Abstract: Why “Small Data” is important for advancing the AI? Will Big Data models work correctly, if trained on synthetic data? Can we properly define and train a Neural Network from a limited dataset, by augmenting its data with an extra knowledge (meta-data)?
This talk is about the role of small data in the future of AI. Efforts have already begun in this direction. Although the current mantra of deep learning says ‘you need big data for AI’, more often than not, AI becomes even more intelligent and powerful if it has the capability to be trained with small data. Some AI solutions that rely only on small data outperform those working with big data. Some other AI solutions use big data to learn how to take advantage of small data.
Presenter will illustrate on practical and visual examples from Fashion Retail sector, some approaches and methods for using Small and Synthetic Data for training of the AI and Big Data systems, starting from the “big difference” but common nature of Small Data and Big Data. It will be explained in details the case of a Visual Search service, who’s training is initially based on 3D CAD models and their meta data, combined with real pictures on the later stages of training.
Bio: FashionTech & Digital Entrepreneur for 10+ years. AI and Digital Twins, AI-Driven Design enthusiast. Andrey Golub is a Business Transformation and Technology Innovation professional, 17+ years as R&D manager, contract professor, and a strategy consultant, specialized in the development and launch of innovative DeepTech products and services, mainly Cloud Computing and Artificial Intelligence.
As of technology entrepreneur since 2010, Andrey's best expertise lay in the field of Fashion/Luxury Retail and Manufacturing with ELSE Corp since 2015), now acting as General Manager and Head of AI R&D of ICOL
Group: Smart RoboFactories & Digital Ecosystems.