Path to AI

AI Adoption in the Enterprise | Jacob Wilson, PwC

The 7 Powers of Machine Learning | Lucas Bernardi, Booking

Diversity and Inclusion in Data Science | Noelle Silver, HackerU

Are You Vulnerable to Adversarial Machine Learning? | Celeste Fralick, McAfee

Democratizing Automated Forecasting | Lukas Stroemsdoerfer, Mercedes-Benz

Fueling Digital Transformation Within the Enterprise | Ayan Bhattacharya, Deloitte

Synthetic Data: How Do We Manage Them? | Armando Vieira, Hazy

Cleaner Oceans Through AI and Data Science | Bruno Sainte-Rose, The Ocean Cleanup

Online Conversion Modeling Combining CRM-Data and Online Behavior | DPG Media

Generative Adversarial Networks for Finance | Alexandre Hubert, Dataiku

AI + Human Intuition = Differentiated Insights | Jeff McMillan, Morgan Stanley

AI in Retail: Building a Brilliant Shoe Discovery System | Antonis Argyros, SafeSize

How to Make a Success of Data Science | Jan Teichmann, Zoopla

Ethical Enterprise AI – A Guideline or Compass? | Martin Leijen, Rabobank

Getting from 1 to 100 Users With Dataiku | Roberto Amador, Johnson & Johnson

Data Science Pioneers: Conquering the Next Frontier | Romain Doutrioux, Dataiku

What is to be Done When Everything Can be Faked | Shaun McGirr, Cox Automotive UK

Becoming an Intelligent Organization in the Age of AI | Jeff McMillan, Morgan Stanley

The Growing Pains, Pitfalls & Future for a Data Science Team in a Hyper-Growth Company | Shaun Moate, DAZN

Cyber Risk Analytics: The Next Frontier | Paul Guthrie, Envelop

EGG AMS 2019 Opening Plenary | Hylke Visser, Dataiku

How Mercedes-Benz, Vodafone, and Credit Suisse Super-Sized their Data Initiatives | A Panel

The Law of Averages to Deal with Data Science Frustration | Adrian Badi, Demant

From Us, To Us: An Inclusivity Architecture | Brandeis Marshall, Spelman College

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