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Author: Janna Hastings
Publisher: CRC Press
ISBN: 100088516X
Category : Computers
Languages : en
Pages : 99
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Book Description
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.
Author: Janna Hastings
Publisher: CRC Press
ISBN: 100088516X
Category : Computers
Languages : en
Pages : 99
Get Book Here
Book Description
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.
Author: National Academies of Sciences Engineering and Medicine
Publisher:
ISBN: 9780309714976
Category : Computers
Languages : en
Pages : 0
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Book Description
Over the past century, artificial intelligence (AI) has evolved from computational theory to everyday conversational technologies, capturing the attention and interest of the public and the media. It has also caught the attention of the scientific community, where it has provided a new tool to support inquiry and exploration. While AI in the context of scientific investigation has existed for decades, advances in computational technology and sensing in the physical world have created opportunities to integrate AI into science in unexpected ways, with capabilities that are rapidly accelerating. As a result, AI has been leveraged by an expanding collection of disciplines in the physical and biological sciences, as well as engineering domains. While the opportunities for AI in scientific discovery seem endless, there are numerous questions about what makes for trustworthy and reliable discovery, whether such investigation should be performed without human oversight or intervention, and how best to prioritize the research agenda and allocation of resources without magnifying disparities for individuals and nations alike. In recognition of the timeliness and sizable implications of AI in our world, the National Academies of Sciences, Engineering, and Medicine hosted AI for Scientific Discovery - A Workshop on October 12-23, 2023. Leaders from across the globe in the field of AI, preeminent researchers in various science and engineering disciplines, and experts in ethics, law, and social sciences met to appraise the state of the field and provide guidance on the opportunities, as well as the challenges, that lay ahead. Presentations and discussion explored the future of AI in terms of its role as an autonomous researcher carrying out discovery and considered ethical aspects of AI used for independent scientific discovery.
Author: Kartik Hosanagar
Publisher: Penguin
ISBN: 0525560904
Category : Business & Economics
Languages : en
Pages : 274
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Book Description
A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence.
Author: Jan Zytkow
Publisher: Springer Science & Business Media
ISBN: 9401721246
Category : Psychology
Languages : en
Pages : 229
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Book Description
Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on searching an `instance space' (empirical exploration) and a `hypothesis space' (generation of theories). In scientific discovery, searching must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This book focuses especially on the processes for finding new problem representations and new concepts, which are relatively new domains for research on discovery. Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the `blackboard' of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interactions. In this sense, all research on discovery, including the investigations on individual processes discussed in this book, is social psychology, or even sociology.
Author: Anthony J. G. Hey
Publisher:
ISBN:
Category : Bioinformatics
Languages : en
Pages : 292
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Book Description
Foreword. A transformed scientific method. Earth and environment. Health and wellbeing. Scientific infrastructure. Scholarly communication.
Author: Saso Dzeroski
Publisher: Springer Science & Business Media
ISBN: 354073919X
Category : Language Arts & Disciplines
Languages : en
Pages : 333
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Book Description
This survey provides an introduction to computational approaches to the discovery of communicable scientific knowledge and details recent advances. It is partly inspired by the contributions of the International Symposium on Computational Discovery of Communicable Knowledge, held in Stanford, CA, USA in March 2001, a number of additional invited contributions provide coverage of recent research in computational discovery.
Author: Nathan Brown
Publisher: Royal Society of Chemistry
ISBN: 1839160543
Category : Computers
Languages : en
Pages : 425
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Book Description
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
Author: Tuan D. Pham
Publisher: Springer Nature
ISBN: 303069951X
Category : Science
Languages : en
Pages : 373
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Book Description
Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.
Author: Morton Wagman
Publisher: Praeger
ISBN: 0275966542
Category : Psychology
Languages : en
Pages : 0
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Book Description
Wagman offers a critical analysis of current theory and research in the psychological and computational sciences, directed toward the elucidation of scientific discovery processes and structures. It discusses human scientific discovery processes, analyzes computer scientific discovery processes, and makes a comparative evaluation of the two. This work examines the scientific reasoning of the discoverers of the inhibition mechanism of gene control; scientific discovery heuristics used at different developmental levels; artificial intelligence and mathematical discovery; the ECHO system; the evolution of artificial intelligence discovery systems; the PAULI system; and the KEKADA system. It concludes with an examination of the extent to which computational discovery systems can emulate a set of 10 types of scientific problems.
Author: StoryBuddiesPlay
Publisher: StoryBuddiesPlay
ISBN:
Category : Computers
Languages : en
Pages : 80
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Book Description
DeepMind's AI is revolutionizing scientific research across various fields. This comprehensive guide explores the transformative potential of DeepMind in protein folding, materials science, drug discovery, and medical imaging analysis. Discover how DeepMind empowers citizen scientists, accelerates scientific progress, and paves the way for a future of personalized medicine and sustainable technologies. Learn about the importance of collaboration between AI and human researchers for ethical and responsible advancements. DeepMind, AI, scientific discovery, protein folding, materials science, drug discovery, medical imaging, citizen science, healthcare, artificial intelligence
