[House Hearing, 118 Congress]
[From the U.S. Government Publishing Office]


                   ARTIFICIAL INTELLIGENCE: ADVANCING
                INNOVATION TOWARDS THE NATIONAL INTEREST

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

                                HEARING

                               BEFORE THE

                      COMMITTEE ON SCIENCE, SPACE,
                             AND TECHNOLOGY

                                 OF THE

                        HOUSE OF REPRESENTATIVES

                    ONE HUNDRED EIGHTEENTH CONGRESS

                             FIRST SESSION

                               __________

                             JUNE 22, 2023

                               __________

                           Serial No. 118-18

                               __________

 Printed for the use of the Committee on Science, Space, and Technology
 
 [GRAPHIC NOT AVAILABLE IN TIFF FORMAT]

        Available via the World Wide Web: http://science.house.gov
        
                               __________

                   U.S. GOVERNMENT PUBLISHING OFFICE                    
52-499PDF                  WASHINGTON : 2024                    
          
-----------------------------------------------------------------------------------           
 
              COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY

                  HON. FRANK LUCAS, Oklahoma, Chairman
                  
BILL POSEY, Florida                  ZOE LOFGREN, California, Ranking 
RANDY WEBER, Texas                       Member
BRIAN BABIN, Texas                   SUZANNE BONAMICI, Oregon
JIM BAIRD, Indiana                   HALEY STEVENS, Michigan
DANIEL WEBSTER, Florida              JAMAAL BOWMAN, New York
MIKE GARCIA, California              DEBORAH ROSS, North Carolina
STEPHANIE BICE, Oklahoma             ERIC SORENSEN, Illinois
JAY OBERNOLTE, California            ANDREA SALINAS, Oregon
CHUCK FLEISCHMANN, Tennessee         VALERIE FOUSHEE, North Carolina
DARRELL ISSA, California             KEVIN MULLIN, California
RICK CRAWFORD, Arkansas              JEFF JACKSON, North Carolina
CLAUDIA TENNEY, New York             EMILIA SYKES, Ohio
RYAN ZINKE, Montana                  MAXWELL FROST, Florida
SCOTT FRANKLIN, Florida              YADIRA CARAVEO, Colorado
DALE STRONG, Alabama                 SUMMER LEE, Pennsylvania
MAX MILLER, Ohio                     JENNIFER McCLELLAN, Virginia
RICH McCORMICK, Georgia              TED LIEU, California
MIKE COLLINS, Georgia                SEAN CASTEN, Illinois,
BRANDON WILLIAMS, New York             Vice Ranking Member
TOM KEAN, New Jersey                 PAUL TONKO, New York
VACANCY
                         C  O  N  T  E  N  T  S

                             June 22, 2023

                                                                   Page

Hearing Charter..................................................     2

                           Opening Statements

Statement by Representative Frank Lucas, Chairman, Committee on 
  Science, Space, and Technology, U.S. House of Representatives..    12
    Written Statement............................................    13

Statement by Representative Zoe Lofgren, Ranking Member, 
  Committee on Science, Space, and Technology, U.S. House of 
  Representatives................................................    15
    Written Statement............................................    16

                               Witnesses:

Dr. Jason Matheny, President & CEO, RAND Corporation
    Oral Statement...............................................    17
    Written Statement............................................    19

Dr. Shahin Farshchi, General Partner, Lux Capital
    Oral Statement...............................................    24
    Written Statement............................................    26

Mr. Clement Delangue, Co-founder & CEO, HuggingFace
    Oral Statement...............................................    31
    Written Statement............................................    33

Dr. Rumman Chowdhury, Responsible AI Fellow, Harvard
    Oral Statement...............................................    38
    Written Statement............................................    40

Dr. Dewey Murdick, Executive Director, Center for Security and 
  Emerging Technology
    Oral Statement...............................................    46
    Written Statement............................................    48

Discussion.......................................................    56

              Appendix: Answers to Post-Hearing Questions

Dr. Jason Matheny, President & CEO, RAND Corporation.............    86

Dr. Shahin Farshchi, General Partner, Lux Capital................    97

Mr. Clement Delangue, Co-founder & CEO, HuggingFace..............   100

Dr. Dewey Murdick, Executive Director, Center....................   107

 
                        ARTIFICIAL INTELLIGENCE:
                          ADVANCING INNOVATION
                     TOWARDS THE NATIONAL INTEREST

                              ----------                              


                        THURSDAY, JUNE 22, 2023

                          House of Representatives,
               Committee on Science, Space, and Technology,
                                                   Washington, D.C.

    The Committee met, pursuant to notice, at 10:02 a.m., in 
room 2318 of the Rayburn House Office Building, Hon. Frank 
Lucas [Chairman of the Committee] presiding.
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]

    Chairman Lucas. The Committee will come to order. Without 
objection, the Chair is authorized to declare recess of the 
Committee at any time.
    Welcome to today's hearing entitled ``Artificial 
Intelligence: Advancing Innovation in the National Interest.'' 
I recognize myself for five minutes for an opening statement.
    Good morning, and welcome to what I anticipate will be one 
of the first of multiple hearings on artificial intelligence 
(AI) that the Science, Space, and Technology Committee will 
hold this Congress. As we've all seen, AI applications like 
ChatGPT have taken the world by storm. The rapid pace of 
technological progress in this field, primarily driven by 
American researchers, technologists, and entrepreneurs, 
presents a generational opportunity for Congress. We must 
ensure the United States remains the leader in a technology 
that many experts believe is as transformative as the internet 
and electricity.
    The purpose of this hearing is to explore an important 
question, perhaps the most important question for Congress 
regarding AI. How can we support innovation development in AI 
so that it advances our national interest? For starters, most 
of us can agree that this is in our national interest to ensure 
cutting-edge AI research continues happening here in America 
and is based on our democratic values. Although the United 
States remains the country where the most sophisticated AI 
research is happening, this gap is narrowing.
    A recent study by Stanford University ranked universities 
by the number of AI papers they published. The study found that 
nine of the top 10 universities were based in China. Coming in 
at 10th was the only U.S. institution, the Massachusetts 
Institute of Technology. Chinese-published papers received 
nearly the same percentage of citations as U.S. researchers' 
papers, showing the gap in research quality is also 
diminishing.
    It is in our national interest to ensure the United States 
has a robust innovation pipeline that supports fundamental 
research all the way through to real-world applications. The 
country that leads in commercial and military applications will 
have a decisive advantage in global economic and geopolitical 
competition. The frontlines of the war in Ukraine are already 
demonstrating how AI is being applied to the 21st century 
warfare. Autonomous drones, fake images, audio used for 
propaganda, and real-time satellite imagery analysis are all a 
small taste of how AI is shaping today's battlefields.
    However, while it's critical that the United States support 
advances in AI, these advances do not have to come at the 
expense of safety, security, fairness, or transparency. In 
fact, embedding our values in AI technology development is 
central to our economic competitiveness and national security. 
As Members of Congress, our job is never to lose sight of the 
fact that our national interest ultimately lies with what is 
best for the American people.
    The Science Committee has and can continue to play a 
pivotal role in the service on this mission. For starters, we 
can continue supporting the application of AI and advance 
science and new economic opportunities. AI is already being 
used to solve fundamental problems in biology, chemistry, and 
physics. These advances have helped us develop novel 
therapeutics, design advanced semiconductors, forecast crop 
yields, saving countless amounts of time and money.
    The National Science Foundation's AI research Institutes, 
the Department of Energy's world-class supercomputers, and the 
National Institutes of Standards and Technology's (NIST's) Risk 
Management Framework and precision measurement expertise are 
all driving critical advances in this area. Pivotal to our 
national interest is ensuring these systems are safe and 
trustworthy.
    The Committee understood that back in 2020 when it 
introduced the bipartisan National Artificial Intelligence 
Initiative Act of 2020. This legislation created a broad 
national strategy to accelerate investments in responsible AI 
research, development, and standards. It facilitated new 
public-private partnerships to ensure the United States leads 
the world in the development and use of responsible AI systems. 
Our Committee will continue to build off of this work to 
establish and promote technical standards for trustworthy AI. 
We are also exploring ways to mitigate risks caused by AI 
systems through research and development (R&D) of technical 
solutions, such as using automation to detect AI-generated 
media.
    As AI systems proliferate across the economy, we need to 
develop our workforce to meet changing skill requirements, 
helping U.S. workers augment their performance with AI will be 
a critical pillar in maintaining our economic competitiveness.
    And while the United States currently is the global leader 
in AI research, development, and technology, our adversaries 
are catching up. The Chinese Communist Party (CCP) is 
implementing AI industrial policy at a national scale, 
investing billions through State-financed investment funds, 
designating national AI champions, and providing preferential 
tax treatment to grow AI startups. We cannot and should not try 
to copy China's playbook, but we can maintain our leadership 
role in AI, and we can ensure its development with our values 
of trustworthiness, fairness, and transparency. To do so, 
Congress needs to make strategic investments, build our 
workforce, and establish proper safeguards without 
overregulation, but we cannot do it alone. We need the academic 
community, the private sector, and the open-source community to 
help us figure out how to shape the future of this technology.
    I look forward to hearing the recommendations of our 
witnesses on how this Committee can strengthen our Nation's 
leadership in artificial intelligence and make it beneficial 
and safe for all American citizens.
    [The prepared statement of Chairman Lucas follows:]

    Good morning, and welcome to what I anticipate will be the 
first of multiple hearings on artificial intelligence the 
Science, Space, and Technology Committee will hold this 
Congress.
    As we all have seen, A.I. applications like ChatGPT have 
taken the world by storm. The rapid pace of technological 
progress in this field, primarily driven by American 
researchers, technologists, and entrepreneurs, presents a 
generational opportunity for Congress.
    We must ensure the United States remains the leader in a 
technology that many experts believe is as transformative as 
the Internet and electricity.
    The purpose of this hearing is to explore an important 
question-perhaps the most important question for Congress 
regarding A.I.-- how can we support innovative development in 
A.I. so that it advances our national interest?
    For starters, most of us can agree that it is in our 
national interest to ensure cutting-edge A.I. research 
continues happening here in America and is based on our 
democratic values. Although the United States remains the 
country where the most sophisticated A.I. research is 
happening, this gap is narrowing.
    A recent study by Stanford University ranked universities 
by the number of A.I. papers they published. The study found 
that nine of the top ten universities were based in China.
    Coming in at 10th was the only U.S. institution--the 
Massachusetts Institute of Technology. Chinese-published papers 
received nearly the same percentage of citations as U.S. 
researchers' papers, showing the gap in research quality is 
also diminishing.
    It is in our national interest to ensure the United States 
has a robust innovation pipeline that supports fundamental 
research, all the way through to real-world applications.
    The country that leads in commercial and military 
applications will have a decisive advantage in global economic 
and geopolitical competition.
    The frontlines of the war in Ukraine are already 
demonstrating how A.I. is being applied to 21st-century 
warfare--autonomous drones, fake images and audio used for 
propaganda, and real-time satellite imagery analysis are small 
tastes of how A.I. is shaping today's battlefields.
    However, while it is critical the U.S. support advances in 
A.I., these advances do not have to come at the expense of 
safety, security, fairness, or transparency. In fact, embedding 
our values in A.I.'s technological development is central to 
our economic competitiveness and national security.
    As Members of Congress, our job is to never lose sight of 
the fact that our national interest ultimately lies with what 
is best for the American people.
    The Science Committee has and can continue to play a 
pivotal role in the service of this mission. For starters, we 
can continue supporting the application of A.I. in advancing 
science and new economic opportunities.
    A.I. is already being used to solve fundamental problems in 
biology, chemistry, and physics. These advances have helped us 
develop novel therapeutics, design advanced semiconductors, and 
forecast crop yields, saving countless amounts of time and 
money.
    The National Science Foundation's A.I. Research Institutes, 
the Department of Energy's world-class supercomputers, and the 
National Institutes of Standards and Technology's Risk 
Management Framework and precision measurement expertise are 
all driving critical advances in this arena.
    Pivotal to our national interest is ensuring these systems 
are safe and trustworthy. This Committee understood that back 
in 2020 when it ushered the bipartisan National Artificial 
Intelligence Initiative Act of 2020 into law. This legislation 
created a broad national strategy to accelerate investments in 
responsible A.I. research, development, and standards.
    It facilitated new public-private partnerships to ensure 
the U.S. leads the world in the development and use of 
responsible A.I. systems.
    Our committee will continue to build off of this work to 
establish and promote technical standards for trustworthy A.I. 
We are also exploring ways to mitigate risks caused by A.I. 
systems through research and development of technical 
solutions, such as using automation to detect A.I.-generated 
media.
    As A.I. systems proliferate across the economy, we will 
need to develop our workforce to meet changing skills 
requirements. Helping U.S. workers augment their performance 
with A.I. will be a crucial pillar in maintaining our economic 
competitiveness.
    While the United States currently is the global leader in 
A.I. research, development, and technology, our adversaries are 
catching up. The Chinese Communist Party is implementing A.I. 
industrial policy at a national scale, investing billions 
through state-financed investment funds, designating ``national 
A.I. champions,'' and providing preferential tax treatment to 
grow A.I. startups.
    We cannot and should not try to copy China's playbook. But 
we can maintain our leadership role in A.I., and we can ensure 
it's developed with our values of trustworthiness, fairness, 
and transparency.
    To do so, Congress needs to make strategic investments, 
build our workforce, and establish proper safeguards without 
overregulation.
    But we cannot do it alone.
    We need the academic community, the private sector, and the 
open-source community to help us figure out how to shape the 
future of this technology.
    I look forward to hearing the recommendations of our 
witnesses for how this Committee can strengthen our nation's 
leadership in artificial intelligence and make it beneficial 
and safe for all American citizens.

