There is no shortage of opinions on the potential for AI technologies in business. However, the current round of solutions is often viewed as expensive, proprietary, and complex to deploy and manage. When will AI solutions scale industry-wide? Is it possible
to measure ROI for automation? How does AI rank against other corporate initiatives? The state of AI technology and its future is spoken here. From the development of neuromorphic chipsets to democratizing deep learning toolsets and from the next
wave of machine vision, emotion, gestures, NLG, new algorithms, HPC and quantum computing will all be shared by the industry’s best and brightest.
Thursday, October 24
7:45 am Registration Opens
8:00 Continental Breakfast (Harborview Foyer)
9:00 – 12:55 pm Keynote Session (Harborview)
12:25 pm Networking, Coffee & Dessert in the Expo (Commonwealth Hall)
1:30 pm Opening Remarks
Research Director Cognitive/Artificial Intelligent Systems and Content Analytics, IDC
1:35 AI Growing Pains: Platform Considerations for Moving from POC to Large Scale Deployments
Chhandomay Mandal, Director, Solutions and Vertical Marketing, Dell Technologies
As ML and DL techniques evolve into mainstream adoption, the architectural considerations for platforms that support large scale production deployments of AI applications change significantly as you mature beyond small scale sandbox and proof-of-concept
environments. This session will draw key business and architectural requirements for compute and storage, discuss how enterprises can achieve the maximum benefit from AI platforms aligning with these requirements and will conclude by introducing
the Dell EMC and NVIDIA solution portfolio.
2:05 Using AI to Synthesize New Data
Jan Kautz, PhD, Vice President of Learning and Perception Research, Nvidia
AI is now ubiquitously used to analyze data in large variety of fields, from the sciences to healthcare. However, AI can not only be used to analyze data but also to synthesize new data, such as new visual content. In particular, generative adversarial
networks (GANs) have been shown to excel at this task. For instance, they can translate images from one domain (e.g., day time) to another domain (e.g., night time), synthesize completely new images, and even learn to detect defects by synthesizing
its own training data.
2:30 PANEL: The Impact of Quantum Science on Artificial Intelligence
As the field of Quantum Science develops, theoretical proposals have shown that building quantum algorithms could improve computational tasks with AI, including machine learning. Quantum computing could perform computations that are more efficient than
classical AI algorithms. This panel of researchers discusses the possibilities of addressing traditional computational challenges and likely paths forward in the fundamental advancement of hardware-based machine learning.
Riordan, Vice President, Entanglement Institute, Inc. and former instructor at Ethics & Emerging Military Technology Graduate Program, U.S. Naval War College
Panelists: Celia Merzbacher, PhD, Associate Director, Quantum Economic Development Consortium (QED-C)
Ahmed El Adl, PhD, AI
Consulting & Intelligent Solutions Leader, Accenture
Matt Langione, PhD,
Project Leader, Boston Consulting Group
Yudong Cao, CTO, ZAPATA Computing, Inc.
3:20 Networking Break in the Expo (Commonwealth Hall)
4:05 Emotional Intelligence and Affective Computing
Havasi, PhD, Research Scientist at MIT and AI Lead for Agorai
People often think of computers as being emotionless but when we interact with them it has always been full of emotion. As the first crowdsourced AI project turns twenty, we reflect on the use emotional relationship between people and artificial intelligence, the importance of useable and pragmatic AI, and how we're going to need to go beyond the emotion basics to make a measurable impact in everything from ecommerce to chatbots - and even automation. Lastly, we'll discuss the questions we need to ask to make sure we won't need to remove our emotions to interact with the next time a world-changing technology.
4:35 Increasing the Bandwidth Between Human and AI with Augmented Reality
Nina Simosko, Advisory Board Member, deepsense.ai
Simulation development is very useful in AR and VR technologies. Augmented and virtual reality is developing very quickly in the entertainment industry, however, its potential is much greater. For example, it can be applied in healthcare (assistance for
the doctor during surgery, replacement of trainers in the rehabilitation process) in education, retail and many others. Artificial intelligence allows you to analyze the world you see and then add relevant information to it. This increases the bandwidth
between humans and AI. Information can be given not only in the form of text/numbers or even 2D visualization but immediately placed in the world.
5:05 Networking Reception in the Expo (Commonwealth Hall)
6:30 Meetup Groups (Cityview)
7:30 Close of Day 2