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


.                                  
                         [H.A.S.C. No. 115-122]

                        DEPARTMENT OF DEFENSE'S

    ARTIFICIAL INTELLIGENCE STRUCTURE, INVESTMENTS, AND APPLICATIONS

                               __________

                                HEARING

                               BEFORE THE

           SUBCOMMITTEE ON EMERGING THREATS AND CAPABILITIES

                                 OF THE

                      COMMITTEE ON ARMED SERVICES

                        HOUSE OF REPRESENTATIVES

                     ONE HUNDRED FIFTEENTH CONGRESS

                             SECOND SESSION

                               __________

                              HEARING HELD

                           DECEMBER 11, 2018

                                     
[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]

                                     
                                __________
                               

                    U.S. GOVERNMENT PUBLISHING OFFICE                    
34-978                      WASHINGTON : 2019                     
          
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           SUBCOMMITTEE ON EMERGING THREATS AND CAPABILITIES

                ELISE M. STEFANIK, New York, Chairwoman

BILL SHUSTER, Pennsylvania           JAMES R. LANGEVIN, Rhode Island
RALPH LEE ABRAHAM, Louisiana         RICK LARSEN, Washington
LIZ CHENEY, Wyoming, Vice Chair      JIM COOPER, Tennessee
JOE WILSON, South Carolina           JACKIE SPEIER, California
FRANK A. LoBIONDO, New Jersey        MARC A. VEASEY, Texas
DOUG LAMBORN, Colorado               TULSI GABBARD, Hawaii
AUSTIN SCOTT, Georgia                BETO O'ROURKE, Texas
JODY B. HICE, Georgia                STEPHANIE N. MURPHY, Florida
(Vacancy)
               Eric Snelgrove, Professional Staff Member
              Lindsay Kavanaugh, Professional Staff Member
                 Jamie Jackson, Deputy General Counsel
                          Neve Schadler, Clerk
                           
                           
                           
                           C O N T E N T S

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                                                                   Page

              STATEMENTS PRESENTED BY MEMBERS OF CONGRESS

Langevin, Hon. James R., a Representative from Rhode Island, 
  Ranking Member, Subcommittee on Emerging Threats and 
  Capabilities...................................................     2
Stefanik, Hon. Elise M., a Representative from New York, 
  Chairwoman, Subcommittee on Emerging Threats and Capabilities..     1

                               WITNESSES

Deasy, Dana, Chief Information Officer, Department of Defense....     5
Porter, Dr. Lisa, Deputy Under Secretary of Defense for Research 
  and Engineering, Department of Defense.........................     4

                                APPENDIX

Prepared Statements:

    Deasy, Dana..................................................    32
    Porter, Dr. Lisa.............................................    27
    Stefanik, Hon. Elise M.......................................    25

Documents Submitted for the Record:

    [There were no Documents submitted.]

Witness Responses to Questions Asked During the Hearing:

    [There were no Questions submitted during the hearing.]

Questions Submitted by Members Post Hearing:

    Mr. Larsen...................................................    43
                        
                        
                        DEPARTMENT OF DEFENSE'S

                   ARTIFICIAL INTELLIGENCE STRUCTURE,

                     INVESTMENTS, AND APPLICATIONS

                              ----------                              

                  House of Representatives,
                       Committee on Armed Services,
         Subcommittee on Emerging Threats and Capabilities,
                        Washington, DC, Tuesday, December 11, 2018.
    The subcommittee met, pursuant to call, at 3:30 p.m., in 
room 2118, Rayburn House Office Building, Hon. Elise M. 
Stefanik (chairwoman of the subcommittee) presiding.

 OPENING STATEMENT OF HON. ELISE M. STEFANIK, A REPRESENTATIVE 
FROM NEW YORK, CHAIRWOMAN, SUBCOMMITTEE ON EMERGING THREATS AND 
                          CAPABILITIES

    Ms. Stefanik. Thank you for your patience. The subcommittee 
will now come to order.
    Welcome, everyone, to this open hearing of the House Armed 
Services Subcommittee on Emerging Threats and Capabilities. 
Today we will examine the DOD's [Department of Defense's] 
efforts to transform the delivery of artificial intelligence-
enabled [AI] capabilities to the warfighter.
    AI and machine learning are topics of priority and deep 
interest among the members of this subcommittee as we build a 
blueprint for the battlefield of the future. Over the last 
year, we have explored these technology issues closely and 
heard from numerous outside subject matter experts on the 
emerging opportunities, challenges, and implications of 
adopting commercial artificial intelligence solutions into the 
defense enterprise.
    We have also closely examined our adversaries' investments 
in AI and related technologies, including China's whole-of-
society approach, which threatens our competitive advantage. In 
response, this committee has taken deliberate bipartisan 
actions to better organize the Department of Defense to 
oversee, accelerate, and integrate artificial intelligence and 
machine learning technologies.
    The John S. McCain National Defense Authorization Act for 
Fiscal Year [FY] 2019 directed the Secretary of Defense to 
conduct a comprehensive national review of advances in AI 
relevant to the needs of the military services.
    Section 238 further directed the Secretary to craft a 
strategic plan to develop, mature, adopt, and transition 
artificial intelligence technologies into operational use.
    Additionally, section 1051 established the National 
Security Commission on AI, an independent entity inside the 
executive branch, to take a holistic view of the 
competitiveness of U.S. efforts and elevate the national 
conversation surrounding the national security implications of 
AI.
    Today, we will continue this conversation and hear about 
the DOD's efforts to reorganize and more effectively oversee 
the execution of AI programs across the military services. We 
will also examine the Department's investments in basic 
research to generate groundbreaking AI capabilities for future 
conflict.
    The transformation and prioritization of AI inside the 
Department today will shape the efficiency of DOD's business 
functions, and most importantly, the effectiveness of our 
forces in future battle.
    Let me welcome our witnesses here today: Dr. Lisa Porter, 
Deputy Under Secretary of Defense for Research and Engineering 
at the DOD, and Mr. Dana Deasy, Chief Information Officer at 
the DOD.
    We look forward to your testimony.
    And finally, I want to take this time to recognize and 
express this subcommittee's gratitude to two staff members who 
will be departing the committee this month, Neve Schadler and 
Mark Pepple. Thank you so much for all of your work this past 
year and years prior. Your contributions to this committee are 
appreciated from both sides of the aisle, and we wish you best 
of luck in your next endeavors.
    Let me now turn to Ranking Member Jim Langevin for his 
opening comments.
    [The prepared statement of Ms. Stefanik can be found in the 
Appendix on page 25.]

