( 全體演講講者 )

6月25日(二) | 8:00 am – 12:40 pm

8:00 am Continental Breakfast

8:50 Conference Chair Introduction

Eliot Weinman, Conference Chair, AI World Government

9:00 Keynote: Open Data and AI Drive Digital Transformation in Government

Scott Lundstrom, Group Vice President and General Manager, IDC


Artificial Intelligence is poised to transform every aspect of government over the next decade. Every individual in the transformed organization will be impacted by AI’s ability to inform, augment, and automate decision making - and is just the beginning! Understanding the opportunity for new services and new models for citizen engagement will change the way we look at technology’s role in government. AI technologies bring threats and opportunities that must be managed to every organization, and new policies and guidelines will be required to harness these advances.

In this presentation, Scott Lundstrom, IDC Group Vice President and General Manager, will set the stage by sharing IDC’s Artificial Intelligence Framework, IDC’s “use cases” for Government Digital Transformation, and IDC’s Artificial Intelligence predictions that will impact government IT professionals over the next five years.

9:20 Keynote: AI Update from the White House

Suzette Kent, Federal Chief Information Officer, U.S. Office of Management and Budget

9:45 Plenary Roundtable: Talk Title to be Announced

Moderator: Scott Lundstrom, Group Vice President and General Manager of IDC Government and Health Insights, IDC


William Mark, PhD, President, Information and Computing Sciences, SRI

Anthony Scriffignano, PhD, Senior Vice President & Chief Data Scientist, Dun & Bradstreet




10:40 Coffee Break in the Exhibit Hall

11:15 Keynote: Towards Explainable and Ethical AI

Tolga Kurtoglu, PhD, CEO, PARC


Deep learning AI models are opaque and can institutionalize biases and errors. We are building models that are transparent and make it much easier to spot (and remove) biases in the training data. Such technological advances are necessary but not sufficient. So, we are developing an AI institutional review board (IRB) to review the data collection and modeling methods to ensure that they are ethical.

11:45 Plenary Roundtable: Connecting the Nation’s Healthcare Data

Dr. Siddiqui will discuss the implementation of HHS’s enterprise data strategy focused on leveraging data for decision making. She will also address the Department’s approach to the development of an AI strategy and the elements of institutional capacity building required to fully utilize its data assets.

Moderator: Mona Siddiqui, MD, Chief Data Officer, U.S. Department of Health & Human Services

Panelists: Speakers TBA



12:10 Lunch Break

12:20 Luncheon Keynote: Unlocking the Value of AI/ML – a VMware Perspective

Ames_RobertRobert Ames, Senior Director, National Technology Strategy, VMware Research, VMware


AI/ML offers tremedous opportunities for many organizations, but advancing its use from experimentation to production deployment requires powerful, resilient, and adaptive IT infrasteucture to support the entire AI/ML pipeline, and will describe how ML/AI technigues can be used to deliver on the vision of a high-scale resilient, and secure self-driving data center.

12:40 Dessert and Refreshment in the Exhibit Hall

6月26日(三) | 8:00 am – 12:10 pm

8:00 am Continental Breakfast

8:50 Conference Chair Introduction

Eliot Weinman, Conference Chair, AI World Government

9:30 Keynote: AI and Machine Learning: A Strategic Component of NASA’s Mission

Brian Thomas, PhD, Agency Data Scientist and Program Manager for Open Innovation, NASA



10:30 Coffee Break

10:50 Plenary Roundtable: Talk Title to be Announced

Moderator: Bill Valdez, President, Senior Executives Association

Mattingly-Jordan_Sara11:30 Keynote: Talk Title to be Announced

Sara Mattingly-Jordan, PhD, IEEE Global Initiative for Ethical AI, Assistant Professor Center for Public Administration & Policy Virginia Tech

3:30 pm Panel: Looking to Future in AI in Government 

Eliot WeinmanModerator: Eliot Weinman, AI World Conference Chair, Cambridge Innovation Institute


( AI與大數據的課題克服 )

6月25日(二) | 1:30 - 5:00 pm

Track Chair: Shawn MccarthyShawn McCarthy, Research Director, IDC Government Insights

Track Description: Agencies have been accumulating data for many years. However, organizations also realize they have not gained many benefits from the datasets. Along with an increase in unstructured data, there has also been a rise in the number of data formats. Administrative data, such as notes and articles, as the primary data type have expanded to include images, audio, video, and sensors.

