Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions
Source abstract
Abstract
Despite its dominance over the past three decades, model-centric AI has recently come under heavy criticism in favor of data-centric AI. Indeed, both promise to improve the performance of AI systems, yet with converse points of focus. While the former successively upgrades a devised model (algorithm/code), holding the amount and type of data used in model training fixed, the latter enhances the quality of deployed data continuously, paying less attention to further model upgrades. Rather than favoring either of the two approaches, this paper reconciles data-centric AI with model-centric AI. In so doing, we connect current AI to the field of cybersecurity and natural language inference, and through the phenomena of ‘adversarial samples’ and ‘hypothesis-only biases’, respectively, showcase the limitations of model-centric AI in terms of algorithmic stability and robustness. Further, we argue that overcoming the alleged limitations of model-centric AI may well require paying extra attention to the alternative data-centric approach. However, this should not result in reducing interest in model-centric AI. Our position is supported by the notion that successful ‘problem solving’ requires considering both the way we act upon things (algorithm) as well as harnessing the knowledge derived from data of their states and properties.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Adversarial Robustness in Machine Learning · Explainable Artificial Intelligence (XAI) · Ethics and Social Impacts of AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computer Information Sys, Higher Colleges of Technology, Abu Dhabi P.O. Box 41012, United Arab Emirates
References (77)
Show source-provided references
1. Hamid, O.H., Smith, N.L., and Barzanji, A. (2017, January 24–26). Automation, per se, is not job elimination: How artificial intelligence forwards cooperative human–machine coexistence. Proceedings of the 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Emden, Germany.10.1109/INDIN.2017.8104891 ↗
2. Bhatt, S. (2023, January 03). The Big Fight: RPA vs. Traditional Automation. Available online: https://www.botreetechnologies.com/blog/the-big-fight-robotic-process-automation-vs-traditional-automation.
3. Thoughts on the development of artificial intelligence combined with RPA
4. Khan, Z.A., Imran, S.A., Akre, V., Shahzad, M., Ahmed, S., Khan, A., and Rajan, A. (2020, January 25–26). Contemporary cutting edge applications of IoT (Internet of Things) in industries. Proceedings of the 2020 Seventh International Conference on Information Technology Trends (ITT), Abu Dhabi, United Arab Emirates.10.1109/ITT51279.2020.9320890 ↗
5. Thames, L., and Schaefer, D. (2017). Cybersecurity for Industry 4.0, Springer.10.1007/978-3-319-50660-9 ↗
6. Cloud computing: Opportunities and challenges10.1109/MPOT.2013.2279684 ↗
7. A survey on the edge computing for the Internet of Things10.1109/ACCESS.2017.2778504 ↗
8. Yuan, L., He, Q., Tan, S., Li, B., Yu, J., Chen, F., Jin, H., and Yang, Y. (2021, January 19–23). Coopedge: A decentralized blockchain-based platform for cooperative edge computing. Proceedings of the Web Conference 2021, Ljubljana, Slovenia.10.1145/3442381.3449994 ↗
9. Boubin, J., Banerjee, A., Yun, J., Qi, H., Fang, Y., Chang, S., Srinivasan, K., Ramnath, R., and Arora, A. (2022). PROWESS: An Open Testbed for Programmable Wireless Edge Systems, Association for Computing Machinery.10.1145/3491418.3530759 ↗
10. Durão, L.F., Haag, S., Anderl, R., Schützer, K., and Zancul, E. (2018, January 2–4). Digital twin requirements in the context of industry 4.0. Proceedings of the IFIP International Conference on Product Lifecycle Management, Turin, Italy.10.1007/978-3-030-01614-2_19 ↗
11. Industry 4.0 and the digital twin for building industry
12. Machine learning and deep learning in smart manufacturing: The smart grid paradigm10.1016/j.cosrev.2020.100341 ↗
13. Predicting Music Popularity Using Machine Learning Algorithm and Music Metrics Available in Spotify
14. Murschetz, P.C., and Prandner, D. (2018). Competitiveness in Emerging Markets, Springer.
15. Moriuchi, E. (2021). Cross-Cultural Social Media Marketing: Bridging across Cultural Differences, Emerald Publishing Limited.10.1108/9781838671754 ↗
16. Smith, N., Teerawanit, J., and Hamid, O.H. (2018, January 7–10). AI-Driven Automation in a Human-Centered Cyber World. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.10.1109/SMC.2018.00551 ↗
17. Hamid, O.H. (2022, January 25–26). From Model-Centric to Data-Centric AI: A Paradigm Shift or Rather a Complementary Approach?. Proceedings of the 2022 8th International Conference on Information Technology Trends (ITT), Dubai, United Arab Emirates.10.1109/ITT56123.2022.9863935 ↗
18. Eyuboglu, S., Karlaš, B., Ré, C., Zhang, C., and Zou, J. (2022, January 12). dcbench: A benchmark for data-centric AI systems. Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning, Philadelphia, PA, USA.10.1145/3533028.3533310 ↗
19. Ng, A. (2023, January 03). A Chat with Andrew on MLOps: From Model-Centric to Data-Centric AI. Available online: https://www.youtube.com/watch?v=06-AZXmwHjo.
