Day 1 - 28 November 2018

Data Analytics for AI & IoT

09:00AM

Shriram Ramanathan

Senior Analyst

Lux Research Inc

Associated Talks:

09:00AM - Day 1

View Data Analytics for AI and IoT: Chair’s Welcome and Opening Remarks

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Data Analytics for AI and IoT: Chair’s Welcome and Opening Remarks

. Shriram Ramanathan, Senior Analyst, Lux Research Inc
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09:20AM

Steve Forcum

Technologist

Avaya

Associated Talks:

09:20AM - Day 1

View Digital Mixology

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Digital Mixology

  • Understand the difference between yesterday’s UC & CC platforms and today’s hyper-integrated communications solutions
  • Meet the core ingredients of your Digital Transformation – Artificial Intelligence, the Internet of Things, and Blockchain
  • Shaken, not stirred – See the ingredients put together in use cases which illustrate solutions that are smarter, more responsive and secure than ever before.
  • Unlock the cheat code – Learn how Cloud enables you to skip levels and realize solutions faster than ever.
. Steve Forcum, Technologist, Avaya
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09:50AM

Shriram Ramanathan

Senior Analyst

Lux Research Inc

Associated Talks:

09:00AM - Day 1

View Data Analytics for AI and IoT: Chair’s Welcome and Opening Remarks

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Doug Sauder

Director of Applied Intelligence

John Deere

Associated Talks:

11:20AM - Day 1

View IoT in Agriculture: Growing Tomorrow’s Intelligence from Today’s Technology

09:50AM - Day 1

View Panel: IoT and AI Data Analytics for Intelligent Decision Making

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Eric Jensen

Product Manager

Ubuntu

Associated Talks:

09:50AM - Day 1

View Panel: IoT and AI Data Analytics for Intelligent Decision Making

04:15PM - Day 1

View Monetising IoT: Repeatable & Scalable Hardware Transformation

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Charlie Key

Co-Founder and Chief Executive Officer

Losant

Associated Talks:

09:50AM - Day 1

View Panel: IoT and AI Data Analytics for Intelligent Decision Making

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Panel: IoT and AI Data Analytics for Intelligent Decision Making

  • Identifying target-rich, high-value data that can be used to generate business intelligence
  • Using cloud analytics platforms to derive value from IoT data
  • Discussing the barriers to widespread IoT/ AI /Big Data value delivery and how these might be overcome.
  • Real time data analytics in practice – examples of how IoT / AI data is creating business efficiency and revolutionising working practices
Moderator: . Shriram Ramanathan, Senior Analyst, Lux Research Inc
. Doug Sauder, Director of Applied Intelligence, John Deere
. Eric Jensen, Product Manager, Ubuntu
. Charlie Key, Co-Founder and Chief Executive Officer, Losant
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10:30AM

Diana Shaw

Manager Americas Artificial Intelligence, SAS Global Technology Practice, SAS Institute

SAS

Associated Talks:

10:30AM - Day 1

View Keynote: Leading the way into the new era of IoT analytics with AI

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Keynote: Leading the way into the new era of IoT analytics with AI

Businesses today are looking to leverage all types of data to promote a data-driven decision making culture for their customers as well as own organizations. Specifically in the Internet of Things (IoT) domain, the amount of data being generated from sensors, devices, equipment, and infrastructure is on a very rapid incline. As a result, there is a tremendous need for the use of analytics algorithms and methodologies along with embedded AI to tackle, understand, and process IoT data to derive meaningful business insights. This session will focus on key aspects of AI as it pertains to IoT and a few customer stories across these domains.

. Diana Shaw, Manager Americas Artificial Intelligence, SAS Global Technology Practice, SAS Institute, SAS
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11:00AM

Networking Break

11:40AM

Gavin Cowie

Director of Engineering

Alluvium

Associated Talks:

11:40AM - Day 1

View Industrial Machine Intelligence: The Golden Braid of Data Streams, AI, and Human Expertise

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Industrial Machine Intelligence: The Golden Braid of Data Streams, AI, and Human Expertise

We are now more than a decade into the commercialization of “big data” and “data science,” but these technologies have yet to meet the needs of the businesses whose work exists outside the data center. The commodity stack of big data technologies are fundamentally flawed for use in the rising tide of data streaming from connected machines in industrial settings. There are many reasons for this, as challenges abound when embedding machine intelligence into a production industrial lifecycle. Perhaps the most challenging, however, is understanding how intelligent software systems will support normal business operations and what real benefits they will provide. In this talk I will present the concept of the “golden braid” of industrial machine intelligence: blending massive data, advanced machine intelligence, and human expertise. This approach enables both the human experts and the algorithms to leverage their comparative strengths. To support this, I will provide a case study demonstrating how I have done this in practice.

. Gavin Cowie, Director of Engineering , Alluvium
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12:10PM

Ron Sadi

Sr. Director, Business Development

Zeta Global

Associated Talks:

12:10PM - Day 1

View Case study: Leveraging NLP and Machine Learning to Activate High-Value Consumers

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Case study: Leveraging NLP and Machine Learning to Activate High-Value Consumers

With every passing second, your prospects and customers are generating billions of signals through their digital interactions. By taking this high volume data and refining it into high value audiences, you can gain deeper insights into the attitudes and behaviors at key ‘moments of truth’.

Join Ron Sadi of Zeta Global as he explains how to join real-time behavioral signals with unique, permissioned user profiles to scale personalization and maximize your customer’s lifetime value.