Many organizations fail to consider how quickly a big data project scales. Constantly pausing a project to add additional resources cuts into time for data analysis. Assessing what data exists and its integrity – completeness, accuracy, bias and trust – prolong the analysis effort. This challenge is further compounded by integrating disparate data sources and securing big data.

This track addresses the major challenges faced by Big Data environments with an emphasis on identifying what data you have, how to source additional data, how to organize it, how to clean it, how to prepare the data for use in a machine learning application, and ultimately, how to integrate and scale the application into the Agency’s IT systems.

2:00 pm Panel: Getting Your Data Ready for AI

The basic challenge of working with data is understanding what you have and what you need. From auditing your data to cleaning and labeling it, preparing your data for quality, relevance, and trust is the most important step you will undertake in your Big Data + AI journey. This panel highlights the importance of identifying the agency objectives, creating a strategy for capturing, structuring, and maintaining data, and steps to monitor and govern data performance. 

Iyer_SukumarModerator: Sukumar R. Iyer,CEO, Brillient and Chair of Intelligent Automation Working Group, ACT-IAC

Panelists: Jeff Butler, Director of Data Management, IRS


Devaney_ChrisChris Devaney, Chief Operating Officer Executive - Business Operations, DataRobot

Ruderman_LoriLori Ruderman, Senior Advisor, U. S Department of Health and Human Services, HHS ReImagine BuySmarter 

Michael Conlin, Chief Data Officer, U.S. Department of Defense 

Preble-Edward2:30 Finding Early Success with Intelligent Automation and Big Data

Edward Preble, PhD, Research Data Scientist, Center for Data Science, RTI International


This presentation will discuss what works, and what doesn't, in AI related projects. AI-driven use cases for the Bureau of Justice Statistics (BJS) and the National Center for Health Statistics (NCHS) will be presented along with specifics for how to evaluate projects for AI-readiness, how to pick the right problems to focus on, and how to begin with small projects that then grow into real-world success stories.

3:00 Refreshment Break in the Exhibit Hall

3:30 Government Data Center Analytics 

Shawn McCarthy, Research Director, IDC Government Insights


Shawn will provide a presentation on the state of AI as it applies to Data Center Infrastructure Management, and how that can be used to leverage agencies compliance with the requirements of the federal Data Center Optimization Initiative. The focus of AI in government data centers is on improving energy consumption, network traffic, processor and virtual machine load balancing, and more.

4:15 A Framework for Automating Data Acquisition and Operationalization

Anil Tilbe, Director of Enterprise Measurements & Design, Veterans Experience Office, U.S. Department of Veterans Affairs


Lee Becker, Chief of Staff, Veterans Experience Office, U.S. Department of Veterans Affairs


5:00 Networking Reception in the Exhibit Hall

6:00 Close of Day


( 政府機關的策略性業務中AI應用措施 )

6月25日(二) | 1:30 - 5:00 pm

Track Chair: Adelaide ObrienAdelaide C. O'Brien, Research Director, Government Insights, IDC


Track Description: In evaluating the potential applications for intelligent automation, fundamental questions revolve around “How do I get started in Artificial Intelligence and what are the best applications where AI can and should be deployed?” In many cases, the answers have less to do with technology choices and more to do with evolving the organization’s culture and mindset. As processes transition from Business Intelligence and Performance Management to AI- and data-driven strategic roles and functions, agencies and departments will face common opportunities to refine the future of work.

