IoTFuse Machine Learning

Hundreds of Online Attendees Worldwide, Organizations, and Speakers!

Live Streaming Worldwide: (December 17th, 2020)

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253

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IoTFuse Machine Learning Speakers

Alex Polyakov

Stealth Mode Startup Company

Dr. Raul Villamarin Rodriguez

Woxsen University

Dean of the School of Business

Sponsored By Digi-Key Electronics

Visit our official Virtual Conference Website! Our Confrnz App contains all of the streaming sessions shown on the show schedule for our upcoming Digital Experience!

Why Attend

Machine learning is a critical piece of the Internet of Things (IoT) today and will continue to grow in importance in the future

• The IoT represents not only a growing network of devices, applications and capabilities, it represents an exponentially growing amount of data.

• As the number of nodes in a network expands linearly, the amount of data produced by these nodes expands exponentially.

Taking for example data points from autonomous vehicles, the sensors record thousands of data point which must be processed either in real-time or later on for many different reasons.

• The objectives behind evaluating autonomous vehicle data may be to improve specific elements of how a vehicle operates, such as improved fuel economy, improved individual navigation or predicting the failure of a part such as a break pad.

• However there may also be a larger, systematic approach to evaluating data from vehicles, including reducing the overall number of accidents on a roadway, improving transportation routes, or suggesting delivery start and end times to reduce traffic.

• While the power of devices, wireless networks, security and the applications and software behind IoT is clear, this power becomes multiplied when combined with properly designed machine learning.

Of course this is easier said than done – a great challenge lies within understanding the types and classes of data, use cases and the proper practices to make everything work.

Check out our IoT Machine Learning virtual event to hear from fascinating experts in the field live, and gain access to many other fascinating Internet of Things horizontal industry related events in Medical, Healthcare, Agriculture, Industrial fields as well as technology verticals including Machine Learning, Cellular and Mobile Networks, Long Range and Non-traditional Wireless Networks, Hardware, Autonomous Devices and more!

Agenda

Time (CST)	Title	Description	Speaker Details
December 17, 2020 08:30 am -09:15 am	The Big Data Dichotonomy: Machine vs. Human-generated data	Understanding the bi-polar implementation of Machine vs. Human-generated data	Dr. Raul V. Rodriguez, Dean, School of Business, San Miguel University
December 17, 2020 09:30 am -10:15 am	Security Testing AI-driven Facial Recognition	The use of facial recognition technology is on the rise, and you can find it in different areas o...Read more.. Description The use of facial recognition technology is on the rise, and you can find it in different areas of human activity including social media, smart home devices, ATMs, and stores.The integration of deep learning (DL) in facial recognition is considered a great leap. However, this doesn’t come without security issues. Recently, researchers have discovered that DL algorithms are vulnerable to various attacks such as poisoning and adversarial examples. Numerous studies have been published, which describe the details of these attacks, particularly for image classification tasks.Facial recognition systems have their specific features and attack scenarios. Besides, the systems usually work in the physical environment and their attack surface differs from the digital one. Unfortunately, all examples of attacks and defensive measures were given for various models, datasets, and conditions. This can't help you understand the actual situation.In this talk, I will consider the effectiveness of recent approaches to attacking facial recognition systems and will present our own research conducted in the real environment with various cameras and algorithms	Alex Polyakov, CEO, Founder, Adversa.AI
December 17, 2020 11:30 am -12:15 pm	Simplifying IoT for ML Applications	Collecting and managing data from networks of heterogenous sensors and machines is a complex hurd...Read more.. Description Collecting and managing data from networks of heterogenous sensors and machines is a complex hurdle to overcome for engineering teams evaluating, developing or deploying machine learning based solutions. Applications must implement several network protocols, decode many non-standard data formats to collect data from sensors and machines sourced from different manufacturers, over a long period of time. Because any ML-based application is only as reliable as its data sources, applications must implement real time monitoring for sensors and machines to ensure reliable data streams and to deliver a robust solution.This webinar will show you how Machinechat’s JEDI One IoT Data Management software can offload your ML-based applications from the complexities of IoT data management, accelerating the time from concept to deployment of your ML-based solutions.	Sanjeev Datla, Founder, Machinechat
December 17, 2020 11:30 am -12:15 pm	Simplifying IoT for ML Applications	Collecting and managing data from networks of heterogenous sensors and machines is a complex hurd...Read more.. Description Collecting and managing data from networks of heterogenous sensors and machines is a complex hurdle to overcome for engineering teams evaluating, developing or deploying machine learning based solutions. Applications must implement several network protocols, decode many non-standard data formats to collect data from sensors and machines sourced from different manufacturers, over a long period of time. Because any ML-based application is only as reliable as its data sources, applications must implement real time monitoring for sensors and machines to ensure reliable data streams and to deliver a robust solution.This webinar will show you how Machinechat’s JEDI One IoT Data Management software can offload your ML-based applications from the complexities of IoT data management, accelerating the time from concept to deployment of your ML-based solutions.	Daryl Miller, Head of Platforms, MachineChat
December 17, 2020 01:00 pm -01:45 pm	Natural Language Queries on IoT Data	IoT systems promise to revolutionize the world by making it easy to collect and use data from sen...Read more.. Description IoT systems promise to revolutionize the world by making it easy to collect and use data from sensors and other sources. However, actually getting insights from data remains quite challenging. The data can be complex, and not easy for most users to query and understand. As a result, access to IoT data and analytics has mostly been limited to power users and specialist data scientists with varying degrees of analytical and technical skills. This is where natural language queries and conversational analytics comes in. This paradigm allows any user to ask text and voice questions, in natural language, of their data to a bot and receive back a natural language and visual result. In this talk, I shall present our efforts and experiences in building a conversational analytics platform on a chat-based interface. Our approach combines NLP, dialog management, natural-language generation/narration, data understanding and modeling, augmented analytics and automated visualization generation. I shall describe how we handle complex natural language questions against IoT datasets like "What is the average duration of bike trips in New York by gender and customer type", and further, enable users to dig deeper into the results in a conversational manner to uncover hidden insights.	Anand Ranganathan, Chief AI Officer, Unscrambl, Inc

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