Day 1 - 28 November 2018
Data Analytics for AI and IoT: Chair’s Welcome and Opening Remarks
09:20AM - Day 1
- 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.
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
Manager Americas Artificial Intelligence, SAS Global Technology Practice, SAS Institute
10:30AM - Day 1
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.
Director of Engineering
11:40AM - Day 1
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.
Sr. Director, Business Development
12:10PM - Day 1
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.
12:40PM - Day 1
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.
02:50PM - Day 1
Senior Expert IS Architect - Data
J.B. Hunt Transport, Inc.
02:50PM - Day 1
Chief Product Officer
AI Data Innovations
02:50PM - Day 1
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
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.
Senior Big Data Engineer
04:20PM - Day 1
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.
Head of Artificial Intelligence
03:50PM - Day 1
04:40PM - Day 1
Solo: AI, Big Data and Autonomous Vehicles
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.