14–17 Oct 2019
AI Business Summit
AI will have enormous impact on your business.
Don’t get left behind.
Designed specifically for executives, business leaders, and strategists, the AI Business Summit provides concise, high-level executive briefings on the most promising and important developments in AI for business.
You'll get an insider’s look at the AI implementations that will have the most profound impact on your business. Advice on how to mitigate risk and out-innovate your competitors. Detailed case studies of successful AI projects.
You have critical—and urgent—decisions to make about your AI strategy. Get the insight you need at the AI Business Summit.
Featured Speakers
Platinum pass holders have access to the AI Business Summit Mon–Thurs. Gold and Silver pass holders have access to the AI Business Summit on Tues–Thurs. Bronze pass holders have access to the AI Business Summit on Wed–Thurs.
| Monday-Tuesday 14-15 October: 2-Day Training (Platinum & Training passes) |
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| Tuesday, 15 October: Tutorials (Gold & Silver passes) |
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| Wednesday 16 October: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes) |
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| 9:00 | Location: King's Suite AI Conference Keynotes |
| TBD Morning break |
| Thursday 17 October: Keynotes & Sessions (Platinum, Gold, Silver & Bronze passes) |
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| 9:00 | Location: King's Suite AI Conference Keynotes |
| TBD Morning break |
9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Hilton Meeting Room 1/2
Secondary topics:
Machine Learning
Average rating:
(5.00, 2 ratings)
Angie Ma and Richard Sargeant offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.
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9:00–12:30 Tuesday, 15 October 2019
Location: Buckingham Room - Palace Suite
Secondary topics:
Machine Learning
Average rating:
(3.25, 8 ratings)
While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores the cycle of developing ML-based capabilities (or entire products) and the role of the (product) manager in each step of the cycle.
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11:05–11:45 Wednesday, 16 October 2019
Location: Windsor Suite
Bahman Bahmani (Rakuten)
Average rating:
(3.20, 5 ratings)
Amid fears of sentient killing robots and a freezing AI winter, AI has a true potential to transform the enterprise. Actualizing this potential requires a well-informed organizational strategy and consistent execution of best practices regarding people, processes, and platforms. Bahman Bahmani examines these strategies and best practices and provides insights into their successful execution.
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11:05–11:45 Wednesday, 16 October 2019
Location: Westminster Suite
Secondary topics:
Ethics, Security, and Privacy,
Machine Learning,
Mobile Computing, IoT, Edge,
Text, Language, and Speech
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.
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11:55–12:35 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Konrad Wawruch (7bulls.com)
Average rating:
(4.00, 2 ratings)
Real business usage of most advanced methods for financial time series forecasting (based on winning methods from M4 competition) and assets portfolio optimization (based on Monte Carlo Tree Search with neural networks - Alpha Zero approach). Complete investments platform with the AI workflow and real time integration with the brokers. Real usage demo.
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11:55–12:35 Wednesday, 16 October 2019
Location: Windsor Suite
Ted Malaska (Capital One)
Average rating:
(4.25, 4 ratings)
While at a big tech conference on AI, it's important to reflect on the human components. Ted Malaska walks you through scenarios and strategies to help different groups work together and explains how to evaluate success and sniff out trouble areas. You'll look at every part of the pipeline to see who's involved and how to optimize the interaction points throughout the pipeline—and how to have fun.
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11:55–12:35 Wednesday, 16 October 2019
Location: Westminster Suite
Secondary topics:
Ethics, Security, and Privacy,
Machine Learning
Martin Benson (Jaywing)
Machine learning has been used in credit scoring for three decades. Martin Benson discusses the history of machine learning in credit scoring and the need for explainable and justified decisions made by machine learning systems. Come find out if it's possible to overcome the black box problem and learn how machine learning systems are evolving and how to bypass the challenges to adoption.
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13:45–14:25 Wednesday, 16 October 2019
Location: Windsor Suite
Secondary topics:
Design, Interfaces, and UX,
Ethics, Security, and Privacy,
Machine Learning tools
Ariadna Font Llitjós (Twitter)
Average rating:
(5.00, 3 ratings)
In the rapidly changing world of AI, adopting the right design principles is key. From data scientists and business users to client end users, IBM Watson always seeks to augment their capabilities. Ariadna Font Llitjós examines how IBM Watson applies ethical AI and user-centered design principles from the beginning and leverages them throughout the product development cycle.
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13:45–14:25 Wednesday, 16 October 2019
Location: Westminster Suite
Secondary topics:
Ethics, Security, and Privacy,
Machine Learning
Martin Goodson (Evolution AI)
Average rating:
(4.75, 4 ratings)
Data leakage occurs when the model gains access to data that it shouldn't have. AI systems can fail catastrophically in production if leakage is not dealt with properly. Martin Goodson details the four main manifestations of data leakage and explains how to recognize the warning signs. By mastering several key scientific principles, you can mitigate the risk of failure.
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14:35–15:15 Wednesday, 16 October 2019
Location: Windsor Suite
Secondary topics:
Design, Interfaces, and UX
Data scientists feel naturally comfortable with the language of mathematics, while designers think in the language of human empathy. Creating a bridge between the two was essential to the success of a recent project at an energy company. Tim Daines and Philip Pilgerstorfer detail what they learned while creating these bridges, showcasing techniques through a series of “aha” moments.
