| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910816392603321 |
|
|
Autore |
Mersch Dieter |
|
|
Titolo |
Epistemologies of aesthetics / / Dieter Mersch ; translated by Laura Radosh |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Zurich, Switzerland : , : Diaphanes, , 2015 |
|
©2015 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (177 p.) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Art - Philosophy |
Aesthetics |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Description based upon print version of record. |
|
|
|
|
|
|
Nota di contenuto |
|
Cover; Contents; Introduction; Research in the Realm of Aesthetics; A Short History of 'Truth' in Art; 'Con-Stellare': The Reflexive Epistemic of the Arts; In Conclusion: Epistemic Practices of the Arts |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910731486703321 |
|
|
Autore |
Kampakis Stylianos |
|
|
Titolo |
Predicting the Unknown : The History and Future of Data Science and Artificial Intelligence / / by Stylianos Kampakis |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
|
|
Edizione |
[1st ed. 2023.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (270 pages) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Artificial intelligence |
Data structures (Computer science) |
Information theory |
Artificial intelligence - Data processing |
Computer science |
Logic programming |
Machine learning |
Artificial Intelligence |
Data Structures and Information Theory |
Data Science |
Computer Science |
Logic in AI |
Machine Learning |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
|
|
|
|
|
Nota di contenuto |
|
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. |
|
|
|
|
|
|
Sommario/riassunto |
|
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 . |
|
|
|
|
|
|
|
| |