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Cloud and systems

7 must-see presentations from AWS re:Invent 2019

Spotting deepfakes, indoor farming, precision cancer treatment, and more.

By Arun Krishnan
January 27, 2020
2 min read
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  1. We’ve handpicked seven insightful talks from AWS re:Invent 2019 to showcase how machine learning and AI are helping drive innovation in a wide variety of fields like medicine, media and urban planning. Pietro Perona, Amazon Fellow, AWS also explores the role of machine learning in society to understand, measure, and systematically mitigate bias in machine learning models, and drive fairness in AI.

  2. Deepfakes, audiofakes, and the future of media

    Deepfakes, or AI-generated images, have captured the public imagination with examples of image and video manipulation that was the purview of artists and movie studios until recently. Deepfakes along with their audio analog -- audiofakes -- have the potential to create an "information disorder" where it can become impossible to discern if a piece of content is real. In this talk, Delip Rao, vice president, research, AI Foundation, contextualizes multimedia fakes in the broader misinformation landscape and explores some state-of-the-art solutions for detecting deepfakes and audiofakes.

    AWS re:Invent 2019: Deepfakes, audiofakes, and the future of media (MLS210-5)

  3. Precision Medicine and positive outcomes

    Raphael Gottardo from Fred Hutch explains how the combination of machine learning and deep neural networks is helping researchers tailor immunotherapies to cancer patients.

    AWS re:Invent 2019: How ML can Help Tailor Immunotherapies to Cancer Patients (MLS210-1)

  4. From seed to store: AI to optimize the indoor farms of the future

    For the last 10,000 years, large scale agriculture has lived outdoors, optimized to withstand unpredictable environmental conditions and long supply chains. But what possibilities can you unlock when you can control every single environmental factor, from the light intensity to nutrient mix to air flow? In this talk, Bowery Farming EVP of science & technology, Henry Sztul, shares how machine learning and computer vision can be used in indoor vertical farms to optimize and scale agricultural production, creating higher yielding, better tasting, safer and more sustainably locally grown produce in cities around the world.

    AWS re:Invent 2019: From seed to store: AI to optimize the indoor farms of the future (MLS210-6)

  5. Big data for tiny patients: Applying ML to pediatrics

    Despite the increase of machine learning applications in research and clinical care, there is minimal work being done specifically in pediatrics, which presents a unique environment compared to adult care. Dr. Judith Dexheimer, associate professor, University of Cincinnati, discusses the role and impact of AI and ML in research with electronic health records, explores unique aspects of conducting work in pediatrics with biomedical informatics, and examines what is being done now with ML and what the future might hold.

    AWS re:Invent 2019: Big data for tiny patients: Applying ML to pediatrics (MLS210-7)

  6. Building the smart cities of tomorrow

    AI and machine learning can play a powerful role in helping connected cities become smarter, cleaner, and more efficient. In this session, Guido Jouret, chief digital officer, ABB, discusses how advanced analytical systems can optimize smart city operations—from waste disposal to transportation to energy distribution.

    AWS re:Invent 2019: AI will drive the industrial IoT to the cloud (MLS210-3)

  7. Deep learning in deep nets: Helping fish farmers feed the world

    70% of the world is covered by water, yet the ocean provides only 5% of the world's protein. However, fish farming is the fastest growing sector of food production. In this talk, Bryton Shang, founder and CEO, Aquabyte, shares how his organization is building IoT computer vision and machine learning software for the next generation of fish farming. Aquabyte uses real-time fish farming data to understand fish growth, optimize fish farming operations, and provide a more environmentally sustainable source of protein in the long-term.

    AWS re:Invent 2019: Deep learning in deep nets: Helping fish farmers feed the world (MLS210-4)

  8. Machine learning and society: Bias, fairness and explainability

    Using real world examples, Pietro Perona, Amazon Fellow, AWS, explores the role of Machine Learning in society today to understand, measure, and systematically mitigate bias in machine learning models. Understanding the current landscape helps to ensure we are developing this technology in a responsible way, by working to support fairness and explainability.

    AWS re:Invent 2019: Machine learning and society: Bias, fairness, and explainability (MLS210-8)

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Arun Krishnan
Arun Krishnan is a contributor to Amazon Science and the editor-in-chief for Alexa Events.

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