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14:35–15:15 Wednesday, 16 October 2019
Location: Westminster Suite
Secondary topics:
Machine Learning tools,
Text, Language, and Speech
Tobias Martens (whoelse.ai)
Average rating:
(3.50, 2 ratings)
More than 50% of all interactions between humans and machines are expected to be speech-based by 2022. The challenge: Every AI interprets human language slightly different. Tobias Martens details current issues in NLP interoperability and uses Chomsky's theory of universal hard-wired grammar to outline a framework to make the human voice in AI universal, accountable, and computable.
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16:00–16:40 Wednesday, 16 October 2019
Location: Windsor Suite
Average rating:
(3.50, 2 ratings)
The Analytics Impact Index gives organizations an understanding of the value potential of analytics as well as the capabilities required to capture the most value. Anastasia Kouvela and Bharath Thota walk you through the 2019 results and the analytics journey of leading global organizations and empower companies to develop a case for change.
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16:00–16:40 Wednesday, 16 October 2019
Location: Westminster Suite
Secondary topics:
Ethics, Security, and Privacy,
Machine Learning,
Text, Language, and Speech
Danielle Deibler (MarvelousAI)
Average rating:
(4.57, 7 ratings)
Danielle Deibler examines an approach to detecting bias, fine-grained emotional sentiment, and misinformation through the detection of political narratives in online media. As building blocks, the methodology uses human-in-the-loop, alongside other natural language processing and computational linguistics techniques, with examples focused on the 2020 US presidential election.
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16:50–17:30 Wednesday, 16 October 2019
Location: Windsor Suite
Charlotte Han (Independent)
Average rating:
(5.00, 1 rating)
According to research by AI2, China is poised to overtake the US in the most-cited 1% of AI research papers by 2025. The view that China is a copycat but not an innovator may no longer be true. Charlotte Han explores what the implications of China's government funding, culture, and access to massive data pools mean to AI development and how the world could benefit from such advancement.
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11:05–11:45 Thursday, 17 October 2019
Location: Windsor Suite
Secondary topics:
Reinforcement Learning
Average rating:
(4.33, 3 ratings)
In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. Rebecca Gu and Cris Lowery explore how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace, which industries are likely to be subject to greater restrictions or scrutiny, and what future digital regulation might look like.
Read more.
11:55–12:35 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Secondary topics:
Computer Vision,
Deep Learning,
Health and Medicine,
Machine Learning,
Temporal data and time-series
Average rating:
(5.00, 1 rating)
In many countries, policy decisions are disconnected from data, and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data.
Read more.
11:55–12:35 Thursday, 17 October 2019
Location: Windsor Suite
Secondary topics:
Ethics, Security, and Privacy,
Machine Learning
Katharine Jarmul (KIProtect)
Average rating:
(5.00, 2 ratings)
Katharine Jarmul sates your curiosity about how far we've come in implementing privacy within machine learning systems. She dives into recent advances in privacy measurements and explains how this changed the approach of privacy in machine learning. You'll discover new techniques including differentially private data collection, federated learning, and homomorphic techniques.
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13:45–14:25 Thursday, 17 October 2019
Location: Windsor Suite
Umit Cakmak (IBM)
In every AI initiative, there’s a demand from businesses to protect or increase market share or decrease operational costs. Your competitors are a growing threat, seemingly adopting new technologies better than you. Umit Cakmak examines critical steps from countless client engagements on how to consistently deliver successful AI projects.
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14:35–15:15 Thursday, 17 October 2019
Location: Windsor Suite
Secondary topics:
Machine Learning,
Machine Learning tools
Paco Nathan (derwen.ai)
Average rating:
(4.50, 2 ratings)
Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way.
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16:00–16:40 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Secondary topics:
Machine Learning,
Temporal data and time-series,
Text, Language, and Speech
Weifeng Zhong (Mercatus Center at George Mason University)
Average rating:
(4.00, 1 rating)
Weifeng Zhong explores a novel method to learn structural changes embedded in unstructured texts based on the Policy Change Index (PCI) framework developed by economists Julian Chan and Weifeng Zhong. He explains how an off-the-shelf application of deep learning—with an important twist—can help you detect structural break points in time series text data.
Read more.
16:00–16:40 Thursday, 17 October 2019
Location: Windsor Suite
Mark Madsen (Teradata)
The growing complexity of data science leads to black box solutions that few people in an organization understand. Mark Madsen explains why reproducibility—the ability to get the same results given the same information—is a key element to build trust and grow data science use. And one of the foundational elements of reproducibility (and successful ML projects) is data management.
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16:50–17:30 Thursday, 17 October 2019
Location: Windsor Suite
Secondary topics:
Text, Language, and Speech
Andreas Kaltenbrunner (NTENT)
Voiced-based AI continues to gain popularity among customers, businesses, and brands, but it’s important to understand that, while it presents a slew of new data at our disposal, the technology is still in its infancy. Andreas Kaltenbrunner examines three ways voice assistants will make big data analytics more complex and the various steps you can take to manage this in your company.
Read more.
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