  STATEMENT OF HON. JAMES R. LANGEVIN, A REPRESENTATIVE FROM 
RHODE ISLAND, RANKING MEMBER, SUBCOMMITTEE ON EMERGING THREATS 
                        AND CAPABILITIES

    Mr. Langevin. Thank you, Madam Chair, and I want to thank 
and welcome our witnesses here today.
    This year the Emerging Threats and Capabilities 
Subcommittee has placed a significant emphasis on how 
artificial intelligence, machine learning, and associated 
technologies can be used to advance U.S. warfighting and 
deterrence capabilities and bring efficiencies to business 
processes and systems in the Department.
    In June, the subcommittee held an industry roundtable where 
we focused largely on the implementation of AI in the defense 
innovation system and how the Department of Defense can best 
leverage in-house and commercial capabilities to support 
military functions.
    During the roundtable discussion I expressed serious 
concerns about what I perceived as a disjointed, ad hoc 
approach by DOD in developing Department-wide AI policies, 
strategies, and programs. Since then, I am pleased to see that 
the Department has made some strides toward refining and 
refocusing its AI programs and initiatives.
    Most notably, the Department launched the Joint Artificial 
Intelligence Center [JAIC]. I look forward to better 
understanding how this center, located under the Chief 
Information Officer, will bring synergy to Department-wide 
efforts.
    More specifically, I hope to hear today about the center's 
structure, mission, roles and responsibilities, coordination 
with the military services, and plans for delivering and 
scaling critical AI capabilities.
    Finally, I would like to better understand how the center 
fits into the Department's cloud initiative.
    Now, many claim that data is the new oil. Access to data, 
data integrity, and data labelling are key issues facing the 
Department.
    In addition to hearing about the Joint AI Center, I look 
forward to hearing from Mr. Deasy about how he is setting 
standards and issuing other guidance to the services, agencies, 
and other entities pertaining to these issues.
    In August, through Chairwoman Stefanik's leadership, we 
successfully authorized the National Security Commission on 
Artificial Intelligence. I commend the Chair for her work. I 
was proud to join her in that effort.
    The commission has been tasked with comprehensively 
examining U.S. advances in AI with regard to investments in 
basic and advanced research, efforts to recruit top-notch 
talent, ethical and safety considerations for military 
applications, and strengthening our global competitive 
advantage in the field.
    I appreciate the DOD's partnership in standing up the 
commission and look forward to hearing more about its plans to 
prioritize funding and resources for the commission during 
today's hearing.
    I also look forward to hearing more about the division of 
roles and responsibilities for the AI portfolio between the 
Department's Under Secretary of Defense for Research and 
Engineering and the Chief Information Officer, as well as 
efforts to synthesize AI strategies and plans with the 
services.
    There is enormous momentum around AI, and it is exciting, 
and it is critical that the U.S. capitalize on this momentum in 
order to maintain its technological edge. As a matter of 
national security, I strongly encourage the Department to 
continue to strengthen its partnerships with academia and the 
private sector, better leverage Federal labs, invest in 
cutting-edge research, and continue to explore applications of 
AI with the interagency to ensure that we remain at the 
forefront of AI innovation.
    Before I yield back, I, too, want to join Chairwoman 
Stefanik in recognizing Dr. Mark Pepple, and Neve Schadler, 
clerk, for their service to the committee as well as they 
depart at the end of the year. I want to thank them for their 
work. They have made great contributions to our work here on 
the committee, and we are grateful for their service.
    So thank you. And I yield back.
    Ms. Stefanik. Thank you, Ranking Member Langevin.
    I also want to welcome the chairman of the full committee, 
Chairman Thornberry, who is here with us today. And this issue 
is of deep interest to him, as reflective of the interest of 
committee members beyond this subcommittee.
    Without objection, the witnesses' prepared statements will 
be made part of the record. I ask that you please keep your 
opening remarks to no more than 5 minutes.
    And Dr. Porter, we will begin with you.

STATEMENT OF DR. LISA PORTER, DEPUTY UNDER SECRETARY OF DEFENSE 
      FOR RESEARCH AND ENGINEERING, DEPARTMENT OF DEFENSE

    Dr. Porter. Good afternoon, Chairwoman Stefanik, Ranking 
Member Langevin, and distinguished members of the subcommittee. 
Thank you for inviting me to appear before you today to discuss 
artificial intelligence, particularly as it relates to national 
security applications.
    As this subcommittee knows, artificial intelligence, or AI, 
is not a new thing. As long as there have been computers, there 
have been engineers who have dreamed of enabling machines to 
think the way humans do. In fact, DARPA [Defense Advanced 
Research Projects Agency] funded much of the early work in AI 
decades ago.
    Today we are experiencing an explosion of interest in a 
subfield of AI called machine learning, where algorithms have 
become remarkably good at classification and prediction tasks 
when they can be trained on very large amounts of data.
    There are numerous examples of successful applications of 
machine learning techniques. Some of the obvious ones include 
facial recognition in photographs and voice recognition on 
smartphones.
    However, there has also been a significant amount of hype 
and confusion regarding the current state of the art. It is the 
USD (R&E) [Under Secretary of Defense for Research and 
Engineering] position that we must not abandon the tenets of 
scientific rigor and discipline as we pursue the opportunities 
that AI presents.
    Today's AI capabilities offer potential solutions to many 
defense-specific problems. Examples include object 
identification in drone video or satellite imagery and 
detection of cyber threats on networks. However, there are 
several issues that must be addressed in order to effectively 
apply AI to national security mission problems.
    First, objective evaluation of performance requires the use 
of quantitative metrics that are relevant to the specific use 
case. In other words, AI systems that have been optimized for 
commercial applications may not yield effective outcomes in 
military applications.
    Second, current AI systems require enormous amounts of 
training data, and the preparation of that data in a format 
that the algorithms can use, in turn, requires an enormous 
amount of human labor.
    Furthermore, AI systems that have been trained on one type 
of data typically do not perform well on data that are 
different from the training data. For example, algorithms that 
are trained on internet images will generally underperform when 
used on drone or satellite imagery.
    Another well-known limitation of current systems is that 
they cannot explain what they do, making them hard to trust.
    Furthermore, current systems require robust processing 
power.
    And finally, current systems are susceptible to various 
forms of spoofing, known as adversarial AI.
    We are working to address these challenges and 
vulnerabilities through multiple efforts, most of which will 
lever the complementary roles of the Joint Artificial 
Intelligence Center, the JAIC, and the USD(R&E) enterprise.
    The JAIC will offer a means to rapidly determine the 
appropriate metrics for operational impact for a variety of 
applications, as well as the operational performance 
limitations of current tools. And these insights will help 
inform algorithm and system development across multiple 
USD(R&E) efforts.
    Furthermore, the JAIC's focus on scaling and integration 
will drive innovation and data-curation techniques, while DARPA 
will pursue algorithms that can be robustly trained with much 
less data.
    In order to address AI's trust issue, DARPA's Explainable 
AI program aims to create machine learning techniques that 
produce more explainable models while maintaining a high level 
of performance. The High Performance Computing Modernization 
Program is designing new systems that will provide ample 
processing power for AI applications on the battlefield. 
Finally, countering adversarial AI is one of the key focus 
areas of DARPA's AI Next campaign.
    Ultimately, as we look to the future, we anticipate a focus 
on developing AI systems that have the ability to reason as 
humans do, at least to some extent. Such a capability would 
greatly amplify the utility of AI, enabling AI systems to 
become true partners with their human counterparts in problem 
solving.
    It is important that we continue to pursue cutting-edge 
research in AI, especially given the significant investments 
our adversaries are making. We are therefore grateful for the 
leadership and support that the members of the subcommittee 
have shown regarding AI.
    We also appreciate the establishment of the National 
Security Commission on AI, whose charter is appropriately 
focused on key areas that must be assessed objectively to 
assure that the U.S. maintains a leadership position in AI-
enabled technologies and systems.
    Thank you for your interest in this important topic, and I 
look forward to answering your questions.
    [The prepared statement of Dr. Porter can be found in the 
Appendix on page 27.]
    Ms. Stefanik. Thank you.
    Mr. Deasy.

