14–17 Oct 2019

Schedule: Mobile Computing, IoT, Edge sessions

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11:0511:45 Wednesday, 16 October 2019
Location: Westminster Suite
Adithya Hrushikesh (Vodafone)
Average rating: ****.
(4.00, 4 ratings)
Every day, millions of Vodafone Germany customers reach out through various social media channels about issues related to mobile, internet, signal issues, etc. Adithya Hrushikesh details how to build and deploy an ensemble model to classify 26 (originally 56) complaint classes using machine learning over deep learning. He also touches on the business case, data product development, and GDPR. Read more.
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11:0511:45 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Yan Zhang (Microsoft), Mathew Salvaris (Microsoft)
Average rating: ***..
(3.67, 3 ratings)
When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices. Read more.
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11:0511:45 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Alex Ingerman (Google)
Average rating: ****.
(4.29, 7 ratings)
Federated learning is the approach of training ML models across many devices without collecting the data in a central location. Alex Ingerman explores learning concepts and the use cases for decentralized machine learning, drawing on Google's real-world deployments. You'll learn how to build your first federated models with the open source TensorFlow Federated. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Paris Buttfield-Addison (Secret Lab), Tim Nugent (Lonely Coffee)
Average rating: ****.
(4.75, 8 ratings)
On-device ML and AI is the future for privacy-conscious, cloud-averse users of modern smartphones. Paris Buttfield-Addison and Tim Nugent explore what's possible using CoreML, Swift, and associated frameworks in tandem with the powerful ML-tuned silicon in modern Apple iOS hardware. They demonstrate and create ML and AI features with Swift to show how much you can do without touching the cloud. Read more.
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16:5017:30 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Average rating: ****.
(4.25, 12 ratings)
Developing perception algorithms for autonomous vehicles is incredibly difficult, as they need to operate in thousands of driving conditions and locations. Adam Grzywaczewski explores the challenges involved in data collection, processing, and management, as well as model development and validation. He also provides an overview of the necessary hardware and software infrastructure. Read more.
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10:1010:25 Thursday, 17 October 2019
Location: King's Suite
Raffaello D’Andrea (Verity | ETH Zurich)
Average rating: ****.
(4.69, 13 ratings)
It's hard ignore the attention given to autonomy and robotics. The impact is significant and the reach is extensive, hitting transportation with self-driving cars, logistics and supply with mobile robots, and remote sensing applications with aerial vehicles or drones. Raffaello D'Andrea explores how autonomous indoor drones will drive the next wave of autonomous robotics development and growth. Read more.
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11:0511:45 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Zaid Tashman (Accenture Labs)
Average rating: *****
(5.00, 3 ratings)
Today traditional approaches to predictive maintenance fall short. Zaid Tashman dives into a novel approach to predict rare events using a probabilistic model, the mixed membership hidden Markov model, highlighting the model's interpretability, its ability to incorporate expert knowledge, and how the model was used to predict the failure of water pumps in developing countries. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Westminster Suite
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Average rating: ****.
(4.80, 5 ratings)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would benefit from the new opportunities enabled by deep learning techniques. Siddha Ganju and Meher Kasam walk you through optimizing deep neural nets to run efficiently on mobile devices. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Alasdair Allan (Babilim Light Industries)
Average rating: ****.
(4.50, 4 ratings)
The future of machine learning is on the edge and on small, embedded devices that can run for a year or more on a single coin-cell battery. Alasdair Allan dives deep into how using deep learning can be very energy efficient and allows you to make sense of sensor data in real time. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: Westminster Suite
Jameson Toole (Fritz AI)
Average rating: *****
(5.00, 1 rating)
Getting machine learning models ready for use on device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce aren’t small enough or fast enough to ship. Jameson Toole walks you through optimization, pruning, and compression techniques to keep app sizes small and inference speeds high. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Tuhin Sharma (Binaize), Bargava Subramanian (Binaize)
Average rating: ****.
(4.50, 2 ratings)
There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learning—which is privacy preserving and doesn't require data to be moved to the cloud—for data quality and cybersecurity. Read more.

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