[House Hearing, 115 Congress]
[From the U.S. Government Publishing Office]


GAME CHANGERS: ARTIFICIAL INTELLIGENCE PART II, ARTIFICIAL INTELLIGENCE 
                       AND THE FEDERAL GOVERNMENT

=======================================================================

                                HEARING

                               BEFORE THE

                            SUBCOMMITTEE ON
                         INFORMATION TECHNOLOGY

                                 OF THE

                         COMMITTEE ON OVERSIGHT
                         AND GOVERNMENT REFORM
                        HOUSE OF REPRESENTATIVES

                     ONE HUNDRED FIFTEENTH CONGRESS

                            SECOND SESSSION

                               __________

                             MARCH 7, 2018

                               __________

                           Serial No. 115-66

                               __________

Printed for the use of the Committee on Oversight and Government Reform


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              Committee on Oversight and Government Reform

                  Trey Gowdy, South Carolina, Chairman
John J. Duncan, Jr., Tennessee       Elijah E. Cummings, Maryland, 
Darrell E. Issa, California              Ranking Minority Member
Jim Jordan, Ohio                     Carolyn B. Maloney, New York
Mark Sanford, South Carolina         Eleanor Holmes Norton, District of 
Justin Amash, Michigan                   Columbia
Paul A. Gosar, Arizona               Wm. Lacy Clay, Missouri
Scott DesJarlais, Tennessee          Stephen F. Lynch, Massachusetts
Blake Farenthold, Texas              Jim Cooper, Tennessee
Virginia Foxx, North Carolina        Gerald E. Connolly, Virginia
Thomas Massie, Kentucky              Robin L. Kelly, Illinois
Mark Meadows, North Carolina         Brenda L. Lawrence, Michigan
Ron DeSantis, Florida                Bonnie Watson Coleman, New Jersey
Dennis A. Ross, Florida              Stacey E. Plaskett, Virgin Islands
Mark Walker, North Carolina          Val Butler Demings, Florida
Rod Blum, Iowa                       Raja Krishnamoorthi, Illinois
Jody B. Hice, Georgia                Jamie Raskin, Maryland
Steve Russell, Oklahoma              Peter Welch, Vermont
Glenn Grothman, Wisconsin            Matt Cartwright, Pennsylvania
Will Hurd, Texas                     Mark DeSaulnier, California
Gary J. Palmer, Alabama              Jimmy Gomez,California
James Comer, Kentucky
Paul Mitchell, Michigan
Greg Gianforte, Montana

                     Sheria Clarke, Staff Director
                    William McKenna General Counsel
     Troy Stock, Information Technology Subcommittee Staff Director
                Sarah Moxley, Senior Professional Member
                    Sharon Casey, Deputy Chief Clerk
                 David Rapallo, Minority Staff Director
                                 ------                                

                 Subcommittee on Information Technology

                       Will Hurd, Texas, Chairman
Paul Mitchell, Michigan, Vice Chair  Robin L. Kelly, Illinois, Ranking 
Darrell E. Issa, California              Minority Member
Justin Amash, Michigan               Jamie Raskin, Maryland
Blake Farenthold, Texas              Stephen F. Lynch, Massachusetts
Steve Russell, Oklahoma              Gerald E. Connolly, Virginia
Greg Gianforte, Montana              Raja Krishnamoorthi, Illinois
                            
                            
                            C O N T E N T S

                              ----------                              
                                                                   Page
Hearing held on March 7, 2018....................................     1

                               WITNESSES

Mr. John O. Everett, Ph.D., Deputy Director, Information 
  Innovation Office, Defense Advanced Research Projects Agency, 
  U.S. Department of Defense
    Oral Statement...............................................     3
Mr. Keith Nadasone, Deputy Assistant Commissioner, Acquisition, 
  Information Technology Category Acquisition Management, U.S. 
  General Services Administration
    Oral Statement...............................................     4
    Written Statement............................................     7
Mr. James F. Kurose, Ph.D., Assistant Director, Computer and 
  Information Science, and Engineering, National Science 
  Foundation
    Oral Statement...............................................    13
    Written Statement............................................    15
Mr. Douglas Maughan, Ph.D., Division Director, Cybersecurity 
  Division, Homeland Security Advanced Research Projects Agency, 
  U.S. Department of Homeland Security
    Oral Statement...............................................    27
    Written Statement............................................    29

                                APPENDIX

Representative Gerald E. Connolly Statement......................    50

 
GAME CHANGERS: ARTIFICIAL INTELLIGENCE PART II, ARTIFICIAL INTELLIGENCE 
                       AND THE FEDERAL GOVERNMENT

