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

作為一個社會，我們不斷努力控制不確定性並預測未知。我們常常認為科學領域和理論是相互獨立的。但是，更仔細的調查可以揭示出將它們聯繫在一起的共同線索。從ChatGPT到Amazon的Alexa，再到Apple的Siri，數據科學和計算機科學已經成為我們生活的一部分。與此同時，對數據科學家的需求也在增加，因為這個領域被越來越稱為“最性感的職業”。

這本書試圖填補數據科學、機器學習和人工智能（AI）之間的文獻空白。歷史上如何處理不確定性，並且它在此之後如何演變？哲學、數學和工程學中存在哪些思想流派，它們在數據科學的發展中扮演了什麼角色？它以數據科學的歷史作為一個基石，解釋未來可能會帶來什麼。

《預測未知》提供了一個框架，幫助您了解人工智能的發展方向，以及如何在未來幾年中作為一個社會和企業做好最佳準備。它不涉及技術，避免使用方程式或技術解釋，但是寫給對知識好奇的讀者和對歷史細節感興趣的技術專家，這些細節可以幫助我們理解我們是如何到達現在的。

您將學到什麼：
- 探索數據科學的大局，並了解如何最好地預測該領域的未來變化
- 理解機器學習、人工智能和數據科學
- 通過引人入勝的歷史和以人為本的敘述來研究數據科學和人工智能

這本書適合對數據科學和人工智能思考方式感興趣的企業領導者和技術愛好者。

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。