20. Ng, A. (2023, January 03). AI Doesn’t Have to Be Too Complicated or Expensive for Your Business. Available online: https://hbr.org/2021/07/ai-doesnt-have-to-be-too-complicated-or-expensive-for-your-business.
21. Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Rojas, W.G., Diamos, S., Diamos, G., He, L., Kiela, D., and Jurado, D. (2022). DataPerf: Benchmarks for Data-Centric AI Development. arXiv.
22. McCarthy, J. (2023, January 03). What is Artificial Intelligence?. Available online: https://cse.unl.edu/~choueiry/S09-476-876/Documents/whatisai.pdf.
23. Horvatić, D., and Lipic, T. (2021). Human-Centric AI: The Symbiosis of Human and Artificial Intelligence. Entropy, 23.10.3390/e23030332 ↗
24. AI-based computer-aided diagnosis (AI-CAD): The latest review to read first10.1007/s12194-019-00552-4 ↗
25. He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7–13). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.123 ↗
26. Mastering the game of go without human knowledge10.1038/nature24270 ↗
27. Deepstack: Expert-level artificial intelligence in heads-up no-limit poker10.1126/science.aam6960 ↗
28. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv.
29. Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv.
30. ImageNet classification with deep convolutional neural networks
31. Deep learning10.1038/nature14539 ↗
32. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function10.1016/S0893-6080(05)80131-5 ↗
33. Bender, E.M., Gebru, T., McMillan-Major, A., and Shmitchell, S. (2021, January 3–10). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Toronto, ON, Canada.10.1145/3442188.3445922 ↗
34. BusinessWire (2023, January 03). Data Creation and Replication Will Grow at a Faster Rate Than Installed Storage Capacity, According to the IDC Global DataSphere and StorageSphere Forecasts. Available online: https://www.businesswire.com/news/home/20210324005175/en/Data-Creation-and-Replication-Will-Grow-at-a-Faster-Rate-Than-Installed-Storage-Capacity-According-to-the-IDC-Global-DataSphere-and-StorageSphere-Forecasts.
35. Hack, U. (2023, January 03). What Is The Real Story behind the Explosive Growth of Data?. Available online: https://www.red-gate.com/blog/database-development/whats-the-real-story-behind-the-explosive-growth-of-data.
36. Reinsel, D., Rydning, J., and Gantz, J.F. (2023, January 03). Worldwide Global DataSphere Forecast, 2021–2025: The World Keeps Creating More Data—Now, What Do We Do with It All?. Available online: https://www.marketresearch.com/IDC-v2477/Worldwide-Global-DataSphere-Forecast-Keeps-14315439/.
37. Lowe, D. (2023, January 03). Machine Learning Deserves Better than This. Available online: https://www.science.org/content/blog-post/machine-learning-deserves-better.
38. Risk of bias in studies on prediction models developed using supervised machine learning techniques: Systematic review10.1136/bmj.n2281 ↗
39. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans10.1038/s42256-021-00307-0 ↗
40. Molnar, C. (2023, January 03). Interpretable Machine Learning. Available online: https://christophm.github.io/interpretable-ml-book.
41. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2013). Intriguing properties of neural networks. arXiv.
42. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20–25). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.10.1109/CVPR.2009.5206848 ↗
43. Goodfellow, I.J., Shlens, J., and Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv.
44. Adversarial attacks and defenses in deep learning10.1016/j.eng.2019.12.012 ↗
45. Deep learning-based image recognition for autonomous driving10.1016/j.iatssr.2019.11.008 ↗
46. Sharma, P., Austin, D., and Liu, H. (2019, January 5–6). Attacks on machine learning: Adversarial examples in connected and autonomous vehicles. Proceedings of the 2019 IEEE International Symposium on Technologies for Homeland Security (HST), Boston, MA USA.10.1109/HST47167.2019.9032989 ↗
47. Shortcut learning in deep neural networks10.1038/s42256-020-00257-z ↗
48. Finlayson, S.G., Chung, H.W., Kohane, I.S., and Beam, A.L. (2018). Adversarial attacks against medical deep learning systems. arXiv.
49. Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S.R., and Smith, N.A. (2018). Annotation artifacts in natural language inference data. arXiv.10.18653/v1/N18-2017 ↗
50. Poliak, A., Naradowsky, J., Haldar, A., Rudinger, R., and Van Durme, B. (2018). Hypothesis only baselines in natural language inference. arXiv.10.18653/v1/S18-2023 ↗
51. Zhang, G., Bai, B., Zhang, J., Bai, K., Zhu, C., and Zhao, T. (2019). Mitigating Annotation Artifacts in Natural Language Inference Datasets to Improve Cross-dataset Generalization Ability. arXiv.