                              ----------                              


                        Wednesday, March 7, 2018

                  House of Representatives,
            Subcommittee on Information Technology,
              Committee on Oversight and Government Reform,
                                                   Washington, D.C.
    The subcommittee met, pursuant to call, at 2:03 p.m., in 
Room 2154, Rayburn House Office Building, Hon. Will Hurd 
[chairman of the subcommittee] presiding.
    Present: Representatives Hurd, Mitchell, Amash, Farenthold, 
Kelly, Lynch, Connolly, and Krishnamoorthi.
    Mr. Hurd. The Subcommittee on Information Technology will 
come to order. And without objection, the chair is authorized 
to declare a recess at any time.
    Good afternoon. Welcome to the Oversight and Government 
Reform hearing on artificial intelligence. This is the second 
hearing in a series of hearings on artificial intelligence, and 
this series is an opportunity for the subcommittee to take a 
deep dive into this issue.
    I have three main objectives when it comes to AI in 
government. First, it should make every interaction an 
individual has with the Federal Government take less time, cost 
less money, and be more secure. I have wonderful caseworkers on 
staff who spend their time working to help constituents receive 
their veterans' benefits or to help with Social Security. They 
are speaking every day with people who are frustrated with how 
long it takes to resolve problems in the Federal Government. I 
believe with the adoption of AI, we can improve the response 
time and, in some cases, prevent these problems in the first 
place.
    Second, AI should produce efficiencies and cost savings 
that will help us do more for less money and help to provide 
better, more transparent citizen-facing services. This should 
help to restore the bonds of trust between citizens and their 
governments. We have innovative companies, brilliant minds, 
hardworking people, and the rule of law. So we, the United 
States, should lead on AI, and the Federal Government needs to 
be an active participant. Whether it is through basic and 
applied research and development that DARPA, NSF, and DHS are 
doing, or GSA's work on procurement, the AI within the 
government needs to benefit those whom the government serves.
    I thank the witnesses for being here today, and I look 
forward to the hearing and learning from all of you. And I will 
be honest, at the beginning of this endeavor I was prepared to 
see not much use of AI throughout the Federal Government, and I 
think our panelists here today are going to show how we are 
doing some very interesting things in the government.
    Mr. Hurd. And, as always, it is an honor to explore these 
very important issues in a bipartisan fashion with my friend 
and ranking member, the one and only Robin Kelly from Illinois.
    Ms. Kelly. Thank you, Mr. Chairman.
    Mr. Chairman, thank you for calling today's important 
hearing on Federal agencies' adoption of artificial 
intelligence, or AI. This is the second hearing in a three-part 
series of hearings on AI. Today's hearing focuses on the 
Federal Government's adoption of this technology.
    AI has the potential to make government more efficient and 
decrease costs across agencies. To fully realize the benefits 
of AI, the U.S. must maintain its leadership role in promoting 
technological innovation, yet preserving the United States' 
leadership role in technologies like AI will require robust 
Federal funding for research and development.
    But at our first hearing on AI, Intel's chief technology 
officer for AI warned us that, quote, ``Current Federal funding 
levels are not keeping pace with the rest of the industrialized 
world.'' In fact, President Trump's proposed budget for fiscal 
year 2019 cuts or flattens nonmilitary agency budgets for R&D.
    [Slide.]
    Ms. Kelly. As you can see on the screens, the trend is so 
clear that the National Science Board and the National Science 
Foundation believe that China will surpass the United States in 
R&D investments by the end of this year. The chart displayed 
demonstrates China's rapidly growing investment and the U.S. 
ceding its position as a leader in AI.
    The future of U.S. innovation is at stake. This should be a 
cause of concern for everyone. Outside of the Department of 
Defense, the President's budget proposes an overall cut to 
research and development of 21.2 percent. Consider, for 
example, the National Science Foundation whose investments in 
R&D have led to innovations that improve our everyday life. 
From Google to Lasik eye surgery to cloud computing all can be 
traced to NSF investments in technology.
    [Slide.]
    Ms. Kelly. This chart shows President Trump's precipitous 
drop in nondefense R&D spending. In an agency like the National 
Science Foundation which supports basic research in colleges 
and universities and in the private sector, this budget 
represented almost a 29 percent decrease from the agency's 
actual spending levels in 2017. These budget cuts take the 
United States in the wrong direction.
    Another troubling trend for the U.S. is that we are not 
making the critical investments today to educate the workforce 
we need to sustain these industries of the future.
    [Slide.]
    Ms. Kelly. The displayed chart shows a number of science 
and engineering undergraduates in China compared to the United 
States. As you can see, we are not keeping pace with China, 
which is displayed in red.
    Yet another troubling factor is this administration's 
hostility to immigrants. Until recently, the U.S. was able to 
attract Ph.D. students from other countries to help supplement 
the domestic workforce. The New York Times reported last year 
that not only is Google opening AI innovation hubs in Canada 
because of concerns with American immigration policies but that 
the U.S. has already turned away promising people in the AI 
field. Unfortunately, this administration's science, 
immigration, and education policies are all working together to 
reduce the U.S. lead in AI technologies. I hope today we can 
discuss the policies and funding necessary to ensure we remain 
competitive in this area.
    Again, I thank you, Mr. Chairman, for having this hearing.
    Mr. Hurd. Thank you.
    And I am pleased now to introduce our witnesses. Our first 
is Dr. John Everett. He is the deputy director of the 
Information Innovation Office for DARPA. Mr. Keith Nakasone, he 
is the deputy assistant commissioner for the Office of 
Information Technology Category for the Federal Acquisitions 
Service at GSA. Say that three times fast. Dr. James Kurose is 
the assistant director for Computer and Information Science and 
Engineering at National Science Foundation. It is always a 
pleasure to have you here, sir. And last but not least, Dr. 
Douglas Maughan is the division director of the Cybersecurity 
Division in the Homeland Security Advanced Research Project 
Agency at DHS.
    Welcome to you all. And pursuant to committee rules, all 
witnesses will be sworn in before you testify, so please rise 
and raise your right hand.
    [Witnesses sworn.]
    Mr. Hurd. Thank you. Please let the record reflect that all 
witnesses answered in the affirmative.
    In order to allow time for discussion, please limit your 
testimony to five minutes. Your entire written statement will 
be made part of the record. And as a reminder, the clock in 
front of you shows the remaining time you have. It is going to 
turn yellow when you have 30 seconds left, and when it flashes 
red, that means your time is up. And also remember to press the 
button to turn your microphone on before speaking.
    Now, I would like to recognize Dr. Everett for your five 
minutes of opening remarks.

                       WITNESS STATEMENTS

                  STATEMENT OF JOHN O. EVERETT

    Mr. Everett. Good afternoon, Chairman Hurd, Ranking Member 
Kelly, and distinguished members of the committee. I appreciate 
the invitation to give testimony on the state of AI research 
today. My name is John Everett, and I'm the deputy director of 
the Information Innovation Office at the Defense Advanced 
Research Projects Agency, DARPA.
    DARPA's mission is to create and prevent technological 
surprise. We do so by funding research programs, each with a 
specific goal to advance the state of the art in a particular 
area. This strategy has served the country well by leveraging 
academia and industry R&D labs to develop the enabling 
technologies for new defense capabilities and to plant the 
seeds for new industries such as the internet and self-driving 
cars.
    Since the 1960s, we have funded more than 50 programs in 
AI. AI technologies have developed in two waves. The first wave 
focused on abstract logic and reasoning tools that require 
explicit representations of knowledge in the form of 
handcrafted rules. The second wave, machine learning, uses 
algorithms to extract implicit representations of knowledge 
from large amounts of data.
    The first wave started in the 1950s and explored many hard 
problems in reasoning, understanding natural language, and 
robotics. It produced many algorithms that are in common use 
today such as planning and scheduling systems.
    Researchers quickly discovered the importance of world 
knowledge in solving problems and created expert systems that 
use rules to represent knowledge about a particular subject 
area such as diagnosing infectious diseases. An early DARPA-
funded expert system rivaled human performance in this area. 
However, as we all know, for every rule, there's an exception, 
and the work necessary to capture sufficient knowledge proved 
impractical in many cases.
    The second wave started in the 1990s in reaction to the 
difficulty of capturing world knowledge by writing it down. The 
most successful form of machine learning today is called the 
neural network because it is inspired by the structure of the 
human brain. Machine learning uses large amounts of data to 
train an algorithm to do a specific task such as recognize 
speech, drive a car, or search for pictures of, say, people 
playing frisbee on a beach. However, these algorithms cannot 
explain their conclusions, which makes them hard to trust. 
Also, researchers have shown that sometimes imperceptible 
changes to an input image, say, of a panda, can cause the 
algorithm to confidently misclassify it as a monkey.
    Nonetheless, the second wave of AI has yet to crest, and 
researchers will continue to improve the technology and develop 
interesting and innovative applications. We believe that the 
next wave of AI will combine insights from the first and second 
waves to produce systems that are aware of context so they can 
interact more effectively with people. This will require major 
advances in commonsense reasoning and natural language 
processing.
    Context in--is the shared understanding that people have 
with each other and enables highly concise communication 
through speech, intonation, facial expressions, and gestures. 
Such communication is extremely difficult for current 
algorithms to understand, making this an ideal area for DARPA 
research. Thank you.
    Mr. Hurd. Thank you, sir.
    Mr. Nakasone, you are now recognized for five minutes.