    Chairman Lucas. I now recognize the Ranking Member, the 
gentlewoman from California, for her statement.
    Ms. Lofgren. Thank you. Thank you, Chairman Lucas, for 
holding today's hearing. And I'd also like to welcome a very 
distinguished panel of witnesses.
    Artificial intelligence opens the door to really untold 
benefits for society, and I'm truly excited about its 
potential. However, AI could create risks, including with 
respect to misinformation and discrimination. It will create 
risks to our Nation's cybersecurity in the near term, and there 
may be medium and long-term risks to economic and national 
security, some have even posited existential risks to the very 
nature of our society.
    We're here today to learn more about the benefits and risks 
associated with artificial intelligence. This is a topic that 
has caught the attention of many lawmakers in both chambers 
across many Committees. However, none of this is new to the 
Science Committee. As the Chairman has pointed out, in 2020, 
Members of this Committee developed and enacted the National AI 
Initiative Act to advance research, workforce development, and 
standards for trusted AI. The Federal science agencies have 
since taken significant steps to implement this law, including 
notably NIST's work on the AI Risk Management Framework. 
However, we're still in the early days of understanding how AI 
systems work and how to effectively govern them, even as the 
technology itself continues to rapidly advance in both 
capabilities as well as applications. I do believe regulation 
of AI may be necessary, but I'm also keenly aware that we must 
strike a balance that allows for innovation and ensures that 
the United States maintains leadership.
    While the contours of a regulatory framework are still 
being debated, it's clear we will need a suite of tools. Some 
risks can be addressed by the laws and standards already on the 
books. It's possible others may need new rules and norms. Even 
as this debate continues, Congress can act now to improve trust 
in AI systems and assure America's continued leadership in AI. 
At a minimum, we need to be investing in the research and 
workforce to help us develop the tools we need going forward.
    Let me just wrap up with one concrete challenge I'd like to 
address in this hearing. One is--it's the intersection of AI 
and intellectual property. Whether addressing AI-based inputs 
or outputs, it's my sincere hope that the content creation 
community and AI platforms can advance their dialog and arrive 
at a mutually agreeable solution. If not, I think we need to 
have a discussion on how the Congress should address this.
    Finally, research in infrastructure workforce challenges 
are also top of mind. One of the major barriers to developing 
an AI-capable workforce and ensuring long-term U.S. leadership 
is a lack of access to computing and training data for all but 
large companies and the most well-researched institutions. 
There are good ideas already underway at our agencies to 
address this challenge, and I'd like to hear the panel's input 
on what's needed in your view. It's my hope that Congress can 
quickly move beyond the factfinding stage to focus on what this 
institution can realistically do to address the development and 
deployment of trustworthy AI.
    At this hearing, I hope we can discuss what the Science 
Committee should focus on. I look forward to today's very 
important discussion with stakeholders from industry, academia, 
and venture capital. And as a Representative from Silicon 
Valley, I know how important private capital is today to the 
U.S. R&D ecosystem.
    Thank you all for being with us, and I yield back.
    [The prepared statement of Ms. Lofgren follows:]

    Thank you, Chairman Lucas, for holding today's hearing. I 
would also like to welcome our distinguished panel of 
witnesses.
    Artificial Intelligence opens the door to untold benefits 
for society, and I'm truly excited about its potential.
    However, AI could create risks including with respect to 
misinformation and discrimination. It will create significant 
risks to our Nation's cybersecurity in the near term. There may 
be medium- and long-term risks to economic and national 
security. Some have even posited even existential risks to the 
very nature of our society.
    We are here today to learn more about the benefits and 
risks associated with artificial intelligence. This is a topic 
that has caught the attention of many lawmakers in both 
chambers across many committees. However, none of this is new 
to the Science Committee. In 2020, Members of this Committee 
developed and enacted the National AI Initiative Act to advance 
research, workforce development, and standards for trustworthy 
AI.
    The Federal science agencies have since taken significant 
steps to implement this law, including, notably, NIST's work on 
the AI Risk Management Framework. However, we are still in the 
early days of understanding how AI systems work and how to 
effectively govern them, even as the technology itself 
continues to rapidly advance in both capabilities and 
applications. I do believe regulation of AI may be necessary, 
but I am also keenly aware that we must strike a balance that 
allows for innovation and ensures the U.S. maintains 
leadership.
    While the contours of a regulatory framework are still 
being debated, it is clear we will need a suite of tools. Some 
risks can be addressed by the laws and standards already on the 
books. It's possible others may need new rules and norms.
    Even as this debate continues, Congress can act now to 
improve trust in AI systems and ensure America's continued 
leadership in AI. At a minimum, we need to be investing in the 
research and workforce to help us develop the tools we will 
need going forward.
    Let me just wrap up with a few concrete challenges I'd like 
to address in this hearing. One is the intersection of AI and 
intellectual property. Whether addressing AI-based inputs or 
outputs, it is my sincere hope that the content creation 
community and AI platforms can advance their dialogue and 
arrive at a mutually agreeable solution.
    Finally, research infrastructure and workforce challenges 
are also top of mind. One of the major barriers to developing 
an AI-capable workforce and ensuring long-term US leadership is 
a lack of access to computing and training data for all but 
large companies and the most well-resourced institutions. There 
are good ideas already underway at our agencies to address this 
challenge. I'd like to hear the panel's input on what's needed. 
It is my hope that we in Congress can quickly move beyond the 
fact-finding stage to focus on what this institution can 
realistically do to address the development and deployment of 
trustworthy AI.
    At this hearing, I hope we can discuss what the Science 
Committee should focus on.
    I look forward to today's very important discussion with 
stakeholders from industry, academia, and venture capital. As a 
representative from Silicon Valley, I know how important 
private capital is to the U.S. R&D Ecosystem. Thank you all for 
being with us today.
    I yield back.

    Chairman Lucas. The Ranking Member yields back.
    Let me introduce our witnesses for today's hearing. Our 
first witness today is Dr. Jason Matheny, Chair, President, and 
CEO (Chief Executive Officer) of RAND Corporation. Prior to 
becoming the CEO, the doctor led the White House policy on 
technology and national security at the National Security 
Agency in the Office of Science and Technology Policy. He also 
served as Director of the Intelligence Advanced Research 
Projects Activity (IARPA), and I'd also like to congratulate 
him for being selected to serve on the Selection Committee for 
the Board of Trustees for the National Semiconductor Technology 
Center.
    Our next witness is Dr. Shahin Farshchi, the General 
Partner of Lux Capital, one of Silicon Valley's leading 
frontier science and technology investors. He invests in the 
intersection of artificial intelligence and science and has co-
founded and invested in many companies that have gone on to 
raise billions of dollars.
    Our third witness of the day is Dr. Clement Delangue, co-
founder and CEO of HuggingFace, the leading platform for open-
source AI community. It has raised over $100 million, with Lux 
Capital leading their last financing round, and counts over 
10,000 companies and 100,000 developers as users.
    Next, we turn to Dr. Rumman Chowdhury, Responsible AI 
Fellow at Harvard University, and she is a pioneer in the field 
of applied algorithmic ethics, which investigates creating 
technical solutions for trustworthy AI. Previously, she served 
as Director of Machine Learning Accountability at Twitter and 
founder of Parity, an enterprise algorithmic auditing company.
    And our final witness is Dr. Dewey Murdick, the Executive 
Director of Georgetown Center for Security and Emerging 
Technology (CSET). He previously served as the Chief Analytics 
Officer and Deputy Chief Scientist within the Department of 
Homeland Security (DHS) and has also co-founded an office in 
predictive intelligence at IARPA.
    Thank you, all witnesses, for being here today. And I 
recognize Dr. Matheny for the first five minutes to present 
your testimony and overlook my phonetic weaknesses.

                TESTIMONY BY DR. JASON MATHENY,

               PRESIDENT & CEO, RAND CORPORATION

    Dr. Matheny. No problem at all. Thanks so much, Chairman 
Lucas, Ranking Member Lofgren, and Members of the Committee. 
Good morning, and thank you for the opportunity to testify. As 
mentioned, I'm the President and CEO of RAND, a nonprofit and 
nonpartisan research organization, and one of our priorities is 
to provide detailed policy analysis relevant to AI in the years 
ahead. We have many studies underway relevant to AI. I'll focus 
my comments today on how the Federal Government can advance AI 
in a beneficial and trustworthy manner for all Americans.
    Among a broad set of technologies, AI stands out both for 
its rate of progress and for its scope of applications. AI 
holds the potential to broadly transform entire industries, 
including ones that are critical to our future prosperity. As 
noted, the United States is currently the global leader in AI. 
However, AI systems have security and safety vulnerabilities, 
and a major AI-related accident in the United States or a 
misuse could dissolve our lead much like nuclear accidents set 
back the acceptance of nuclear power in the United States.
    The United States can make safety a differentiator for our 
AI industry, just as it was a differentiator for our early 
aviation and pharmaceutical industries. Government involvement 
in safety standards and testing led to safer products, which in 
turn led to consumer trust and market leadership. Today, 
government involvement can build consumer trust in AI that 
strengthens the U.S. position as a market leader. And this is 
one reason why many AI firms are calling for government 
oversight to ensure that AI systems are safe and secure: It's 
good for their business.
    I'll highlight five actions that the Federal Government 
could take to advance trustworthy AI within the jurisdiction of 
this Committee. First is to invest in potential research 
moonshots for trustworthy AI, including generalizable 
approaches to evaluate the safety and security of AI systems 
before they're deployed; second, fundamentals of designing 
agents that will persistently follow a set of values in all 
situations; and third, microelectronic controls embedded in AI 
chips to prevent the development of large models that lack 
safety and security safeguards.
    A second recommendation is to accelerate AI safety and 
security research and development through rapid high-return-on-
investment techniques such as prize challenges. Prizes pay only 
for results and remove the costly barrier to entry for 
researchers who are writing applications, making them a cost-
effective way to pursue ambitious research goals while opening 
the field to non-traditional performers such as small 
businesses.
    A third policy option is to ensure that U.S. AI efforts 
conduct risk assessments prior to the training of very large 
models, as well as safety evaluations and red team tasks prior 
to the deployment of large models.
    A fourth option is to ensure that the National Institute of 
Standards and Technology has the resources needed to continue 
applications of the NIST Risk Management Framework and fully 
participate in key international standards relevant to AI, such 
as ISO/SC 42.
    A fifth option is to prevent intentional or accidental 
misuse of advanced AI systems by requiring that companies 
report the development or distribution of very large AI 
computing clusters, training runs, and train models, such as 
those involving over 10 to the 26th operations.
    Second, include in Federal contracts with cloud computing 
providers requirements that they employ know-your-customer 
screening for all customers before training large AI models.
    And third, include in Federal contracts with AI developers 
know-your-customer screening, as well as security requirements 
to prevent the theft of large AI models.
    Thank you for the opportunity to testify, and I look 
forward to your questions later.
    [The prepared statement of Dr. Matheny follows:]
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    Chairman Lucas. Thank you. And I recognize Dr. Farshchi for 
five minutes to present his testimony.