STATEMENT OF DANA DEASY, CHIEF INFORMATION OFFICER, DEPARTMENT 
                           OF DEFENSE

    Mr. Deasy. Good afternoon, Ms. Chairwoman, Ranking Member, 
and distinguished members of the subcommittee. I thank you for 
this opportunity to testify on the Department's progress in AI 
adoption and the establishment of the Joint Artificial 
Intelligence Center.
    I am Dana Deasy, the Department of Defense Chief 
Information Officer. I am the principal adviser to the 
Secretary of Defense for a set of responsibilities that 
integrate together to ensure that DOD has the information and 
communications technology capabilities needed to enable the 
broad set of missions we perform as a joint force.
    The application of AI is rapidly changing a wide range of 
businesses and industries. The 2018 National Defense Strategy 
[NDS] foresees that ongoing advances in AI will change society 
and, ultimately, the character of war.
    In June, Deputy Secretary Shanahan directed my office to 
establish the Joint Artificial Intelligence Center as a focal 
point for that endeavor. In parallel, DOD submitted its first 
AI Strategy to Congress, an annex to the NDS. JAIC's formation 
also dovetailed section 238 of the latest NDAA.
    Going forward, JAIC will benefit from and help bring into 
reality recommendations of the National Security Commission on 
AI.
    In talking about the Joint Artificial Intelligence Center, 
I would like to highlight three themes today.
    The first is delivering AI-enabled capabilities at speed. 
JAIC is collaborating now with teams across DOD to 
systematically identify, prioritize, and select mission needs, 
and then rapidly execute a sequence of cross-functional use 
cases that demonstrate value and spur momentum.
    Projects fall into two main categories: National Mission 
Initiatives [NMI] and Component Mission Initiatives [CMI]. NMIs 
are driven and executed by JAIC, whereas CMIs are component-led 
and are able to make use of JAIC's common tools, libraries, 
best practices, and more.
    I will note that our new emphasis on rapid, iterative 
delivery of AI complements the Department's ongoing work at the 
other end of the AI spectrum and fundamental research, as Dr. 
Porter shared with you today.
    Two examples of early projects. First, predictive 
maintenance. The NMI helps address Secretary Mattis' direction 
to the services to improve their maintenance readiness rates 
and offers well-defined return on investment criteria.
    A second example, humanitarian assistance and disaster 
relief. This NMI is an open mission to apply AI to saving lives 
and livelihood. We are applying lessons learned and reusable 
tools from the DOD's AI pathfinder, Project Maven, to field AI 
capabilities in support of such events as hurricanes and 
wildfires.
    The second theme is all about scale. JAIC's early projects 
serve a dual purpose: to deliver new capabilities to end-users, 
as well as to incrementally develop the common foundation that 
is essential for scaling AI's impact across the DOD. This means 
shared data, reusable tools, libraries, standards, and AI cloud 
and edge services that help jump-start new projects.
    We will put this in place, this foundation, in a manner 
that aligns with the DOD enterprise cloud adoption. Let me 
underscore that point. Our enterprise approach for AI and 
enterprise cloud adoption via the DOD-wide cloud strategy are 
mutually reinforcing, mutually dependent undertakings.
    The third theme is we build the initial JAIC team. It is 
all about talent. And this will be represented across all the 
services and all components.
    Today we have assembled a force of nearly 30 individuals. 
Going forward, it is essential that JAIC attract and cultivate 
a select group of mission-driven, world-class AI talent, 
including pulling these experts into service from industry.
    In closing, 2 weeks ago, in front of 380 companies and 
academic institutions at DOD's AI Industry Day, I announced we 
had achieved a significant milestone: JAIC is now up and 
running and open for business.
    I look forward to continuing to work with Congress in this 
critical area in an ongoing dialogue on our progress in AI 
adoption and the ways in which JAIC is being used to accelerate 
that progress.
    Thank you for this opportunity to testify this afternoon, 
and I look forward to your questions.
    [The prepared statement of Mr. Deasy can be found in the 
Appendix on page 32.]
    Ms. Stefanik. Thank you for those opening statements.
    I want to ask a broad question to begin. I am deeply 
concerned, as I read headline after headline announcing the 
U.S.'s looming defeat when it comes to the global race for AI 
dominance. It seems like every week there is a new headline.
    I want to quote a recent article, of the fall of this year, 
in Foreign Policy:
    ``There will not be one exclusively military AI arms race. 
There will instead be many AI arms races as countries (and, 
sometimes, violent nonstate actors) develop new algorithms or 
apply private sector algorithms to help them accomplish 
particular tasks.
    ``In North America, the private sector invested some $15 
billion to $23 billion in AI in 2016. That is more than 10 
times what the U.S. Government spent on unclassified AI 
programs that same year.
    ``China says it already holds more than 20 percent of 
patents in the field and plans to build its AI sector to be 
worth $150 billion by 2030.''
    My broad question is, are we falling behind already? If so, 
how far behind? And how do we jump-start it to make sure that 
we do not lose our technological edge when it comes to AI?
    Dr. Porter, I will start with you.
    Dr. Porter. So I would say we are not behind. Right now we 
are actually ahead. However, we are in danger of losing that 
leadership position. So your concern is certainly valid.
    Ms. Stefanik. And let me, I am going to jump in there. How 
are we ahead? How do we measure that?
    Dr. Porter. Absolutely. So there are a lot of ways to 
assess that, but if you look in terms of our talent, 
particularly in our academic base, the United States, along 
with our partners in the U.K. [United Kingdom] and Canada in 
particular, are seen, even by the Chinese, as having quite a 
lead.
    And I will tell you the reason for that--and this is an 
important point to make, and I alluded to it in my opening--
DARPA, in particular, and also the NSF [National Science 
Foundation], have been funding this field for decades. So we 
have built an extremely robust and deep bench in the 
disciplines that are required to advance this field.
    Even when you hear about AI winters in the past and so 
forth, the United States continued to invest robustly in this 
domain for decades. And then we have this vibrant private 
sector that is able to turn around and take that research and 
rapidly convert it to commercial products and create new 
markets.
    So we have a lot going for us, and I believe China has, 
unfortunately--or fortunately if you are from China, I guess--
they have figured out that that is one of the key ingredients 
to our success, that we have a multitiered approach in this 
country to ensuring that we continue to stay on the cutting 
edge. We invest heavily in academia, we invest heavily in our 
labs, and then we figure out how to convert those investments 
quickly and rapidly into products and creating new markets.
    They recognize that, and that is why you are seeing a 
tremendous increase in their investments, particularly in 
academic as well as startup community, which I think you were 
alluding to.
    Ms. Stefanik. What about the race for data? You talked 
about some of the challenges that we face. Obviously, data is 
the fuel for AI. So when we talk about an AI arms race, part of 
that is a race for data and being able to analyze data in a 
comprehensive way. Can you comment on that, Dr. Porter? Beyond 
the challenges, what are our solutions to ensure that we have 
the fuel to help propel our AI research?
    Dr. Porter. Yes. So data is a very key element. I have 
commented on this and so has The New York Times. Probably you 
saw the article a couple weeks ago highlighting how China has 
created these places where people are sitting there and 
essentially labelling data, right, for their needs.
    So one of the things we have to do is we have to be smarter 
about how we understand how these algorithms are working, and 
that is why DARPA always looks at a problem and says: Okay, how 
do we do this better? Our industrial sector also recognizes 
these challenges, so they are going to look at how do we do 
this better.
    So it is not going to be just about how do we get a lot of 
data; it is going to be about how do we develop algorithms that 
don't need as much data; how do we develop algorithms that we 
trust as they are using the data and they are evolving the 
data.
    And this is where the JAIC comes in. I think this is why 
