Fake It to Make It: Augmenting Sparse Training Sets With Synthetic Data

Please note that this event will be held at TCG, 125 Park Ave 3rd Floor (not 4th floor) NY NY. RSVPs will close at 11 a.m. on the day of the event so that names can be sent to building security.

Synthetic data promises massive sets of perfectly generated training data for a fraction of the cost of manually sourced annotated data. But there remains doubt about the efficacy of using synthetic data sets to train machine learning amongst practitioners. In this talk, Daeil Kim, a machine learning researcher and founder of AI.Reverie (https://aireverie.com/) delineates the advantages of synthetic data and how to avoid traps that lead to dead zones and false positives. He also reviews work of simulations for synthetic data in application verticals that are traditionally difficult to manually acquire significant data sets.

Speaker bio:
Daeil Kim believes that we can create a future where issues related to food, shelter and health can be efficiently met with the help of AI.

Daeil grew up in New York City and received a liberal arts degree at Sarah Lawrence College, focusing on literature. An interest in medicine led him to New Mexico to research schizophrenia and to understand mental illness through artificial intelligence. He then pursued a Ph.D. in computer science at Brown University, focusing on the development of scalable machine learning algorithms. Afterwards, his interests in developing tools for investigative journalism led him to pursue a career as a data scientist at The New York Times.

Driven by the passion to create a better world with AI, Daeil created AI.Reverie, a simulation platform to train AI to understand the world and make it better.

Agenda:

• Pizza and socializing begins @ 6:45
• Talk begins @ 7:00 followed by Q&A
• A little extra socializing after the event