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Democratizing AI: From Theory to Practice

Paul Clough D., Jahna Otterbacher
2021 Zenodo  
Through the lens of socio-political democracy, this chapter examines the democratization of AI.  ...  We are witnessing a movement towards Democratizing AI, with a wide variety of tools, platforms and data sources becoming accessible to more people.  ...  We consider the full spectrum of data, techniques, and practices that surround the modern ML pipeline, in our examination of the trend towards Democratizing AI.  ... 
doi:10.5281/zenodo.5788369 fatcat:qeqzz6spwrd73jb4yhrxf24lzy

DLHub: Model and Data Serving for Science [article]

Ryan Chard, Zhuozhao Li, Kyle Chard, Logan Ward, Yadu Babuji, Anna Woodard, Steve Tuecke, Ben Blaiszik, Michael J. Franklin, Ian Foster
2018 arXiv   pre-print
Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications.  ...  Furthermore, few such systems are designed to support the specialized requirements of scientific ML.  ...  Department of Energy under Contract DE-AC02-06CH11357. We thank Amazon Web Services for research credits and Argonne for computing resources.  ... 
arXiv:1811.11213v1 fatcat:zmmandowgnco7n3b5rqd3nao5q

VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Amir Haghighati, Kamran Sedig
2020 Data  
Through social media platforms, massive amounts of data are being produced.  ...  In this paper, we describe VARTTA (Visual Analytics for Real-Time Twitter datA), a visual analytics system that combines data visualizations, human-data interaction, and ML algorithms to help users monitor  ...  Also, it enables real-time user interactions with ML methods and data processing components. Pipeline design for processing a stream from a source (Twitter) to VARTTA's interface.  ... 
doi:10.3390/data5010020 fatcat:dre5vcmexjc7fjipnn2skdqm7u

Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations

Saskia Comess, Alexia Akbay, Melpomene Vasiliou, Ronald N. Hines, Lucas Joppa, Vasilis Vasiliou, Nicole Kleinstreuer
2020 Frontiers in Artificial Intelligence  
We argue that collaborative efforts in data curation and storage, scientific computing, and training are of paramount importance to empower researchers within environmental sciences and the broader public  ...  , a lack of skilled data scientists and appropriate cyber-infrastructures, and limited understanding of possibilities and communication of benefits were among those identified.  ...  Even programs such as Harvard's Health Data Science MS, which is offered through the School of Public Health, only requires one epidemiology course and then places the onus on the student to integrate  ... 
doi:10.3389/frai.2020.00031 pmid:33184612 pmcid:PMC7654840 fatcat:xg3ev4uohfgmvf2vc4ejngmwh4

Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop [article]

Anamaria Crisan, Brittany Fiore-Gartland
2021 arXiv   pre-print
AutoML systems can speed up routine data science work and make machine learning available to those without expertise in statistics and computer science.  ...  In this study, we conduct interviews with 29 individuals from organizations of different sizes to characterize how they currently use, or intend to use, AutoML systems in their data science work.  ...  We also wish to acknowledge members of the Tableau Research, User Research, and Tableau CRM for their feedback on our study and findings.  ... 
arXiv:2101.04296v1 fatcat:rawglgau7fhgvj2npii7if6qwq

Learnings from Frontier Development Lab and SpaceML – AI Accelerators for NASA and ESA [article]

Siddha Ganju, Anirudh Koul, Alexander Lavin, Josh Veitch-Michaelis, Meher Kasam, James Parr
2020 arXiv   pre-print
research projects, measured through NASA's Technology Readiness Levels.  ...  to the ML community's challenges in reproducibility.  ...  FDL serves as a useful sandbox for observing interdisciplinary ML and open science in real-time, over a broad range of science subjects.  ... 
arXiv:2011.04776v1 fatcat:giakfrg4cvcf3a5l24plpzu55y

Multimodal datasets: misogyny, pornography, and malignant stereotypes [article]

Abeba Birhane, Vinay Uday Prabhu, Emmanuel Kahembwe
2021 arXiv   pre-print
These address concerns surrounding the dubious curation practices used to generate these datasets, the sordid quality of alt-text data available on the world wide web, the problematic content of the CommonCrawl  ...  , policy makers and data subjects.  ...  Abeba Birhane was supported, in part, by Science Foundation Ireland grant 13/RC/2094_2  ... 
arXiv:2110.01963v1 fatcat:eiyzq4ywgbb3bnkwgce4smavti

AMPL: A Data-Driven Modeling Pipeline for Drug Discovery [article]

Amanda J. Minnich, Kevin McLoughlin, Margaret Tse, Jason Deng, Andrew Weber, Neha Murad, Benjamin D. Madej, Bharath Ramsundar, Tom Rush, Stacie Calad-Thomson, Jim Brase, Jonathan E. Allen
2019 arXiv   pre-print
The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools.  ...  We have also found that dataset size is directly correlated to prediction performance, and that single-task deep learning models only outperform shallow learners if there is sufficient data.  ...  Data ingestion and curation-related parameters include: • User-defined custom feature classes • Because some types of features are expensive to compute, AMPL supports two kinds of interaction with external  ... 
arXiv:1911.05211v2 fatcat:yo4smamvfrgnjn2cbv6nebir4i

