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Towards Green Automated Machine Learning: Status Quo and Future Directions [article]

Tanja Tornede and Alexander Tornede and Jonas Hanselle and Marcel Wever and Felix Mohr and Eyke Hüllermeier
2022 arXiv   pre-print
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

Yan Hao Tan, Holden Li King Ho
2018 Zenodo  
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]

Kyle McCullough, Andrew Feng, Meida Chen, Ryan McAlinden
2020 arXiv   pre-print
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

Rebecca Ferguson, Simon Buckingham Shum
2012 Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK '12  
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

Chris Lawson, Jose Manuel Martí, Tijana Radivojevic, Sai Vamshi R. Jonnalagadda, Reinhard Gentz, Nathan J. Hillson, Sean Peisert, Joonhoon Kim, Blake A. Simmons, Christopher J. Petzold, Steven W. Singer, Aindrila Mukhopadhyay (+3 others)
2020 Metabolic Engineering  
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]

Christopher A. Tucker
2020 arXiv   pre-print
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]

Christian Helbig, Sandra Hofhues, Marc Egloffstein, Dirk Ifenthaler
2021 Digital Transformation of Learning Organizations  
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

Christine Redecker, Yves Punie
2013 Future Learning  
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

Marion Fourcade, Jeffrey Gordon
2020 Journal of Law and Political Economy  
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]

Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, Yulia Tsvetkov, Oren Etzioni (+3 others)
2022 arXiv   pre-print
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]

Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau
2020 arXiv   pre-print
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]

Andreas Sudmann
2019 The Democratization of Artificial Intelligence  
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]

Ben Hutchinson, Andrew Smart, Alex Hanna, Emily Denton, Christina Greer, Oddur Kjartansson, Parker Barnes, Margaret Mitchell
2021 arXiv   pre-print
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]

Allison McCarn Deiana, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini (+74 others)
2021 arXiv   pre-print
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

Cary Coglianese
2021 Daedalus  
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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