               TESTIMONY BY DR. SHAHIN FARSHCHI,

                  GENERAL PARTNER, LUX CAPITAL

    Dr. Farshchi. Thank you, Mr. Chairman.
    Chairman Lucas, Ranking Member Lofgren, and Members of the 
Committee, my name is Dr. Shahin Farshchi, and I'm a General 
Partner at Lux Capital, a venture capital firm with $5 billion 
of assets under management. Lux specializes in building and 
funding tomorrow's generational companies that are leveraging 
breakthroughs in science and engineering. I have helped create 
and fund companies pushing the state-of-the-art in 
semiconductors, rockets, satellites, driverless cars, robotics, 
and AI.
    From that perspective, there are two important 
considerations for the Committee today. One, preserving 
competition in AI to ensure our country's position as a global 
leader in the field; and two, driving Federal resources to our 
most promising AI investigators.
    Before addressing directly how America can reinforce its 
dominant position in AI, it is important to appreciate how some 
of Lux's portfolio companies are pushing the state-of-the-art 
in the field. HuggingFace, whose founder Clement Delangue is a 
witness on this panel, MosaicML, a member of the HuggingFace 
community, is helping individuals enterprises train, tune, and 
run the most advanced AI models. Mosaic's language models have 
exceeded the performance of OpenAI's GPT-3. Unlike OpenAI, 
Mosaic's models are made available to their customers entirely, 
as opposed to through an application programming interface, 
thereby allowing customers to keep all of their data private. 
Mosaic built the most downloaded LLM (large language model) in 
history MPT-7B, which is a testament to the innovations coming 
into the open source from startups and researchers.
    RunwayML is bringing the power of generative AI to 
consumers to generate videos from simple text and images. 
Runway is inventing the future of creative tools with AI, 
thereby reimagining how we create so individuals can achieve 
the same expressive power of the most powerful Hollywood 
studios. These are just a few examples of Lux companies 
advancing the state-of-the-art in AI, in large part because of 
the vital work of this Committee to ensure America is 
developing its diverse talent, providing the private sector 
with helpful guidance to manage risk, and democratizing access 
to computing resources that fuel AI research and the next 
generation of tools to advance the U.S. national interest.
    To continue America's leadership, we need competitive 
markets to give entrepreneurs the opportunity to challenge even 
the largest dominant players. Unfortunately, there are steep 
barriers to entry for AI researchers and founders. The most 
advanced AI generative models cost more than $100 million to 
train. If we do not provide open, fair, and diverse access to 
computing resources, we could see a concentration of resources 
in a time of rapid change, reminiscent of Standard Oil during 
the Industrial Revolution.
    I encourage this Committee to continue its leadership in 
democratizing access to AI R&D by authorizing and funding the 
National AI Research Resource, NAIRR. This effort will help 
overcome the access divide and ensure that our country is 
benefiting from diverse perspectives that will build the future 
of AI technology and help guide its role in our society. I am 
particularly concerned that Google, Amazon, and Microsoft, 
which are already using vast amounts of personal data to train 
their models, have also attracted a vast majority of 
investments in AI startups because of the need to access their 
vast computing resources to train AI models, further 
entrenching their preexisting dominance in the market. In fact, 
Google and Microsoft are investing heavily in AI startups under 
the condition that their invested dollars are spent solely on 
their own compute resources.
    One example is AI--is OpenAI's partnership with Microsoft. 
Through efforts such as NAIRR, we hope that competitors of 
Google, Microsoft, and Amazon will be empowered to offer 
compute resources to fledgling AI startups, while perhaps even 
endowing compute resources directly to the startups and 
researchers as well. This will facilitate a more competitive 
environment that will be more conducive to our national 
dominance at the global stage.
    Furthermore, Congress must rebalance efforts toward 
providing resources with deep investment into top AI 
investigators. For example, the DOD (Department of Defense) has 
taken a unique approach by allocating funding to top 
investigators, as opposed to the National Science Foundation, 
which tends to spread funding across a larger number of 
investigators. When balanced appropriately, both approaches 
have value to the broader innovation ecosystem. However, deep 
investment has driven discoveries at the frontier, leading to 
the creation of great companies like OpenAI, whose dollars were 
initially funded--whose founders were initially funded by 
relatively large DOD grant dollars. Building on these successes 
is key to America's continued innovation--its continued success 
in AI innovation.
    Thank you for the opportunity to share how Lux Capital is 
working with founders to advance AI in our national interest, 
bolster our national defense and security, strengthen our 
economic competitiveness, and foster innovation right here in 
America. Lux is honored to play a role in this exciting 
technology at this pivotal moment. I look forward to your 
questions.
    [The prepared statement of Dr. Farshchi follows:]
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    Chairman Lucas. Thank you. And I recognize Mr. Delangue for 
five minutes for his testimony.

               TESTIMONY BY MR. CLEMENT DELANGUE,

                 CO-FOUNDER & CEO, HUGGINGFACE

    Mr. Delangue. Chairman Lucas, Ranking Member Lofgren, and 
Members of the Committee, thank you for the opportunity to 
discuss AI innovation with you. I deeply appreciate the work 
you are doing to advance and guide it in the United States. My 
name is Clement Delangue, and I'm the co-founder and CEO of 
HuggingFace. I'm French, as you can hear from my accent, and 
moved to the United States 10 years ago, barely speaking 
English. With my co-founders, Julien and Thomas, we started 
this company from scratch here in the United States as a U.S. 
startup, and we are proud to employ team members in 10 
Different U.S. States today. I believe we could not have 
created this company anywhere else. I am living proof that the 
openness and culture of innovation in the United States allows 
for such a story to happen.
    The reason I'm testifying today is not so much the size of 
our organization or the cuteness of our emoji name HuggingFace. 
And contrary to what you said, Chairman Lucas, I don't hold a 
Ph.D. like all the other witnesses. But the reason I'm here 
today is because we enable 15,000 small companies--startups, 
nonprofits, public organizations, and companies--to build AI 
features and workflows. Collectively on our platform, they have 
shared over 200,000 open models 5,000 new ones just last week, 
50,000 open datasets, and 100,000 applications, ranging from 
data anonymization for self-driving cars, speech recognition 
from visual lip movement for people with hearing disabilities, 
applications to detect gender and racial biases, translation 
tools in low-resource languages to share information globally, 
not only with large language models and generative AI, but also 
with all sorts of machine-learning algorithms with usually 
smaller, customized specialized models in domains as diverse as 
social productivity platforms, finance, biology, chemistry, and 
more.
    We are seeing firsthand that AI provides a unique 
opportunity for value creation, productivity boosting, and 
improving people's lives, potentially at the larger scale and 
higher velocity than the internet or software before. However, 
for this to happen across all companies and at a sufficient 
scale for the United States to keep leading compared to other 
countries, I believe open science and open source are critical 
to incentivize and are extremely aligned with American values 
and interests.
    First, it's good to remember that most of today's progress 
has been powered by open science and open source like the 
Attention Is All You Need paper, the BERT paper, the latent 
diffusion paper, and so many others. The same way without open 
source PyTorch, TensorFlow, Keras, transformers, diffusers, all 
invented here in the United States, the United States might not 
be the leading country for AI.
    Now, when we look toward the future, open science and open 
source distribute economic gains by enabling hundreds of 
thousands of small companies and startups to build with AI. It 
fosters innovation and fair competition between all. Thanks to 
ethical openness, it creates a safer path for the development 
of the technology by giving civil society, nonprofits, 
academia, and policymakers the capabilities they need to 
counterbalance the power of big private companies.
    Open science and open source prevents black box systems, 
make companies more accountable, and help solve today's 
challenges like mitigating biases, reducing misinformation, 
promoting copyrights, and rewarding all stakeholders, including 
artists and content creators, in the value creation process. 
Our approach to ethical openness combines institutional 
policies such as documentation with model cards pioneered by 
our own Dr. Margaret Mitchell, technical safeguards such as 
staged releases, and community incentives like moderation and 
opt-in/opt-out datasets.
    There are many examples of safer AI thanks to openness, 
like Bloom, an open model that has been assessed by Stanford as 
the most compliant model with the EU AI Act or the research 
advancements in watermarking for AI content. Some of that you 
can only do with open models and open datasets.
    In conclusion, by embracing ethical AI development with a 
focus on open science and open source, I believe the United 
States can start a new era of progress for all, amplify its 
worldwide leadership, and give more opportunities to all like 
it gave to me. Thank you very much.
    [The prepared statement of Mr. Delangue follows:]
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    Chairman Lucas. Absolutely. And thank you, Mr. Delangue. 
And I would note probably that there would be some of my 
colleagues who would note that your version of English might be 
more understandable than my Okie dialect.
    But setting that issue aside, I now recognize Dr. Chowdhury 
for five minutes for her testimony.

               TESTIMONY BY DR. RUMMAN CHOWDHURY,

                 RESPONSIBLE AI FELLOW, HARVARD

    Dr. Chowdhury. Thank you, Chairman Lucas, Ranking Member 
Lofgren, and esteemed Members of the Committee. My name is Dr. 
Rumman Chowdhry, and I'm an AI developer, data scientist, and 
social scientist. For the past seven years, I've helped address 
some of the biggest problems in AI ethics, including holding 
leadership roles in responsible AI at Accenture, the largest 
tech consulting firm in the world, and at Twitter. Today, I'm a 
responsible AI Fellow at Harvard University. I'm honored to 
provide testimony on trustworthy AI and innovation.
    Artificial intelligence is not inherently neutral, 
trustworthy, nor beneficial. Concerted and directed effort is 
needed to ensure this technology is used appropriately. My 
career in responsible AI can be described by my commitment to 
one word: Governance. People often forget that governance is 
more than the law. Governance is a spectrum, ranging from codes 
of conduct, standards, open research, and more. In order to 
remain competitive and innovative, the United States would 
benefit from a significant investment in all aspects of AI 
governance.
    I would like to start by dispelling the myth that 
governance stifles innovation. Much to the contrary, I use the 
phrase ``Brakes help you drive faster'' to explain this 
phenomenon. The ability to stop your car in dangerous 
situations is what enables us to feel comfortable driving at 
fast speeds. Governance is innovation.
    This holds true for the current wave of artificial 
intelligence. Recently, a leaked Google memo declared there is 
no moat. In other words, AI will be unstoppable as open-source 
capabilities meet and surpass closed models. There is also a 
concern about the United States remaining globally competitive 
if we aren't investing in AI development at all costs. This is 
simply untrue. Building the most robust AI industry isn't just 
about processors and microchips. The real competitive advantage 
is trustworthiness.
    If there's one thing to take away from my testimony, it's 
that the U.S. Government should invest in public accountability 
and transparency of AI systems. In this testimony, I describe 
how. I make the following four recommendations to ensure the 
United States advances innovation in the national interest: 
First, support for AI model access to enable independent 
research and audit; second, investment in and legal protections 
for red teaming and third-party ethical hacking; third, the 
development of a non-regulatory technology body to supplement 
existing U.S. Government oversight efforts; fourth, 
participation in global AI oversight.
    CEOs of the most powerful AI companies will tell you that 
they spend significant resources to build trustworthy AI. This 
is true. I was one of those people. My team and I held 
ourselves to the highest ethical standards, and as--and my 
colleagues who remain in these roles still do so today. 
However, a well-developed ecosystem of governance also empowers 
individuals whose organizational missions are to inform and 
protect society. The DSA's (Digital Services Act's) article 40 
creates this sort of access for Europeans. Similarly, the U.K. 
Government has announced Google DeepMind and OpenAI will allow 
model access. My first recommendation is the United States 
should match these efforts.
    Next, new laws are mandating third-party algorithmic 
auditing. However, there is currently a workforce challenge in 
identifying sufficiently trained third-party algorithmic 
investigators. Two things can fix this. First, funding for 
independent groups to conduct red teaming and adversarial 
auditing; and second, legal protections so these individuals 
operating in the public good are not silenced with litigation.
    With the support of the White House, I am part of a group 
designing the largest-ever AI red teaming exercise in 
collaboration with the largest open and closed source AI 
companies. We will provide access to thousands of individuals 
who will compete to identify how these models may produce 
harmful content. Red teaming is a process by which invited 
third-party experts are given special-permission access by AI 
companies to find flaws in their models. Traditionally, these 
practices happen behind closed doors, and public information-
sharing is at the company's discretion. We want to open those 
closed doors. Our goals are to educate, address 
vulnerabilities, and importantly, grow a new profession.
    Finally, I recommend investment in domestic and global 
government institutions in alignment with this third-party 
robust ecosystem. A centralized body focus on responsible 
innovation could assist existing oversight by promoting 
interoperable licensing, conducting research to inform AI 
policy, and sharing best practices and resources. Parallels in 
other governments include the U.K. Center for Data Ethics and 
Innovation, of which I'm a board member, and the EU Center for 
Algorithmic Transparency.
    There's also a sustained an increasing call for global 
governance of AI system, among them, experts like myself, 
OpenAI CEO Sam Altman, and former New Zealand Prime Minister 
Jacinda Ardern. A global governance effort should develop 
empirically driven, enforceable solutions for algorithmic 
accountability and promote global benefits of AI systems.
    In sum, innovation in the national interest starts with 
good governance. By investing in and protecting this ecosystem, 
we will ensure AI technologies are beneficial to all. Thank you 
for your time.
    [The prepared statement of Dr. Chowdhury follows:]
   [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    Chairman Lucas. Thank you, Doctor.
    And I now recognize Dr. Murdick for five minutes to present 
his testimony.