                  STATEMENT OF KEITH NAKASONE

    Mr. Nakasone. Good afternoon, Chairman Hurd, Ranking Member 
Kelly, and members of the subcommittee. Thank you for the 
opportunity to appear before you today. My name is Keith 
Nakasone, and I am the deputy assistant commissioner for 
acquisition operations in the Office of Information Technology 
Category at GSA. I've been a participant in the growth of 
emerging technologies in government over the past 20 years, 
including during my years at the Defense Department.
    Mr. Chairman, at the first hearing in this series you 
stated that it was your hope agencies would use today's 
discussion to inform Congress how we plan to use artificial 
intelligence to spend taxpayer dollars wisely and make each 
individual's interactions with government more efficient, 
effective, and secure.
    I would like to discuss four ways in which our agency is 
supporting government AI evaluation and adoption to accomplish 
that. First, our Federal Acquisition Service provides 
contracting vehicles and mechanisms, including Schedule 70, as 
well as several other governmentwide acquisition contracts, 
which encourage competition and help connect agencies and 
businesses to allow government to efficiently procure the most 
effective new AI services and capabilities. GSA's IT Schedule 
70 contracts provides Federal, State, local, and tribal 
government agencies with access to over 7.5 million best-value 
IT and telecommunications products, services, and solutions for 
more than 4,600 pre-vetted vendors, including firms whose 
offerings use AI and similar technologies.
    Since emerging technology businesses frequently tend to be 
startups, Schedule 70 offers two shortcuts, Startup 
Springboard, and FastLane, as part of the Making It Easier 
initiative, which aims to streamline the process for younger 
innovative companies and suppliers to do business with 
government.
    Second, GSA is piloting robotic process automation and 
related technologies designed to augment our workforce and 
achieve more with less while establishing a foundation for 
greater data-driven decision-making through AI.
    GSA has developed a new pilot using AI for prediction of 
regulatory compliance, the solicitation review tool uses 
natural language processing, text mining, and machine learning 
algorithms to substantially alleviate the human resources 
needed to identify, audit, and enforce compliance of 
solicitations posted on FBO.gov.
    Further, GSA recently launched two pilots exploring the use 
of robotic process automation and distributed ledger 
technology, foundational technologies that can open our 
programs to better decision-making through AI. These pilots aim 
to increase GSA's operational efficiency, reduce costs, improve 
processes, increase accuracy, and redeploy staff to higher-
value functions.
    Third, our interagency Emerging Citizens Technology Office 
unites more than 2,000 government managers from over 300 
Federal, State, and local agencies and representatives from 
businesses, startups, and research and civic organizations to 
support and coordinate governmentwide development of citizen-
facing AI and other emerging technology programs, including 
through resources at Emerging.Digital.gov. Recent initiatives 
include the launch of an interagency venture capital advisory 
group and a new education and training pilot.
    Fourth, along with our private sector and Federal agency 
partners, we are pursuing a greater understanding alignment of 
ID modernization through cloud adoption, data services, and 
emerging technologies, including AI, that deliver the greatest 
benefit to the American people. For instance, through Data.gov 
and ECTO, we are learning how to improve the standardization 
and accessibility of government open data to help fuel 
innovation. We have solicited input from industry partners on 
how to improve data hosting so data sets are more easily 
digestible for AI and machine learning.
    GSA is essentially a shared service, and we are constantly 
seeking ways to develop government faster, better, and smarter. 
AI is a tool that can expand the value proposition for Federal 
agencies, vendors, and the American people alike.
    Thank you again for the opportunity to testify. I look 
forward to your questions.
    [Prepared statement of Mr. Nakasone follows:]
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    Mr. Hurd. Dr. Kurose, you now have five minutes.

                  STATEMENT OF JAMES F. KUROSE

    Mr. Kurose. Thank you very much. Good afternoon, Chairman 
Hurd, Ranking Member Kelly, and members of the subcommittee. My 
name is Jim Kurose. I'm the assistant director at the National 
Science Foundation for the Directorate of Computer and 
Information Science and Engineering.
    As you know, NSF contributes to national security and 
economic competitiveness by supporting fundamental research in 
all areas of science and engineering, as well as education for 
the next generation of discoverers. I welcome this opportunity 
to highlight NSF's AI investments.
    Federal investments in foundational AI research are 
critical to achieving and sustaining U.S. science and 
technology leadership. Fundamental AI R&D challenges can be 
broadly classified into two categories. First, there's narrow 
AI that is focused on solving specific tasks in well-defined 
domains such as speech recognition or image classification. 
Here, NSF-funded researchers have pioneered new machine-
learning techniques and applied these techniques, for example, 
to analyze breast cancer and predict sepsis.
    To your opening remarks, Chairman Hurd, NSF is piloting the 
use of AI clustering techniques in its own business processes 
to help program managers select proposal reviewers.
    The second broad category, general AI, is about 
transferring what is learned in one setting to another and 
ultimately appreciating intent, meaning, and understanding. 
Several witnesses in your earlier panel have noted that these 
goals remain an AI grand challenge in which we're also 
investing.
    In fiscal year 2017, the National Science Foundation 
invested more than $120 million in core AI research. AI will 
continue to be an important part of our research portfolio, 
including NSF's Big Ideas. Indeed, NSF Director Dr. France 
Cordova, my boss, recently described AI as, quote, ``the 
universal connector that interweaves all of our big ideas. Data 
science is changing the very nature of scientific inquiry, and 
AI's use of data has the potential to revolutionize everything 
we do in science.
    The AI innovations that we are seeing today are built on 
earlier fundamental research. For example, NSF's investments in 
reinforcement learning decades ago are enabling today's deep 
learning systems in autonomous vehicles. As Eric Schmidt, 
former Google Alphabet CEO, has said NSF is, quote, ``where all 
interesting research gets started.'' Well, you know, yes, we're 
a starter, but we're also more than that. We're part of the 
larger very uniquely American research and innovation ecosystem 
among academia, industry, and government with the flow of 
ideas, artifacts, and people across these sectors. This 
ecosystem has given rise to multibillion-dollar industries 
including AI, but truly, it all begins with investment in 
fundamental long-term research often made with Federal research 
dollars.
    At NSF, we're constantly exploring new partnership models 
to grow this ecosystem. We've partnered with industry on joint 
research solicitations. Recently, we combined $50 million from 
the National Science Foundation with an equivalent amount from 
an industry consortium to advance wireless technologies. These 
public and private partnerships serve as models for potential 
future AI R&D collaborations.
    Federal agencies like my colleagues here are other 
partners. NSF co-chairs the Networking and Information 
Technology Research and Development Subcommittee of the 
National Science and Technology Council, and we co-chaired an 
NST committee that developed the 2016 National AI R&D Strategic 
Plan.
    You've heard that much of the AI revolution has been 
enabled by the availability of large data sets in computing. 
NSF has invested in open training data sets and is committed to 
public access to data resulting from federally funded research. 
NSF has also long invested in high-performance computing. To 
complement these investments, we recently announced a 
partnership with three commercial cloud providers--Amazon, 
Google, and Microsoft--to make $12 million in cloud resources 
available to academic researchers through our BIGDATA program.
    Beyond data and computation, there remains the most 
valuable resources of all: people. NSF investments here include 
research that builds the foundations for rigorous, engaging 
computer science education at all levels, K through 12, 
university, and lifelong learning. For example, working with 
the teaching community and many partners, NSF's support led to 
a new advanced placement computer science principal's course 
whose launch last year was the largest ever in the College 
Board's history. More than 50,000 students took the exam. We 
saw remarkable strides in participation of groups long 
underrepresented in computing as well. More than double the 
number of African Americans, Hispanics, and women took this new 
AP exam in 2017 as compared to the existing CS AP exam the year 
before.
    NSF's investments and partnerships have helped sustain the 
Nation's leadership in AI and enhanced our nation's economic 
competitiveness and security. We at the NSF are committed to 
continuing this investment in fundamental AI research, 
infrastructure, and workforce to maintain U.S. global 
leadership.
    This concludes my remarks, and thank you again for the 
opportunity to address this subcommittee.
    [Prepared statement of Mr. Kurose follows:]
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    Mr. Hurd. Thank you, sir.
    Dr. Maughan, you are now recognized for five minutes.