the powerful connection between R&E and JAIC is so important, 
because we have an opportunity now, as we want to test out new 
ideas, we get to a point where we use something like DIU 
[Defense Innovation Unit], that says, hey, what is going on in 
the private sector; how are they trying out new things; let's 
try to prototype; and then get them out into the operational 
place more quickly and say, how is that actually working?
    So this can be a very powerful way for us to accelerate the 
experimentation that is going to be continual to stay ahead of 
the game, because it can't just be about labelling data. It has 
got to be about being smarter using the data that you have got.
    Ms. Stefanik. Thank you, Dr. Porter.
    Mr. Langevin.
    Mr. Langevin. Thank you, Madam Chair.
    And thank you again to both of our witnesses for your 
testimony today.
    Dr. Porter, I would like to start by asking you to expand 
on what you talked about in terms of the DARPA project and 
using less data to get better outcomes, if you want to talk a 
little bit more about that.
    Dr. Porter. Sure. That is just one of the many areas that 
DARPA has been focusing on. I think some of you are aware of 
their AI Next campaign, which they have announced publicly, and 
they are trying to address all of these weaknesses that--or 
several of the weaknesses, I should say, that I outlined, and 
one of them has to do with this reality of the big data 
problem.
    Folks on the cutting edge are now talking about how we 
can't just be using traditional machine learning. What is the 
next step? How do we combine other elements to get after this?
    And if I can brag about something that DARPA has done 
recently, because I think it will give you some hope when I say 
I think the United States has ways to stay ahead. They recently 
started something called AI Exploration, and this is a way that 
they very rapidly get money out, particularly into academia, 
and the labs, and the small businesses, to say, all right, by 
the time I announce my concept I want you guys to go after, 
within 90 days I am going to get you the money. Not from the 
time I tell you, you are selected, but from the time I post it 
till you get the money.
    They have already done this once and within 90 days they 
had 16 awards out, each about a million dollars or so. And the 
problem they are tackling is, can we bring some physics into 
machine learning so that we don't need as much data and we 
don't have to worry so much about these fragile and brittle 
things that I was talking about.
    So I am telling you this story because I think you have got 
a lot of innovation going on within the DOD enterprise to say: 
How do we get to speed, as well as scale? And this is what Dana 
and I are going to continually try to work together on, is how 
do we move faster, because AI is all about speed. It really is. 
This is one of those domains where things are just going very, 
very quickly.
    Mr. Langevin. Thank you, Dr. Porter.
    Mr. Deasy, as I mentioned in my opening statement, data is 
the fuel that powers AI and machine learning. So what efforts 
are you undertaking to promote policies and practices that 
ensure DOD owns data collected under its authorities?
    Mr. Deasy. So it is interesting, when I joined the 
Department and we kicked off the JAIC, sir, one of the earliest 
questions I got asked was: What are going to be some of the 
earliest stumbling blocks you are going to face in the 
successful standup of JAIC? And I said: I can almost predict 
now that as we roll out the first two, three, four 
applications, the thing that will be hitting us over and over 
again will be data.
    And what do I mean by that? It will be: Where is the single 
source of the truth coming from? How do you ingest it? What are 
its formats? Do we have duplicate data? And how do we bring it 
together.
    Part of the reason why you heard me comment in my opening 
remarks about the integration of cloud: cloud provides us the 
physical capacity to take this enormous amount of data and 
bring it together.
    True, it will still continue to sit in different formats. 
But what we will do in development of JAIC is we will start to 
define with different problem sets and different algorithms 
what is the expectation in terms of the data standards that 
need to be deployed.
    So if we are looking at audio versus we are looking at 
image data, or if we are looking at textual or good old-
fashioned tables, one of the things that the JAIC will need to 
do is--two things--technically describe what it is we need to 
do to ingest the data and what are the tools; and then two is 
what are the policies and standards that need to be put in 
place on the correct formats of data as people develop new 
systems going forward.
    Mr. Langevin. Yeah, but you didn't answer my question about 
what are we doing to ensure that DOD owns the data collected 
under its authorities. I need that. But I also need to ask you, 
how are you incorporating publicly available data sets into 
your efforts, and have you had challenges accessing data sets 
owned by other entities? So those two.
    Mr. Deasy. Yeah. Too early from the standpoint of JAIC, as 
JAIC is just stood up. So we haven't had a program right now 
where we are actually accessing public data. DARPA may be in a 
position to describe what they have done on that standpoint. 
But, indeed, there will be programs eventually where we will 
need to incorporate that, and we will have to be very clear on 
the ownership of that data.
    If the data is truly being created, whether it be from an 
intel [intelligence] community, a mission partner, very clear 
rules of the roads will have to be established early on as to 
the ownership of that data.
    Part of our job in standing up JAIC--and I need to stress 
this throughout today--is that this is going to be an iterative 
learning cycle. We are going to take something in, we are going 
to learn what are the issues.
    One of the issues, the one you probably bring up here, who 
owns the data? Where is the legal authorities for that data? 
And we are going to have to actually take these on a case-by-
case basis, then develop ongoing policy that can be applied for 
more missions as we go forward.
    Mr. Langevin. Okay. I am glad we are thinking about these 
things now for sure.
    I know my time is expired. I have other questions. If we 
get to a second round, I will ask those then. If not, I will 
submit them for the record. But thank you, and I yield back.
    Ms. Stefanik. Thanks.
    Dr. Abraham.
    Dr. Abraham. [Inaudible--off mic] but certainly on other 
sides of this globe. I refer to even gene editing here. Of late 
we have seen that go awry on the eastern part of the globe. So 
I worry about the scientific discipline that will be involved 
with our data.
    To follow up on Jim's question a little bit, Mr. Deasy, the 
algorithms that are constructed by, I am assuming, commercial 
industry, they own that data. Am I correct there, the way the 
law stands as of now?
    Mr. Deasy. Yes. So in the case of some solutions that we 
built, for example, in Maven, where we have used partners, part 
of that case will be commercial available solutions and 
algorithms that they will own.
    Dr. Abraham. And you said JAIC wants to incorporate people 
from industry to be part of the total family.
    Mr. Deasy. Absolutely.
    Dr. Abraham. Is that a correct statement?
    Mr. Deasy. It will be a combination of those solutions that 
will be developed by our own organization, JAIC, and those that 
will be developed through partners. So there will be no single 
solution where we will probably come from either all 
commercial, internally, but we will be using a combination of 
both.
    Dr. Abraham. But I just go back to a few years ago where 
the VA [Department of Veterans Affairs] had a physician develop 
a drug that was used, and who owned that particular patent was 
a big mess.
    So I just implore--and I am sure you are ahead of the curve 
here--but if we have the rules of the roads in place before 
those algorithms are developed and then we have to get into 
this debate, I think it is prudent to do that.
    Madam Chair, I yield back. Thank you.
    Ms. Stefanik. Thank you, Dr. Abraham.
    Mr. Larsen.
    Mr. Larsen. Thank you very much. Thanks for coming today.
    So one of the criticisms or, I guess, concerns when we 
compare ourselves to our competitors, especially China, is they 
can take a top-down approach, sort of drive everything through 
state-owned enterprises, through what they consider public 
financing, where we have to have a more of a bottom-up approach 
because we have such a very active private sector innovative 
economy.
    And so how are you trying to balance that? How are you 
trying to drive the innovation so that it creates options for 
the DOD to pick from? Because we are probably not going to 
drive it to any one solution, but a set of solutions, and then 
