Predicting the Unknown: The History and Future of Data Science and Artificial Intelligence
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
Dr. Stylianos (Stelios) Kampakis is a data scientist, data science educator and blockchain expert with more than 10 years of experience. He has worked with decision makers from companies of all sizes: from startups to organizations like the US Navy, Vodafone ad British Land. His work expands multiple sectors including fintech (fraud detection and valuation models), 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 blockchain and he has two patents pending to his name. He has also helped many people follow a career in data science and technology.
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 and tokenomics auditor at Hacken. As a well-known data-science educator, he has published two books, both of them getting 5 stars on Amazon. His personal website gets more than 10k visitors per month, and he is also a data science influencer on LinkedIn.
Dr. Stylianos (Stelios) Kampakis是一位資料科學家、資料科學教育家和區塊鏈專家，擁有超過10年的經驗。他曾與各種規模的公司的決策者合作，從初創企業到美國海軍、Vodafone和British Land等組織。他的工作涵蓋多個領域，包括金融科技（詐騙檢測和估值模型）、體育分析、健康科技、通用人工智能、醫學統計、預防性維護等。他曾使用多種不同的技術，從統計模型到深度學習再到區塊鏈，並且有兩項專利正在等待批准。他還幫助許多人在資料科學和技術領域追求職業生涯。
他是英國皇家統計學會的成員，倫敦大學學院區塊鏈技術中心的名譽研究員，倫敦商學院的資料科學顧問，以及The Tesseract Academy的首席執行官和Hacken的代幣經濟學審計師。作為知名的資料科學教育家，他出版了兩本書，並在亞馬遜網站上獲得了5星評價。他的個人網站每月有超過1萬名訪客，他也是LinkedIn上的資料科學影響者。