and computer science have become part of our lives. In the meantime, the demand for data scientists has grown, as the field has been increasingly called the “sexiest profession.”
This book attempts to specifically cover this gap in literature between data science, machine learning and artificial intelligence (AI). How was uncertainty approached historically, and how has it evolved since? What schools of thought exist in philosophy, mathematics, and engineering, and what role did they play in the development of data science? It uses the history of data science as a stepping stone to explain what the future might hold.
Predicting the Unknown provides the framework that will help you understand where AI is headed, and how to best prepare for the world that’s coming in the next few years, both as a society and within a business. It is not technical and avoids equations or technical explanations, yet is written for the intellectually curious reader, and the technical expert interested in the historical details that can help contextualize how we got here.
You will:
- Explore the bigger picture of data science and see how to best anticipate future changes in that field
- Understand machine learning, AI, and data science
- Examine data science and AI through engaging historical and human-centric narratives
As a society, we’re in a constant struggle to control uncertainty and predict the unknown. Quite often, we think of scientific fields and theories as being separate from each other. But a more careful investigation can uncover the common thread that ties many of those together. From ChatGPT, to Amazon’s Alexa, to Apple’s Siri, data science, and computer science have become part of our lives. In the meantime, the demand for data scientists has grown, as the field has been increasingly called the “sexiest profession.”
This book attempts to specifically cover this gap in literature between data science, machine learning and artificial intelligence (AI). How was uncertainty approached historically, and how has it evolved since? What schools of thought exist in philosophy, mathematics, and engineering, and what role did they play in the development of data science? It uses the history of data science as a stepping stone to explain what the future might hold.
Predicting the Unknown provides the framework that will help you understand where AI is headed, and how to best prepare for the world that’s coming in the next few years, both as a society and within a business. It is not technical and avoids equations or technical explanations, yet is written for the intellectually curious reader, and the technical expert interested in the historical details that can help contextualize how we got here.
What You’ll Learn
- Explore the bigger picture of data science and see how to best anticipate future changes in that field
- Understand machine learning, AI, and data science
- Examine data science and AI through engaging historical and human-centric narratives
Who is This Book For
Business leaders and technology enthusiasts who are trying to understand how to think about data science and AI
Examine how data science can be applied in a variety of contexts and how it has evolved Teaches data science and AI through an engaging historical human-centered narrative Understand modern developments in AI through a broad historical lens
Autor*in
Stylianos Kampakis
Themen in »Predicting the Unknown«
data science for business leaders data science for non-experts Web 3.0 AI blockchain statistical modelling Data science machine learning uncertainty big data statistics
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“Kampakis’ book clearly and readably covers the essence of uncertainty and the human efforts to address it, written for both professional data scientists and anyone attempting to predict life’s unknowable and unexpected outcomes.” (Harry J. Foxwell, Computing Reviews, November 29, 2023)
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