Enterprise AI Strategies Theater
Tuesday December 4, 2018
1:00 PANEL: AI in Government
As IT-based enterprise organizations deploy intelligent automation across their business and industries, the opportunity emerges for fedreal, state, and local government agencies to become fast-followers. Whether in the pursuit of delivering enhanced
services to constituents, increasing worker productivity, reducing operational cost, or merely accelerating the digital transformation efforts underway within the agency. Attendees to this panel will hear:
- How agencies are utilizing automation and machine learning to improve service delivery and response time
- The unique challenges faced by public sector IT organizations compared to private enterprise
- The evolving discussion around AI ethics, safety, and regulatory requirements
John Desmond, Editor, AI Trends
Marc Mancher, US Government and Public Services Robotics & Intelligent Automation Lead, Deloitte Consulting LLP
Wayne Haubner, Chief Innovation Officer, Synergi Partners
Nguyen Anh Tuan, CEO, Boston Global Forum
Brad Mascho, Chief Artificial Intelligence Officer,
1:45 PANEL: Succeeding with AI System Integrators
Application integration services involve any work done to link or integrate multiple AI technologies together, as well as the work completed to tie in AI software with existing software or systems. AI can only provide significant benefits
when it is deployed seamlessly, with potential process interruptions (such as exceptions) identified and managed prior to full implementation. Much of the work done may revolve around the modification of existing systems, processes,
and job functions to ensure that the new AI technology can enhance speed, accuracy, or productivity, and seamlessly sit within or alongside of existing, mission-critical applications.
- What market drivers and conditions are driving the market for AI system integration services?
- How should AI budgets for AI integration be structured and managed to ensure optimal efficiency and success?
- What criteria should be used to evaluate systems integrators?
Keith Kirkpatrick, Principal Analyst, Tractica
Scot Whigham, CEO, Function (AI)
Larry Ross, Sr Operations Manager, InterContinental Hotels Group (IHG)
Kwame Monthrope, Global CRM Executive, Accenture
Carl Horton, Associate Partner, IBM
2:30 PANEL: Use Cases for AI and High-Performance Computing
As enterprises begin to scale artificial intelligence (AI) pilot programs, which often incorporate deep learning (DL), machine learning (ML), and natural language processing (NLP), across the enterprise, the need for a high-performance
compute and storage environment becomes clear. HPC environments, with their massive processing capability and low-latency access to data, are seen by many observers as a clear complementary technology to AI.
- What are the top AI use cases that are likely to be powered by HPC systems now, and in the future?
- What technological and operational challenges exist with deploying HPC systems that can support AI pilot programs or full-scale rollouts?
- What operational models are enterprises using to deploy AI technology that is powered by HPC
- What security and regulatory issues need to be considered when using HPC systems to power AI use cases?
Keith Kirkpatrick, Principal Analyst, Tractica
Christopher Carothers, PhD, Technology & Acadamic Advisor, Research & Development, Lucd
Gary Tyreman, CEO, Univa
Margrit Betke, PhD, Professor of Computer Science, Boston University
3:15 Breaking Bias
Kesha Williams, Senior Software Engineer, Chick-fil-A Corporate
Machines, like children, only model what they've been taught. Machines start out bias free but can quickly learn and even amplify bad human behavior. Learn how to mitigate bias by first understanding how it is
introduced and then by being intentional about its removal. After removing bias, introduce transparency in how predictions are made and even expose how good an algorithm is at making predictions. Attend this talk
to learn how you can start to build systems that are bias free. S. A. M. (Suspicious Activity Monitor), a predictive policing algorithm, is used as the case study during this talk.