      TESTIMONY BY DR. DEWEY MURDICK, EXECUTIVE DIRECTOR,

          CENTER FOR SECURITY AND EMERGING TECHNOLOGY

    Dr. Murdick. Thank you, Chairman Lucas, Ranking Member 
Lofgren, and everyone on the Committee, to be--have this 
opportunity to talk about how we can make AI better for our 
country.
    There are many actions Congress can do to support AI 
innovation, protect key technology from misuse, and ensure 
customers or consumers are safe. I'd like to highlight three 
today.
    First, we need to get used to working with AI as a society 
and individually. We need to learn what--when we can trust our 
AI teammates and when to question or ignore them. I think this 
takes a lot of training and time.
    Two, we need skilled people to build future AI systems and 
to increase AI literacy.
    Three, we need to keep a close eye on the policies that we 
do enact. We need to make sure that every policy is being--
action is being monitored and make sure it's actually doing 
what we think it's doing and update that as we need to. This is 
especially true when we're facing peer innovators, and 
especially in a rapidly changing area like artificial 
intelligence.
    China is such a peer innovator, but we need to remember 
they're not 10 feet tall, and they have different priorities 
for their AI than we do. China's AI leadership is evident 
through aggressive use of State power and substantial research 
investments, making it a peer innovator for us and our allies, 
never far ahead and never far behind either. China focuses on 
how AI can assist military decisionmaking and mass surveillance 
to help maintain societal control. This is very unlike the 
United States, thankfully. Managing that control means they're 
not letting AI run around all willy nilly. In fact, the 
deploying of--the deployment of large language models does not 
appear to be a priority for China's leadership precisely for 
that reason. We should not let the fear of China surpassing us 
deter our oversight of AI industry and AI technology. Instead, 
the focus should be on developing methods that allow 
enforcement of AI risk and harm management and guiding the 
innovation and advancement of AI technology.
    I'd like to return to my first three points and expand on 
them a little bit. Going back to my opening points, the first 
one was we must get used to working with AI via effective human 
machine teaming, which is central to AI's evolution, in my 
opinion, in the next decade. Understanding what an AI system 
can and cannot do and should and shouldn't do, and when to rely 
on them and when to avoid using them should guide our future 
innovation and also our training standards.
    One thing that keeps me up at night is when human partners 
trust machines when they shouldn't, and there's interesting 
examples of that. They fail to trust AI when they should or are 
manipulated by a system. And there are some instances of that 
also.
    We've witnessed rapid AI advancements, and the convergence 
between AI and other sectors promises widespread innovation in 
areas from medical imaging to manufacturing. Therefore, 
fostering AI literacy across the population is critical for 
economic competitiveness. But also--and I think even more 
importantly--it is essential for democratic governance. We 
cannot engage in a meaningful societal debate about AI if we 
don't understand enough about it. This means an increasingly 
large fraction of the U.S. citizens will encounter AI daily.
    So that's the second point; we need skilled people working 
at all levels. We need innovators from technical and non-
technical backgrounds. We need to attract and retain diverse 
talent from across our Nation and internationally. And 
separately from those who are building the AI systems, these 
future and current ones, we need comprehensive AI training for 
the general population, K through 12 curricula, certifications. 
There's a lot of good ideas there. AI literacy is the central 
key, though.
    So what else can we do? I think we can promote better 
decisionmaking by gathering information now that we need to 
make decisions. For example, tracking AI harms via incident 
reporting is a good way to learn where things are breaking, 
learning how to request key model and training data for 
oversight to make sure it's being used in important 
applications correctly. We don't know how to do that. 
Encouraging and developing third-party auditing, an ecosystem, 
the red teaming ecosystem, excellent.
    If we are going to license AI software, which is a common 
proposal we hear, we're probably going to need to update 
existing authorities for existing agencies, and we may need to 
create a new one, a new agency or organization. This new 
organization could check how AI is being used and overseen by 
existing agencies. It can be the first to deal with problems 
directing those in need to the right solutions, either in the 
government or private sector, and fill gaps in sector-specific 
agencies.
    My last point--and I see I'm going too long--we need to 
make sure our policies are monitored and effectively 
implemented. There's really great ideas in the House and Senate 
on how to increase the analytic capacity to do that.
    I look forward to this discussion because I think this is a 
persistent issue that is just not going to go away, and CSET 
and I have dedicated our professional lives to this. So thank 
you so much.
    [The prepared statement of Dr. Murdick follows:]
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    Chairman Lucas. Thank you, Doctor. And thank you to the 
entire panel for some very insightful opening comments.
    Continuing with you, Dr. Murdick, making AI systems safer 
is not only a matter of regulations, but also requires 
technical advances in making the systems more reliable, 
transparent, and trustworthy. It seems to me that the United 
States would be more likely than China to invest in these 
research areas, given our democratic values. So, Doctor, can 
you compare or expand on your comments earlier? Can you compare 
how the Chinese Communist Party and the United States' 
political values influence their research and development 
priorities for AI systems?
    Dr. Murdick. Sure. I think that the large language models, 
which is the obsession right now of a lot of the AI systems, 
provides a very interesting example of this. China is very 
concerned about how it can destabilize their societal structure 
by having a system that they can't control and might say things 
that would be offensive about--they might bring up the 
Tiananmen Square or might associate the president with Winnie 
the Pooh or something, and that can be very destructive to 
their way of--their societal control. Because of this, they're 
really limiting control. They're passing regulations and laws 
that are very constraining of how that's going. So that is a 
difference between our societies and how we--what we view as 
acceptable.
    I do think the military control, the command-and-control 
emphasis, as well as the desire to maintain control through 
mass surveillance, if you look at their research portfolio, 
most of where they're leading is--could be very well associated 
with those types of areas, so I think those are differences 
that are pretty significant.
    We can go on, but I'm going to just pause there to make 
sure other people have----
    Chairman Lucas. Absolutely. Dr. Matheny, some advocated for 
a broad approach to AI regulations such as restricting entire 
categories of AI systems. In the United States, many agencies 
already have the existing authorities to regulate the user 
cases of AI in their jurisdiction. For example, the Department 
of Transportation can perform set performance benchmarks that 
autonomous vehicles (AVs) must meet to drive on U.S. roads. 
What are your opinions regarding outcomes of user case-driven 
approach to AI regulation versus an approach that places broad 
restrictions on AI development or deployment?
    Dr. Matheny. Thank you, Mr. Chairman. I think that in many 
of the cases that we're most concerned about related to risks 
of AI systems, especially these large foundation models, we may 
not know enough in advance to specify the use cases. And those 
are ones then where the kind of testing and red teaming that's 
been described here is really essential. So having terms and 
conditions in Federal contracts with compute providers actually 
might be one of the highest leverage points of governance: We 
could require that models trained on infrastructure that is 
currently federally contracted involve red teaming and other 
evaluation before models are trained or deployed.
    Chairman Lucas. Mr. Delangue, in your opening--in my 
opening statement, I highlighted the importance of ensuring we 
continue to lead in AI research and development because 
American-built systems are more likely to reflect democratic 
values. Given how critical it is to ensure we maintain our 
leadership in AI, how do you recommend Congress ensure that we 
do not pass regulations that stifle innovation?
    Mr. Delangue. I think a good point that you made earlier is 
that AI is so broad and the impact of AI could be so widespread 
across use cases, domain sectors, is--leads to the point that 
for regulation to be effective and not stifle innovation at 
scale, you need it to be, you know, customized and focused on 
specific domains, use cases, and sectors where there are more 
risks and then kind of like empower the whole ecosystem to keep 
growing and keep innovating.
    The parallel that I like to draw is with software, right? 
It's such kind of like a broad, applicable technology that the 
important thing is to regulate the final use cases in the 
specific domains of application of software rather than 
software in general.
    Chairman Lucas. In my remaining moments, Dr. Farshchi, 
advances in civilian AI--civilian R&D often help progress 
defense technologies and enhance national security. How have 
civilian R&D efforts in AI transformed in advances in defense 
applications, and how do you anticipate that relationship 
evolving over the next few years?
    Dr. Farshchi. I expect that relationship to evolve in a 
positive way. I expect it to--I expect there to be a further 
strengthening of the relationship between the private sector 
and dual use and government-targeted products. Thanks to the 
open source, there are many technologies that otherwise would 
have not been available, would it have to be--would have had to 
be reinvented are now made available to build on top of. The 
venture capital community is very excited about funding 
companies that have dual-use products and companies that sell 
to the U.S. Government. Palantir was an example where investors 
profited greatly from investing in a company that was 
targeted--that was targeting the U.S. Government as customers, 
same with SpaceX. And so it's my expectation that this trend 
will continue, that more private dollars will go into funding 
companies that are selling to the U.S. Government and 
leveraging technologies that come out of the open source to 
build on top of to continue innovating the sector.
    Chairman Lucas. Thank you. My time has expired, and I now 
recognize the Ranking Member, Ms. Lofgren.
    Ms. Lofgren. Thank you, Mr. Chairman. And thanks to our 
panelists. This is wonderful testimony. And I'm glad this is 
the first of several hearings because there's a lot to wrap our 
heads around.
    Dr. Chowdhury, as you know, large language models have 
basically vacuumed up all the information from the internet, 
and there is a dispute between copyright holders who feel they 
ought to be compensated, others who feel it's a fair use of the 
information. You know, it's in court. They will probably decide 
it before Congress will.
    But here's the question I've had. What techniques are 
possible to identify copyrighted material in the training 
corpus of the large language models? Is it even possible to go 
back and identify protected material?
    Dr. Chowdhury. Thank you for the excellent question. It's 
not easy. It is quite difficult. What we really need is 
protections for the individuals generating this artwork because 
they are at risk of having not only their work stolen, but 
their entire livelihood taken.
    Ms. Lofgren. No, I understand but the question is----
    Dr. Chowdhury. Yes.
    Ms. Lofgren [continuing]. Retroactively, is it possible to 
identify?
    Dr. Chowdhury. It's difficult to, but yes, it can. One can 
use digital imaging matching. But it's also important to think 
through, you know, what we are doing with the data, what it is 
being used for----
    Ms. Lofgren. No, I understand that as well.
    Dr. Chowdhury. Absolutely. Thank you.
    Ms. Lofgren. One of the things I'm interested in, you 
mentioned CFAA (Computer Fraud and Abuse Act), and that's a 
barrier to people trying to do third-party analysis. I had a 
bill--actually, after Aaron Swartz's untimely and sad demise, I 
had a bill named after him to allow those who are making non-
commercial use to actually do what Aaron was doing. And Jim 
Sensenbrenner was my co-sponsor, since retired, and we couldn't 
get anywhere. There were large vested interests who opposed 
that. Do you think if we approached it just as those who 
register rather than be licensed with the government as non-
commercial entities that are doing redshirting, whether that 
would be a viable model for what you're suggesting?
    Dr. Chowdhury. I think so, yes. I think that would be a 
great start. I do think there would need to be a follow up to 
ensure that people are indeed using it for non-commercial 
purposes.
    Ms. Lofgren. Correct. I'm interested in the whole issue of 
licensing versus registration. I'm mindful that the Congress 
really doesn't know enough in many cases to create a licensing 
regime. And the technology is moving so fast that I fear we 
might make some mistakes or Federal agencies might make some 
mistakes. But licensing, which is giving permission, would be 
different than registration, which would allow for the capacity 
to oversee and prevent harm. What's your thought on the two 
alternatives, anyone who wants to speak?
    Dr. Chowdhury. I can speak to that. I think overly onerous 
licensing would actually prevent people from doing one-off 
experimental or fun exercises. What we have to understand is, 
you know, there is a particular scale of impact, number of 
people being--you know, using a product that maybe would 
trigger licensing, rather than saying everybody needs to 
license. There are college students, high school kids who want 
to use these models and just do fun things, and we should allow 
them to do it.
    Ms. Lofgren. You know, I guess I have some qualms--and 
other Members may--you know, we've got large model AIs, and to 
some extent, they are a black box. Even the creators don't 
fully understand what they're doing. And the idea that we would 
have a licensing regime, I think, is very daunting, as opposed 
to a registration regime where we might have the capacity for 
third parties to do auditing and the like. I'll just lay that 
out there.
    I want to ask anybody who can answer this question, when it 
comes to large language models, generative AI, the computing 
power necessary is so immense that we've ended up with 
basically three very large private-sector entities who have the 
computing capacity to actually do that. Mr. Altman was here, I 
