東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Predicting the unknown = the history...

Kampakis, Stylianos.

FindBook

Google Book

Amazon

博客來

Predicting the unknown = the history and future of data science and artificial intelligence /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predicting the unknown/ by Stylianos Kampakis.
其他題名:	the history and future of data science and artificial intelligence /
作者:	Kampakis, Stylianos.
出版者:	Berkeley, CA :Apress : : 2023.,
面頁冊數:	xvii, 264 p. :ill. (some col.), digital ;24 cm.
內容註:	1. Where Are We Now? A Brief History of Uncertainty -- 2. Truth, Logic and the Problem of Induction -- 3. Swans and Space Invaders -- 4. Probability: To Bayes, or not to Bayes? -- 5. What's Maths Got to Do With It? The Power of Probability Distributions -- 6. Alternative Ideas: Fuzzy Logic and Information Theory -- 7. Statistics: the Oldest Kid on the Block -- 8. Machine Learning: Inside the Black Box -- 9. Causality: Understanding the 'Why' -- 10. Forecasting, and Predicting the Future: The Fox and the Trump -- 11. The Limits of Prediction (Part A): A Futile Pursuit? -- 12. The Limits of Prediction (Part B): Game Theory, Agent-based Modelling and Complexity (Actions and Reactions) -- 13. Uncertainty in Us: How the Human Mind Handles Uncertainty -- 14. Blockchain: Uncertainty in transactions -- 15. Economies of Prediction: A New Industrial Revolution -- Epilogue: The Certainty of Uncertainty.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-1-4842-9505-2
ISBN:	9781484295052

Predicting the unknown = the history and future of data science and artificial intelligence /
Kampakis, Stylianos.

Predicting the unknownthe history and future of data science and artificial intelligence /[electronic resource] :by Stylianos Kampakis. - Berkeley, CA :Apress :2023. - xvii, 264 p. :ill. (some col.), digital ;24 cm.

1. Where Are We Now? A Brief History of Uncertainty -- 2. Truth, Logic and the Problem of Induction -- 3. Swans and Space Invaders -- 4. Probability: To Bayes, or not to Bayes? -- 5. What's Maths Got to Do With It? The Power of Probability Distributions -- 6. Alternative Ideas: Fuzzy Logic and Information Theory -- 7. Statistics: the Oldest Kid on the Block -- 8. Machine Learning: Inside the Black Box -- 9. Causality: Understanding the 'Why' -- 10. Forecasting, and Predicting the Future: The Fox and the Trump -- 11. The Limits of Prediction (Part A): A Futile Pursuit? -- 12. The Limits of Prediction (Part B): Game Theory, Agent-based Modelling and Complexity (Actions and Reactions) -- 13. Uncertainty in Us: How the Human Mind Handles Uncertainty -- 14. Blockchain: Uncertainty in transactions -- 15. Economies of Prediction: A New Industrial Revolution -- Epilogue: The Certainty of Uncertainty.

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.

ISBN: 9781484295052

Standard No.: 10.1007/978-1-4842-9505-2doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: QA76.9.D35

Dewey Class. No.: 005.7

Predicting the unknown = the history and future of data science and artificial intelligence /
LDR:03607nmm a2200325 a 4500 001 2332475
003 DE-He213
005 20230615133322.0
006 m d
007 cr nn 008maaau
008 240402s2023 cau s 0 eng d
020 $a 9781484295052 $q (electronic bk.)
020 $a 9781484295045 $q (paper)
024 7 $a 10.1007/978-1-4842-9505-2 $2 doi
035 $a 978-1-4842-9505-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D35
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.D35 $b K15 2023
100 1 $a Kampakis, Stylianos. $3 3445836
245 1 0 $a Predicting the unknown $h [electronic resource] : $b the history and future of data science and artificial intelligence / $c by Stylianos Kampakis.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2023.
300 $a xvii, 264 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 1. Where Are We Now? A Brief History of Uncertainty -- 2. Truth, Logic and the Problem of Induction -- 3. Swans and Space Invaders -- 4. Probability: To Bayes, or not to Bayes? -- 5. What's Maths Got to Do With It? The Power of Probability Distributions -- 6. Alternative Ideas: Fuzzy Logic and Information Theory -- 7. Statistics: the Oldest Kid on the Block -- 8. Machine Learning: Inside the Black Box -- 9. Causality: Understanding the 'Why' -- 10. Forecasting, and Predicting the Future: The Fox and the Trump -- 11. The Limits of Prediction (Part A): A Futile Pursuit? -- 12. The Limits of Prediction (Part B): Game Theory, Agent-based Modelling and Complexity (Actions and Reactions) -- 13. Uncertainty in Us: How the Human Mind Handles Uncertainty -- 14. Blockchain: Uncertainty in transactions -- 15. Economies of Prediction: A New Industrial Revolution -- Epilogue: The Certainty of Uncertainty.
520 $a 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.
650 0 $a Artificial intelligence. $3 516317
650 0 $a Data structures (Computer science) $3 527210
650 0 $a Computer science $x History. $3 1066446
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Computer Science. $3 626642
650 2 4 $a Logic in AI. $3 3386372
650 2 4 $a Machine Learning. $3 3382522
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-9505-2
950 $a Professional and Applied Computing (SpringerNature-12059)