52. Belinkov, Y., Poliak, A., Shieber, S.M., Van Durme, B., and Rush, A.M. (2019). Do not take the premise for granted: Mitigating artifacts in natural language inference. arXiv.10.18653/v1/P19-1084 ↗
53. Motamedi, M., Sakharnykh, N., and Kaldewey, T. (2021). A data-centric approach for training deep neural networks with less data. arXiv.
54. Berscheid, D. (2023, January 03). Data-Centric Machine Learning: Making Customized ML Solutions Production-Ready. Available online: https://dida.do/blog/data-centric-machine-learning.
55. Morrish, J., and Hatton, M. (2023, January 03). Global IoT Market to Grow to 24.1 Billion Devices in 2030, Generating $1.5 Trillion Annual Revenue. Available online: https://transformainsights.com/news/iot-market-24-billion-usd15-trillion-revenue-2030.
56. IoT Business News (2023, January 03). Transforma Insights Makes Powerful New IoT Forecast Resource Available for All. Available online: https://transformainsights.com/news/powerful-new-iot-forecast-tool.
57. Ji, X., Tian, Q., Yang, Y., Lin, C., Li, Q., and Shen, C. (2023, January 03). Improving Adversarial Robustness with Data-Centric Learning. Available online: http://alisec-competition.oss-cn-shanghai.aliyuncs.com/competition_papers/20211201/rank5.pdf.
58. Hamid, O.H., and Braun, J. (2019). Computational Intelligence: Proceedings of the 9th International Joint Conference, IJCCI 2017, Funchal-Madeira, Portugal, 1–3 November 2017, Springer. Revised Selected Papers.
59. Inhibition in selective attention10.1111/nyas.14304 ↗
60. Schlegl, T., Stino, H., Niederleithner, M., Pollreisz, A., Schmidt-Erfurth, U., Drexler, W., Leitgeb, R.A., and Schmoll, T. (2022). Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images. arXiv.
61. Miranda, L.J. (2023, January 03). Towards Data-Centric Machine Learning: A Short Review. Available online: https://ljvmiranda921.github.io/notebook/2021/07/30/data-centric-ml.
62. LabelMe: A database and web-based tool for image annotation10.1007/s11263-007-0090-8 ↗
63. Activeclean: Interactive data cleaning for statistical modeling10.14778/2994509.2994514 ↗
64. Vartak, M., Subramanyam, H., Lee, W.E., Viswanathan, S., Husnoo, S., Madden, S., and Zaharia, M. (July, January 26). ModelDB: A system for machine learning model management. Proceedings of the Workshop on Human-In-the-Loop Data Analytics, San Francisco, CA, USA.
65. Continuous integration of machine learning models with ease. ml/ci: Towards a rigorous yet practical treatment
66. Zhang, H., Li, Y., Huang, Y., Wen, Y., Yin, J., and Guan, K. (2020, January 12–16). Mlmodelci: An automatic cloud platform for efficient mlaas. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.10.1145/3394171.3414535 ↗
67. Jiang, Y., Zhu, Y., Lan, C., Yi, B., Cui, Y., and Guo, C. (2020, January 4–6). A unified architecture for accelerating distributed {DNN} training in heterogeneous {GPU/CPU} clusters. Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), Online.
68. Chen, T., Moreau, T., Jiang, Z., Zheng, L., Yan, E., Shen, H., Cowan, M., Wang, L., Hu, Y., and Ceze, L. (2018, January 8–10). {TVM}: An automated {End-to-End} optimizing compiler for deep learning. Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), Carlsbad, CA, USA.
69. Sharma, R., Allen, J., Bakhshandeh, O., and Mostafazadeh, N. (2018, January 15–20). Tackling the story ending biases in the story cloze test. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Melbourne, Australia.10.18653/v1/P18-2119 ↗
70. Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press.
71. Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., and Abbeel, P. (2018, January 21–25). Overcoming exploration in reinforcement learning with demonstrations. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.10.1109/ICRA.2018.8463162 ↗
72. Emotion in reinforcement learning agents and robots: A survey10.1007/s10994-017-5666-0 ↗
73. Irwin, T. (1989). Aristotle’s First Principles, Clarendon Press.10.1093/0198242905.003.0001 ↗
74. LeCun, Y. (2022, June 27). A Path towards Autonomous Machine Intelligence, Version 0.9. 2. Available online: http://openreview.net.
75. Pearl, J., and Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect, Penguin Random House.
76. Schölkopf, B. (2022). Probabilistic and Causal Inference: The Works of Judea Pearl, ACM Books.
77. Prefrontal cortex as a meta-reinforcement learning system10.1038/s41593-018-0147-8 ↗