think, last month and opined when he met with us at the Aspen 
Institute breakfast that it might not even be possible to catch 
up in terms of the pure computing power. We've had discussions 
here on whether the government should create the computing 
power to allow not only private sector but academics to be 
competitive. Is that even viable at this point, whoever could 
answer that?
    Dr. Farshchi. I can take that real quick. I believe so. I 
think it is possible. Bear in mind that the compute resources 
that were created by these three entities were initially meant 
for internal consumption----
    Ms. Lofgren. Correct.
    Dr. Farshchi [continuing]. And they have been repurposed 
now for training AI. And semiconductor development, which is 
the core of this technology, there is ultimately a tradeoff 
between narrow functionality and--or breadth of functionality 
and performance at a single use case. And so if there was a 
decision made to build resources that were targeted at training 
large language models, for example, I think it would be 
possible to quickly catch up and build that resource, the same 
way we built specific resources during World War II for a 
certain type of warfare and then again during the Persian Gulf 
War. So I think it's--we as a Nation are capable of doing that.
    Ms. Lofgren. I see my time has expired. I thank you so 
much. And my additional questions we'll send to you after the 
hearing, and I yield back.
    Mr. Obernolte [presiding]. The gentlewoman yields back.
    I will recognize myself for five minutes for my questions. 
And I want to thank you for the really fascinating testimony. 
But, Dr. Matheny, I'd like to start with you. You'd brought up 
the concept of trustworthy AI, and I think that that's an 
extremely important topic. I actually dislike the word 
trustworthy AI because it imparts to AI something that it 
doesn't have, human qualities. It's just a piece of software. I 
was interested, Dr. Murdick, when you said sometimes human 
partners trust AI when they shouldn't and fail to trust it when 
they should, and I think that that is a better way of 
expressing what we mean when we talk about AI.
    But this is an important conversation to have because we in 
Congress, as we contemplate establishing a regulatory framework 
for AI, we need to be explicit when we say we want it to be 
trustworthy. You know, we can talk about efficacy or robustness 
or repeatability, but we need to be very specific when we talk 
about what we mean by trustworthy. It's not helpful to use 
evocative terms like, well, AI has to share its values, which 
is something that's in the framework of other countries' 
approach to AI. Well, that's great. You know, values, that's a 
human term. What does that mean for AI to have values?
    So the question for you, Doctor, is what do we mean when we 
say trustworthy AI, and, you know, in what context should us as 
lawmakers think about AI as trustworthy?
    Dr. Matheny. Thank you. When we talk about trustworthiness 
of engineered systems, we usually mean do they behave as 
predicted? Do they behave in a way that is safe, reliable, and 
robust given a variety of environments? So, for example, do we 
have trust in our seatbelt? Do we have trust in the antilock 
braking system of a car? Do we have trust in the accident 
avoidance system on an airplane? So those are the kinds of 
properties that we want in our engineered systems is, are they 
safe, are they reliable, are they robust.
    Mr. Obernolte. I would agree. And I think that we can put 
metrics on those things. I just don't think that calling AI 
trustworthy is helpful because we're already having this 
perceptual problem that people are thinking of it in an 
anthropomorphic way. And it isn't. It is just software. And, 
you know, we can talk about the intent. When we deploy it, we 
can talk about the intent when we create it of us as humans, 
but to impart those qualities to the software I think is 
misleading to people.
    Dr. Chowdhury, I want to continue the Ranking Member's line 
of questioning, which I thought was excellent, on the 
intersection between copyright holders and content creators and 
the training of AI because I think that this is going to be a 
really critical issue for us to grapple with. And I mean, 
here's the problem. The--if we say, as some content creators 
have suggested, that no copyrighted content can be used in the 
training of AI, you know, which from their point of view is a 
completely reasonable thing to be saying, but if we do that, 
then we're going to wind up with AI that is fundamentally 
useless in a lot of different domains.
    And let me give you a specific example, because I'd like 
your thoughts on it. I mean, one trademarked term is Super 
Bowl, right? The NFL (National Football League) would not like 
someone using the word Super Bowl in a commercial sense. If you 
own a bar, you have to talk about, you know, the party to watch 
the big game, nudge, nudge, wink, wink, right? Which is, from 
their point of view, completely reasonable. But if you 
prohibited the use of the word Super Bowl in training AI, you'd 
come up with a large language model that if you said what time 
is the Super Bowl, it would have no idea what you were talking 
about. You know, it would lack the--you know, the context to be 
able to answer questions like that. So how do we navigate that 
space?
    Dr. Chowdhury. I think you bring up an excellent point. I 
think these technologies are going to push the upper limits of 
many of the laws we have, including protections for copyright. 
I don't think there's a good answer. I think this is what we 
are negotiating today. The answer will lie somewhere in the 
spectrum. There will be certain terms. I think a similar 
conversation happened about the term ``taco Tuesday'' and the 
ability to use it widely, and it was actually decided you could 
use it widely. I think some of these will be addressed on a 
case-by-case basis. But more broadly, I think the thing to keep 
an eye on is whether or not somebody's livelihood is being 
impacted. It's not really about a word or picture. It is 
actually about whether someone is taken out of a job because of 
a model that's being built.
    Mr. Obernolte. I partially agree. You know, I think it is--
it gets into a very dicey area when we talk about if someone's 
job is being impacted because AI is going to be extremely 
economically disruptive. And our job as lawmakers is to make 
sure that disruption is largely positive for the median person 
in our society. But, you know, jobs will be impacted. We hope 
that most will be positive and not negative.
    I actually think--and I am going to run out of time here, 
but I--we have a large body of legal knowledge already on this 
topic around the concept of fair use, and I think that that 
really is the solution to this problem. There'll be fair use of 
intellectual property in AI, and there'll be things that are--
clearly are not fair use or infringing. And I think that we can 
use that as a foundation, but I'd love to continue the 
discussion later.
    Dr. Chowdhury. Absolutely.
    Mr. Obernolte. Next, we'll recognize the gentlewoman from 
Oregon. Ms. Bonamici, you are recognized for five minutes.
    Ms. Bonamici. Thank you, Mr. Chairman, Ranking Member. 
Thank you to the witnesses for your expertise.
    I acknowledge the tremendous potential of AI but also the 
significant risks and concerns that I've heard about, including 
what we just talked about, potential job displacement, privacy 
concerns, ethical considerations, bias, which we've been 
talking about in this Committee for years, market dominance by 
large firms and in the hands of scammers and fraudsters, a 
whole range of nefarious possibilities to mislead and deceive 
voters and consumers. Also, the datasets take up an enormous 
amount of energy to run. We know--we acknowledge that. So we 
need responsible development with ethical guidelines to 
maximize the benefits and minimize the risks of course.
    So, Dr. Chowdhury, as AI systems move forward, I remain 
concerned about the lack of diversity in the workforce. So 
could you mention how increasing diversity will help address 
bias?
    Dr. Chowdhury. What an excellent point. Thank you so much, 
Congresswoman. So, first of all, we need a wide range of 
perspectives in order to understand the impact of artificial 
intelligence systems. I'll give you a specific example from my 
time at Twitter. We held the first algorithmic bias bounty. 
That meant we opened up a Twitter model for public scrutiny. 
And we learned things that my team of highly educated Ph.D.'s 
wouldn't think of. For example, did you know if you put a 
single dot on a photo, you could change how the algorithm 
decided where to crop the photo? We didn't know that. Somebody 
told us this. Did you know that algorithmic cropping tended to 
crop out people in camouflage because they blended in with 
their backgrounds. It did what camouflage is supposed to do. We 
didn't know that. So we learn more when we bring more people 
in. So you know, open--more open access, independent researcher 
funding, red teaming, et cetera, opening doors to people will 
be what makes our systems more robust.
    Ms. Bonamici. Absolutely, appreciate that so much.
    And I want to continue, Dr. Chowdhury. I want to talk about 
the ethics. And I expect that those in this room will all agree 
that ethical AI is important to align the systems with values, 
respect fundamental rights, contribute positively to society 
while minimizing potential harms and gets to this 
trustworthiness issue, which you mentioned and that we've been 
talking about. So who defines what ethical is? Is there a 
universal definition? Does Congress have a role? Is this being 
defined by industry? I know there's a bipartisan proposal for a 
Blue Ribbon Commission to develop a strategy for regulating AI. 
Would this be something that they would handle or would NAIRR 
be involved?
    And also, I'm going to tell you the second part of this 
question and then let you respond. In your testimony, you talk 
about ethical hackers and your testimony explains the role that 
they play, but how can they help design and implement ethical 
systems? And how can policy differentiate between bad hackers 
and ethical hackers?
    Dr. Chowdhury. Both great questions. So, first, I want to 
address the first part of what you brought up is who defines 
ethics. And, you know, fortunately, this is not a new problem 
with technology. We have grappled with this in the law for 
many, many years. So, you know, I recognize that. Previously, 
someone mentioned that we seem to think a lot of problems are 
new with technology. This is not a new problem. And usually 
what we do is we get at this by a diversity of opinions and 
input and also ensuring that our AI is reflective of our 
values. And we've articulated our democratic values, right, for 
the United States. We have the blueprint of the AI Bill of 
Rights. We have the NIST AI Risk Management Framework. So we've 
actually as a nation sat down and done this.
    So to your second question on ethical hackers, ethical 
hackers are operating in the public good, and there is a very 
clear difference. So what an ethical hacker will do is, for 
example, identify a vulnerability in some sort of a system, and 
often, they actually go to the company first to say, hey, can 
you fix this? But often, these individuals are silenced with 
threats of litigation, so what we need to do is actually have 
increasing protections for these individuals who are operating 
in the public good, have repositories where people can share 
this information with each other, and also allow companies to 
be part of this process. For example, what might responsible 
disclosure look like? How can we make this not an adversarial 
environment where it's the public versus companies but the 
public as a resource for companies to improve what they're 
doing above and beyond what they're able to do themselves?
    Ms. Bonamici. I appreciate that. And I want to follow up on 
the earlier point about--yes, and I'm aware of the work that's 
been done so far on the ethical standards. However, I'm just 
questioning whether this is something that--does it need to be 
put into law, to regulation? Does everyone agree? And could 
this--is there hope that there could be some sort of universal 
standard?
    Dr. Chowdhury. I do not----
    Ms. Bonamici. Ethical standard.
    Dr. Chowdhury. I do not think a universal ethical standard 
is possible. We live in a society that reflects diversity of 
opinions, thought, and we need to respect that and encourage 
that. But how do we prevent--how do we create the safeguards 
and identify what is harmful? In social media, we think a lot 
about what is harmful content, toxic content, and all of this 
lives on the spectrum. And I think any governance that's 
created has to respect that society changes, people change, the 
words and terms we use to reflect things change, and our ethics 
will change as well. So creating a flexible framework by which 
we can create ethical guidelines to help people make smart 
decisions is a better way to go.
    Ms. Bonamici. And just to confirm, that would be voluntary, 
not mandatory? Oh, I see my time has expired. I must yield 
back. Thank you. Could you--can she just answer that question, 
just yes or no?
    Mr. Obernolte. Certainly.
    Dr. Chowdhury. Yes.
    Ms. Bonamici. Thank you.
    Mr. Obernolte. The gentlewoman yields back.
    We'll go next to my colleague from California. Congressman 
Issa, you're recognized for five minutes.
    Mr. Issa. Thank you. I'll continue right where she left 
off, Dr. Chowdhury. The--boy, the last answer got me just 
perfect because--so it's going to be voluntary? So who decides 
who puts that dot in a place that affects the cropping?
    Dr. Chowdhury. I'm going to continue my answer. So it can 
be voluntary, but in certain cases, in high risk cases, in high 
impact----
    Mr. Issa. We don't do voluntary here in Congress. You know 
that.
    Dr. Chowdhury. I do. But I also do recognize that the way 
that we have structured regulation in the United States is 
context-specific, you know, financial authorities----
    Mr. Issa. OK. Well, let's get a context because----
    Dr. Chowdhury. Yes.
    Mr. Issa [continuing]. And it's for a couple of you. If 
you've got a Ph.D., you're eligible to answer this question. 
And if you've published a book, you're eligible to answer this 
question. Now, the most knowledgeable person on AI up here at 
the dais that I know of is sitting in the chair right now, so--
but he happened to say fair use. And of course, that gets my 
hackles up as the Chairman of the Subcommittee that determines 
what fair use is. Now, how many of you went to college and 
studied from books that were published for the pure purpose of 
your reading them, usually by the professor who wrote them? 
Raise your hand. OK. Nothing has changed in academia.
    So is it fair use to not pay for that book, absorb the 
entire content, and add it to your learning experience? So then 
the question--and I'll start with you because both Twitter time 
and academia time, and so on. Today, everyone assumes that the 
learning process of AI is fair use. Is there any basis for it 
being fair use rather than maybe a standard essential 
copyright, one that must be licensed? But if you're going to 
absorb every one of your published books and every one of the 
