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Towards Green Automated Machine Learning: Status Quo and Future Directions
[article]
2022
arXiv
pre-print
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Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine learning pipeline - tailored ...
Green AutoML: design of AutoML systems, benchmarking, transparency and research incentives. ...
of Green AutoML at the panel discussion. ...
arXiv:2111.05850v2
fatcat:tx7qrhstnffijgsmn6gc5ud23y
Experimental Machine Learning Of Finger Photoplethysmography (Ppg) For Autonomous Hospital Bed Pushing Framework Using Polynomial Regression
2018
Zenodo
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Polynomial regression machine learning of 65 one-hour sets of finger PPG data from a single subject were collected and studied. ...
The data was processed by polynomial regression machine learning technique to output the degree of polynomial with highest cross validation score mean. ...
In terms of PPG convergence, it was observed that both pre-journey and journey sets converges to the same degree of polynomial which facilitates the establishment of a status quo in PPG vitals for the ...
doi:10.5281/zenodo.1465050
fatcat:a4fazdbfkrfgxk6qq7ovvhgrpy
Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions
[article]
2020
arXiv
pre-print
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using machine learning algorithms and neural networks. ...
A goal of this research is to allow automated monitoring for largescale infrastructure projects, such as railways, to determine reliable metrics that define and predict the direction construction initiatives ...
ACKNOWLEDGEMENTS The authors would like to thank the two primary sponsors of this research: Army Futures Command (AFC) Synthetic Training Environment (STE), and the Office of Naval Research (ONR). ...
arXiv:2009.00185v1
fatcat:mjnyemrktbawrm7jg4nuod3fze
Social learning analytics
2012
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK '12
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We conclude by revisiting the drivers and trends, and consider future scenarios that we may see unfold as SLA tools and services mature. ...
We consider some of the concerns that learning analytics provoke, and suggest that Social Learning Analytics may provide ways forward. ...
from researchers and practitioners who have found these ideas valuable in their own work. ...
doi:10.1145/2330601.2330616
dblp:conf/lak/FergusonS12
fatcat:lk6gaeuohnd33hyayajnaedux4
Machine learning for metabolic engineering: A review
2020
Metabolic Engineering
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Finally, the future perspectives and most promising directions for this combination of disciplines are examined. ...
Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. ...
and the U. ...
doi:10.1016/j.ymben.2020.10.005
pmid:33221420
fatcat:hac34yggd5hnrhkikd7elbzz3m
A machine-learning software-systems approach to capture social, regulatory, governance, and climate problems
[article]
2020
arXiv
pre-print
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class sectors) and operational efficiency. ...
It will finish with a discussion of these and other historical implications. ...
The problem So lies the conundrum today manifest as an irony between despite an increasing connectivity and communication globally, trappings by unwise policy and unwavering faith in status quo leads to ...
arXiv:2002.11485v1
fatcat:l4dpmae6wfc5laync7ee4drsym
Digital Transformation in Learning Organizations
[chapter]
2021
Digital Transformation of Learning Organizations
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have in learning organizations. ...
In conclusion, this chapter leads back to the starting point of the anthology: the project #ko.vernetzt and the question of what significance the dimensions and design perspectives of digital transformation ...
mobility and cooperation in education, science, research and culture" (OeAD) from September 2019 to August 2020. ...
doi:10.1007/978-3-030-55878-9_14
fatcat:l36faznntjexxoadcstygtf6eu
The Future of Learning 2025: Developing a vision for change
2013
Future Learning
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and Tim Reader (Futurelab) . Special thanks must go to Richard Sandford for his leadership in theoretical and methodological issues and for his practical management at the heart of the project. ...
Acknowledgements This report is the result of the work of a large number of individuals and institutions, in particular, the members of the Beyond Current Horizons team at DCSF and Futurelab: ...
Indeed, the goal following these types of events is often to ensure a return to the status quo. ...
doi:10.7564/13-fule12
fatcat:pmfgb63yyvbcrh4sdzz53svytm
Learning Like a State: Statecraft in the Digital Age
2020
Journal of Law and Political Economy
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What does it mean to sense, see, and act like a state in the digital age? We examine the changing phenomenology, governance, and capacity of the state in the era of big data and machine learning. ...
First, what we call the dataist state may be less accountable than its predecessor, despite its promise of enhanced transparency and accessibility. ...
public record for affected parties to challenge in notice-and-comment rulemaking and in court than does the status quo (Engstrom et al. 2020) . ...
doi:10.5070/lp61150258
fatcat:kl3yhw7khngr5nfvsmrclxypri
Can Machines Learn Morality? The Delphi Experiment
[article]
2022
arXiv
pre-print
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As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof. ...
Empirical results shed novel insights on the promises and limits of machine ethics; Delphi demonstrates strong generalization capabilities in the face of novel ethical situations, while off-the-shelf neural ...
TPU machines for conducting experiments were generously provided by Google through the TensorFlow Research Cloud (TFRC) program. ...
arXiv:2110.07574v2
fatcat:rm3vipytkrexhjewrn2bnmve2a
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
[article]
2020
arXiv
pre-print
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Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. ...
By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms. ...
current status quo. ...
arXiv:2002.05651v1
fatcat:ma6buukl7jad5klewgrzsrfbva
The Democratization of Artificial Intelligence
[chapter]
2019
The Democratization of Artificial Intelligence
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Net Politics in the Era of Learning Algorithms" in Bochum, September 2018 led by Andreas Sudmann and with the members of the ITEA3 project "Industrial-grade Verification and Validation of Evolving Systems ...
and dissemination, media philosophy and media theory. ...
It is about connecting the dots, building an infrastructure on an already existing infrastructure to direct a data f low towards machine learning algorithms. ...
doi:10.1515/9783839447192-001
fatcat:7yjb2m5p7jdt7dvkpzsuttbvf4
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
[article]
2021
arXiv
pre-print
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However the datasets which empower machine learning are often used, shared and re-used with little visibility into the processes of deliberation which led to their creation. ...
Which domain experts were consulted regarding how to model subgroups and other phenomena? How were questions of representational biases measured and addressed? Who labeled the data? ...
INTRODUCTION Machine learning faces a crisis in accountability. ...
arXiv:2010.13561v2
fatcat:5gq5klvzfnbp5fbau4slcmyrzm
Applications and Techniques for Fast Machine Learning in Science
[article]
2021
arXiv
pre-print
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In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing ...
training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. ...
Both the event-by-event triggering and fast directional reconstruction can be addressed with fast machine learning. ...
arXiv:2110.13041v1
fatcat:cvbo2hmfgfcuxi7abezypw2qrm
Administrative Law in the Automated State
2021
Daedalus
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Not only might a highly automated state readily meet long-standing administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in ...
In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? ...
As a result, transparency concerns are reasonable when considering a future of an automated state based on machine learning systems. ...
doi:10.1162/daed_a_01862
fatcat:jsr3r7utcvddhaaqygdzworo2y
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