                  STATEMENT OF DOUGLAS MAUGHAN

    Mr. Maughan. Chairman Hurd, Ranking Member Kelly, and 
members of the subcommittee, good afternoon, and thank you for 
this opportunity today.
    I will be sharing important aspects of how the Department 
of Homeland Security's Science and Technology Directorate, or 
S&T as it is known, is using artificial intelligence-based 
technologies in research and development and working across all 
DHS mission areas to integrate innovative technologies into 
everyday use.
    As the R&D arm of DHS, S&T develops the tools, 
technologies, and knowledge products for DHS operators and 
State and local first responders, ensuring the R&D coordination 
across the Department to develop solutions for the needs of 
today and tomorrow.
    S&T partners with Federal agencies, as Jim said, industry, 
academia, and international government, to create and test 
solutions that help the Nation's homeland security officials 
prevent, respond to, and recover from all hazards and threats. 
S&T's goal is to provide real-world solutions in a realistic 
time frame.
    AI offers much promise. From a government perspective, it 
holds the potential for enhanced insight into public service 
operations and improved delivery of citizen services. Examples 
span the range from helping people navigate immigration systems 
to predicting and preempting threats and enabling resilient 
critical infrastructures that today are under attack.
    AI technology is improving our knowledge and actions. 
Fueled by sensors, data digitization, and ever-increasing 
connectedness, AI filters, prioritizes, classifies, measures, 
and predicts outcomes which can have significant impact on 
people.
    Private industry is leading the way in AI development 
because many see its implementation as a key competitive 
advantage. Government must be informed and ensure AI technology 
is being used to create efficiencies and enhance the public 
good.
    At DHS S&T, AI is a part of several ongoing cybersecurity 
division research initiatives, which are using AI and machine-
learning techniques for predictive analysis of malware 
evolution against future malware variance; detecting anomalous 
network traffic and behaviors to inform decision-making; and 
helping identify, categorize, and score adversarial telephony 
denial-of-service techniques. For example, S&T developed a 
machine-learning-based policy engine capable of blocking more 
than 120,000 calls per month, including robocalls. This same 
technology can be used to defend 911 centers against life-
threatening distributed denial-of-service attacks.
    DHS S&T also is working closely with the Nation's startups 
on AI through our Silicon Valley Innovation Program, or SVIP. 
Launched in 2015, the Department is connecting with innovation 
communities across the Nation to harness the commercial R&D 
ecosystem for technologies with government applications and 
help accelerate transition to market with the goal of reshaping 
how government entrepreneurs and industry work together to find 
cutting-edge solutions for the Department operators.
    SVIP and Customs and Border Protection are partnering on AI 
and machine-learning topics, including visualization, 
predictive models, and entity resolution and currently are 
funding startups to exchange information on intelligence, build 
capacity, and increase worldwide security and compliance 
standards.
    Looking forward in AI, DHS continues to support the design 
of AI systems in a manner that makes the actions and decision-
making of technologists, government officials, and other users 
both transparent and understandable. The design, development, 
implementation, and evaluation of AI solutions should generate 
trust that the government and industry are innovating 
responsibly by demonstrating that the government is balancing 
risks and delivering on its mission to serve the public fairly 
and justly and influence responsible evolution and the role for 
AI in the private sector.
    In order for the government to be relevant in this fast-
moving and competitive future that is being defined by AI, 
innovation should be advanced through an emphasis on 
responsible R&D. In addition, AI R&D should involve multiple 
disciplines and those perspectives that involve experts not 
only from computer science but also the other physical and 
social sciences.
    Mr. Chairman and members of the subcommittee, AI is here to 
stay. This reality means that S&T must aggressively work with 
its research, development, test, and evaluation partners 
throughout government and industry so homeland security 
applications of AI and machine learning are both effective and 
trusted.
    Thank you for your thoughtful leadership on these issues. I 
look forward to your questions.
    [Prepared statement of Mr. Maughan follows:]
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    Mr. Hurd. Thank you, Dr. Maughan.
    My first question to the panel, it is for all of you. And 
you all are here as the representation of some of the best 
things that are happening when it comes to AI across the 
Federal Government. And one of the things that we heard in the 
last panel and we have heard in conversation on this topic, two 
things that the Federal Government can be doing: research 
obviously, right, continued basic research, applied research 
like some of the things that Dr. Maughan is doing at DHS. I am 
in. We get it. We are going to try to figure out how to do 
that, right? This is a bipartisan issue.
    Second thing we have heard is also data, you know, how do 
we unlock data that the Federal Government has that can be used 
to train and teach these various algorithms. I get those two 
things. But I am asking each one of you all--and this is not to 
apply to just your agency but across the Federal Government, 
what is one thing that the Federal Government should be doing 
now in implementing artificial intelligence, something that is 
available, something that can be used that we should be doing? 
Is that a fair question? Dr. Maughan, you are shaking your 
head. Yes, Dr. Maughan?
    Mr. Maughan. Sure. So, I mean, we--things we are doing 
already include, as I mentioned with our Customs and Border 
Protection folks, they have something called the Global Travel 
Assessment System, GTAS, which they make available to all of 
our international partners as well. And we have been working 
with them to add in capability into that open-source system 
that are AI-based. And so we're starting to see that roll out 
as new capability for not only CBP but all of our international 
partners.
    Mr. Hurd. Dr. Kurose?
    Mr. Kurose. Well, thank you. And thank you for mentioning 
the importance of funding basic research and open data as well.
    You know, I had mentioned in my own testimony about what 
the National Science Foundation is doing testing the use of 
some AI techniques actually built with open software on making 
recommendations for panelists for program managers. And it's an 
example of the broader challenge, I think, and opportunity of 
adopting AI tools and having folks in government use AI tools 
to help inform decision-making that they are. It's not going to 
be a magic press-the-button-and-get-the-answer-out but using 
that to help complement already-existing activities.
    Dr. Nakasone, maybe a more specific question for you, you 
mention 7.5 million different kinds of applications and tools 
that GSA makes available. Are there tools that other agencies 
are not taking advantage of and they should when it comes to 
this topic?
    Mr. Nakasone. So when we speak about the 7.5 solution sets 
that we're talking about, you know, it crosses the scope of 
telecommunications, IT services, supplies, commodities, right? 
So we have access and we are learning every day on how to build 
these solution sets by looking at use cases, best practices, 
and things like with of course the working groups that we have 
to understand how we can deliver these broader acquisition 
solutions to cover, you know, things like distributed ledger 
technology with the robotic process automation and the--and --
--
    Mr. Hurd. So let me ask it this way. We bring in a lot of 
Federal CIOs when we go through the FITARA scorecard talking 
about how are they modernizing their digital infrastructure. 
Give me a question I should be asking them, you know? Are you 
using--fill in the blank.
    Mr. Nakasone. Right. So what--you know, something that we 
could be asking is what emerging technologies are you using to 
do your IT modernization uplift? You know, we recently have--
GSA has a big part in the IT modernization plan, and I think 
one of the things that we need to look at is how are we 
leveraging emerging technologies and injecting it into our 
infrastructure.
    Mr. Hurd. Dr. Everett, wrap it up for us.
    Mr. Everett. I think there's a temptation to think of AI as 
magic and as being able to solve all our problems. When you 
talk about implementing something that would be effective for 
the Federal Government, I think we should take the perspective 
of first understanding what the actual problems are and then 