Machine learning-guided design and development of metallic structural materials

Jinxin Yu, Shengkun Xi, Shaobin Pan, Yongjie Wang, Qinghua Peng, Rongpei Shi, Cuiping Wang, Xingjun Liu
2021 Journal of Materials Informatics  
In recent years, the advent of machine learning (ML) in materials science has provided a new tool for accelerating the design and discovery of new materials with a superior combination of mechanical properties  ...  We also present our perspectives regarding the further acceleration of data-driven discovery, development, design and deployment of metallic structural materials and the adoption of ML-based techniques  ...  We speculate that it is very likely in the future that ML interpretability techniques will form a substantial part of ML workflows and pipelines.  ... 
doi:10.20517/jmi.2021.08 fatcat:7r3wdmxgl5dbdmci27me6wvzdy

Analysis validation has been neglected in the Age of Reproducibility

Kathleen E. Lotterhos, Jason H. Moore, Ann E. Stapleton
2018 PLoS Biology  
Increasingly complex statistical models are being used for the analysis of biological data.  ...  We find that different fields of science have historically failed to meet all criteria, and we suggest ways to implement meaningful validation in training and practice.  ...  Acknowledgments We thank Casey Greene and Daniel Himmelstein, University of Pennsylvania, Yishi Wang, UNCW, S.  ... 
doi:10.1371/journal.pbio.3000070 pmid:30532167 pmcid:PMC6301703 fatcat:mh3lk3sdzfcfpkjtl2af57aldm

Towards Interactive Data Exploration [chapter]

Carsten Binnig, Fuat Basık, Benedetto Buratti, Ugur Cetintemel, Yeounoh Chung, Andrew Crotty, Cyrus Cousins, Dylan Ebert, Philipp Eichmann, Alex Galakatos, Benjamin Hättasch, Amir Ilkhechi (+10 others)
2019 Lecture Notes in Business Information Processing  
Enabling interactive visualization over new datasets at "human speed" is key to democratizing data science and maximizing human productivity.  ...  Furthermore, we present the results of building IDEA, a new type of system for interactive data exploration that is specifically designed to integrate seamlessly with existing data management landscapes  ...  QuIC-M exposes a simple model building interface allowing domain experts to seamlessly interleave data exploration with curation of machine learning pipelines.  ... 
doi:10.1007/978-3-030-24124-7_11 fatcat:ixxjchbhe5awrlcjvr5kocqgmq

Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives

Asheesh Kumar Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Arti Singh
2018 Trends in Plant Science  
Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain  ...  Finally, we outline several avenues of research leveraging current and future DL tools in plant science.  ...  Acknowledgments This work was supported by the Iowa State University (Presidential Initiative for Interdisciplinary Research) to all authors; the Plant Science Institute to A.K.S., B.G., S.S.; the Iowa  ... 
doi:10.1016/j.tplants.2018.07.004 pmid:30104148 fatcat:32sw4te2izenpj4vmyhztdcgqa

A Framework for Democratizing AI [article]

Shakkeel Ahmed, Ravi S. Mula, Soma S. Dhavala
2020 arXiv   pre-print
However, accessibility and affordability are all but a few of the facets of democratizing the field.  ...  As one can imagine, democratizing AI is a multi-faceted problem, and it requires advancements in science, technology and policy. At mlsquare, we are developing scientific tools in this space.  ...  For a curated list of papers, and tools, see [24] . [27] certainly looks very interesting as it makes graph and tabular data interoperable seamlessly.  ... 
arXiv:2001.00818v1 fatcat:hdhacgdt7fboxdaqw4ge7dqnme

Serverless on FHIR: Deploying machine learning models for healthcare on the cloud [article]

Bell Raj Eapen, Kamran Sartipi, Norm Archer
2020 arXiv   pre-print
Machine Learning (ML) plays a vital role in implementing digital health. The advances in hardware and the democratization of software tools have revolutionized machine learning.  ...  ML models undergo constant improvement of their accuracy and predictive power with a high turnover rate.  ...  The uniform format of the input and output allows the chaining of ML models as a pipeline.  ... 
arXiv:2006.04748v1 fatcat:ivk2yffpynbkndbjry7av5ma3m

Towards the Modelling of Veillance based Citizen Profiling using Knowledge Graphs

Siraj Munir, Syed Imran Jami, Shaukat Wasi
2021 Open Computer Science  
It uses veillance (Surveillance and Sousveillance) for data acquisition. For representation of Citizen Profile Temporal Knowledge Graph has been used through which we can answer semantic queries.  ...  Our contribution is towards enriching the data acquisition process by adding sousveillance mechanism and facilitating semantic queries through representation of Citizen Profiles using Temporal Knowledge  ...  Making a user's private data open for world is indeed violation of democratic laws and policies.  ... 
doi:10.1515/comp-2020-0209 fatcat:zay5zfuhkvfdfnzoxe353pjpa4
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