Small Data, Big Opportunities: Making the Most of AI
November 2nd, 2022
New York City or Remote
November 2nd, 2022
New York City or Remote
To date, much of the impact AI has had in the industry has benefitted large internet-scale tech companies for search, recommendations, user-identification and the like. AI has seen less success in the more general day-to-day businesses, such as financial services. Even among large companies, only a small percentage benefit from AI, with 60%-85% of AI projects failing to achieve the return on investment promised during the planning stage.
In addition, in the last 2 years, the extreme business changes from the COVID-19 pandemic caused ML and AI models based on large amounts of historical data to become less relevant.
The good news is surprisingly solid and flexible AI systems can be built around a smaller data set. While the narrative for the last 10 years has been mostly about big data, it is not about “big data only” anymore. Taking a small-data approach is a more targeted, tailored way to feed data into an AI model and inject continuous feedback once the model is deployed. One key feature of this approach is to build the tools that allow the experts to codify and engineer the data and information in a way that lets them express the domain knowledge.
In this workshop, we look to bringing together practitioners from industry and academia to discuss approaches and developments of AI capabilities with small data approaches. We are also particularly interested in exploring best practices in injecting domain knowledge from experts in the AI system.
Invited Speakers
Dr. Harvey J. Stein is a Senior Vice President in the Labs' group at Two Sigma. Dr. Stein is well known in the industry, having published and lectured on mortgage backed security valuation, CVA calculations, interest rate and FX modeling, credit exposure calculations, financial regulation, and other subjects. Dr. Stein is also on the board of directors of the IAQF, an adjunct professor at Columbia University, a board member of the Rutgers University Mathematical Finance program and of the NYU Enterprise Learning program, and organizer of the IAQF/Thalesians financial seminar series. He received his BA in mathematics from WPI in 1982 and his PhD in mathematics from UC Berkeley in 1991.
Swami Chandrasekaran is a managing director at KPMG's AI Innovation & Enterprise Solutions. He leads the architecture, technology, creation of AI + emerging tech offerings as well as innovation efforts. He has led the creation of products and solutions that have solved a wide range of problems in areas such as tax and audit, industrial automation, aviation safety, contact centers, insurance claims, field service, multimedia enrichment, social care, digital marketing, M&A, and KYC. These solutions have leveraged automation, ML/DL, NLP, advanced analytics, as well as RPA, cloud and IoT capabilities. He is currently also driving explainable and trusted AI efforts.
Marius is a Chief Solutions Architect in the Financial Services team at Red Hat, where he acts as trusted advisor on industry trends, technology strategy, and best practices to more than 30 Fortune 500 banks in the US and Canada. Using his expertise in modern application development and data strategy, Marius is helping customers accelerate their digital transformation using artificial intelligence and machine learning techniques.
Anand Rao is a Principal with PwC’s US Advisory practice, with over 24 years of industry and consulting experience, helping senior executives structure, solve and manage critical issues facing their organizations. He has worked extensively on business, technology, and analytics issues across a wide range of industry sectors including Financial Services, Healthcare, Telecommunications, Aerospace & Defense, across US, Europe, Asia and Australia. His work has included behavioral economics, simulation modeling, global growth strategies, marketing, sales, and distribution strategies, online, mobile, social media strategies, customer experience, multi-channel integration, risk management and compliance, large scale program mobilization and management. Before his consulting careers, Anand was the Chief Research Scientist at the Australian Artificial Intelligence Institute, a boutique research and software house.