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IPP Symposium

Analytics, Cloud computing and Crowdsourcing: How to destroy my job

Piero P. Bonissone, Chief Scientist, GE Global Research

We are witnessing the resurgence of analytics as a key differentiator for creating new services, the emergence of cloud computing as a disrupting technology for service delivery, and the growth of crowdsourcing as a new phenomenon in which people play critical roles in creating information and shaping decisions in a variety of problems. After introducing the first two well-known concepts, we analyze some opportunities created by the advent of crowdsourcing. Then we explore the intersection of these three concepts. We examine it from the perspective of a machine-learning researcher and show how his job and roles have evolved over time.

In the past, analytic model creation was an artisanal process, as models were handcrafted by experienced, knowledgeable model-builders. More recently, the use of meta-heuristics (such as evolutionary algorithms) has provided us with limited levels of automation in model building and maintenance. In the future, we expect analytic models to become a commodity. We envision having access to a large number of data-driven models, obtained by a combination of crowdsourcing, crowd-servicing, cloud-based evolutionary algorithms, outsourcing, in-house development, and legacy models. In this context, the critical issue will be model ensemble selection and fusion, rather than model generation. We address this issue by proposing customized model ensembles on demand, inspired by Lazy Learning. In our approach, referred to as Lazy Meta-Learning, for a given query we find the most relevant models from a DB of models, using their meta-information. After retrieving the relevant models, we select a subset of models with highly uncorrelated errors (unless diversity was injected in their design process.) With these models we create an ensemble and use their meta-information for dynamic bias compensation and relevance weighting. The output is a weighted interpolation or extrapolation of the outputs of the models ensemble. The confidence interval around the output is reduced as we increase the number of uncorrelated models in the ensemble. This approach is agnostic with respect to the genesis of the models, making it scalable and suitable for a variety of applications. We have successfully tested this approach in a regression problem for a power plant management application, using two different sources of models: bootstrapped neural networks, and GP-created symbolic regression models evolved in the cloud.

Finally, we illustrate research trends, future challenges and opportunities for ML techniques in this emerging context of big data and cloud computing.

Piero received the PhD in EECS from UC Berkeley in 1979. He is a Fellow of the IEEE, AAAI, and IFSA. He is also a Coolidge Fellow at GE Global Research for lifetime achievements. He is the recipient of the 2012 Fuzzy Systems Pioneer Award from IEEE CIS. In 2010, he became President of the Scientific Committee of the European Centre of Soft Computing. In 2008, he received the II Cajastur International Prize for Soft Computing from the European Centre of Soft Computing. In 2005, he received the Meritorious Service Award from the IEEE CIS. He served as Editor in Chief of the International Journal of Approximate Reasoning for 13 years. He is in the editorial board of five technical journals and is Editor-at-Large of the IEEE Computational Intelligence Magazine. He has co-edited six books and has over 150 publications in refereed journals, book chapters, and conference proceedings, with an H-Index of 32 (by Google Scholar). He received 67 patents issued from the US Patent Office (plus 19 pending patents). From 1982 until 2005 he was an Adjunct Professor at Rensselaer Polytechnic Institute, in Troy NY, where he supervised 5 PhD theses and 33 Master theses. He co-chaired 12 scientific conferences (nine of which sponsored by CIS) focused on Multi-Criteria Decision-Making, Fuzzy sets, Diagnostics, Prognostics, and Uncertainty Management in AI. Dr. Bonissone is very active in the IEEE, where he served as a member of the Fellow Evaluation Committee from 2007 to 2009. In 2002, while President of the IEEE Neural Networks Society (now CIS), he was also a member of the IEEE Technical Activities Board (TAB). He has been an IEEE CIS Distinguished Lecturer from 2004 to 2011.