This track looks at alternatives to building Data Science teams and strategies for enabling a data-driven workforce.

Lofdahl_Corey2:00 Bridging Policy and the Mission with Computer-Based Models

Corey Lofdahl, PhD, Principal Engineer, Systems & Technology Research (STR)


Academic researchers have for decades investigated how computers and Artificial Intelligence (AI) can help address complex government policy problems, but few of these efforts have paid off or proven workable. This talk covers the key policy problems faced by senior decision makers, the early promise of AI, why AI research has been slow to transition to real-world applications, and how an increased appreciation of human factors supports that transition. 

Sheppard_Lindsey2:30 Personnel, Supply Chain & Logistics

Lindsey Sheppard, Associate Fellow, International Security Program, Center for Strategic & International Studies (CSIS)


Explore the common challenges and opportunities faced in these public sector roles and functions. If we are a nation where we are doing better by our people, how can government personnel be empowered to create more efficient processes supported by data? This talk examines the organizational challenges to implementing data-driven projects in Personnel, Supply Chain & Logistics.

3:00 Refreshment Break in the Exhibit Hall

3:30 Panel: Leveraging AI in the Automation of Government Accounting and Reporting

Moderator: Adelaide C. O'Brien, Research Director, Government Insights, IDC


Professionals frequently perform activities that may not require their expertise. Utilizing AI could free up their time to perform higher-value tasks. Accountants, for instance, may analyze hundreds of contracts looking for patterns and anomalies, which relies more on reading skills than accounting skills. The use of intelligent automation and AI technologies could reduce human error and increase workflow by scanning and extracting contract terms. Similarly, rules exist for standard reporting and compliance content. Automation could speed the process by automatically generating reports for human review. This talk separates fact from fiction about the use of and value from automation in government accounting and reporting. 

4:15 Panel: Intelligent Automation and AI at NASA

In the latest NSF Statement on AI for American Industry, "The effects of AI will be profound. To stay competitive, all companies will, to some extent, have to become AI companies." Compared to both industry and academia, NASA and its research sites have specific challenges as well as resources that are particularly adapted to the use of AI. They have a wealth of data and information to leverage and "learn" from. And many science- and mission-oriented applications have been identified that can benefit from learning on previous data and from domain and expert knowledge. This panel of representatives from multiple NASA research centers share how intelligent automation and AI is advising mission planning and operations, discovering correlations in large amounts of science data, and enabling new tools and intelligent user interfaces to improve outcomes. 

Moderator: Jeff Orr, AI World Content Director and AI Trends Editor, Cambridge Innovation Institute

Crichton_DanielPanelists: Daniel Crichton, Program Manager, Principal Investigator, and Principal Computer Scientist, NASA's Jet Propulsion Laboratory

Oza_NikunjNikunj Oza, PhD, Leader of the Data Sciences Group, NASA Ames Research Center

Thompson_BarbaraBarbara Thompson, Solar Physicist, Lead of the Center for HelioAnalytics, NASA Goddard Space Flight Center




5:00 Networking Reception in the Exhibit Hall

6:00 Close of Day


( 使用AI的服務加速推動實現智慧城市的作業 )

6月25日(二) | 1:30 - 5:00 pm

Savoie_Curt Track Chair: Curt Savoie, Program Manager, Global Smart Cities Strategies, IDC


Track Description: To achieve the title of Smart City, municipalities must enhance existing services, while at the same time innovate and deploy new applications and capabilities. For existing services, organizations are utilizing predictive models to gain operational efficiency, such as using data to enhance asset location. Big data is also aiding in the delivery of a better user experience (UX). Artificial intelligence can also be applied in a host of other specific areas, such as the preparation for autonomous vehicles and smart mobility systems, as well as planning and regulating of new service delivery.