you can choose partners as you move forward. Who might be best 
to answer that?
    Mr. Deasy. Well, I will talk about it from an operational 
production.
    Mr. Larsen. Yeah, sure.
    Mr. Deasy. We will let Dr. Porter discuss it more from a 
research and science.
    Mr. Larsen. Yeah.
    Mr. Deasy. So the way that JAIC is being established is 
going to be very much a hub-and-spoke model. There will be a 
physical entity that we are creating in the Washington, DC, 
area. But we recognize that we are going to need talents that 
are going to exist, for example, outside in the academia 
environment.
    So part of our spoke model is we will be establishing 
locations next to academic environments, we are actually in the 
process of selecting those right now, where they will have 
certain skill sets. And so what we are actually doing is going 
through an inventory process of identifying what are the 
problems we believe are most in need to solve for and what 
institutions.
    Between that and the fact in our AI Day that we ran 
recently, the reason we ran that day was we are now getting in 
white papers that are coming in from the commercial sector, as 
well as the academic sector, starting to describe what are 
their solutions against the problem sets we are trying to 
solve. We are right now in the case of actually building out an 
inventory of these solution sets.
    Mr. Larsen. Interesting. And Dr. Porter.
    Dr. Porter. So one of the things that when Dana and I talk 
about this--and this may be a helpful thing if you can 
visualize it--we think about near, mid, and long term. And in 
the near term, of course, that is where JAIC resides.
    And in the mid term gets to kind of your question. This is 
where DIU, for example, says: All right, what is going on in 
the private sector? Because those problems that I articulated 
that we have to address, the private sector has to address as 
well.
    So if an algorithm isn't very robust, my recommender system 
doesn't tell you that the movie that I am recommending to you 
makes any sense to you, you are not going to use my system 
either, and that is going to cause revenue problems. So I have 
got to solve that problem.
    So the DIU, in places like that, they look and say: Well, 
what are they coming up with in the near term or the mid term 
that we can fold back in and test.
    And so DIU has a very effective way of basically doing 
proof of principle and projects at a lower level and say: Okay, 
JAIC, I think we have got this; we want you to scale it up and 
really test it, and wring it out, and tell us where we are 
missing things and continue to iterate. So that is kind of a 
unique capability.
    Now, if you go a little further out, to your point, some of 
these problems the commercial sector are not going to solve, 
because they are hard or they are not relevant to their 
markets. That is why we need a DARPA. That is why we need our 
national labs. That is why we need our underpinning across that 
entire spectrum of our academic experts who can guide us in the 
near, mid, and far term to think about what can we solve now 
and where do we need to have long-term strategic investment.
    And, again, the willingness as a nation to continue to 
invest in the hard problems even as some of those are going to 
lead to missteps and we are going to have to try again, right? 
That is that high-risk, high-payoff realm. So we have to cover 
that entire spectrum. If we do that, we can optimize on 
benefitting from the private sector, as well as pushing to 
solve the problems we care about most that are hard.
    I am sorry, I went over the time.
    Mr. Larsen. No, that is fine. I have a little less than a 
minute.
    So the Center for Strategic and International Studies just 
published a report late last month on AI and national security, 
and the argument they made was the need for robust supporting 
capabilities or an ecosystem around AI, especially within DOD.
    And I don't know if your folks have evaluated that. But it 
might be--it is not an easy read for people like me, but it is 
a good read for folks like you to use it maybe as a marker 
standard to compare yourself against. There are other folks 
writing about this as well. But I would commend that to you.
    And they outline a variety of areas: trust and security of 
AI, the people part of it, the digital capability, and the 
policy. Which you have already outlined some of those concerns 
and the things you are trying to focus on. I would just lay 
that out there if you are thinking about how to compare 
yourself to where maybe you ought to be versus where you are 
today.
    And I will wait for a second round. Thanks.
    Ms. Stefanik. Mr. Hice.
    Mr. Hice. Thank you, Madam Chair.
    China has identified AI as a strategic technology for them, 
and they plan to develop an AI industry worth over $21 billion 
by 2020.
    As we all know here in this room, China also has a strong 
history of both government and industrial espionage, and this 
just creates a great deal of concern personally.
    So what are we doing to protect ourselves, specifically 
from China, but really from anyone, from hackers? What are we 
doing to make sure our AI program remains ours?
    Mr. Deasy. Okay, I will start.
    So I would say a couple things on that. Interestingly 
enough, we are actually going to apply AI to help us address 
this problem.
    So I mentioned earlier we have two types of initiatives, 
National Mission Initiatives and Component Mission Initiatives. 
We are actually doing some work right now to start to evaluate 
with U.S. Cyber Command, how is it we can apply AI in pattern 
recognition in signatures, where are you looking for anomalies 
that are going on in your network, and how can you use AI to 
quickly assess that there has been a change to what is a normal 
pattern.
    If you think about how hackers actually try to penetrate, 
they will go to the point of least resistance, and once they 
are in, they will go laterally. And then what you are looking 
for is exfiltration.
    And so we believe actually AI will be a very good machine 
use case for looking at how we look at signatures and patterns 
of data across our network and actually use that to help ensure 
that we don't have exfiltration occurring from folks like the 
Chinese.
    Mr. Hice. Dr. Porter, would you like to add anything to 
that?
    Dr. Porter. Sure. I think you are highlighting an extremely 
important point. I think there are specific technical 
approaches that we are going to be working. And that example is 
a good one, because we are not going to get it all right, and 
it is going to be iterative, and DARPA, in fact, is also 
looking at this from their perspective.
    But I would want to emphasize the broader point you are 
making. I think we have to be vigilant and aware of this 
problem. I actually spent time at In-Q-Tel and I have spent 
time in the intel community. And I know this committee was 
briefed, I think back in June, about the Thousand Talents 
Program that China has, and I know you guys were told at that 
time exactly your point: They have a goal of facilitating both 
legal and illicit transfer of U.S. technology, intellectual 
property, and know-how, and we have to be cognizant of that in 
the community.
    So across our research domain and spectrum we are thinking 
about that. It isn't just about protecting against hacking, 
although that is certainly a big part of it, it is all of those 
ways that they have to try to capture that intellectual 
property, which I think is what you were alluding to.
    Mr. Hice. Absolutely it is. And I know some of this 
probably would be best served in another environment than this.
    Dr. Porter. Right.
    Mr. Hice. But I would like to dive deeper into this issue 
if we can in another setting.
    But going back to what Mr. Larsen said, I want to just get 
a little more clarity. How do you plan to recruit talented data 
engineers and scientists? Specifically in the near term, I 
guess.
    Mr. Deasy. Right. So right now, we have approximately 30 
people inside. It is a combination of civilians, which are DOD 
employees, as well as military.
    The philosophy is over time we are going to need to 
actually build out an internal capability that will include 
people inside the military.
    So what we have done recently is we brought in 10 very 
highly talented, skilled individuals from the various services 
into JAIC. We are going to team them with data scientists, 
``been there, done it'' people that we are recruiting. And the 
idea is to use this pairing system so people can leave JAIC, go 
back into the services, and then use that to increase the 
flywheel.
    How we are recruiting people is a combination of commercial 
contacts, academia contacts, think tank contacts. We have quite 
a list of people that we are currently identifying.
    We expect at some point we may have to put something in 