working our way back towards how AI could actually address 
those problems and not just up front but looking at what is the 
lifecycle cost of implementing those technologies.
    Mr. Hurd. Thank you, Dr. Everett.
    The gentlewoman from Illinois is now recognized.
    Ms. Kelly. Thank you, Mr. Chair.
    I think we all agree that research and development is 
essential to continuing to improve the government's use of 
artificial intelligence, and in my opening statement I talked 
about the concern about China passing us by. Are there any 
other countries that you think are putting a lot of money into 
research and development and are passing us by or could pass us 
by when it comes to AI? Whoever wants to answer.
    Mr. Everett. The one that I hear about all the time is 
China. Certainly the international community, however, is very 
broad, and the AI community started out internationally in 
1970, so the basis for the technology is international. Whether 
or not that is an issue at the individual country level I don't 
know, aside from China.
    Ms. Kelly. All right.
    Mr. Kurose. I would again just add I think it is 
international, so absolutely the comments that you had made in 
your opening statements about China I note that DeepMind that 
Google has acquired is from the U.K., that Microsoft acquired a 
really topflight AI research company from Canada. And so really 
it's a global phenomenon.
    Ms. Kelly. And I think we all would agree that funding is 
extremely important so that you can continue the good work that 
you're already doing, and we can progress further.
    The other question is, besides funding, finding people that 
are educated and trained to help us progress in this area and 
what are suggestions that you have on what we can do to find 
people interested in the field that want to get involved in the 
field.
    Mr. Maughan. So my suggestion is we need to make--so at the 
core of AI is computer science, and so it's making computer 
science attractive and so the--again, depending upon the 
application area, cybersecurity, which is what some of us work 
on, is one of the most attractive but we're still not 
attracting as many as we need. So we have to find those things 
that make it exciting. We've been, for example, funding 
competitions, high school and collegiate competitions as a way 
to try to get students interested in cybersecurity and computer 
science as early as possible, and I think we just need to 
continue to push that agenda earlier in the school system. The 
sooner we can get youth interested in computer science as a 
career, they use the tools all day anyway, so let's teach them 
that there's a career in that direction.
    Ms. Kelly. I know when you--the statistic I showed, it is 
amazing the difference between us and China, people in the 
field and the Ph.D.'s graduating.
    Mr. Kurose. So I'd like to second Doug's recommendation 
about the focus on pipeline and also the importance of a broad 
computer science education. In my testimony I had mentioned the 
computer science principal's AP exam and how popular that has 
been. There are other investments that the National Science 
Foundation is making in exploring computer science in the 
middle schools. I think at the undergraduate level also 
computer science now is becoming a much more popular major and 
also programs such as what's called Computer Science Plus X, so 
it's the application of computing in other disciplines and two 
grand challenges and two challenges that the country faces, and 
there's also a movement afoot of AI Plus X, so applying AI and 
data science for good.
    So I think at both the high school and the undergraduate 
level that, you know, there are programs afoot and universities 
innovating in that space. At the Ph.D. level we always face a 
challenge in that keeping Ph.D. students in academia. There are 
lots of interesting challenges to be addressed in industry, and 
it's important to keep our Ph.D. pipeline cranking at full 
speed as well.
    Mr. Nakasone. So I guess one thing that GSA focuses on, you 
talked about recruiting, and we actively search out, go to 
universities, and also when we look at the diversity aspects, 
we are, you know, recruiting from minority perspective.
    I just want to say for GSA's overall workforce, you know, 
40 percent are minorities and 46 percent are female. And within 
the IT field, we have 39 percent that are minorities and 33 
percent that are female, so, you know, we work hard to try to 
recruit talented and the best of the ability to try to get 
highly educated people into the workforce. And we have to--as 
we build out these emerging technology solution sets, I think 
by showing us--or we Federal Government agencies need to figure 
out how to get that message out there that, no kidding, we are 
leaning forward and building out and using emerging technology 
solutions.
    Ms. Kelly. How much do you rely on--do you think AI relies 
on students educated outside of the United States to supplement 
the workforce? Whoever wants to answer. You can answer.
    Mr. Nakasone. Sorry.
    Ms. Kelly. Don't be shy.
    Mr. Nakasone. As far as that, I don't have that data in 
front of me, but however, we can take that back as a question.
    Ms. Kelly. Okay.
    Mr. Hurd. The distinguished gentleman from the great State 
of Michigan is now recognized for his first round of questions.
    Mr. Mitchell. Thank you, Mr. Chairman.
    I think one of the issues that has arisen as we look at 
adoption of AI and expansion of it is really getting a broader 
understanding of what it is and how impacts our lives 
currently. I have read a couple of articles recently where it 
seems to me to be an innate fear of what AI is. How do we at 
the Federal level overcome or get the level of understanding 
among the population as a whole, not the tech weenie 
population. They all think it is cool. It is the other folks 
about what AI is, how it makes decisions, and why it is of 
value to them if we are going to continue to expand investment 
and get more people into training. Anybody who wants to tackle 
that, please go ahead. Mr. Maughan, you smile and chuckle. You 
are going to pass it off to Dr. Kurose?
    Mr. Maughan. I am waiting to see if Dr. Kurose wants to go 
first.
    Mr. Kurose. Actually, I'm very happy to go first. It's a 
really great question, and it really comes to the question of 
decision-making and, you know, there's--there have been 
computer software-assisted decision-making for a long period of 
time. And when we do predictions, we do regression analysis. 
So, I mean, these are--there's a long history of relying on 
computation to help in making decisions. And I think the key 
phrase that you mentioned is AI making decisions. And in the 
end it needs to be people making decisions, and it needs to be 
people making decisions with AI software.
    Mr. Mitchell. How do we get enough transparency of how that 
happens so that people understand that in the real world 
outside of here and a handful of other places? How do we 
achieve that? Because we need to do that if we are going to get 
the level of acceptance and engagement and education that we 
want. How do we achieve that, folks?
    Mr. Kurose. Right, well, so I agree 100 percent. It's 
absolutely a question of outreach. I think with some AI 
techniques that are in use today there's an issue of 
explainability, which I think Dr. Everett and DARPA's had a 
program on explainability of AI, so maybe I could pass it down 
to my right.
    Mr. Mitchell. Ping-pong. Go ahead, Dr. Everett.
    Mr. Everett. We are just starting a new program called 
Explainable AI, and it directly addresses the issue that a lot 
of the machine-learning software that we have today cannot 
explain why it has ----
    Mr. Mitchell. Right.
    Mr. Everett.--come up with a particular answer, and so the 
objective of the research is to say tell me why you think this 
is a certain kind of bird, and it will tell you, well, I think 
it's got a red crest and a black stripe on the wing, and then 
it will show you that it is actually looking at the right part 
of the image to start to build trust.
    Another aspect of this is assuring autonomous systems. So 
we have an autonomous ship called Sea Hunter, and to make it--
to ensure that it would operate safely within shipping lanes, 
for example, it has to pass the commercial collision ----
    Mr. Mitchell. Right.
    Mr. Everett.--regulations. So we're looking at ways in 
which to do--to use mathematical techniques to verify that the 
software will behave as expected in a wide range of 
circumstances that it might encounter in the real world.
    Mr. Mitchell. You're not likely to get beyond an autonomous 
ship in the near future, Doctor, but I have to be honest, how 
soon would that research and that information become available 
to the population at a broader level do you think?
    Mr. Everett. I think it will diffuse rather slowly, 
particularly as the popular culture tends to portray AI with a 
mix of science and science fiction.
    Mr. Mitchell. Yes. And evil at some level or fear of evil. 
Let's put it that way. But let me get to my second question as 
