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.

Make sure to go to http://thedatascientist.com for more content on Web3.0 and AI.
About Dr Kampakis

Dr Stylianos (Stelios) Kampakis is a data scientist and tokenomics expert with more than 10 years of experience.

He has worked with companies of all sizes: from startups to organisations like the US Navy, Vodafone and British Land. His work expands multiple sectors including fintech, sports analytics, health-tech, general AI, medical statistics, predictive maintenance and others.

He has worked with many different types of technologies, from statistical models, to deep learning, to large language models. He has 2 patents pending to his name, and has published 3 books on data science, AI and data strategy.

He has helped many people follow a career in data science and technology.

His seminal work in token economics has led to many successful token economic designs using tools such as agent based modelling and game theory.

He is a member of the Royal Statistical Society, honorary research fellow at the UCL Centre for Blockchain Technologies, a data science advisor for London Business School and CEO of The Tesseract Academy.

🤖 http://tesseract.academy
🤖 http://thedatascientist.com
🤖 http://beyond-machine.com
🤖   / stelios_thedatascientist  
🤖   / s_kampakis  

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