Monday, December 3 | Tuesday, December
| Wednesday, December 5
TUESDAY, DECEMBER 4
8:20 Continental Breakfast & Presentation Sponsored by Alegion
AI Was Never Meant for Humans
Nathaniel Gates, CEO, Alegion
Most people see AI as an easier way for humans to interact with computers. However, the day is coming when the vast majority of AI interactions are with other AI. Nathaniel will talk about the new intelligent-API that will make this routine.
8:55 Chairperson's Remarks
Eliot Weinman, Founder and Conference Chair, AI World
9:00 KEYNOTE: Algorithmic Models of Investor Behavior
Andrew W. Lo, the Charles E. and Susan T. Harris Professor, Professor of Finance, Director of the Laboratory for Financial Engineering, MIT Sloan School of Management
Financial AI seems so close, yet so far. We have automated trading algorithms, electronic exchanges, robo advisors, and cryptocurrencies, but machines still haven’t replaced portfolio managers, financial advisors, and bankers. So what’s missing? Not artificial intelligence. We have yet to develop an algorithmic understanding of human behavior as it is, rather than as it should be. We need a theory of artificial stupidity.
9:30 KEYNOTE: Getting on the Road to Artificial General Intelligence
Danny Lange, PhD, Vice President, AI and Machine Learning, Unity Technologies
this keynote session by AI visionary Danny Lange to discuss the role of intelligence in biological evolution and learning. Mr. Lange will demonstrate why a game engine is the perfect virtual biodome for AI’s evolution. Attendees will recognize
how the scale and speed of simulations is changing the game of AI while learning about new developments in reinforcement learning. They will also understand how this shift can be applied to their own organizations.
10:00 KEYNOTE: AI at Work: from Programming to Learning
Jay Bellissimo, Managing Partner, Cognitive Process Transformation, IBM Services
We are in The Learning Era, a new technology era in which the winners will be the companies that have the knowledge and the ability to train the systems to get the most value out of them. Enterprises that have years of accumulated know-how will differentiate
themselves and win in this era by infusing AI into customer engagement, employee expertise and business processes.
10:20 Grand Opening Coffee Break in the Exhibit Hall with Poster Viewing
10:50 EXECUTIVE ROUNDTABLE: What Successful Adoption of AI Looks Like
Machine learning may be poised to transform business, but actually adopting it is challenging. How have actual companies
put it into practice? How did they hire the right people, how did they set up their teams, and what kinds of projects did they start with? This panel will explore the practical barriers that companies face to adopting AI – and to getting value
Walt Frick, Senior Editor, Harvard Business Review
Norbert Monfort, Vice President, IT Transformation & Innovation, Assurant
Bogucki, CTO, deepsense.ai
Tsvi Gal, CTO Infrastructure, Morgan Stanley
Anju Gupta, PhD, Head of Sustainability Campaign, Syngenta
11:30 EXECUTIVE ROUNDTABLE: Adoption & Integration of AI in Healthcare
Business and technology leaders from the healthcare sector assess how close AI has come to transforming the industry, predicting patient outcomes and impacting cost. This panel evaluates adoption and integration of AI in Healthcare
in addition to exploring the regulatory, technical, and patient data security challenges.
David Ledbetter, Lead Data Scientist, Children’s Hospital Los Angeles
Paul Bleicher, CEO, OptumLabs
and others to be announced
5:00 KEYNOTE: Beyond the Hype: Accessible and Actionable AI
Chad Steelberg, Chairman & CEO, Veritone
The explosion of narrowly focused, highly specialized cognitive engines combined with ever-increasing sources of unstructured data will require novel approaches to fully realize the future of artificial intelligence.
The best business and technology
strategies to accelerate AI deployment and ROI, applicable to virtually any industry or organization, will include the following key components:
- Aggregation of specialized cognitive engines in a diverse ecosystem
- Simplicity of access and use for end users via SaaS platforms
- Continuously learning orchestration systems
- A spectrum of deployment from centralized (cloud) to highly decentralized (Edge/IoT)
- The melding of hardware and softwareEngines building engines
What does this mean for the AI community? We now have the ability to propel AI forward faster, make it more scalable for the enterprise and enable it to become more accessible for all.