published books that gave you the education you have and call 
it fair use, are we in fact turning upside down the history of 
learning and fair use just because it's a computer?
    Dr. Chowdhury. I think the difference here is--there's a 
difference between me borrowing a book from my friend, 
learning, and my individual impact on society----
    Mr. Issa. Oh, because it's cumulative, because you've 
borrowed everyone's book and read everyone's book?
    Dr. Chowdhury. Well, and because it's at scale. I cannot 
impact hundreds of millions of people around the world the way 
these models can. The things I say will not change the shape of 
democracy.
    Mr. Issa. So--I'll accept that. Anyone want to have a 
slightly different opinion on stealing 100% of all the works 
from--of all time, both copyrighted and not copyrighted, and 
calling it fair use and saying because you took so much, 
because you stole big, that, in fact, you're fine? Anyone want 
to defend that?
    Dr. Chowdhury. I'm sorry, I don't think that at all. I 
think it's wrong to do that. I think it's wrong to----
    Mr. Issa. So do--is one of the essential items that we must 
determine the rights of the information that goes in to be 
properly compensated, assuming it's under copyright?
    Dr. Chowdhury. Yes.
    Mr. Issa. You all agree with that? OK. Put them in your 
computers and give us an idea of how we do it because, 
obviously, this is something we've never grappled with. We've 
never grappled with universal copyright absorption. And 
hopefully, since all of you do publish and do think about it 
and the RAND Corporation has the dollars to help us, please 
begin the process because it's one of the areas that is so--
it's going to emerge incredibly quickly.
    Now, obviously, we could switch from that to--we're talking 
about job losses and so on. One of my questions of course is 
if--for anyone who wants to hypothecate this, if we put all of 
the information in and we turn on a computer and we give it the 
funding of the Chinese Communist Party and we say patent 
everything, do we in fact eliminate the ability to--or--to 
independently patent something and not be bracketed by a 
massive series of patents that are all or in part produced by 
artificial intelligence? I'm not looking at 2001: A Space 
Odyssey or Terminator. I'm looking at destruction of the 
building blocks of intellectual property that have allowed for 
innovation for hundreds of years. Most courageous, please.
    Dr. Murdick. Yes, I don't know why I'm turning the mic on 
at this moment. But I think there's a core--for the--both two 
questions you asked, I think money actually matters. The reason 
that we haven't litigated fair use fully is there hasn't been 
much money made in these models that have been incorporating 
all the copyrighted material----
    Mr. Issa. Trust me, Google was sued when they were losing 
money, too.
    Dr. Murdick. But I do think the fact that there's a view 
that there's a market will change the dynamics about how 
because people are saying, wait, you're profiting off of my 
content. And there will be--so I do think money changes this a 
little bit. And I think--and it goes to the second question, 
too. I think the pragmatics of money will change the dynamics. 
Anyway, it's a fairly simple observation on this point.
    Mr. Issa. Thank you. And I apologize for dominating on 
this, but I'm going to Nashville this weekend to meet with a 
whole bunch of songwriters that are scared stiff, so I--anyone 
that wants to follow up? Yes, if the Chairman doesn't mind?
    Mr. Delangue. Yes, I think an interesting example, some 
people have been describing HuggingFace as some sort of--kind 
of like a giant kind of like library. And in a sense, we accept 
that people can rent books at the library because it 
contributes to kind of like public and global kind of like 
progress. I think we can see the same thing there where if we 
kind of like give access to this content for open source, for 
open science, I think it should be accepted in our society 
because it contributes to public good. But when it's for 
private, commercial interests, then it should be approached 
differently. And actually, in that sense, you know, open 
science and open source is some sort of a solution to this 
problem because it gives transparency. There is no way to know 
what copyright content is used in black box systems like most 
of the systems that we have today in order to take a decision--
--
    Mr. Issa. That knocking says the rest for the record. He's 
been very kind. Thank you.
    Mr. Delangue. Thank you.
    Mr. Obernolte. The gentleman yields back.
    We'll go next to the gentlewoman from Michigan. Ms. 
Stevens, you're recognized for five minutes.
    Ms. Stevens. And thank you, Mr. Chair. I'm hoping I can 
also get the extra minute given that I am so excited about what 
we're talking about here today. And I want to thank you all for 
your individual expertise and leadership on a topic that is 
transforming the very dialog of society and the way we are 
going to function and do business yet again at the quarter 21st 
century mark after we've lived through many technological 
revolutions already before us.
    And to my colleague who mentioned some very interesting 
points, I'll just say, we're in a race right now. I'm also on 
the Select Committee on Competitiveness with the Chinese 
Communist Party. And I come from Michigan if you can't tell 
from my accent. And artificial intelligence is proliferating 
with autonomous vehicle technology. And we're either going to 
have it in this country or we're not. That's the deal, right? 
We could ask ourselves, what happened to battery manufacturing 
and why we're overly reliant on the 85% of battery 
manufacturing that takes place in China when we would like it 
to be Michigan manufacturers, when we'd like it to be United 
States manufacturers? And we're catching up, so invest in the 
technology.
    I mean, certainly, our witness here from the venture 
capital company with $5 billion is going to be making those 
decisions, but our Federal Government has got to serve as a 
good steward and partner of the technology proliferation 
through the guardrails that Dr. Chowdhury is talking about or 
our lunch will be eaten. This is a reality. We've got one AV 
manufacturer that is wholly owned in the United States. That's 
Cruise, a subsidiary of General Motors, and they've got to 
succeed. And we've got a pass the guardrails to enable them to 
make the cars because right now they can only do 2,500.
    So the question I wanted to ask is--because yesterday, I 
sent a note. It's a letter to the Secretary of State, Mr. 
Blinken, and I asked about this conversation, this point that 
has come up multiple times in the testimony about how we're 
going to dialog at the international level to put up these 
proper AI guardrails.
    And so, Dr. Chowdhury, you've brought this up in your 
testimony. And what do you think are the biggest cross-national 
problems in need of global oversight with regard to artificial 
intelligence right now?
    Dr. Chowdhury. Thank you. What a wonderful question, 
Congresswoman. I don't think every problem is a global problem. 
The important thing to ask ourselves is simply what is the 
climate change of AI? What are the problems that are so big a 
given country or a given company can't solve it themselves? 
These are things like information integrity, preserving 
democratic values and democratic institutions, CSAM, child 
sexual abuse material, radicalization. And we do have 
organizations that have been set up that are extra-governmental 
to address these kinds of problems. But there's more--what we 
need is a rubric or a way of thinking through the biggest 
problems and how we're going to work on them in a 
multistakeholder fashion.
    Ms. Stevens. Right. And, Dr. Murdick, could you share 
with--which existing authorities you believe would be best to 
convene the global community to abide by responsible AI 
measures?
    Dr. Murdick. Well, I'm not sure I can name chapter and 
verse----
    Ms. Stevens. Treaties----
    Dr. Murdick. Well, so I think just one point about the core 
goal. The United States plus its allies is stronger than China. 
We--by a lot of different measures you look at, you know, 
everything from research production, to technology, to 
innovation, to talent-space, size, to companies, to--you know, 
to investment levels, I think it's important that we're 
stronger together if we can work together. If we're ever going 
to implement any kind of export controls effectively, we have 
to do them together, as opposed to individually.
    So there's a lot of venues, international multiparty bodies 
that work on a variety of things. There's a lot of treaties. 
There's plurilateral, there's multilateral agreements, and I 
think we do have to work together to be able to implement 
these.
    Ms. Stevens. Yes.
    Dr. Murdick. And I think any and all of them are relevant.
    Ms. Stevens. Well, and allow me to just say Mr. Delangue, 
we have a great French-American presence in my home State of 
Michigan, and we'd love to see you look at expanding to my 
State. We've got a lot of exciting technology happening. And, 
as I mentioned with autonomous vehicles, just to level set 
this, we've got CCP-owned autonomous vehicle companies that are 
testing in San Francisco and all along the West Coast, but we 
cannot test our technology in China. We cannot sell our 
technology, our autonomous vehicle technology in China. That's 
an example of a guardrail.
    I love what you just said, Dr. Murdick, about working with 
our allies, working with open democratic societies to make, 
produce, and innovate and win the future.
    Thank you so much, Mr. Chair. I yield back.
    Mr. Obernolte. The gentlewoman yields back.
    We'll go next to the gentleman from Ohio. Mr. Miller, 
you're recognized for five minutes.
    Mr. Miller. Thank you, Mr. Chairman and Ranking Member 
Lofgren, for holding this hearing today.
    Dr. Murdick, as you highlighted in your written testimony, 
AI is just not a field to be left to Ph.D.'s and engineers. We 
need many types of workers across the AI supply chain, from 
Ph.D. computer scientists, to semiconductor engineers, to lab 
technicians. Can you describe the workforce requirements needed 
to support the entire AI ecosystem, and what role individuals 
with technical expertise play?
    Dr. Murdick. Now, this is a great question, and I will try 
to be brief because it's such a fascinating conversation that 
when you look at the AI workforce, it's super easy to get 
fixated on your Ph.D. folks. And I think that was a big 
mistake. One of the first applications that I started seeing 
discussed was a lawn--a landscaping business using--the 
proprietor would--had trouble writing, and they started using 
large language models to help communicate with customers to 
give them proposals when they couldn't do it before. That 
proliferation--now, I don't mean that in a proliferation way. 
Sorry. That expansion of capabilities into--and users that are 
much wider than your standard users is really the space we are 
living today.
    So in the deployment of any AI capability, you have your 
core tech team, you have your team that surrounds them or 
implements the software engineers and other things like that. 
You have your product managers. When I worked in Silicon 
Valley, the product managers were my favorite human beings 
because they could take a technical concept and connect it to 
reality in a way that a lot of people couldn't. And then you 
have your commercial and marketing people. You have a variety 
of different technologies showing up at the intersection of 
energy manufacturing. And so I think this AI literacy is 
actually relevant because I think all of us are going to be 
part of this economy in some way or another. So the size of 
this community is growing, and I think we just need to make 
sure that they're trained well and they have that expertise to 
be able to implement implementations that are respectful of the 
values that we've been discussing.
    Mr. Miller. Yes. Thank you for that answer. I'm a big 
believer that technical education and CTE (career and technical 
education) should be taught at a K through 12 level for every 
American to go ahead for an alternative career pathway. And I 
believe that the American dream has been distorted within this 
reality for younger generations of what they think that that 
is. It used to be, you know, a nice house, a white picket fence 
and to provide for your family and a future generation. Now, 
it's a little bit different and distorted in my view, so thank 
you very much for that answer.
    Another question, what role will community and technical 
colleges play in AI workforce development in the short term, 
you know, more recently than we think down the road? What about 
your recommended 2-year programs are well-suited to recruit and 
train students in AI-related fields?
    Dr. Farshchi. I'm a product of the California community 
college system, so I can comment on that. So I feel like the 
emphasis in the community college system right now, at least 
what I experienced back in the late 1990's, was two things, was 
preparation for transfer into a four-year institution and 
vocational skills to enter into an existing kind of technical 
workforce. And I feel like there's an opportunity in the 
community college system, which is excellent, by the way, which 
is the reason why I'm sitting here today, to also focus on 
preparing an existing workforce instead of just being 
vocational into existing jobs, into jobs that are right over 
the horizon.
    So in the middle, in between the kind of get ready to 
become a mechanic, to get ready to transfer to UC (University 
of California) Berkeley, in between those two things, there are 
jobs that are going to be evolving as a result of AI, 
therefore, upskilling you as an existing worker to these new 
jobs that will be emerging in the next couple of years. So I 
think that in-between kind of service to the community through 
the community college system I think would be extremely 
valuable the community.
    Dr. Murdick. Just to add to that, I think that the--I spent 
after high school 12 years in school. That's a lot of time to 
grind away at a particular technology and concept. As the world 
accelerates, I don't think that that kind of investment is for 
everyone and never really was for everyone, but I think it 
needs to be more and more focused. And I think community 
colleges provide the opportunity to pick up skills. Maybe you 
graduated as an English major and now you're like, wait, I'd 
really like to use data science and that kind of environment of 
quick training, picking up the skills, stacking up 
certifications so that you can actually use that is a perfect 
venue for community colleges to be able to execute rapid 
training and that adaption--adaptation that's necessary in a 
very quick world working--wow, excuse me--a rapidly moving 
economy.
    Dr. Chowdhury. If I may add quickly, at the--at our 
generative AI red teaming event, we're working with--funded by 
the Knight Foundation, working with CDI (Center for Digital 
Information) to bring hundreds of community college students 
from around America to hack these models. So we--I absolutely 
support your endeavors.
    Mr. Miller. Nice. Well, thank you all very much. Thank you, 