time is running a little short. What is your agency's approach 
in dealing with a difficult question of ethics in the use of 
AI, which kind of goes to what you are suggesting? How are you 
approaching that with the general population or even within 
your agency?
    Mr. Maughan. Certainly, I think that in the case of the 
research piece, we need to look at that. As I mentioned in my 
testimony, you need to make sure that the--kind of the AI 
itself is transparent and understandable and you can actually 
see the decisions being made are balancing risks and are fair 
and just to the recipient of those, and that requires us to 
have not only the AI piece of it but kind of watching the AI. 
How do I ensure that the AI is working and doing what it wants? 
I think we're still early in the day, but certainly agree that 
the ethics question was raised in your industry panel as well.
    Mr. Mitchell. Anybody else have any input? Go ahead, sir.
    Mr. Kurose. Yes, I'd like to say that I think the ethics 
question is also often very tied up with data and how data is 
used in inferences from data. It's an active research area. NSF 
is funding a number of activities there. I think it also calls 
to the front the importance of interdisciplinary collaboration 
here because it's not just computer scientists and engineers. 
It's also social, behavioral, and economic scientists who have 
to be involved in this as well.
    Mr. Mitchell. I appreciate it. My time is expired. Thank 
you, Mr. Chair.
    Mr. Hurd. The distinguished gentleman from the Commonwealth 
of Massachusetts is now recognized for his five minutes.
    Mr. Lynch. Thank you very much, Mr. Chairman, and to 
Ranking Member Kelly for your persistent attention on something 
I think that should be a huge priority for both Democrats and 
Republicans in this Congress.
    I do want to note that Ms. Kelly in her opening remarks had 
put up a good slide there that demonstrated that the Chinese 
recent announcement--or in the last few years announcement on 
AI, their intense focus and funding on that, you know, has them 
eclipsing the U.S. investment not only because of their 
additional funding but because the Trump administration has 
backed off somewhat on research and development funding for a 
number of our agencies. I know that NSF is looking at a cut in 
funding of I think $9 billion, and I know that DHS as well. 
Especially in your Science and Technology Directorate, you are 
looking at I think it is a $1.3 billion cut.
    So I am concerned about whether we are recognizing the 
priority with our budget as well. And, you know, you have been 
very helpful in terms of demonstrating the importance of this 
issue, but do you see any need for additional funding? And 
also, you know, Dr. Everett and Mr. Nakasone, you see this as 
well. I know that DARPA has been considering projects from 
companies in my district from, you know, underwater radar 
systems to, you know, enhanced antibiotics, you know, for use 
against these resistant strains of bacteria to climate change. 
And so we really do need, as Dr. Kurose has said, an 
interdisciplinary approach, but all of that is affected by the 
amount of available dollars for research and development.
    And we have had such great success in the past through NASA 
and other agencies where basically nondefense research has 
really helped us enormously across society. And I am just 
wondering, Dr. Everett or any of you, for the whole panel, do 
you see that the lack of funding here could trip up or 
basically prevent some of the wonderful discoveries and 
advancements that we anticipate in this field?
    Mr. Everett. Well, DARPA supports the President's budget 
request for our agency. We are a projects-based agency. Our 
projects last roughly four years. Our PMs are not civil service 
but rather they come from industry and academia for a limited 
period of time. So what that means is that every year 25 
percent of our programs are turning over, 25 percent of our PMs 
are new. This enables us to rapidly shift our budget to meet 
current priorities that we see emerging in the technology 
space.
    Mr. Lynch. Okay. Do you have any opinions about the 
National Science Foundation or any part of HHS that also might 
benefit from further funding or are we just talking about 
DARPA?
    Mr. Everett. I'm speaking for DARPA.
    Mr. Lynch. Okay. All right. We have got other witnesses as 
well. Mr. Nakasone?
    Mr. Nakasone. Sure. Thank you for your question. When it 
comes to funding, as far as GSA is concerned, you know, I just 
want to thank Emily Murphy, who is our new GSA administrator, 
and Alan Thomas, who's our FAS commissioner, and Kay T. Ely, 
who I work under, supports the efforts on the distributed 
ledger technology and the robotic process automation and ----
    Mr. Lynch. I am sorry. You are eating all my time.
    Mr. Nakasone. Yes, sir.
    Mr. Lynch. I can't go with this. In English, do you think 
more money would help?
    Mr. Nakasone. For--from a GSA perspective ----
    Mr. Lynch. Well, that is who you represent.
    Mr. Nakasone. Yes. I think ----
    Mr. Lynch. Okay. That is good. Mr. Kurose? That is all I am 
asking for.
    Mr. Kurose. Thank you.
    Mr. Lynch. Nothing complicated.
    Mr. Kurose. Just to say that the President's fiscal year 
2019 budget request with the addendum funding NSF at $7.5 
billion, which is the '17-enacted level.
    But to your question, I want to stress there is capacity to 
do more. When I mentioned that the National Science Foundation 
funds $122 million in AI core research, if we look at proposals 
that were not funded but rated either competitive or highly 
competitive, that's $174 million in proposals there, so there 
is ----
    Mr. Lynch. Okay.
    Mr. Kurose.--capacity to do more.
    Mr. Lynch. That is helpful. Thank you very much.
    Mr. Chairman, I yield back.
    Mr. Hurd. My esteemed colleague from the Commonwealth of 
Virginia is now recognized.
    Mr. Connolly. I thank you, Mr. Chairman. And by the way, 
congratulations I think on your re-nomination last night, 
right?
    Mr. Hurd. That is right.
    Mr. Connolly. All right. Let's stipulate you all support 
the President's budget and it is perfect and you wouldn't 
change a word or a number. Let's stipulate that so you don't 
have to demonstrate any further loyalty. We got it. But let's 
talk a little bit about the relationship between R&D and 
technological innovation and its impact on the economy. And I 
am particularly interested in Federal R&D.
    So, Dr. Everett, there used to be something called 
DARPANET, correct?
    Mr. Everett. That's correct.
    Mr. Connolly. And what is it called today?
    Mr. Everett. The internet.
    Mr. Connolly. The internet. So DARPANET, when we first--
when your agency was smart enough to make that critical 
investment, were the commercial dollars flowing into that R&D 
effort at the time?
    Mr. Everett. No, certainly not.
    Mr. Connolly. No. It was entirely a Federal R&D effort, is 
that correct?
    Mr. Everett. That's correct.
    Mr. Connolly. And somewhere along the line someone decided 
this is so nifty. This is so useful to us internally that maybe 
it might have some other applications. Is that correct?
    Mr. Everett. Yes. And I'd like to point out that it became 
NSFNET before it became ----
    Mr. Connolly. And then it became ----
    Mr. Everett.--the internet.
    Mr. Connolly.--NSFNET. Thank you very much. Good point. So 
would that be the same story of GPS technology?
    Mr. Everett. That would be.
    Mr. Connolly. So GPS, which is now ubiquitous, we all take 
it for granted, you can't even lie about getting lost going to 
a meeting anymore, kind of put paper maps out of business. But 
GPS was also a Federal R&D investment, is that correct?
    Mr. Everett. That's correct.
    Mr. Connolly. Your agency?
    Mr. Everett. Yes.
    Mr. Connolly. What about robotics? Did your agency get 
involved in robotics at all?
    Mr. Everett. We just concluded the DARPA robotics 
challenge, so yes.
    Mr. Connolly. Yes, so a lot of the research in robotics, 
again a Federal investment, your agency being one of the 
pioneers?
    Mr. Everett. Yes.
    Mr. Connolly. Drones, developed by the private sector or 
was that a Federal R&D investment as well?
    Mr. Everett. Initially, a Federal R&D.
    Mr. Connolly. My goodness. What about noise cancelation 
technologies?
    Mr. Everett. I'm not directly familiar with that 
technology.
    Mr. Connolly. Well, for example, we did a lot--during the 
Cold War, we did a lot of hush-hush work, no longer hush-hush, 
on noise cancelation technologies for reasons you can surmise. 
But after the Cold War when we were looking at civilian 
application for R&D in our possession of the Federal 
Government, we took noise cancelation out of your agency and 
out of the Pentagon and we applied it to things like cars and 
even other things like parts of a room that we could cancel 
noise to allow privacy. We use it in courtrooms today. That all 
came out of Federal R&D technologies for defense at the time.
    Human genome, was that your area, Dr. Kurose, human genome 