place like the Cyber Excepted Service, which is going to allow 
us to recruit in a way that has a lot of additional speed. It 
is going to have to handle compensation differently. And it is 
going to handle how we onboard them in a much better fashion 
than you would normally onboard into government.
    Mr. Hice. Okay. Thank you very much. I yield back.
    Ms. Stefanik. Mr. Veasey.
    Mr. Veasey. Thank you, Madam Chair.
    I wanted to ask Dr. Porter or Mr. Deasy about the $2 
billion that DARPA has announced as a multiyear investment for 
AI Next.
    Can you explain to me exactly what the $2 billion is going 
to be used for? Is it just to sort of develop a kind of a basic 
groundwork on how we should move forward? Or is it going to 
advance specific technologies?
    Dr. Porter. So it is kind of both, because that is what 
DARPA does. Now, to be clear, the $2 billion is over 5 years, 
so it is roughly $400 million a year. And they have several 
thrust areas that target these problems that I was talking 
about.
    So one I already told you about, this Exploration program, 
and this is that really rapid getting stuff out there and 
getting really great ideas funded. So we do exactly what you 
just said and provide that foundation for larger efforts.
    There is also a lot of focus on what I mentioned, 
adversarial AI. This is where it has been proven, and if you 
read the popular press there are these examples that people are 
publishing almost daily now, where they can spoof AI systems 
pretty easily.
    One of the ones that is notable, because in the self-
driving car community they really took note of this, is there 
is a team at Berkeley, at the school out in California, they 
put tape on stop signs. And when you put the tape on the stop 
signs, the AI system thought the stop sign was a speed limit 
sign for 45 miles an hour. So you can imagine that is a little 
bit of a problem, right?
    And there are countless examples of this now. It is almost 
a game now where people are showing all the ways they can spoof 
these systems.
    So, obviously, if we are going to trust this and we are 
going to apply it to things where there are high stakes, i.e., 
the DOD mission, we have got to do much better at understanding 
how we ensure that people can't spoof our systems. There is a 
lot of research to be done there, and that is one of the key 
thrust areas in the AI Next program.
    Mr. Veasey. As we try to gain a better understanding, is 
the $400 million, is that like a good starting number? Or where 
does the number ideally need to be in order for us to sort of 
stay on track?
    And right now, I think you had mentioned earlier that when 
it comes to China and other competitors, that we actually are 
ahead. But financially, like where do we need to be to make 
sure that we stay ahead and that we can continue to work on 
things like making this AI more smarter, to where tape can't 
throw it off?
    Dr. Porter. You have got it.
    I think it is a reasonable investment level. And one of the 
things DARPA likes to emphasize, which I fully agree with, is 
it is not just the amount of money you invest in, it is how you 
do it.
    So DARPA has a model, right, where they try things that a 
lot of people won't try because it is risky and it may not pay 
off. And if it doesn't work, no harm, no foul, we will try 
something else, because we are trying to pursue the really hard 
things.
    That whole model that DARPA has is pretty unique, and, in 
fact, when you couple that with a robust funding effort, as the 
$2 billion over 5 years is, you can actually get significant 
jump-aheads. And that is really what I am trying to emphasize 
here. That is one of our unique secret sauce ingredients in the 
United States.
    Mr. Veasey. Thank you very much.
    Madam Chair, I yield back.
    Ms. Stefanik. Mr. Bacon.
    Mr. Bacon. Thank you both for being here. I am grateful for 
your expertise and sharing it.
    It seems to me that until a few years back, or maybe even a 
decade ago, DOD would drive a lot of the technology. The 
private sectors would then leverage that. And then we saw a 
period of time where there was probably a lot of even synergy.
    But in my visits recently to the private sector and some of 
the larger companies, it seems to me they are producing 
technology faster than DOD can install it, or with the 
requirements process, testing, by the time we do field it, it 
is already 2 to 3 years out of date, if not more.
    Are we positioning ourselves right in the AI to stay 
abreast and not fall behind?
    Dr. Porter. So I will start, and then I will, because I 
think this is a joint answer.
    I think you highlighted the problem we are very much 
interested, the two of us, in trying to address. If we do 
nothing else, we are going to still have this problem, because 
even if DARPA gets us ahead of the game and the private sector 
takes off with those ideas and goes their own way and creates 
these great products, we have got to have a way to more rapidly 
transition that innovation back in, learn from it, and continue 
the cycle.
    And both Dana and I have talked multiple times about the 
speed challenge, and this is why we are really trying very hard 
to figure out, how do we coordinate that cycle, so that spin 
cycle, if you will, so that we get multiple spins very quickly, 
rather than three, four, five multiple-year cycles just to 
insert something.
    It is not solved, but I think what you are seeing here is a 
real serious attempt by the DOD to say, let's line this up so 
that we can improve this.
    Would you not agree?
    Mr. Deasy. Yeah. As someone who spent the majority of my 
career in the private sector, I am often asked when I arrive, 
what is it I have noticed most, and I say clock speed. How fast 
we can embrace, either decide to work with something, get rid 
of it and move on.
    And one of the reasons we created the relationship we 
created was you need two things in AI to be successful. You 
need a maniacal focus on the here and now of operationalize and 
getting things up and running, and that is that flywheel I talk 
about. But you also need an intense focus on where the future 
is going, where the science is going.
    And you need a place to take that science. In this case, 
what DARPA develops. Bring it in, rapidly decide whether or not 
it can work or not work. If it doesn't work, move on. Tell 
DARPA that is the case. Or if it is working and it just needs 
tweaking, then let's do that.
    This is why we think this model we have put in place is 
actually going to help to address the very problem you raise on 
how do we get the flywheel of innovation moving at a lot faster 
clock speed.
    Mr. Bacon. Are we confident DARPA is abreast of all of what 
the various private sector companies are doing? I mean, do they 
have their fingers on the pulse of a lot of different 
companies? Are we confident of that?
    Dr. Porter. So we are confident, but that is why we also 
have DIU in our quiver. Because, as you know, DIU sits out in 
Silicon Valley, but also sits in Austin, Texas, sits in Boston, 
and it is keeping its finger on the pulse.
    And, again, where they are going to really see the 
innovation is a little bit nearer term, but it is that nice 
bridge between where DARPA may be looking a little further out, 
DIU is going to see where opportunities are in the next 12 to 
24 months, which is much shorter than where DARPA typically 
looks.
    So we try to cover that landscape appropriately----
    Mr. Bacon. Right
    Dr. Porter [continuing]. So that we are seeing everything 
we should be seeing.
    Mr. Bacon. Two follow-on questions. Do we need to make any 
revisions to our acquisition rules processes to help you out, 
one.
    Two, when I was recently visiting a company this past week, 
they would say they come up with new technology, but because 
the DOD didn't have a requirement for it, they didn't want to 
really look at it. However, later on, they would say, yeah, 
basically the requirements were shortsighted because they 
didn't realize what the technology--what could be executed or 
applied.
    So my question, two of them, do we need to make any 
revisions to our acquisition system? And two, do your 
requirements keep up with some of the far-ranging technologies 
that you are seeing in AI?
    Thanks.
    Mr. Deasy. So I will start with the first half of that, the 
acquisition.
    What I tell people often is, one of the things that we 
struggle with at the DOD and government is what I call a 
startup mentality. How you start AI is a very iterative 
process. And many times the acquisition cycles are asking you 
to define 30, 60, 90, 2, 3, 1 year, 2 years out, what the end 
state will look like.
    I am just trying to get the end state identified for the 
next 90 days, 120 days, and then allow us to create this, what 
I will call this iterative approach for how we are going to 