This track examines the design and governance of the Smart City utilizing data and intelligent automation. Focus is given to three specific aspects of the Smart City: digital government and citizen services, transportation, and public safety.


2:00 pm Delivering Effective Citizen Services

Madelene Stolpe, Head of Digital Strategy, Health & Human Services, City of Oslo, Norway


The world’s population is growing and become more in need of public services. Our current treatment model will not be sustainable in the future. As AI and other technologies are emerging – could this be used preventively and make public servants guide our citizens well before they even know they’ll need it? 

2:30 Panel: Identifying Targeted Public Safety Applications for Your AI Digital Transformation


Public safety agencies globally are leveraging AI in their day-to-day operations to work faster, smarter, and to redress some of the additional difficulties being created by the digital deluge. This panel explores some best practice examples of agencies on the cutting edge of AI and ML implementations, as well as discusses how to deploy AI responsibly. This is critical to meeting citizen expectations about police capabilities, as well as help with information sharing endeavors, and rebuilding trust in an era that has witnessed the decline of public confidence in law enforcement agencies. Attendees will learn: 

  • What are the obvious and less obvious ways in which AI can fundamentally transform data-driven public safety? 
  • What are some of the lesser known implementation inhibitors for law enforcement agencies? 
  • What are best practices recommendations from mature AI agencies and organizations? 

Brooks_AlisonModerator: Alison Brooks, PhD, Research Director, Smart Cities Strategies & Public Safety, IDC

Brown_RichPanelists:  Rich Brown, Director, Project VIC International

Spitzer-Williams_NoahNoah Spitzer-Williams, Principal Product Manager, Redaction AI and Transcription AI, Axon Technologies

3:00 Refreshment Break in the Exhibit Hall

3:30 Panel: Strategies for Developing AI-Based Applications & Services for Transportation

As autonomous vehicles come closer to closer to reality in cities and on the nation’s roadways, the decision-making around AI can have significant impacts for government, not only for road safety and traffic management but for urban society at large. This panel session presents various strategies and perspectives on the topic from an auto OEM to that of a city to capture the progress and thinking on AI decision-making in cars, and where the dialogue stands today between industry and government

Zannoni_MarkModerator: Mark Zannoni, Research Director, Smart Cities & Transportation, IDC

Panelists: Diana Furchtgott-Roth, Deputy Assistant Secretary for Research and Technology, U.S. Department of Transportation

4:15 Panel: AI in Smart Cities, Campuses, and Communities

From public safety to resilience and environmental monitoring, to population health and the government consumer experience, there are many uses cases for AI in smart ecosystems and communities. This panel will explore government services that rely heavily on large amounts of data and that could be transformed via AI and automation. Thie discussion will focus not only on the transformative effect of AI, but the necessary short and medium terms steps needed to develop effective AI platforms. This is especially important when looking at services that often transcend municipal boundaries and require the participation of many agencies, community groups, and private sector stakeholders. Takeaways for attendees include:

  • What services and programs can be transformed by AI and automation to deliver key outcomes for public health and safety? 
  • What must be in place now to develop these services in the future? What do government organizations need to put in place around data architecture, IT policies, and IT infrastructure to enable AI?
  • What are best practices for how groups of stakeholders can effectively work together to work on large-scale challenges? 


Ruthbea ClarkeModerator: Ruthbea Clarke, Vice President IDC Government Insights, IDC

Nguyen_ThanhPanelists: Thanh Van Nguyen, Minister of Public Security Ministry, Former Governor of Hai Phong, Vietnam

5:00 Networking Reception in the Exhibit Hall

6:00 Close of Day


( 使用AI的大數據所帶來的服務及好處 )

6月26日(三) | 1:15 - 4:00 pm

Track Description: Once the initial Big Data challenges have been overcome, what does an organization do with the data? How can it use AI to accelerate digital transformation strategies? Having more data doesn’t necessarily lead to actionable insights. A key challenge for data science teams is to identify a clear objective and determine the most impactful questions. Once key patterns have been identified, agencies must also be prepared to act and make necessary changes in order to demonstrate value from them.