research?
    Mr. Kurose. Excuse me, not my personal area of research, 
but certainly bioinformatics and computation plays an 
absolutely critical role there.
    Mr. Connolly. But the Human Genome Project, so that was run 
by some private entity in New York, right? Golly. It is not a 
trick question, Dr. Kurose.
    Mr. Kurose. Okay.
    Mr. Connolly. The answer is of course not.
    Mr. Kurose. Of course.
    Mr. Connolly. It was a Federal ----
    Mr. Kurose. Federal.
    Mr. Connolly.--R&D investment. And I am trying to make a 
point here. Now, there is a lot of loose talk about the 
government can't do anything right. That is not true. You four 
represent the face of the Federal Government that has 
transformed the world with its R&D investment, and we are not 
even talking pharmacological research. Almost all basic 
research in pharmacological areas is Federal because the 
private sector won't take the risk. And right now, we are 
counting on the Federal Government to save us from antibiotic-
resistant bacteria that could unfortunately transform health 
worldwide because it is not profitable for the private sector 
to engage in that R&D right now, so we got to do it.
    But we have transformed the world. So when we say we are 
going to cut a couple of billion dollars out of Federal R&D and 
I look at this record, I tremble at what are we cutting? Is it 
the next GPS? Is it the next drone? Is it the next Human Genome 
Project? Is it the next internet? We don't know. But the 
opportunity cost I fear is enormous.
    And so it may be that DARPA is happy with the budget it has 
got, but this Member of Congress trembles at a 21 percent cut 
that Ms. Kelly pointed out to us at the beginning in her 
opening statement because there is an opportunity cost we can't 
calculate. We can't even know for us. But I do know this: 
Whatever amount of money we spent on DARPANET, it was worth 
every cent. The return on that investment cannot be calculated. 
And that is true for GPS, and that is true for drones, and it 
is true for the Human Genome Project. These are investments 
worth making. And America does not make itself great again when 
it retreats from the field of R&D.
    So thank you for being here and know that a number of us up 
here are going to continue to push hard for your budgets for 
the sake of the country. Thank you.
    Mr. Hurd. Mr. Krishnamoorthi, you are now recognized.
    Mr. Krishnamoorthi. Thank you, Mr. Chairman. Thank you, 
Ranking Member Kelly. I really appreciate the opportunity to be 
able to ask a few questions of our distinguished panel.
    Last month, I, along with others, including my 
distinguished colleague Paul Mitchell, who is on this 
subcommittee, introduced a bill called the AI Jobs Act, which 
basically requires for the first time that the Department of 
Labor study the impact of artificial intelligence on our 
workforce, you know, what areas of the economy are going to be 
impacted the most? How do we prepare our workforce for this 
artificial intelligence revolution and make sure that they are 
ready to take advantage of it, as some of you have talked 
about?
    I wanted to just start out with Mr. Kurose. What specific 
industries do you think are most likely to kind of experience 
the impact of artificial intelligence in our economy?
    Mr. Kurose. Well, thank you for your question and the 
interest here. Actually, I want to do a short promo if I might 
for National Academies study on information technology and the 
U.S. workforce that came out just late last year and was funded 
by the National Science Foundation. And it was actually written 
both by economists. The committee that chaired this was an 
economist Erik Brynjolfsson from MIT and the machine-learning 
professor from Carnegie Mellon Tom Mitchell, and what I found 
very interesting about this, to answer your question, is that 
they talk about the broad application of IT technology and AI 
technology specifically across the whole U.S. workforce. So 
it's not so much a question of which jobs will be lost, which 
jobs will be created but really how AI will transform work 
across broad, broad swatches of the U.S. workforce and again 
not just even in automation in terms of robots replacing jobs 
but also AI's software helping doctors and lawyers and high-
cognitive-skilled jobs. And so I would recommend this to you 
and to everybody, just very insightful report.
    Mr. Krishnamoorthi. Right. Right. Well, thank you so much. 
I don't know if robots will replace Members of Congress. We 
might write a bill about that beforehand.
    Mr. Lynch. Sounds good.
    Mr. Krishnamoorthi. Well, I want to switch subjects to 
something that Congresswoman Kelly brought up before, which I 
thought it was really important which is kind of the rise of 
China in the field of artificial intelligence. I want to ask 
kind of the corollary set of questions, which is how do we 
catch up and overtake them? What are our strengths in this area 
that we need to leverage to basically come back and eclipse 
them over the shorter long term? Dr. Everett, can you go for 
it?
    Mr. Everett. We have a very different system than China, 
but I think we can leverage it. So, as Mr. Connolly pointed 
out, a lot of the original investments came from DARPA and from 
other parts of the DOD and the Federal Government that 
ultimately led to inventions such as the cell phone. If we look 
at the DARPA Grand Challenge, which in 2004 put up a $1 million 
prize for an car to complete 132-mile course in the Nevada 
desert driving autonomously, we then--no cars did, so we then 
had a 2005 challenge. Five cars did at that time. That laid the 
basis for the self-driving car industry. So I believe that we 
are effective in de-risking technologies at the Federal level 
so that we can then enable venture capitalists and well-funded 
companies to take on the substantial business risk to bring 
them to market and to make them reliable for consumers.
    Mr. Krishnamoorthi. Mr. Nakasone?
    Mr. Nakasone. Yes, thank you for the question. I think one 
of the things we can do is do a lot of cross-collaboration, 
leveraging the Emerging Citizens Technology Office to convene, 
facilitate, collaborate, and help rapidly deploy and also from 
an acquisitions standpoint is have this private-public 
engagement and provide acquisition solutions so that we can 
support the entire Federal, State, local government.
    Mr. Krishnamoorthi. If I might add, it sounds like--I mean, 
both your answers kind of include an element of the private 
sector playing a substantial role in the development of 
artificial intelligence. If I might, it sounds like one 
strength we have is that we are not necessarily going to pick 
and choose what is the best technology in any given sector of 
artificial intelligence. We may let the best one bloom and then 
the private sector helps to fuel it, whereas in China they 
might kind of decide something is the best and it may not end 
up being the best. Is that a fair point, Dr. Maughan? Do you 
want to comment?
    Mr. Maughan. Certainly. I believe, you know, let the market 
decide. Let's let these new technologies come out that are AI-
based, and those that are successful in helping people in their 
applications, they'll survive, and things that don't, they'll 
die, right? So let the market decide.
    Mr. Krishnamoorthi. Great. Thank you so much. Thank you.
    Mr. Hurd. Mr. Mitchell.
    Mr. Mitchell. Dr. Everett, you know, my colleagues left 
unfortunately. I asked them to stay because I promised them it 
would be interesting. I appreciate the new things I learn every 
day as a new Member of Congress, better over 14 months. I found 
out something new you just shared with me. So the internet was 
invented by DARPA?
    Mr. Everett. That's correct.
    Mr. Mitchell. So it wasn't a former politician, formerly a 
Vice President?
    Mr. Everett. It might have been popularized by a former 
politician.
    Mr. Mitchell. Well, that helps a lot. I was confused about 
that up until just a few moments ago.
    A question for all of you, a serious question sort of and 
sort of not, but I think I want to make a point. If I had a 
magic wand and could invent a giant wad of cash, a big bushel 
basketful of cash--around here, it would have to be $1,000 
bills or something, maybe $1 million bills--are any of you 
going to turn it down? Anybody here going to turn down more 
money? No takers. Exactly my point, which is priorities have to 
be made by your agencies, by the President of the United 
States, by Congress in terms of how we prioritize funding to 
get things accomplished on a broad range of things. And it is 
not a bushel basket that suddenly regenerates cash or, as I 
tell my teenage children, no, the cash tree out back is going 
to be bare. It takes priorities. And at some point in time 
there isn't enough cash sort of thing.
    So I suggest that if people want to spend more on this--we 
had a hearing this morning on transportation infrastructure on 
the highways and putting more money in the Federal Highway 
Trust Fund. Decisions have to be made where that cash comes 
from, and I am hopeful that those concerns about artificial 