build out. We are going to try a solution, we may acquire a 
product. We will say that product didn't quite meet the needs, 
and then we are going to need to go back out in a very rapid 
cycle.
    So, yes, I do believe there will need changes, and I 
believe it is going to be, how do we move to a more startup 
mentality when looking at technologies like AI?
    Ms. Stefanik. We will now move to the second round of 
questions and get through as many as we can before they call 
votes.
    My second question has to do with a previous testimony 
before this committee. I believe it was Deputy Secretary 
Shanahan talked about the fact that there are hundreds of AI 
projects and programs within the DOD.
    Can you speak, Mr. Deasy, to how we plan on integrating 
those programs into the JAIC and how that process is going? And 
can you also highlight one of the best examples of an AI 
program that was started within the DOD that we can learn from?
    Mr. Deasy. Yeah. So clearly what the Deputy was referring 
to is there are a lot of programs that are using data learning, 
machine learning, cognitive. You have to be quite thoughtful 
when describing what is the universe of AI. I would argue that 
some of those programs, when you really kind of dig under the 
covers, are more business analytics, as they are as to true, 
what I will call, machine learning.
    With that said, there is no doubt that one of the biggest 
benefits that JAIC will bring is trying to reduce the 
replication and the duplication of tools, processes, and, 
frankly, methodologies that are being used.
    A good example of this--and it actually brings DIU into 
it--is you think about the predictive maintenance. So this is 
an area where how do you look at helicopters, planes, ships, 
anything where there is a need to reduce the waste and the 
cycle time of readiness. This is an example where DIU went out 
and did some work, found some solutions in the marketplace. 
They are now bringing that to us.
    One of our first initiatives is predictive maintenance, and 
we are actually going to use the learnings from DIU and the 
commercial offerings as a way we are going to jump-start the 
predictive maintenance.
    Ms. Stefanik. Thank you.
    Mr. Langevin.
    Mr. Langevin. Thank you.
    So for Mr. Deasy, our military force projection 
capabilities are developed and tested almost entirely within 
the continental United States, but the nature of warfighting is 
largely expeditionary. That is, the great majority of 
warfighting is going to occur far away from the wide 
infrastructure and domestic regulatory constraints of the U.S., 
requiring flexible access to maneuver within a different 
electromagnetic environment.
    What role do you see for AI in overcoming this challenge? 
And how would it potentially apply to other domains like space 
and cyber?
    Mr. Deasy. So interesting enough, I just came back from a 
Five Eyes  meeting over in the U.K. in which we 
discussed with our mission partners what is the role that AI 
can play in a lot of spaces. You mentioned the one, 
electromagnetic spectrum. I mean, the nature of electronic 
warfare is such that trying to degrade, spoof, and change the 
nature of spectrum is such that clearly AI can play a role in 
being able to quickly assess where spectrum has been 
compromised and how do you then change the nature of the use of 
that spectrum.
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     Australia, Canada, New Zealand, United Kingdom, and United 
States intelligence alliance.
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    Another example is, if you think about mission partner 
networks and how we need to share data in a classified or 
confidential manner, we see that AI will be able to use, much 
to my earlier comment, patterns and changes of behavior as we 
are sharing data across our mission partner networks. And so we 
have had conversations recently with our partners on what is it 
that we should be doing more joined up in the matter of these 
AI initiatives.
    Mr. Langevin. Okay. Thank you. That is encouraging.
    Also, getting back to data, what efforts are you taking to 
set standards and guidance for data integrity? And finally, 
what efforts are being taken to provide for a common lexicon 
for AI and machine learning?
    Mr. Deasy. So on the data front, one of the things that we 
are doing, for example, is we are working now with the CMO 
[Chief Management Officer] office. They have actually hired a 
Chief Data Officer. And on the reform side, we are doing some 
early work, because I have been quite a proponent of saying 
that we are going to have to solve for do we really understand 
where the sources of our data come from, what I like to refer 
to as the single source of the truth.
    So we are partnering with the CMO and the Chief Data 
Management Officer to start to identify what are those going to 
be, those problematic data sets, where we are going to have to 
get clearer standards, especially in the back office area of 
reform. That is the area we are focusing on right now in the 
Chief Data Management Officer.
    Mr. Langevin. Dr. Porter, do you have anything to add to on 
that?
    Dr. Porter. Regarding the data integrity issue I think----
    Mr. Langevin. Press your mic [microphone].
    Dr. Porter. Oops. I am sorry about that. Regarding data 
integrity there is also a research component to that as well. 
And, again, it gets back to, as people recognize how important 
your data is to training your algorithms, they are going to try 
to mess with your data, right?
    And so there is both the how do you ensure you are thinking 
about AI not in an isolated way, but as was raised earlier in 
the context of cybersecurity and other elements in your system 
that have to work together.
    And so one of the things I like to emphasize, which I think 
you were touching on when you asked questions about space and 
so forth, AI doesn't really mean anything until you think about 
it in the context of the larger system that you are using it 
in.
    So how does it apply to your mission usually means it has 
to be part of a larger system. How does it get integrated in a 
way that you don't open up vulnerabilities because you have 
forgotten that, wow, if my data is really easy to get into 
someone is going to mess with it, so that I am training on the 
wrong thing, as an example.
    So there are research elements of this, because we have to 
take a system-level approach, as we do with all technology, 
when we think about integrating it into operations.
    Mr. Langevin. So the last question I had is, to what extent 
are the Department's challenges based on development of AI 
technology--e.g., data processing and neural network 
algorithms--versus a lack of infrastructure, such as big data 
repositories, compute power, and cloud capabilities?
    Mr. Deasy. [Indaudible]
    Mr. Langevin. Microphone.
    Mr. Deasy. Thank you, sir. I will start with that.
    So I mentioned earlier the cloud. If you kind of step back 
for a second and say, what has happened that has allowed AI to 
suddenly be on the forefront of all conversations? And I would 
argue there is the data science behind this, and I would say we 
have entered an era now where there is unlimited compute power. 
AI needs a massive amount of computer power, a massive amount 
of storage, and, of course, you need the algorithms behind it.
    The reason why I have been so vocal and energized about 
wanting to get to an enterprise cloud capability is I want to 
provide the Department of Defense with a way to handle that 
unlimited compute capacity, unlimited storage, on demand, as 
needed, with high integrity.
    And this is why I have been such a strong advocate about 
pushing the need for an enterprise cloud solution, because the 
enterprise cloud is going to become the foundation for which 
all the data and all that compute power will reside on top of 
and those algorithms will use.
    And understand that when I talk about cloud, I am not 
talking about a centralized, single repository. I am talking 
about a world where we need to work in a decentralized world. 
If you are out at the warfighter, tactical edge, and we need to 
be able to work in what I will call a compromised, degraded 
mode. So it is clouds that can handle the edge, all the way 
that clouds can handle the central.
    Mr. Langevin. Thank you. My time has expired, so I yield 
back. Thanks.
    Ms. Stefanik. Thank you very much to our witnesses.
    Votes have been called. For other members who we didn't get 
to your second round of questions, please submit your questions 
for the record.
    And thank you, Dr. Porter and Mr. Deasy, for the testimony 
today. We look forward to discussing this in the next Congress. 
And I know I look forward to working with Mr. Langevin on it. 
Thanks.
    Mr. Deasy. Thank you.
    Ms. Stefanik. The hearing is adjourned.
    [Whereupon, at 4:30 p.m., the subcommittee was adjourned.]

