509: Accelerating Start-up Growth with A.I. Specialists
Parinaz Sobhani joins us to discuss the cutting-edge work of Georgian, a collaborative company that helps start-ups implement and scale machine learning and AI.
In this episode you will learn:
Parinaz’s work at Georgian [5:35]
Use cases of Georgian’s work [14:35]
Tools and approaches Parinaz uses [32:27]
Environmental concerns of machine learning [42:52]
Hiring at Georgian and what Parinaz looks for [48:18]
How did Parinaz become interested in this? [56:19]
Fairness in AI [1:09:01]
Additional materials: www.superdatascience.com/509
Live from TWIMLcon! Operationalizing Responsible AI - #310
An often forgotten about topic garnered high praise at TWIMLcon this month: operationalizing responsible and ethical AI. This important topic was combined with an impressive panel of speakers, including: Rachel Thomas, Director, Center for Applied Data Ethics at the USF Data Institute, Guillaume Saint-Jacques, Head of Computational Science at LinkedIn, and Parinaz Sobahni, Director of Machine Learning at Georgian Partners, moderated by Khari Johnson, Senior AI Staff Writer at VentureBeat.
Trust and AI with Parinaz Sobhani - TWiML Talk #208
In today’s episode we’re joined by Parinaz Sobhani, Director of Machine Learning at Georgian Partners.
In our conversation, Parinaz and I discuss some of the main issues falling under the “trust” umbrella, such as transparency, fairness and accountability. We also explore some of the trust-related projects she and her team at Georgian are working on, as well as some of the interesting trust and privacy papers coming out of the NeurIPS conference.
Epsilon Software for Private Machine Learning with Chang Liu - TWiML Talk #135
In this episode, our final episode in the Differential Privacy series, I speak with Chang Liu, applied research scientist at Georgian Partners, a venture capital firm that invests in growth stage business software companies in the US and Canada. Chang joined me to discuss Georgian’s new offering, Epsilon, a software product that embodies the research, development and lessons learned helps in helping their portfolio companies deliver differentially private machine learning solutions to their customers. In our conversation, Chang discusses some of the projects that led to the creation of Epsilon, including differentially private machine learning projects at BlueCore, Work Fusion and Integrate.ai. We explore some of the unique challenges of productizing differentially private ML, including business, people and technology issues. Finally, Chang provides some great pointers for those who’d like to further explore this field. The notes for this show can be found at twimlai.com/talk/135