Attendees will learn:
- How human bias is passed on to machines
- Practical strategies for identifying and removing bias
- Tips for making machine learning algorithms transparent
4:00 PANEL: Digital Transformation in Financial Services
The financial services industry is one of the most progressive enterprise verticals adopting AI technologies for use in internal business applications and customer-facing services. Intelligent automation enables
the financial services field to grow and scale beyond the power of human intelligence. This is accomplished by optimizing existing business processes and enhancing the way that financial organizations engage
with external customers. Panelists representing insurance, banking, mortgage, and investment segments discuss how intelligent automation is changing the financial services organization to compete in the data-driven
economy. Attendees to this panel will learn:
- Can intelligent automation bring back the human connection?
- Why clean, trusted, and governed data is essential for AI to succeed
- How are financial service organizations measuring the impact from intelligent automation?
Denise Valentine, Sr Analyst, Aite Group
Norbert Monfort, VP, IT Transformation & Innovation, Assurant
Brian Pearce, Senior Vice President, AI Enterprise Solutions, Wells Fargo
Srinivas Krovvidy, Director, Advanced Analytics Enablement, Fannie Mae
Chida Khatua, CEO, EquBot
4:45 PANEL: RPA/Intelligent Automation
Transaction-based roles and organizations are constantly under pressure to reduce operational costs while increasing the quality of output, reducing false-positive triggers, and scaling the capability faster
than the market demands. This is the domain of robotic process automation (RPA) and intelligent automation. While RPA was initially small snippets of software (bots) that captured screen and form content
using existing business rules to fill out enterprise application fields, the introduction of machine learning has added intelligence to repetitive business tasks. Attend this session to learn:
- The difference between rules-based automation and machine learning RPA
- If RPA helps employee productivity or is it replacing jobs
- How an enterprise can measure ROI using intelligent automation
Moderator: Pegasystems Speaker to be announced
Kashyap Kompella, CEO, RPA2ai
Lalitha Kompella, VP & CTO, Global Head of Intelligent Automation, Genpact
5:35 PANEL: AI in Legal
Artificial intelligence (AI) and intelligent automation are starting to impact the legal field. In many cases, it is augmenting the repetitive, procedural work being done and empowering professionals
to focus on complex tasks. Corporate legal teams as well as independent law firms gain from these advances. Attend this talk to hear:
- How AI-powered software is improving the legal document analysis process, including discovery, fact checking, and due diligence
- Predicting legal outcomes and identifying best-client fit
- Integrating legal into all facets of the enterprise business
Matt Karlyn, JD, Co-Chair Technology Industry Team, Foley & Lardner LLP
Nick Brestoff, JD, CEO, Intraspexion
David Colarusso, PhD, Professor, Suffolk University
Amanda L. Brown, JD, Legal Innovation Advisor + Counsel, InnoLegal Services, PLLC
Antigone Peyton, JD, Chair, Intellectual Property and Technology Group, Protorae Law PLLC
Wednesday, December 5, 2018
1:00 The Importance of Enterprise AI Industry Standards, According to ISO/IEC
Diab, Chair of ISO/IEC Standards JTC 1/42 Working Group; Steering Committee, Industrial Internet Consortium; Sr. Director, Huawei
Artificial Intelligence (AI) is an enabling horizontal technology. While the field of AI is not new, an international standards committee looking at the entire AI ecosystem is a recent
development. ISO/IEC JTC 1/SC 42 is the first of its kind international standards committee that is looking at the entire AI ecosystem. To enable mass deployment and adoption of AI
in the commercial and industrial fields, standards are required. Attendees to this session will learn:
- How common terminology can be used by all stakeholders to enable clear communication and sound decision making
- Which use cases, their requirements, and best practices for application of the technology will guide technology development.
- Like other transformational IT technologies, how pervasive AI requires addressing issues of trustworthiness from the get-go.
- Why standardization of algorithms and computational techniques will allow a higher level of adoption, use, and interoperability.
1:30 Recognizing Early Signals of Breakthrough Innovation and Disruption Using Artificial Intelligence and Machine Learning
Krishnan Nandabalan, President and CEO, InveniAI Corporation