Differential Privacy at Bluecore with Zahi Karam - TWiML Talk #133
In this episode of our Differential Privacy series, I'm joined by Zahi Karam, Director of Data Science at Bluecore, whose retail marketing platform specializes in personalized email marketing. I sat down with Zahi at the Georgian Partners portfolio conference last year, where he gave me my initial exposure to the field of differential privacy, ultimately leading to this series. Zahi shared his insights into how differential privacy can be deployed in the real world and some of the technical and cultural challenges to doing so. We discuss the Bluecore use case in depth, including why and for whom they build differentially private machine learning models. The notes for this show can be found at twimlai.com/talk/133
1 Maj 201838min
Differential Privacy Theory & Practice with Aaron Roth - TWiML Talk #132
In the first episode of our Differential Privacy series, I'm joined by Aaron Roth, associate professor of computer science and information science at the University of Pennsylvania. Aaron is first and foremost a theoretician, and our conversation starts with him helping us understand the context and theory behind differential privacy, a research area he was fortunate to begin pursuing at its inception. We explore the application of differential privacy to machine learning systems, including the costs and challenges of doing so. Aaron discusses as well quite a few examples of differential privacy in action, including work being done at Google, Apple and the US Census Bureau, along with some of the major research directions currently being explored in the field. The notes for this show can be found at twimlai.com/talk/132.
30 Apr 201842min
Optimal Transport and Machine Learning with Marco Cuturi - TWiML Talk #131
In this episode, i’m joined by Marco Cuturi, professor of statistics at Université Paris-Saclay. Marco and I spent some time discussing his work on Optimal Transport Theory at NIPS last year. In our discussion, Marco explains Optimal Transport, which provides a way for us to compare probability measures. We look at ways Optimal Transport can be used across machine learning applications, including graphical, NLP, and image examples. We also touch on GANs, or generative adversarial networks, and some of the challenges they present to the research community. The notes for this show can be found at twimlai.com/talk/131.
26 Apr 201832min
Collecting and Annotating Data for AI with Kiran Vajapey - TWiML Talk #130
In this episode, I’m joined by Kiran Vajapey, a human-computer interaction developer at Figure Eight. In this interview, Kiran shares some of what he’s has learned through his work developing applications for data collection and annotation at Figure Eight and earlier in his career. We explore techniques like data augmentation, domain adaptation, and active and transfer learning for enhancing and enriching training datasets. We also touch on the use of Imagenet and other public datasets for real-world AI applications. If you like what you hear in this interview, Kiran will be speaking at my AI Summit April 30th and May 1st in Las Vegas and I’ll be joining Kiran at the upcoming Figure Eight TrainAI conference, May 9th&10th in San Francisco. The notes for this show can be found at twimlai.com/talk/130
23 Apr 201840min
Autonomous Aerial Guidance, Navigation and Control Systems with Christopher Lum - TWiML Talk #129
Ok, In this episode, I'm joined by Christopher Lum, Research Assistant Professor in the University of Washington’s Department of Aeronautics and Astronautics. Chris also co-heads the University’s Autonomous Flight Systems Lab, where he and his students are working on the guidance, navigation, and control of unmanned systems. In our conversation, we discuss some of the technical and regulatory challenges of building and deploying Unmanned Autonomous Systems. We also talk about some interesting work he’s doing on evolutionary path planning systems as well as an Precision Agriculture use case. Finally, Chris shares some great starting places for those looking to begin a journey into autonomous systems research. The notes for this show can be found at twimlai.com/talk/129.
19 Apr 201852min
Infrastructure for Autonomous Vehicles with Missy Cummings - TWiML Talk #128
In this episode, I’m joined by Missy Cummings, head of Duke University’s Humans and Autonomy Lab and professor in the department of mechanical engineering. In addition to being an accomplished researcher, Missy also became one of the first female fighter pilots in the US Navy following the repeal of the Combat Exclusion Policy in 1993. We discuss Missy’s research into the infrastructural and operational challenges presented by autonomous vehicles, including cars, drones and unmanned aircraft. We also cover trust, explainability, and interactions between humans and AV systems. This was an awesome interview and i'm glad we’re able to bring it to you! The notes for this show can be found at twimlai.com/talk/128.
16 Apr 201843min
Hyper-Personalizing the Customer Experience w/ AI with Rob Walker - TWiML Talk #127
In this episode, we're joined by Rob Walker, Vice President of decision management and analytics at Pegasystems, a leading provider of software for customer engagement and operational excellence. Rob and I discuss what’s required for enterprises to fully realize the vision of providing a hyper-personalized customer experience, and how machine learning and AI can be used to determine the next best action an organization should take to optimize sales, service, retention, and risk at every step in the customer relationship. Along the way we dig into a couple of key areas, specifically some of the techniques his organization uses to allow customers to manage the tradeoff between model performance and transparency, particularly in light of new laws like GDPR, and how all this ties to an enterprise’s ability to manage bias and ethical issues when deploying ML. We cover a lot of ground in this one and I think you’ll find Rob’s perspective really interesting. The notes for this show can be found at twimlai.com/talk/127.
12 Apr 201841min
Information Extraction from Natural Document Formats with David Rosenberg - TWiML Talk #126
In this episode, I’m joined by David Rosenberg, data scientist in the office of the CTO at financial publisher Bloomberg, to discuss his work on “Extracting Data from Tables and Charts in Natural Document Formats.” Bloomberg is dealing with tons of financial and company data in pdfs and other unstructured document formats on a daily basis. To make meaning from this information more efficiently, David and his team have implemented a deep learning pipeline for extracting data from the documents. In our conversation, we dig into the information extraction process, including how it was built, how they sourced their training data, why they used LaTeX as an intermediate representation and how and why they optimize on pixel-perfect accuracy. There’s a lot of interesting info in this show and I think you’re going to enjoy it. The notes for this show can be found at twimlai.com/talk/126.
9 Apr 201845min
