컨퍼런스 프로그램 & 트랙






( 전체 세션에 등장할 예정인 연사 )


6월 25일(화) | 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


Panelists:

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

6월 26일(수) | 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



OVERCOMING THE AI & BIG DATA CHALLENGE

( AI와 빅데이터의 문제 해결 )

각국의 정부기관은 수년간 데이터를 축적하고 있으나, 이 데이터세트가 충분히 활용되지 않고 있다는 점도 인식하고 있습니다. 그 배경에는 비구조화 데이터의 증가뿐만 아니라 데이터 포맷의 종류도 증가하고 있다는 문제가 자리하고 있습니다. 기록 및 조항 등의 행정 데이터는 최근 범위가 확대되고 있으며, 주요 데이터 유형으로서 이미지 및 음성, 영상, 센서 데이터까지 포함되고 있습니다.

다수의 정부기관이 빅데이터 프로젝트의 규모가 급속히 확대한다는 점을 고려하지 않아 프로젝트를 중단하여 새롭게 리소스를 추가하고, 시간을 할애하여 데이터 분석을 시행해야 하는 사태가 자주 발생합니다. 데이터의 종류와 정합성(완전성, 정확성, 바이어스, 신뢰성)을 평가하기 위해서는 애널리스트에 의한 장시간의 작업이 필요하며, 다른 데이터 소스의 통합 및 빅데이터 보호 등의 작업이 추가되면서 이 문제는 한층 더 심각화합니다.

이 트랙에서는 보존되고 있는 데이터 종류의 식별, 추가 데이터 수집, 데이터 체계화, 클린 데이터 확보, 기계학습에서 이용하는 데이터를 준비하는 방법, 정부기관의 IT 시스템에 애플리케이션을 통합, 확장하는 방법 등에 초점을 맞추며, 빅데이터 환경이 직면하는 주요 문제에 대해 논의합니다.

6월 25일(화) | 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


USING AI FOR STRATEGIC GOVERNMENT FUNCTIONS

( 정부기관의 전략적 업무에 AI 활용 )

인텔리전트 자동화의 유망 응용 분야 평가시 "AI 도입 작업에 어떻게 착수해야 하는가, AI의 도입이 가능하거나 도입해야 할 최적의 응용 분야는 어디인가?"라는 근본적인 의문에 대한 해답을 찾을 필요가 있습니다. 이 의문에 대한 해답은 때때로 기술의 선택보다 조직의 문화 및 사고의 진화에 큰 영향을 미칩니다. 비지니스 인텔리전스 및 퍼포먼스 관리로부터 AI 및 데이터를 활용한 전략적 임무 및 기능으로 프로세스를 이동시키는 과정에서 정부기관 및 부처의 향후 작업을 개선할 기회가 생성됩니다.

이 트랙에서는 데이터 사이언스팀을 구성하기 위한 방안 및 데이터 중시 작업 환경을 실현하기 위한 전략에 초점을 맞춥니다.

6월 25일(화) | 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


ACCELERATING SMART CITIES WITH AI-POWERED SERVICES

( AI를 이용한 서비스에 의한 스마트 시티 실현의 가속 )

Track Description: 자치체가 스마트 시티의 타이틀을 얻기 위해서는 기존 서비스를 강화함과 동시에 새로운 응용 기술 및 기능을 개발, 배치할 필요가 있습니다. 기존 서비스에 관해서는 많은 조직이 예측 모델을 이용한 업무 효율의 개선에 주력하고 있으며, 데이터를 이용한 어셋 로케이션의 강화 등의 동향도 생성되고 있습니다. 또한 빅데이터는 사용자 경험(UX)의 개선에 유용하게 활용되며, 자율주행차 및 스마트 모빌리티 시스템의 도입을 향한 준비, 새로운 서비스를 제공하기 위한 계획 입안 및 규제 정비 등 개별 분야에 AI를 응용하는 동향도 확대되고 있습니다.

이 트랙은 데이터와 인텔리전트 자동화 기술을 이용한 스마트 시티의 설계와 거버넌스에 대해 생각하는 것으로, 디지털 정부와 시민 서비스, 운송, 공공안전 등 스마트 시티의 3가지 구체적 측면에 초점을 맞춥니다.

6월 25일(화) | 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.

Stolpe_Madelene

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


SERVICES & BENEFITS OF AI-POWERED BIG DATA

( AI를 이용한 빅데이터가 초래하는 서비스와 혜택 )

초반에 지적된 빅데이터 문제가 해결된 후 부상한 것은 데이터를 이용하여 무엇을 하는가, AI를 이용하여 디지털 전환 전략을 가속시키기 위해서는 어떻게 해야 하는가라는 질문이며, 데이터 양의 증가가 필연적으로 실용적 인사이트로 연결되는 것은 아니라는 사실이었습니다. 현재 데이터 사이언스 연구팀은 명확한 목표를 식별하여 가장 영향이 큰 문제를 결정한다는 중요한 문제에 주력하고 있으며, 정부기관측도 주요 패턴이 특정된 후 빅데이터가 초래하는 가치를 실증하기 위한 작업을 진행하고 필요한 개혁을 시행하기 위한 준비를 갖출 필요가 있습니다.

이 트랙에서는 학습 기능을 갖춘 시스템을 이용하여 각종 서비스 및 응용 기술을 실현하는 방법에 대해 생각합니다.

6월 26일(수) | 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


EMERGING AI TECHNOLOGIES

( 새로운 AI 기술 )

알고리즘 모델을 이용한 데이터 분석 및 정보 수집에 대한 관심은 최근 높아지고 있으나, 기본적 방법 및 프로토콜의 유효성은 수십년 전에 실증되었으며 현재 많은 연구자가 유효성이 증명된 이 프레임워크를 이용한 새로운 아이디어의 실험을 진행하고 있습니다.

이 트랙에서는 향후 수년간 AI 기술의 진화에 관한 전망이 제시되고, 향후 기계학습 솔루션의 중요 요소가 되는 신뢰성과 설명 가능성의 문제를 해결하기 위한 방안, 곧 실용화되어 생산성 향상에 기여하는 새로운 종류의 응용 기술로 연결될 가능성이 있는 최신 AI 솔루션 및 기술, AI에 대해 최적화된 차세대 하드웨어의 양상, 차세대 바이오메트릭 기술에 포함되는 기능 등이 소개됩니다.

6월 26일(수) | 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

 


USING INTELLIGENT AUTOMATION FOR COMPLIANCE, SECURITY & TRUST

( 컴플라이언스, 보안, 신뢰성의 강화를 향한 인텔리전트 자동화 기술의 활용 )

향후 디지털 플랫폼으로의 이동이 진행되면 거버넌스 및 위기 관리, 컴플라이언스, 보안 등의 분야에서 자동화 기술을 효과적으로 활용할 수 있게 됩니다. 현재 데이터 관리 분야에서는 데이터 소유권, 보유, 공적 기록 관리 등에 관한 개혁이 진행되고 있으며, 알고리즘 모델링 솔루션에 의한 효율적인 분석도 가능해졌으나, 투명성과 감사 가능성이 요구되는 영역에서의 정보 입수 방법에 관한 '블랙박스' 문제는 해결되지 않고 있습니다.

이 트랙에서는 AI 및 자동화 기술을 기존 컴플라이언스 리포트 작성 업무에 이용할 가능성, 데이터 프라이버시와 보호에 관한 새로운 입법 조치에 대한 준비 등의 토픽에 초점을 맞춥니다.

6월 26일(수) | 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

* 주최측 사정에 따라 사전 예고없이 프로그램이 변경될 수 있습니다.

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