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| 005 | 20231130095721 | |
| 008 | 231124s2023 flua b 001 0 eng | |
| 010 | ▼a 2022056543 | |
| 020 | ▼a 9781032128771 ▼q (hardback) | |
| 020 | ▼a 9781032124841 ▼q (paperback) | |
| 020 | ▼z 9781003226642 ▼q (ebook) | |
| 035 | ▼a (KERIS)REF000020206895 | |
| 040 | ▼a LBSOR ▼b eng ▼e rda ▼c LBSOR ▼d 211009 | |
| 042 | ▼a pcc | |
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| 084 | ▼a 006.3 ▼2 DDCK | |
| 090 | ▼a 006.3 ▼b A7918 ▼c 1 | |
| 100 | 1 | ▼a Hastings, Janna. |
| 245 | 1 0 | ▼a AI for scientific discovery / ▼c Janna Hastings. |
| 250 | ▼a 1st ed. | |
| 260 | ▼a Boca Raton : ▼b CRC Press, ▼c 2023. | |
| 264 | 1 | ▼a Boca Raton : ▼b CRC Press, ▼c 2023. |
| 300 | ▼a xiii, 119 p. : ▼b ill. ; ▼c 20 cm. | |
| 336 | ▼a text ▼b txt ▼2 rdacontent | |
| 337 | ▼a unmediated ▼b n ▼2 rdamedia | |
| 338 | ▼a volume ▼b nc ▼2 rdacarrier | |
| 490 | 1 | ▼a AI for everything series ; ▼v [v. 1] |
| 504 | ▼a Includes bibliographical references and index. | |
| 505 | 0 | ▼a Introduction : AI and the digital revolution in science -- AI for managing scientific literature and evidence -- AI for data interpretation -- AI for reproducible research -- Limitations of AI and strategies for combating bias -- Conclusion : AI and the future of scientific discovery. |
| 520 | ▼a "AI for Scientific Discovery provides an accessible introduction to the wide-ranging applications of artificial intelligence technologies in scientific research and discovery across the full breadth of scientific disciplines. Artificial intelligence technologies support discovery science in multiple different ways. They support literature management and synthesis, allowing the wealth of what has already been discovered and reported on to be integrated and easily accessed. They play a central role in data analysis and interpretation - in the context of what is called 'data science'. AI is also helping to combat the reproducibility crisis in scientific research, by underpinning the discovery process with AI-enabled standards and pipelines, support the management of large-scale data and knowledge resources so that they can be shared, integrated and serve as a background 'knowledge ecosystem' into which new discoveries can be embedded. However, there are limitations to what AI can achieve and its outputs can be biased and confounded thus should not be blindly trusted. The latest generation of hybrid and 'human-in-the-loop' AI technologies have as their objective a balance between human inputs and insights and the power of the number-crunching and statistical inference at massive scale that AI technologies are best at"--Provided by publisher. | |
| 650 | 0 | ▼a Research ▼x Data processing. |
| 650 | 0 | ▼a Science ▼x Data processing. |
| 650 | 0 | ▼a Artificial intelligence. |
| 830 | 0 | ▼a AI for everything series; ▼v [v. 1]. |
| 945 | ▼a ITMT |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.3 A7918 1 | 등록번호 121264837 (2회 대출) | 도서상태 대출중 | 반납예정일 | 예약 예약가능 | 서비스 |
컨텐츠정보
책소개
AI for Scientific Discovery provides an accessible introduction to the wide-ranging applications of artificial intelligence (AI) technologies in scientific research and discovery across the full breadth of scientific disciplines. AI technologies support discovery science in multiple ways. They support literature management and synthesis, allowing the wealth of what has already been discovered and reported on to be integrated and easily accessed. They play a central role in data analysis and interpretation in the context of what is called ‘data science’. AI is also helping to combat the reproducibility crisis in scientific research by underpinning the discovery process with AI-enabled standards and pipelines and supporting the management of large-scale data and knowledge resources so that they can be shared and integrated and serve as a background ‘knowledge ecosystem’ into which new discoveries can be embedded. However, there are limitations to what AI can achieve and its outputs can be biased and confounded and thus should not be blindly trusted. The latest generation of hybrid and ‘human-in-the-loop’ AI technologies have as their objective a balance between human inputs and insights and the power of number-crunching and statistical inference at a massive scale that AI technologies are best at.
AI for Scientific Discovery provides an accessible introduction to the wide-ranging applications of artificial intelligence technologies in scientific research and discovery across the full breadth of scientific disciplines.
정보제공 :
목차
Preface. Acknowledgements. About the Author. 1 Introduction: AI and the Digital Revolution in Science. 2 AI for Managing Scientific Literature and Evidence. 3 AI for Data Interpretation. 4 AI for Reproducible Research. 5 Limitations of AI and Strategies for Combating Bias. 6 Conclusion: AI and the Future of Scientific Discovery. Index.