Panel discussion with Ramon Lopez de Mantaras, Nuria Oliver and JoEllen Lukavec Koester.
What is the future we want to create, and what can we do – starting today – to actively shape that future with general AI? This talk outlines a vision for the future of humankind once AI reaches human or superhuman levels, and leads the audience through the steps one research group is taking to get there. From the economics of smart robots and job replacement, to bionic humans exploring the universe through space travel, the talk offers a window into the work of 30 researchers focused on AI development and safety, and explains what attendees can do themselves to help make that future happen.
JoEllen is the AI Safety Ambassador and Head of PR for GoodAI, a Prague-based general AI research and development company. A high school teacher by trade, she has a bachelor’s degrees in English and Philosophy from Seattle University, a master’s degree in Transatlantic Studies from Charles University in Prague, and is the recipient of Fulbright grant. JoEllen is particularly interested in how AI will affect international government and political relations.
Possibly the most important lesson we have learned after 60 years of AI research is that what seemed to be very difficult to achieve, such as accurate medical diagnosis to playing chess at the level of a Grand Master, turned out to be relatively easy whereas what seemed easy, such as visual object recognition or deep language understanding, turned out to be extremely difficult. In my talk I will try to explain the reasons for this apparent contradiction by briefly reviewing the past and present of AI and projecting it into the near future.
Ramon Lopez de Mantaras is Research Professor of the Spanish National Research Council (CSIC) and Director of the Artificial Intelligence Research Institute of the CSIC. Technical Engineer EE (Electrical Engineering) from the Technical Engineering School of Mondragón (Spain) in 1973. Master of Sciences in Automatic Control from the University of Toulouse III (France) in 1974, Ph.D. in Physics from the University of Toulouse III (France), in 1977, with a thesis in Robotics (done at LAAS, CNRS). Master of Science in Engineering (ComputerScience) from the University of California at Berkeley (USA) in 1979. Ph.D. in Computer Science, from the Technical University of Catalonia, Barcelona (Spain) in 1981.
Fintech startups are taking business away from traditional institutions like banks, exchanges, and brokerages. One of the reasons that these startups are able to compete with $30B+ behemoths like Credit Suisse and Goldman Sachs is their advanced decision making capabilities. By leveraging new data sources and better predictive analytics, companies like Ferratum Bank can make more accurate decisions in a fraction of the time.
This talk will cover:
- Types of decisions you can automate
- Challenges in building predictive, financial apps
- First-hand, real-world examples
Greg Lamp is the co-Founder and CTO of Yhat. In this role, Greg leads development of Yhat's core products and infrastructure and is the principal architect of the company's cloud and on-premise enterprise software applications. Greg was previously a product manager at OnDeck, a fintech startup in New York and before that an analyst at comScore. Greg is a graduate of the University of Virginia.
The best services have one thing in common: a superb customer experience. Banking services are no exception to this rule, and indeed the quest for an effortless, well informed, and personalized customer experience is one of the main goals of today's innovation in digital banking services. According to what Maslow has described in his "pyramid of needs", customers are seeking a more intimate and meaningful experience where banking services can actively assist the customer in performing and managing their financial life. Predictive APIs have a fundamental role in all this, as they enable a new set of customer journeys such as automatic categorization of transactions, detecting and alerting recurrent payments, pre-approving credit requests or provide better tools to fight fraud without limiting legitimate customer transactions. In this talk, I will focus on how to provide better banking services by using predictive APIs. I will describe the path on how to get there and the challenges of implementing predictive APIs in a strictly audited and regulated domain such as banking. Finally, I will briefly introduce a number of data science techniques to implement those customer journeys and describe how big/fast data engineering can be used to realize predictive data pipelines.
Natalino is currently Enterprise Data Architect at ING in the Netherlands, where leads the strategy, definition, design and implementation of big/fast data solutions for data-driven applications, for personalized marketing, predictive analytics, and fraud/security management. All-round Software Architect, Data Technologist, Innovator, with 15+ years experience in research, development and management of distributed architectures and scalable services and applications.