This track explores the delivery of services and applications powered by learning systems.

1:15 pm Talk Title to be Announced

Daniel Duffy, PhD, NASA Center for Climate Simulation, NASA

1:40 Panel: Adoption, Best Practices, and Successful Deployment of Process Automation

The federal government is facing unprecedented operating challenges as they manage mounting budget constraints while trying to be more agile to increase mission objectives. Unable, in many cases, to hire more employees, federal agencies are forced to spend dollars on contractor support or shift resources away from mission-critical work to handle routine, manual tasks. Robotic process automation (RPA) provides federal agencies the capability to operate more efficiently with reduced resources. Hear from government thought leaders and subject matter experts who will discuss their adoption, best practices, and successful deployment of RPA.

Singh_PrabhdeepModerator: Prabhdeep (PD) Singh, Vice President, AI, UiPath

Panelists: Speakers TBA

2:15 Networking Break

2:25 Using NLP and Big Data to Deliver High-Value Decision Making

Abhivyakti Sawarkar, MD, Biomedical Informatician, Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER), U.S. Food & Drug Administration

Sung-Woo Cho3:00 Planning for Desired Outcomes with Recommender Systems

Sung-Woo Cho, PhD, Senior Associate/Scientist, Social and Economic Policy, Abt Associates


The abundant data that are regularly collected from federal agencies are ripe for the application of artificial intelligence, provided that they are collected in a secure manner with the benefit of service recipients as the sole reason for these solutions. Predictive analytics and recommender systems can provide these agencies with the necessary tools to help guide their service recipient clients towards optimal outcomes, by leveraging structured and unstructured data alike.

4:00 Close of AI World Government 2019


( 最新AI技術 )

6月26日(三) | 1:15 - 4:00 pm

Track Chair: Jeff OrrJeff Orr, AI World Conference Content Director and AI Trends Editor, Cambridge Innovation Institute


Track Description: Despite the recent interest in using algorithmic models for data analysis and insight, the underlying methodologies and protocols have been proven for decades. Researchers are experimenting with new ideas that leverage these time-tested frameworks.

This track provides attendees with a roadmap for the evolution of AI technologies in the next few years. How will trust and explainability be resolved by the industry to become integral components of future machine learning solutions? Which emerging AI solutions and technologies will be evolving out of research labs in the near term, enabling new classes of productive applications? What will the next generation of AI-optimized hardware look like? What can we expect from the next generation of biometric technologies?

1:15 pm Explainable AI: The Need for Transparency and Auditability of “Black Box” Systems

Speaker TBA


Organizations and end-users need a way to explain why the AI made a prediction. Government watchdogs and regulators are reluctant to embrace intelligent systems without some explanation of how the data input generated the machine output. This talk further explores the need to audit and report on decision-making and why human interpretable explanations are necessary for multiple audiences. 

  • Discuss what is meant by explainable AI and what is it that agencies and regulators want to know about predictions 
  • Understand the trade-off between AI transparency and performance along with the implications for intellectual property 
  • What is the current state of the technology in delivering truly explainable AI systems? 
  • As narrow AI implementation scales to address complex business judgments and Artificial General Intelligence (AGI), does the demand for explainable AI increase? 

1:40 Panel: Implementing Advanced AI Technologies

Machine learning (ML) is currently viewed as a single tool. However, ML is not a static environment. Researchers have already developed advanced technology to evolve ML to process larger amounts of data even faster. Some developers, for example, are examining how ML can incorporate blockchain for safety and security within the ML model. ML in its various forms are being integrated into and with other highly advanced intelligent systems such as NLP, image processing, etc. for multitudes of applications. This panel of AI and data science researchers is pushing the bleeding edge of emerging technology and identifying the future of ML.

