Accessibility and Computer Vision - #425
Digital imagery is pervasive today. More than a billion images per day are produced and uploaded to social media sites, with many more embedded within websites, apps, digital documents, and eBooks. Engaging with digital imagery has become fundamental to participating in contemporary society, including education, the professions, e-commerce, civics, entertainment, and social interactions. However, most digital images remain inaccessible to the 39 million people worldwide who are blind. AI and computer vision technologies hold the potential to increase image accessibility for people who are blind, through technologies like automated image descriptions. The speakers share their perspectives as people who are both technology experts and are blind, providing insight into future directions for the field of computer vision for describing images and videos for people who are blind. To check out the video of this panel, visit here! The complete show notes for this episode can be found at twimlai.com/go/425

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Trust and AI with Parinaz Sobhani - TWiML Talk #208

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.

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Unbiased Learning from Biased User Feedback with Thorsten Joachims - TWiML Talk #207

Unbiased Learning from Biased User Feedback with Thorsten Joachims - TWiML Talk #207

In the final episode of our re:Invent series, we're joined by Thorsten Joachims, Professor in the Department of Computer Science at Cornell University. We discuss his presentation “Unbiased Learning from Biased User Feedback,” looking at some of the inherent and introduced biases in recommender systems, and the ways to avoid them. We also discuss how inference techniques can be used to make learning algorithms more robust to bias, and how these can be enabled with the correct type of logging policies.

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Language Parsing and Character Mining with Jinho Choi - TWiML Talk #206

Language Parsing and Character Mining with Jinho Choi - TWiML Talk #206

Today we’re joined by Jinho Choi, assistant professor of computer science at Emory University. Jinho presented at the conference on ELIT, their cloud-based NLP platform. In our conversation, we discuss some of the key NLP challenges that Jinho and his group are tackling, including language parsing and character mining. We also discuss their vision for ELIT, which is to make it easy for researchers to develop, access, and deploying cutting-edge NLP tools models on the cloud.

5 Dec 201847min

re:Invent Roundup Roundtable 2018 with Dave McCrory and Val Bercovici - TWiML Talk #205

re:Invent Roundup Roundtable 2018 with Dave McCrory and Val Bercovici - TWiML Talk #205

I’m excited to present our second annual re:Invent Roundtable Roundup. This year I’m joined by Dave McCrory, VP of Software Engineering at Wise.io at GE Digital, and Val Bercovici, Founder and CEO of Pencil Data. If you missed the news coming out of re:Invent, we cover all of AWS’ most important ML and AI announcements, including SageMaker Ground Truth, Reinforcement Learning, DeepRacer, Inferentia and Elastic Inference, ML Marketplace and much more. For the show notes visit https://twimlai.com/ta

3 Dec 20181h 7min

Knowledge Graphs and Expert Augmentation with Marisa Boston - TWiML Talk #204

Knowledge Graphs and Expert Augmentation with Marisa Boston - TWiML Talk #204

Today we’re joined by Marisa Boston, Director of Cognitive Technology in KPMG’s Cognitive Automation Lab. We caught up to discuss some of the ways that KPMG is using AI to build tools that help augment the knowledge of their teams of professionals. We discuss knowledge graphs and how they can be used to map out and relate various concepts and how they use these in conjunction with NLP tools to create insight engines. We also look at tools that curate and contextualize news and other text-based data sour

29 Nov 201846min

ML/DL for Non-Stationary Time Series Analysis in Financial Markets and Beyond with Stuart Reid - TWiML Talk #203

ML/DL for Non-Stationary Time Series Analysis in Financial Markets and Beyond with Stuart Reid - TWiML Talk #203

Today, we’re joined by Stuart Reid, Chief Scientist at NMRQL Research. NMRQL is an investment management firm that uses ML algorithms to make adaptive, unbiased, scalable, and testable trading decisions for its funds. In our conversation, Stuart and I dig into the way NMRQL uses ML and DL models to support the firm’s investment decisions. We focus on techniques for modeling non-stationary time-series, stationary vs non-stationary time-series, and challenges of building models using financial data.

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Industrializing Machine Learning at Shell with Daniel Jeavons - TWiML Talk #202

Industrializing Machine Learning at Shell with Daniel Jeavons - TWiML Talk #202

In this episode of our AI Platforms series, we’re joined by Daniel Jeavons, General Manager of Data Science at Shell. In our conversation, we explore the evolution of analytics and data science at Shell, discussing IoT-related applications and issues, such as inference at the edge, federated ML, and digital twins, all key considerations for the way they apply ML. We also talk about the data science process at Shell and the importance of platform technologies to the company as a whole.

21 Nov 201845min

Resurrecting a Recommendations Platform at Comcast with Leemay Nassery - TWiML Talk #201

Resurrecting a Recommendations Platform at Comcast with Leemay Nassery - TWiML Talk #201

In this episode of our AI Platforms series, we’re joined by Leemay Nassery, Senior Engineering Manager and head of the recommendations team at Comcast. In our conversation, Leemay and I discuss just how she and her team resurrected the Xfinity X1 recommendations platform, including the rebuilding the data pipeline, the machine learning process, and the deployment and training of their updated models. We also touch on the importance of A-B testing and maintaining their rebuilt infrastructure.

19 Nov 201847min

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