14–17 Oct 2019

Schedule: Health and Medicine sessions

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10:1510:30 Wednesday, 16 October 2019
Location: King's Suite
Jeff Jonas (Senzing)
Average rating: ****.
(4.36, 11 ratings)
Entity resolution—determining “who is who” and “who is related to whom”—is essential to almost every industry, including banking, insurance, healthcare, marketing, telecommunications, social services, and more. Jeff Jonas details how you can use a purpose-built real-time AI, created for general-purpose entity resolution, to gain new insights and make better decisions faster. Read more.
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11:5512:35 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Biraja Ghoshal (Tata Consultancy Service)
Average rating: *....
(1.00, 2 ratings)
Deep learning, which involves powerful black box predictors, has achieved state-of-the-art performance in medical imaging analysis, such as segmentation and classification for diagnosis, but knowing how much confidence there is in a prediction is essential for gaining clinicians' trust. Biraja Ghoshal explores probabilistic modeling with TensorFlow Probability in cancer prediction. Read more.
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14:3515:15 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Abhishek Kumar (Publicis Sapient)
Abhishek Kumar outlines how to industrialize capsule networks by detailing capsule networks and how capsule networks help handle spatial relationships between objects in an image and how to apply them to text analytics and tasks such as NLU or summarization. Join in to see a scalable, productionizable implementation of capsule networks over KubeFlow. Read more.
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16:5017:30 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
James Fletcher (Grakn)
Average rating: ***..
(3.50, 2 ratings)
Statistical approaches alone are not sufficient to tackle the complexity of AI challenges today. Being smarter with the data we already have is critical to achieving machine understanding of any complex domain. James Fletcher explains how knowledge graph convolutional networks (KGCNs) demonstrate the usefulness of combining a connectionist deep learning approach with a symbolic approach. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Ganes Kesari (Gramener), Soumya Ranjan (Gramener)
Average rating: *****
(5.00, 1 rating)
In many countries, policy decisions are disconnected from data, and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Manas Ranjan Kar (Episource)
Natural language processing (NLP) is hard, especially for clinical text. Manas Ranjan Kar explains the multiple challenges of NLP for clinical text and why it's so important that we invest a fair amount of time on domain-specific feature engineering. It’s also crucial to understand to diagnose an NLP model performance and identify possible gaps. Read more.

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