Mining the Vatican Secret Archives with TensorFlow w/ Elena Nieddu - TWiML Talk #243
Today we’re joined by Elena Nieddu, Phd Student at Roma Tre University, who presented on her project “In Codice Ratio” at the TF Dev Summit. In our conversation, Elena provides an overview of the project, which aims to annotate and transcribe Vatican secret archive documents via machine learning. We discuss the many challenges associated with transcribing this vast archive of handwritten documents, including overcoming the high cost of data annotation.
27 Mars 201943min
Exploring TensorFlow 2.0 with Paige Bailey - TWiML Talk #242
Today we're joined by Paige Bailey, TensorFlow developer advocate at Google, to discuss the TensorFlow 2.0 alpha release. Paige and I talk through the latest TensorFlow updates, including the evolution of the TensorFlow APIs and the role of eager mode, tf.keras and tf.function, the evolution of TensorFlow for Swift and its inclusion in the new fast.ai course, new updates to TFX (or TensorFlow Extended), Google’s end-to-end ML platform, the emphasis on community collaboration with TF 2.0, and more.
25 Mars 201939min
Privacy-Preserving Decentralized Data Science with Andrew Trask - TWiML Talk #241
Today we’re joined by Andrew Trask, PhD student at the University of Oxford and Leader of the OpenMined Project, an open-source community focused on researching, developing, and promoting tools for secure, privacy-preserving, value-aligned artificial intelligence. We dig into why OpenMined is important, exploring some of the basic research and technologies supporting Private, Decentralized Data Science, including ideas such as Differential Privacy,and Secure Multi-Party Computation.
21 Mars 201933min
The Unreasonable Effectiveness of the Forget Gate with Jos Van Der Westhuizen - TWiML Talk #240
Today we’re joined by Jos Van Der Westhuizen, PhD student in Engineering at Cambridge University. Jos’ research focuses on applying LSTMs, or Long Short-Term Memory neural networks, to biological data for various tasks. In our conversation, we discuss his paper "The unreasonable effectiveness of the forget gate," in which he explores the various “gates” that make up an LSTM module and the general impact of getting rid of gates on the computational intensity of training the networks.
18 Mars 201932min
Building a Recommendation Agent for The North Face with Andrew Guldman - TWiML Talk #239
Today we’re joined by Andrew Guldman, VP of Product Engineering and R&D at Fluid to discuss Fluid XPS, a user experience built to help the casual shopper decide on the best product choices during online retail interactions. We specifically discuss its origins as a product to assist outerwear retailer The North Face. In our conversation, we discuss their use of heat-sink algorithms and graph databases, challenges associated with staying on top of a constantly changing landscape, and more!
14 Mars 201947min
Active Learning for Materials Design with Kevin Tran - TWiML Talk #238
Today we’re joined by Kevin Tran, PhD student at Carnegie Mellon University. In our conversation, we explore the challenges surrounding the creation of renewable energy fuel cells, which is discussed in his recent Nature paper “Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution.” The AI Conference is returning to New York in April and we have one FREE conference pass for a lucky listener! Visit twimlai.com/ainygiveaway to enter!
11 Mars 201933min
Deep Learning in Optics with Aydogan Ozcan - TWiML Talk #237
Today we’re joined by Aydogan Ozcan, Professor of Electrical and Computer Engineering at UCLA, exploring his group's research into the intersection of deep learning and optics, holography and computational imaging. We specifically look at a really interesting project to create all-optical neural networks which work based on diffraction, where the printed pixels of the network are analogous to neurons. We also explore practical applications for their research and other areas of interest.
7 Mars 201942min
Scaling Machine Learning on Graphs at LinkedIn with Hema Raghavan and Scott Meyer - TWiML Talk #236
Today we’re joined by Hema Raghavan and Scott Meyer of LinkedIn to discuss the graph database and machine learning systems that power LinkedIn features such as “People You May Know” and second-degree connections. Hema shares her insight into the motivations for LinkedIn’s use of graph-based models and some of the challenges surrounding using graphical models at LinkedIn’s scale, while Scott details his work on the software used at the company to support its biggest graph databases.
4 Mars 201946min