Mr. Chairman. And I have a few more questions I'm going to 
enter into the record, but thank you very much. This is a joy. 
I yield back.
    Mr. Obernolte. The gentleman yields back.
    We'll hear next from the gentleman from New York. Mr. 
Bowman, you're recognized for five minutes.
    Mr. Bowman. Thank you so much, Mr. Chairman. And thank you 
to all the witnesses. Hey, y'all, I'm over here. Thank you to 
all the witnesses for being here today and for sharing your 
expertise and your testimony.
    So America has a series of challenges that I would describe 
as national security challenges. We have millions of children 
going to bed hungry every single day. We have gun violence as 
the No. 1 killer of children in our country. We have crippling 
inequality and a racial and economic caste system that 
persists. We have issues with climate change. We have issues of 
children who go to school in historically marginalized and 
neglected communities not receiving the same education as 
children who go to schools in wealthier or more suburban 
communities.
    My question is--and I'll start with Dr. Murdick--can you 
speak to the risks that we face with people intentionally and 
unintentionally using AI to exacerbate these issues? What must 
be done to ensure that these critical areas are improved? 
Because, to my mind, if we're not using AI to better humanity, 
what the heck are we using it for? It's not just about 
commercialization. It's not just about military, but my--it's 
about our collective humanity and solving the crippling 
challenges that persist in our country before we even think 
about China or another foreign adversary.
    Dr. Murdick. I really appreciate your stepping back and 
saying, you know, as--in governance, you have to look at the 
full priorities of what we're trying to do, and just obsessing 
about a little gnat of a technology is not the right way of 
thinking. Looking at where it integrates with our entire 
societal needs is really relevant. And I think there are a lot 
of benefits to AI that can help with some of those issues. 
However, your point about intentional misuse of technology to 
tear apart our society, I think, both from internal as well as 
external, there's the attack surface if you may is higher, and 
being able to have tools that can make that--those attacks 
easier to operate or more cost-effective is a very risk--big 
risk.
    So it's often referred to in the category of 
disinformation, misinformation about how this is used, and it 
can be done in different modalities. It can be done in text, it 
can be done in images, it can be done in audio, and I think 
being able to figure out how to manifest this is actually a 
democratic process discussion, which is why I think that the AI 
literacy needs to be brought up. We have to be engaging in 
this. And because there's a lot of creativity needed, it's 
really, really hard to detect fake text. It's hard to know what 
was created by a system and what isn't. There's stories in the 
news where people thought they had a method and they tried to 
punish all their students because they thought they had figured 
out a tool to be able to determine whether or not they can 
detect it. It didn't work so well.
    So the problem is it's going to be a--requires our full 
stakeholder--of all the people in the United States to be 
participatory, giving ideas, figuring out how to do this, also 
that awareness of images. And I think image and audio really is 
a problem because we're not--we're used to trusting everything 
we hear and we see. Everything we read, maybe not so much. So I 
think there's a situational awareness that we need to bring up 
across our entire population to help.
    I can go on, but hopefully, that's helpful.
    Dr. Chowdhury. If I may?
    Mr. Bowman. Please, yes.
    Dr. Chowdhury. What you're bringing up is a concept of 
algorithmic bias. And why this is critically important to talk 
about today is to build on a point from earlier. This is where 
regulation comes in. We already have laws today that prohibit 
certain implementations and certain outcomes from happening, 
independent of the technology or the solution being used. But 
in addition, we're going to have to identify where the gaps 
are, that existing regulation is insufficient, and create new 
ones.
    So a couple of reasons this happens, one is just a lack of 
diversity in creating these technologies. These technologies 
are gate-kept, not just by education, but literally 
geographically gate-kept. People who sit in California try to 
dictate to school districts in New York how their schools 
should be run, and that's not how this should work. And second, 
this techno solutionism, this idea that humanity is flawed and 
technology will save us all. And that is also incorrect. 
Humanity is something to be rejoiced in and something to be 
nurtured and kept, you know--we need to make sure that people 
are having the best kinds of lives that they can, mediated by 
technology.
    So algorithmic bias starts with datasets, it continues with 
implementation, and it continues with the lack of ability to 
understand and have model visibility. And it continues with a 
lack of good governance.
    Mr. Bowman. Thank you so much. I ran out of time. I have a 
few more questions. Mr. Chair, can I submit the questions for 
the record? Absolutely. I yield back. Thank you.
    Mr. Obernolte. The gentleman yields back. We'll go next to 
the gentleman from Texas. Mr. Babin, you're recognized for five 
minutes.
    Mr. Babin. Thank you, Mr. Chairman. And thank you, 
witnesses, for being here as well.
    Dr. Murdick, would you--this question is directed at you. 
By many metrics, China has caught up or surpassed the United 
States in research and commercial capabilities. As Chairman 
Lucas referenced in his opening statements, Chinese 
universities are publishing many more research papers than U.S. 
institutions, and they receive nearly the same share of 
citations as U.S. papers. How concerned are you with the pace 
of AI progress in China, and what can we do about that?
    Dr. Murdick. Wonderful question. And I--just a quick 
comment on methods of how we determine who's leading. Research 
is an easy place to start. It's an easy place to count. I 
think, you know, I just want to say what is probably very 
obvious. You know, patenting, investments, talent, numbers, job 
postings, all these other datasets are really important to take 
into account. Also, it's really important to mention scale. I 
think in 2021, China's population was 4 1/4 times the U.S. 
population. So therefore, just on sheer statistics, you're 
going to see quantity numbers going to be outstripped by China. 
So that's just some observations about the measures about how 
concerned I am, which I think is a very interesting question.
    My image of going forward--I've been following China 
since--for over 15--well over 15 years, and it's been written 
on the wall for a long time that they were going to be a pure 
innovator. We've known it was coming. So the metaphor that I 
come into is a kind of a grappling situation. China and the 
United States and its allies will be grappling with its 
different values and different approaches for the foreseeable 
future. And when you're in a grappling, you don't freak out 
when someone, you know, puts an arm on a shoulder. You know how 
to respond, and it's a give-and-take. And I believe that that 
metaphor kind of expresses how I see this going, so I'm not 
going to freak out about them having a larger number. It's--
we're going to have to just figure out how to walk through 
this, which is why I mentioned the policy monitoring concept is 
whatever we do, we need to make sure it's working and be able 
to adapt because they're going to counter whatever we do at any 
given instance.
    Mr. Babin. All right. OK. Thank you. The American research 
enterprise currently operates in a transparent and open manner 
with basic research being published without restrictions. How 
can we maintain a balance between ensuring the openness of 
basic AI research, while also mitigating potential threats to 
national security that might arise from sharing such 
information? And this is for Dr. Matheny and Dr. Murdick as 
well. How can we solve this problem?
    Dr. Matheny. Thanks so much. I think that for the largest 
of so-called foundation models where we're just learning about 
how they can be misused, how they can be used either in 
developing cyber weapons, biological weapons, and massive 
disinformation attacks, we should be really cautious. And so 
before publishing or open sourcing the largest models, I 
recommend that we require some amount of red teaming and 
evaluation in order to understand the risk scenarios before we 
unleash this thing into the world and we're unable to take it 
back.
    Mr. Babin. I got you. One quick thing, I believe that all 
of us here today agree that it's important for the United 
States to lead in technological advances, especially in the 
cutting-edge field of AI. Unfortunately, I've seen instances 
where overregulation hinders our ability to compete globally, 
thereby forcing growth overseas or allowing others to step in 
and lead. And I've seen--also seen that the private-public 
partnerships and how successful they've been have put the 
United States in the driver's seat and look at our space 
industry as an example.
    So, Dr. Murdick, how do we make sure that we do not 
overregulate, but rather innovate through the facilitation of 
bottom-up, consensus-based standards rather than top-down 
regulations? And can you speak to the role NIST has played in 
enabling innovation, particularly for AI, and how we can 
leverage that approach going forward in the rest of the time 
that we have?
    Dr. Murdick. The distributed innovation system within the 
United States is extremely important. I think Congress has 
levers of--I think one of the core tenets of this hearing was 
how do we figure out how to innovate going forward. And I think 
that there--we need all areas to be innovated in. And, for 
example, compute has helped move us forward. Data has helped 
move us forward. Talent helps us move, and we need to make sure 
whatever investment we do, it looks at like a balanced 
portfolio of all those to be able to move forward.
    In terms of like the space industry, I think it's really 
exciting to see how we have led, but we have made policy 
actions that have damaged parts of our industry, for example, 
with the satellite industry, when we put in places--put in 
rules that we could not deal with China. We actually--the size 
of the U.S. market dropped, and Japan and European countries 
developed ITAR- (International Traffic in Arms Regulations-) 
free satellite technology that allowed them to increase their 
market share.
    So I think we have to be very--going back to that 
wrestling--grappling concept, we have to be very cognizant of 
everything we do will quickly be adapted and tried to be used 
against us, and we have to be able to adjust our policies and 
monitor them. So I think that monitoring action of like how 
well is this working is actually a core part of anything that 
Congress is going to do going forward.
    Mr. Babin. Thank you.
    Mr. Delangue. If I can add on the previous points about 
kind of like ensuring that open source and open science is 
safe, I think it's important to recognize that when we keep 
things more secret, when we try to hurt open science, we 
actually slow down more the United States than China, right? I 
think that's what's been happening for the past few years. And 
it's important to recognize that and make sure we keep our 
ecosystem open to help the United States.
    Mr. Babin. Merci. Thank you, yield back.
    Mr. Obernolte. The gentleman yields back.
    We'll hear next from my colleague from North Carolina, the 
gentlewoman. Congresswoman Ross, you're recognized for 5 
minutes.
    Ms. Ross. Thank you very much, Mr. Chairman and Ranking 
Member Lofgren, and to all of the witnesses for being here 
today.
    I will not go into intellectual property. I serve on the 
same Committee with all of them.
    As we all know, artificial intelligence permeates all areas 
of business, industry, the arts, and influences decisions that 
we make in our daily lives. In my district, North Carolina 
State University launched the AI Academy in 2020 that will 
prepare up to 5,000 highly qualified AI professionals from 
across the Nation through their workforce development program. 
The AI Academy is one of the Labor Department's current 28 
public-private apprenticeship programs, and I look forward to 
hearing from you about that. I know we've talked a little bit 
about community colleges.
    But my first question is about cybersecurity. I'm concerned 
that we're not ready for AI-enabled attacks. I mean, we're not 
even ready for the attacks that we have already. And one of the 
best defenses against phishing emails is that they're often 
poorly drafted with misspellings. But generative AI provides an 
easy solution for malicious actors.
    And so, Dr. Matheny and Dr. Murdick, and then anybody 
else--but I do have another question, so be quick--are current 
cybersecurity standards and guidelines equipped to be able to 
handle AI-enabled cybersecurity and attacks, and what could the 
Federal Government do?
    Dr. Matheny. Thank you for the question. We're not equipped 
yet. And I think that this has been, I know, a priority for 
DHS/CISA (Department of Homeland Security/Cybersecurity and 
Infrastructure Security Agency) to take this on. And there's a 
lot of thoughtful work there looking at the impact of AI on 
cybersecurity, including advances in spearfishing, which can be 
made more cost-effective through the use of these language 
models, but also through the use of these large language models 
to generate not human language, but computer code. So these 
cogeneration tools can be used to create offensive cyber 
weapons. And it's possible that in the future those cyber 
weapons could be quite capable and very cost-effective and 
generated at scale, a scale that right now isn't possible even 
for State programs, so I think that's quite worrisome.
    AI can also, though, enable stronger cyber defenses. And so 
figuring out how to invest in AI capabilities that will 
ultimately create an asymmetric advantage for defense over 
offense is an important research priority.
    Ms. Ross. OK. And, Dr. Murdick, could you be very brief? 
Because my next question is for you.
    Dr. Murdick. I think your sense of this being an important 
priority is actually right because I think AI plus 
cybersecurity is probably one of the earliest spaces where 
we're going to see AI manifesting. And I'll just agree with 
Jason otherwise, good points. We need to work on this.
    Ms. Ross. Great, thank you. And, Dr. Murdick, to you, 
American leadership begins with a strong workforce, as we've 
discussed, one that nurtures both domestic talent and attracts 
the best global minds. Many international students come here to 
conduct innovative research in emerging technologies, and a 
report from your organization revealed that two-thirds of 
graduate students in AI-related programs are international 
students. And so although they want to stay here, our 
immigration laws keep them from staying here. I've done a lot 
of work with the so-called documented DREAMers who came here 
with their parents, and then at 21 have to self-deport with all 
