Big Data Days 2021

Online Edition

28-30 Cентября

онлайн

Sagar Kurandwad

Machine Learning Developer

Canada, AI Redefined

Биография

Sagar Kurandwad is a researcher and developer with nearly 10 years of experience in leading teams and developing end-to-end custom solutions in Computer Vision, Natural Language Processing/Generation/Understanding, and Recommender Systems for organizations in IoT, Renewable Energy, Smart Cities, Real Estate, Advertising, and Media domains. As a Machine Learning Developer at AI Redefined Inc, he invests his time in discovering potential opportunities and researching and developing AI algorithms, systems, and technologies to enhance the company’s offerings. Sagar earned his Masters in AI from the University of Edinburgh, UK in 2011.

Clodéric Mars

AI Product Engineer

Canada, AI Redefined

Биография

Clodéric Mars is working on Product & R&D strategy at AI Redefined. For more than 15 years, he has pursued one goal: fostering the collaboration between Humans and AIs. At INRIA then at Golaem and MASA Group he has worked on explicit AI techniques applied to video games, simulation and special effects. Clodéric contributed to the development of AIs used, for instance, to create digital extra for the tv show « Game of Thrones » or for military training in more than 20 countries. In 2015, he co-founded craft ai to focus on making machine learning explainable and performant, he helped build a multidisciplinary team and product that ran AIs in production for 10+ B2B customers. In 2020, he joined AI Redefined to help build a product to further the synergy between Humans and AIs.

Доклад

Towards Human-AI Teaming: Challenges and Opportunities of Human in the Loop AI Training

The goal of our presentation is to stress the need for Human-in-the-Loop and Hybrid-Systems based thinking in AI driven systems. We present solutions with experimental results to formulate and implement such systems. During the session you will hear about the distributing learning processes to build automated AI systems, find out how to ensemble created models to accelerate next-level training processes as well as listen about the architecting communication to facilitate human-AI interactions. We will also focus on the designing operational structure of agents and humans to arrange their communications and evaluating component-level performance of Human-AI teaming to optimize human and AI efficiencies.

Ключевые слова

ML
Multi-Agent Systems
Reinforcement Learning

« Hазад