Moderator: Ola Olude-Afolabi, PhD, Adjunct Prof., Morgan State University

Mascho_BradPanelists: Brad Mascho, Chief Artificial Intelligence Officer, NCI Information Systems, Inc.

Jackson_JesusJesus Jackson, Senior Director of Technology Strategy, eGlobalTech (eGT)

Kashyap_KompellaKashyap Kompella, CFA, CEO and Chief Analyst, rpa2ai

2:15 Networking Break

2:25 Application Concepts for AI at the Edge

Antigone PeytonAntigone Peyton, JD, Chair, Intellectual Property and Technology Law Group, Protorae Law PLLC


As organizations develop a deeper understanding of how AI might be used to support their missions, they must also confront challenges regarding deployment of intelligence in equipment and devices at the edge of networks or connected through the Internet of Things. This talk will share design considerations for “skinny AI,” use cases ranging from smart cities to field deployment, practical pointers relating to security, anonymity, and system trust, and edge AI training trends. 

3:00 Hardware's New Frontier: Non Deterministic Analog Super Turing Machines

Wood_LarsLars Wood, CEO & Co-Founder, QAI.ai LLC


Current machine learning is restricted to computable numbers, which limits their application to solving narrowly defined solutions with inherent bias and the tendency to completely forget previously learned information upon learning new information. Non deterministic super Turing machines solve problems like biological brain networks with uncomputable real numbers. This talk provides an overview of the history of super Turing machines, their first proof of principle, and how to design and build machine learning systems that use non computable real number analog networks to develop adaptive AI systems.

4:00 Close of AI World Government 2019



( 合規、安全性、信賴度的強化之中智慧自動化技術的應用 )

6月26日(三) | 1:15 - 4:00 pm

Track Chair: Ronald SchmelzerRonald Schmelzer, Managing Partner, Principal Analyst, Cognilytica

Track Description: Organizations can effectively leverage automation in governance, risk management, compliance and security as they move to a digital platform for the future. Change in stewardship of data is afoot including how data ownership, retention, and public records are managed. Algorithmic modeling solutions deliver efficient analysis, though the “black box” question of how insights are arrived at remains an open issue where transparency and auditability are needed.

This track highlights the opportunity to use AI and automation to meet existing compliance reporting, as well as prepare for new legislation on data privacy and protection.

Kuehn_David1:40 De-Identification of Video Data for Public Sector Research

David Kuehn, Program Director, Exploratory Advanced Research Program, Federal Highway Administration 


The Second Strategic Highway Research Study (SHRP2) collected over one million hours of driving data from over 3,000 volunteers.  To preserve privacy, researchers only can view images of drivers which are critical for understanding behavior, available to more researchers at a secure data enclave.  To make driver image data, which are critical for understanding behavior, available to more researchers, the government is developing machine learning tools that mask driver identity while preserving head pose and facial behavior.  

2:15 Networking Break

Wu_Daniel2:25 The Regulatory Landscape and Designing Trust into Data-Driven Systems

Daniel Wu, JD, PhD, Privacy Counsel and Legal Engineer, Immuta


To put you one step ahead of the curve, we offer 7 legal principles and 3 tools. The principles give you a framework to interpret and prioritize existing and new data regulations, while the tools help you protect your customer’s data -- and trust -- by embedding it into the very design of your data operations. 

Heider_Jun3:00 Creating Organizational Value from Machine Learning

Jun Heider, CTOO, RealEyes Media


The public sector needs to meet compliance standards with limited resources. As media volume grows, compliance success becomes increasingly difficult for human workers alone. Learn to successfully leverage machine learning to optimize and automate media compliance and monitoring workflows. Attendees will be provided with the knowledge and resources to get started and accelerate their transition to compelling machine learning workflows: redaction, transcription, translation, and media compliance monitoring.

4:00 Close of AI World Government 2019

* 活動內容有可能不事先告知作更動及調整。

Choose your language