Séminaire Donnees et APprentissage ArtificielRSS

Analytics, Cloud-Computing, and Crowdsourcing - or How To Destroy My Job...


04/10/2012
Intervenant(s) : Piero P. Bonissone (Chief Scientist, SSA, 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 will analyze some of the opportunities created by the advent of crowdsourcing.
Then, we will explore the intersections of these three concepts. We will examine their evolution from the optics of a professional machine-learning researcher and try to understand how his job and roles have evolved over time.
In the past, analytic model building was an artisanal process, as models were handcrafted by an experienced, knowledgeable model-builder. 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 not so distant 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, crowdservicing, cloud-based evolutionary algorithms, outsourcing, in-house development, and legacy models.
In this new 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 regressors evolved on a cloud.

Piero P. Bonissone
A Chief Scientist at GE Global Research, Dr. Bonissone has been a pioneer in the field of fuzzy logic, AI, soft computing, and approximate reasoning systems applications since 1979. Recently he has led a Soft Computing (SC) group in the development of SC application to diagnostics and prognostics of processes and products, including the prediction of remaining life for each locomotive in a fleet, to perform efficient assets selection. His current interests are the development of multi-criteria decision making systems for PHM and the automation of intelligent systems lifecycle to create, deploy, and maintain SC-based systems, providing customized performance while adapting to avoid obsolescence.
He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), of the Association for the Advancement of Artificial Intelligence (AAAI), of the International Fuzzy Systems Association (IFSA), and a Coolidge Fellow at GE Global Research. He is the recipient of the 2012 Fuzzy Systems Pioneer Award from the IEEE Computational Intelligence Society. Since 2010, he is the 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 Computational Intelligence Society. He has received two Dushman Awards from GE Global Research. 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 30 (by Google Scholar). He received 65 patents issued from the US Patent Office (plus 15 pending patents). From 1982 until 2005 he has been an Adjunct Professor at Rensselaer Polytechnic Institute, in Troy NY, where he has supervised 5 PhD theses and 33 Master theses. He has co-chaired 12 scientific conferences and symposia focused on Multi-Criteria Decision-Making, Fuzzy sets, Diagnostics, Prognostics, and Uncertainty Management in AI. Dr. Bonissone is very active in the IEEE, where is has been a member of the Fellow Evaluation Committee from 2007 to 2009. In 2002, while serving as President of the IEEE Neural Networks Society (now CIS) he was also a member of the IEEE Technical Board Activities (TAB). He has been an Executive Committee member of NNC/NNS/CIS society since 1993 and an IEEE CIS Distinguished Lecturer since 2004.
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