. Ron Sadi, Sr. Director, Business Development, Zeta Global
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12:40PM

Ashwin Krishnan

COO

Uberknowledge

Associated Talks:

12:40PM - Day 1

View The collision course between Big data, AI, Privacy, Ethics and Regulations in the IoT world

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The collision course between Big data, AI, Privacy, Ethics and Regulations in the IoT world

With the much heralded GDPR, General Data Protection Regulation, now in place, a great deal has been revealed about what good data privacy hygiene looks like. That involves collecting and storing as little information as possible so that exposure is minimal and ensuring consumer rights to revocation or transportation can be accommodated easily. This marks a U-turn for most forward-leaning organizations. These companies are transitioning from taking snapshot backups every hour and storing those backups in ever-cheaper cloud storage, worrying about the exposure later, to now actively changing policies to take more thoughtful backups and not retain that information longer than necessary.

Now, shift the focus to IoT. IoT is everywhere, from Industrial to Healthcare to Consumer, and the one crucial ingredient that underpins the success of AI with IoT is large swathes of data. The more data there is, the better the algorithms can be trained. Autonomous cars are a great example. In order for these cars to determine a street light from a tall, thin man, they need to have seen enough street lights in daylight, dusk, and night. The tall, thin men should represent all races so the car has enough skin-color data sets to make the algorithm effective. And that requires lots of data.

Therein lies the conflict. The regulation and privacy gurus will be advocating for limiting the amount of data collected in order to provide for a safer and more trustworthy customer experience. The IoT and AI engineers and business owners will be pulling the wagon in the opposite direction in order to make the AI more effective and extract ROI from their IoT deployments.

. Ashwin Krishnan, COO, Uberknowledge
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01:10PM

Networking Break

02:50PM

Ryan Martin

Principal Analyst

ABI Research

Associated Talks:

09:40AM - Day 1

View Bluetooth Fireside Chat – Smart Industry

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Emilio Billi

CTO

A3Cube Inc

Associated Talks:

02:50PM - Day 1

View Panel: The (Big) Data challenge

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Samantha Daugherty

Senior Expert IS Architect - Data

J.B. Hunt Transport, Inc.

Associated Talks:

02:50PM - Day 1

View Panel: The (Big) Data challenge

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Jeremy Gu

Senior Data Scientist

Uber

Associated Talks:

03:30PM - Day 1

View Multi-armed bandits (MAB) Platform for Continuous Experiments at Uber – A new case study to optimize the financial impact of CRM campaigns

02:50PM - Day 1

View Panel: The (Big) Data challenge

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Tom White

Chief Product Officer

AI Data Innovations

Associated Talks:

02:50PM - Day 1

View Panel: The (Big) Data challenge

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Panel: The (Big) Data challenge

  • How to collect data
  • Quality versus quantity
  • The labelling challenge
  • How to handle noisy data – random noise versus systematic noise
Moderator: . Ryan Martin, Principal Analyst, ABI Research
. Emilio Billi, CTO, A3Cube Inc
. Samantha Daugherty, Senior Expert IS Architect - Data, J.B. Hunt Transport, Inc.
. Jeremy Gu, Senior Data Scientist, Uber
. Tom White, Chief Product Officer, AI Data Innovations
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03:30PM

Jeremy Gu

Senior Data Scientist

Uber

Associated Talks:

03:30PM - Day 1

View Multi-armed bandits (MAB) Platform for Continuous Experiments at Uber – A new case study to optimize the financial impact of CRM campaigns

02:50PM - Day 1

View Panel: The (Big) Data challenge

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Multi-armed bandits (MAB) Platform for Continuous Experiments at Uber – A new case study to optimize the financial impact of CRM campaigns

  • A high summary of the Uber’s engineering development on the MAB experiments
  • How to use bandits experiments to optimize Eats campaigns in Europe.
  • How to maximize the conversion rates in the bandits experiments.
  • How to measure the short-term and the long-term financial impacts of the bandits experiments.
. Jeremy Gu, Senior Data Scientist, Uber
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03:50PM

Networking Break

04:20PM

Michael Berkovsky

Senior Big Data Engineer

Trulia

Associated Talks:

04:20PM - Day 1

View Case Study: Big Data In Real Estate

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Case Study: Big Data In Real Estate

In today’s market property buyers and renters demand more then just real-estate information – they want to know all about home location.

This poses 3 type challenges for data provides:

Accumulate and combine data from various sources (general neighborhood information, school quality, ease of commute, crime scores, proximity to shopping centers, etc)

  • Utilize both static and real-time listing data
  • Provide high data quality.

This session will focus on building pipelines with Spark to address these and other challenges.

. Michael Berkovsky, Senior Big Data Engineer, Trulia
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04:40PM

Daniel Weimer

Head of Artificial Intelligence

Volkswagen

Associated Talks:

03:50PM - Day 1

View Panel: The Next Generation of Connected Cars and Vehicles

04:40PM - Day 1

View Solo: AI, Big Data and Autonomous Vehicles

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Solo: AI, Big Data and Autonomous Vehicles

. Daniel Weimer, Head of Artificial Intelligence, Volkswagen
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05:10PM

Meetup: Hosted by Tech Thinkers and Makers

IoT Innovations in Food

M2M technology is changing the future of food production.  The sector landscape is slowly being populated with monitoring crops, transport temperature data collection, and livestock wellness wearables. IoT will be at the forefront of revolutionizing agriculture in advanced and developing countries.

Halle Baksh will provide an overview of the sector, opportunities and IoT innovations in the food production ecosystem.  Halle is an independent consultant to investors and institutional capital, with extensive experience in financial valuation of tech companies, revenue strategy development and industry sector analysis.

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06:00PM

Session Close