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Finding solutions to artificial intelligence data challenges

artificial intelligence

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.

According to a SAS Institute look at the history of AI, the term was first conceived in 1956. However, “AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvement in computing power and storage.”

Artificial intelligence is a powerful tool. It automates repetitive learning and discovery through data, adds intelligence, adapts through progressive learning algorithms, analyzes more and deeper data, achieves incredible accuracy and gets the most out of data.

A novel approach to AI

Rafid Mahmood, a Telfer assistant professor, has received a Discovery grant from the Natural Sciences and Engineering Research Council of Canada (NSERC). His AI research project proposes a novel approach toRafid Mahmood developing an operational research framework for data-centric artificial intelligence development.

Mahmood will combine machine learning (ML) methods (e.g. active learning, uncertainty estimation) used to valuate data with operational research models (e.g. optimization, queuing, simulation) to generate algorithms for decisions around managing data collection, annotation and model training.

Machine learning is a subset of artificial intelligence that allows computers to automatically learn, improve and hone their skills based on what they’re exposed to. Research on ML has studied what data can improve a model’s downstream accuracy. However, there’s been little work on how to manage the operations of the data-centric pipelines needed to field large ML projects.

Research benefits

Mahmood’s research aims to benefit those looking to implement AI systems. It could improve the success rate of AI deployment in enterprises with limited ML expertise. AI technology companies may be interested in this research to minimize annotation costs and workflow delays from having to address data limitations mid-project. Finally, emerging data collection and annotation companies may benefit from optimizing their current data management workflows.

About the Author

Zoï Coucopoulos a occupé le poste de coordonnatrice des initiatives stratégiques, où elle soutenait la croissance de la qualité et de l'intensité de la recherche de l’École de gestion Telfer en aidant les chercheurs et les groupes de recherche à élaborer des programmes de recherche interdisciplinaires et des activités de sensibilisation. De plus, elle travaillait à faciliter le développement de groupes de recherche stratégiques et à aider les membres du corps professoral et les étudiantes et étudiants diplômés à contribuer au développement d'une culture solide basée sur la recherche.</br></br>Zoï Coucopoulos previously held the position of Coordinator of Strategic Initiatives, where she supported the growth of the Telfer School of Management’s research quality and intensity by assisting researchers and research groups in developing interdisciplinary research programs and outreach activities. She also facilitated the development of strategic research groups and helped faculty members and research-based graduate students contribute to the development of a strong research culture.