intelligence or how we fund our highways, that discussions can 
be held not just about how we either go further in debt or we 
tax more, how we save some money and actually find a way to pay 
for these things rather than expecting the American public to 
just go deeper in debt or pay more taxes. I have not heard a 
lot of that from some of my colleagues. He left. It is too bad. 
So I appreciate your time, and I apologize for the little bit 
snarky question, but I think it made the point. I appreciate 
it. Thank you.
    I yield back, Mr. Chair.
    Mr. Hurd. Dr. Everett, you said earlier one of the things 
we should be looking at in the Federal Government what is the 
problem set that you have, what is the problem that you are 
trying to solve, and maybe there are tools that use machine 
learning or artificial intelligence. How do we get a senior 
manager in the government in that mindset? Who would they go to 
to say here is my problem; are there other tools that I should 
be using to help improve citizen-facing services?
    Mr. Everett. Well, that's a very broad question. And ----
    Mr. Hurd. Does GSA have anything in their toolkit, the NSF 
or DHS have a way or, you know, here are some potential tools 
that could solve this problem that may or may not be being 
used? Do we need folks within the government to better define 
the problem set and maybe Dr. Maughan goes out and finds, you 
know, some company that may be doing it and do some of that 
applied research you guys are so good at? Is that how we should 
be thinking about this problem, Dr. Everett?
    Mr. Everett. Well, a few years ago we ran a program called 
XDATA, and it was in the area of big data analytics. We open-
sourced much of the software that we developed there. That 
software has subsequently been used by startups in the private 
sector. IBM has made a major investment in Spark, which is a 
big data platform. So this information that we--we have 
published it and made it available to the private sector 
directly. That doesn't get directly to your question but it 
does at least start to enable the private sector to create 
solutions in this area.
    Mr. Hurd. Dr. Kurose?
    Mr. Kurose. Within the National Science Foundation, we're 
taking up a process of agency reform, and one of the pillars 
there is making IT work for us. And so, for example, 
understanding what are the open-source tools that we may be 
able to combine to help us do our work more efficiently, 
exactly what you were saying. The example that I mentioned 
earlier about using AI clustering techniques to help program 
managers actually identify the most appropriate--really the 
best panelists and reviewers for proposals is one example of 
that. So having those decisions made locally and knowing what's 
available has proven to be very valuable.
    Mr. Hurd. So if we had someone in the U.S. Census Bureau 
that wanted to learn more, how would they do that?
    Mr. Kurose. Well, for this particular project, we could 
certainly put them in touch with people inside the NSF who are 
working on this project.
    Mr. Hurd. Dr. Maughan, it looks like you were getting ready 
to say something.
    Mr. Maughan. Well, I was just going to say that, you know, 
when we talk to operators in the field and they're looking for 
solutions, they don't necessarily say I need an AI solution to 
my problem, right? They come to us and say I need a new widget 
or a new this, but I--it looks like this, and then our job is 
to go find the researchers at the universities or the companies 
or--and a lot of it ends up being how they think about solving 
it and do they think about solving it in an efficient manner 
that can take advantage of new technologies? Because we are in 
an innovative country, an innovative mindset, and I think 
that's one of the benefits from an earlier question is we do 
have an innovative community out there that really is trying to 
bring cutting-edge solutions to the operations community. The 
operations community don't know they need an AI-based solution, 
but if you give them a solution that solves their problem, they 
don't care if it's AI-based or not. They'll use it, they'll 
deploy it, the companies can be successful.
    Mr. Hurd. If we had Mr. Mitchell's cash tree and let's say 
we had $100 million, in what kind of basic research should we 
be putting that towards? And, Dr. Everett and Dr. Kurose, if 
you had ----
    Mr. Kurose. So I will say--and I will just echo what Doug 
Maughan said. We have a very, very creative community, and many 
of the best ideas are--they're bottom-up ideas, so we say to 
the community, here are broad areas that are very important. I 
might label a couple of grand--some of the grand challenges I 
talked about earlier. Explainable AI, fairness, accountability, 
transparency, and decision-making, for example, are all really, 
really important areas. The ideas are going to come from the 
research community itself.
    Mr. Hurd. I think you referenced $145 million worth of 
research proposals that you have been given that you haven't 
been able to fund. I am assuming ----
    Mr. Kurose. That's right, $174 million ----
    Mr. Hurd. A hundred and seventy-four.
    Mr. Kurose.--in artificial intelligence.
    Mr. Hurd. Wow.
    Mr. Kurose. Right.
    Mr. Everett. So in our area we are more project-driven, so 
we would have--reach out to the community to find people 
interested in starting programs in the relevant areas. One area 
that I think is very important for us to be looking at is 
commonsense reasoning. That is what people are--it's--people 
are so good at it, it's hard to even describe.
    Mr. Hurd. I know some people that may need help with that.
    Mr. Everett. Computers are definitely challenged in this 
area, but if we're going to move past graphical interfaces with 
computers where they're simply tools and computers are going to 
become more active partners in decision-making, we're going to 
need to imbue them with common sense.
    Mr. Hurd. And my last question--and whoever would like to 
answer it--what is the equivalent of going to the moon with 
artificial intelligence? I will say this, it has been 
interesting as we have been looking at this and talking to 
folks--I went out on the plaza of the Capitol and asked people 
what do you think about artificial intelligence? Is it good or 
bad? I was shocked at how many people are scared of it. You 
know, I think we have had too many movies where the robot with 
the plastic face that is getting ready to snatch you, right? 
You know, Will Smith. And so one of the things that we all 
understand the importance of this, and I always--you know, if 
Vladimir Putin said that whoever master's AI is going to be the 
sole hegemon and we should listen, but to be able to explain 
this in a way to folks that don't have you all's experience or 
background, what is that moonshot in artificial intelligence, 
Dr. Kurose?
    Mr. Kurose. So maybe if I could start, I'd come back to 
earlier in my testimony talking about narrow AI versus general 
AI, and in narrow AI it's what we're hearing about, image 
classification, speech understanding, phenomenal leaps forward 
on that. But if you look at, for instance, what an 18-month-old 
child can do and how the child can transfer learning from one 
environment to another, how a child can understand intent and 
meaning, that that's really the grand challenge. General AI 
still remains a very grand challenge.
    Mr. Everett. I would second that. Right now, we're building 
tools, and the popular press makes it seem as if these are 
going to become autonomous and think for themselves, but that 
is very far from the actual case of things. Right now, we know 
that people learn by having as few as one example, and yet we 
need terabytes of data to get our systems to learn. We may look 
back on this time as the era of incredibly inefficient machine 
learning, so a moonshot might take us to the point where 
computers actually do understand us in ways that our tools 
today don't. But what we have today are tools.
    Mr. Maughan. I would just add to something earlier said by 
Dr. Everett, which is the program at DARPA, which is the 
explainable AI, it may just be that the moonshot is AI that is 
just in and around us all the time and we don't even think 
about it. We don't call it AI; it just is, it works, and it 
becomes part of our everyday life and we don't worry about it. 
And that ----
    Mr. Hurd. It is not locking us out of our house.
    Mr. Maughan. It doesn't lock you out of the house, but it 
might just be that explainable piece that might be the 
moonshot.
    Mr. Hurd. Excellent. Well, I appreciate that, Dr. Maughan.
    I want to thank all the witnesses for appearing before us 
today, and we are going to hold the record open for two weeks 
for any member to submit an opening statement or questions for 
the record.
    And if there is no further business, without objection, the 
subcommittee stands adjourned.
    [Whereupon, at 3:13 p.m., the subcommittee was adjourned.]


                                APPENDIX

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