     
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                            A P P E N D I X

                           December 11, 2018
      
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              PREPARED STATEMENTS SUBMITTED FOR THE RECORD

                           December 11, 2018

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              QUESTIONS SUBMITTED BY MEMBERS POST HEARING

                           December 11, 2018

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                   QUESTIONS SUBMITTED BY MR. LARSEN

    Mr. Larsen. How do you assess the ability of military recruits to 
work with AI? What recommendations would you make to improve K-12 and 
community college curricula in order to make sure military recruits 
have the necessary skills and are appropriately prepared to work with 
AI-related applications?
    Mr. Deasy. While some people joining the military today may have 
skills suited for working with AI, overall we assess that the current 
state of the existing workforce and military recruitment pipeline is a 
critical shortfall for DOD. Although means of quantifying this 
shortfall are still emerging, directional industry benchmarks indicate 
DOD should build capacity of several thousand people with AI-specific 
skills, such as data scientists and data coders. National investments 
in skills training and high-quality K-12 and community college 
education would be a significant force multiplier for DOD. Classes in 
computational thinking as early as middle school and again in high 
school will help establish a foundation for AI skills that will pay 
dividends in the DOD workforce. Other recommendations to ensure 
military recruits have the necessary skills and are appropriately 
prepared to work with AI-related applications include the following:
    1. Accelerate the use of digital content and ``flipped classroom'' 
pedagogy. There has been a renaissance in digital content such as 
massive open online courses (MOOC), ebooks, and YouTube videos. This 
content represents a new category of learning experience that presents 
several advantages for K-12 and community college--generally high 
quality, low cost, scalable, and adaptable to the needs of an 
individual or community. In-person teachers can complement the online 
content, resulting in faster and more enjoyable learning experiences 
(``flipped classroom pedagogy'').
    2. Evaluate guidelines, measurements, and incentives for AI 
education in K-12. To establish consistent, measurable standards for AI 
education and training, guidelines, measurements, and incentives should 
be established across the country for curricula or key skills. As an 
example of an external effort underway, the Association for the 
Advancement of Artificial Intelligence (AAAI) and the Computer Science 
Teachers Association (CSTA) are in the process of formulating 
guidelines that will define what students in each grade should know in 
AI.
    3. Launch public-private partnerships, including open missions to 
use AI to solve problems of societal significance. The use of public-
private partnerships can bring AI education to more K-12 classrooms 
throughout the country. One type of partnership involves bringing to K-
12 and community colleges national security challenges and forming an 
open mission to produce innovative AI technology to address real-world 
problems. Such initiatives would enhance AI education, generate 
excitement about working with the government, and inform potential 
recruits of AI-related opportunities within the military. Similar 
cyberspace initiatives have been very successful.
    4. Establish clear pathways between K-12 and AI-enabled roles in 
military service. Establishing a career track for computer scientists 
in the military services provides potential recruits a clear path to 
obtain sophisticated AI-related training and education. Designating AI-
related career fields allows for recruiting incentives such as 
scholarships and bonuses.
    5. Prioritize continued learning within military. The unique pace 
of technological change in AI means that relevant knowledge decays more 
rapidly than ever before. After entry, incentivizing continual learning 
within military is imperative to maintain an ``AI ready'' workforce. 
This should include expanding opportunities for internships, 
fellowships, and exchanges between DOD and leading commercial AI 
companies.

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