of the investment that we have made in their education. So can 
you discuss better ways to not just attract but retain this 
talent, particularly in the AI space? And then, if there's 
time, anybody else.
    Dr. Murdick. So I will give one point. I think we've, 
clearly through our own surveys, seen that people want to stay 
in the United States. It's one of our strongest strengths. The 
United States attracting high quality talent is the thing that 
has driven a lot of our innovation, and being able to pull from 
the world's best is really key. So we see this from China, we 
see this from all countries, and I think we need to very much 
invest in this and continue to invest in it.
    I think a lot of the inhibitors are bureaucratic. And I do 
think there's--for our high-skilled talent base, we've seen 
China implement some really innovative--not China, Canada, 
sorry, Canada----
    Ms. Ross. Yes.
    Dr. Murdick [continuing]. Invest in some really interesting 
ways of when someone comes to the--to Canada and they meet 
their--they give everybody in their family the opportunity to 
start working the same day that the person who was approved. 
That's pretty amazing, and it really can make a big decision 
when you're trying to decide whether to move to Canada or the 
United States or some other place. And so I think those kinds 
of innovations are strongly within the--your hands, and I think 
you can use them very effectively.
    Ms. Ross. Thank you so much, and I yield back.
    Mr. Obernolte. The gentlewoman yields back.
    We'll go next to my colleague from Georgia. Mr. McCormick, 
you're recognized for five minutes.
    Mr. McCormick. Thank you, Mr. Chair, and thank you to the 
witnesses for your thoughtful answers.
    As Members of the House Committee on Science, Space, and 
Technology, it is our responsibility to address the challenges 
and opportunities presented by this rapidly advancing 
technology. Artificial intelligence has the potential to 
revolutionize our national security strategies and capabilities 
by harnessing AI's power to analyze vast amounts of data. We 
can enhance early threat detection, intelligent--intelligence 
analytics, and decisionmaking processes. However, we must 
proceed with caution, recognizing the ethical considerations, 
algorithmic biases, and risks associated with AI deployment.
    As policymakers, we must strike a delicate balance between 
fostering innovation and protecting our national security 
interests. This requires investing in research and development 
to safeguard AI systems against adversarial attacks, ensuring 
transparency and accountability, and collaborating with 
international partners to address emerging challenges and 
establishing norms. By doing so, we can harness and--the 
transformative potential of AI to strengthen our defense 
capabilities, while safeguarding our values and security.
    In fact, that whole statement was produced by AI at 
ChatGPT, which I thought was fun. And matter of fact, it 
probably could have said it a lot better than me, too. That's 
where we're at right now.
    I think it's kind of funny. I'm about to replace my entire 
legislative staff with ChatGPT and save a lot of money--sorry, 
guys--since they came up with that. And I say that tongue-in-
cheek, but I thought you made a really, Dr. Chowdhury, I 
thought you made a huge statement when you said we are looking 
at AI like our savior and like it's going to replace us. And I 
really thought it was great to say that you're not going to 
save us, and also the government's not going to save us either, 
by the way. I want to put that in. Maybe that's my little 
political statement.
    But it's interesting. It is time to replace some of us. I'm 
an ER physician, and I'm watching radiologists being replaced. 
There'll be in a supervisory capacity, and you're going to see 
pathologists next. And eventually, we just had a recent survey 
that says I'd rather interact with AI than a physician because 
they give me easy-to-understand answers and they're nicer to 
me. And they're not in a rush. So I think it's just a matter of 
time, right? We're seeing that happen right now.
    And in the defense industry, I'm worried because I've seen 
those documentaries like Terminator and Star Trek. And I 
understand that we have the potential for systems to actually 
start outthinking us and actually out-reacting and has a 
potential to damage us.
    So I'm curious, what kind of guardrails should we put in 
place to keep us not only employed but actually safe as far 
as--Dr. Chowdhury, since you had the most insightful comments 
so far, I'll let you start.
    Dr. Chowdhury. I appreciate that. So I think you're talking 
about two things here. One is job displacement, which has 
happened with many technologies in the past. And I think some 
of my colleagues on this panel have raised the need for 
upskilling, retraining, easier access, lower barriers to entry, 
investment in jobs programs. Frankly, these are almost 
standard, but now we need to apply them to technology.
    And the second part is really parsing out what we mean by 
risks and harms. So some of the Terminator type narratives, we 
are very, very far from these things. But there are harms that 
we are seeing today, people being denied medical care or 
insurance because of an incorrectly specified model. We have 
examples of people of color being denied kidney transplants 
because an algorithm is incorrectly determining whether or not 
they should be on a kidney transplant list. These are harms 
that are happening today. We can't even figure out how to fix 
these now. We don't have enough to fix these now. We are so far 
away from a Terminator, and really what we should be focusing 
on are the harms in technology we're building today.
    Mr. McCormick. Great, thanks. One thing--I'm not even sure 
who to ask this for, but I'll let the panel decide who's the 
best person. I have a real sincere concern that 100% of our AI 
chips right now are produced in Taiwan, with the posturing we 
have in China talking about they are going to take over Taiwan. 
Having an adversarial country owning 100% of the production of 
the most influential technology in world history deeply 
concerns me. Now, I know AMD has some processes that they want 
to produce AI outside of Taiwan in the next couple years, but 
in the meantime, what do we do? I feel like all of our eggs are 
in one basket. Please.
    Dr. Matheny. Yes, RAND has done a lot of work on this topic 
because it's been quite concerning about what the economic 
impacts would be and the national security impacts if a Taiwan 
invasion occurred and disrupted the microelectronics supply 
chain, given that we're dependent for 90% of our advanced 
microelectronics, the most leading-edge chips coming from 
Taiwan. And it would be an economic catastrophe. So among the 
policy options that we have to deal with that is to deter an 
invasion of Taiwan by ensuring that Taiwan has the self-
defenses needed for a so-called porcupine defense.
    Mr. McCormick. Great tie-in to my ask. Thank you very much. 
And with that, I yield.
    Chairman Lucas. The gentleman yields back.
    The Chair recognizes the gentleman from Illinois, Mr. 
Sorensen, for five minutes.
    Mr. Sorensen. I'd like to begin by thanking Chairman Lucas 
and Ranking Member Lofgren for convening this hearing and for 
our witnesses today.
    Building AI systems responsibly is vital. I believe 
Congress is instrumental in providing the guardrails with which 
AI can be implemented. However, there's much that the private 
sector must do as well. Dr. Chowdhury, how should we audit AI 
systems to ensure that they do not provide unintended output? 
How do we ensure that companies that create the algorithms are 
using accurate datasets when training the system?
    And also, I've met with Amazon, Microsoft, and Google and 
each have different stances on the need for guardrails within 
their companies. One representative of one of these companies 
says it's actually Congress' job. So do we need a system like 
the European Union's AI Act, which includes the concept of 
triangles of risk? And how can Congress learn from the 
Europeans concept?
    Dr. Chowdhury. You are speaking to my heart. This is what 
I've been spending the past seven years of my life doing. 
Interesting to note that I'm not a computer scientist by 
background. I'm a social scientist, so I fundamentally think 
about impact on people in society.
    I would actually direct you to think about the way the 
Digital Services Act is constructed where they've actually 
defined five areas. These are including things like impact on 
elections and democracy, impact on things like mental health, 
you know, and more like socially developed issues and 
developing audits around them for companies that have the at-
scale level of impact. Also, I am an audit consultant for the 
Digital Services Act helping them construct these 
methodologies.
    I will add that it is not easy. And not only for 
traditional machine learning and AI models but in generative 
AI, now we have a whole other ballgame. So really, what the 
investment is needed here is in this workforce development of 
critical thinkers and algorithmic auditors.
    Mr. Sorensen. Great. Follow up question, you know, when we 
go to a search engine or we have a video conference, do you 
believe that there should be safeguards so that consumers 
understand if the data that they're receiving is organic and 
believable and, most important, trustworthy?
    Dr. Chowdhury. Yes.
    Mr. Sorensen. Thank you. Dr. Farshchi, first of all, I'd 
like to say that I was a little nervous when I first had my 
electric car, my Chevy Volt, drive itself down the road, when 
it came up to that first curve and it did it itself, all right? 
But I had a steering wheel to be able to control it to take 
over.
    Nine days ago, I met with local union leaders from our bus 
systems in Bloomington and Rockford, Illinois, and they had 
concerns about autonomous bus systems. What does it mean for 
the safety of those that are on their buses or the safety of 
those folks that are standing on a sidewalk? AVs are 
complicated technology with extensive programming. How do we 
ensure safety if we're going to put children on school buses? 
How do we protect our autonomous vehicles from those that might 
want to hack them and cause problems?
    Dr. Farshchi. So the technology is still very early, but 
there are lessons that we've learned in the past that have 
helped create a certain level of safety for complicated 
machines and technologies. And I think the best lesson there is 
in aviation. So in aviation, there were certain guidelines that 
had to be met for an airplane to be able to fly. And now, 
aviation has become one of the most safe forms of 
transportation.
    I feel like we're still kind of in the, you know, early 
part of the 20th century on the AV side. We still don't know 
exactly what the failure modes of these machines are. There is 
still rapid iteration going on. They're perhaps 98% safe, but 
they have to be 99.999% safe. And then once the technologies 
are matured, then, when we identify what the weak points of 
these technologies are, we will come up with a framework to 
audit the technologies to make sure that, OK, if we--if a 
vehicle passes these tests, just like what we have, for 
example, with crashworthiness, if a vehicle passes these tests, 
we have this level of certainty that this vehicle will be safe 
in most circumstances.
    And I think that safety should be higher than that of a 
human driver. So if a human driver--because humans make errors. 
If a human driver makes an error that leads to an accident 
every 100,000 miles, then these vehicles have to be audited to 
be safe for at least 100,000 miles. And once they pass that 
threshold, then we should consider them for use. I think we're 
still two steps away from being able to identify that 
regulatory framework to be able to audit these machines to be 
sure that they are safer than human drivers.
    Mr. Sorensen. Thank you very much for your testimony today, 
and I yield back the balance of my time.
    Chairman Lucas. The Chair recognizes the gentlelady from 
North Carolina, Mrs. Foushee, for five minutes.
    Mrs. Foushee. Thank you, Mr. Chairman. And thank you all 
for your testimonies here today.
    My first question is to Dr. Chowdhury. And I think that I 
was really struck while reading your testimony by a particular 
line where you say, ``It is important to dispel the myth that 
governance stifles innovation,'' that--during your career that 
you have found--and you talked about this a little earlier, 
governance is innovation. Can you please elaborate on that 
notion that governance is innovation in the context of AI, and 
how can the Federal Government and certainly Congress fulfill 
our oversight duties to get this right?
    Dr. Chowdhury. Thank you. And I would love to elaborate on 
this. And here's where we make a distinction between what is 
research and what ends up being applied. In my time helping 
Fortune 500 companies implement AI systems, 99% of the time, 
the reason they did not implement it is because they could not 
reliably trust or predict what the outcome was. They did not 
know if they were operating within the appropriate safeguards, 
if they would be breaking the law, what it even meant to 
possibly break the law, so they just decided to not do it. So 
by creating the appropriate set of standards, guidelines, 
laws--and this is all within the remit of Congress--you 
actually help innovation by showing companies where the safe 
area to innovate and play is.
    Mrs. Foushee. Would anyone else like to speak to this?
    OK. So also, with the rise of artificial intelligence, 
researchers are increasingly in need of computing and data 
resources at scale. Unfortunately, there is a general lack of 
access to AI resources outside of the large tech companies. 
This has resulted in a steep resource divided between the top 
tech companies and smaller AI firms. Dr. Farshchi, and perhaps 
Mr. Delangue, can you speak to the needs of enabling innovation 
for smaller companies to access computer--computing, rather, 
and data resources?
    Mr. Delangue. Yes, I can start there. I think it's 
important to realize that it's not just compute. I think 
there's been a very interesting study by the Center for 
Security and Emerging Technology saying that, you know, for 
researchers to thrive and for companies to thrive, they need 
not only compute, but good data and system access for AI. So 
when we look at kind of like providing the resources for all 
companies to thrive with AI, I think we need to be kind of like 
looking at all of that, compute, people, and system access and 
provide more transparency.
    What we've seen on the platform and the HuggingFace 
platform is that when you give these tools to companies, they 
thrive and they manage to use AI responsibly, right? We've seen 
everywhere from kind of like marble cutters, small businesses 
in the United States using AI to detect material, printshop 
using image generation to generate image and generate kind of 
like T-shirts or phone covers using AI. So I think by like 
enabling and giving access to these resources more broadly, we 
can enable all companies to make use of AI.
    Dr. Farshchi. Congresswoman, just to add, I think the 
Federal Government has a role to play here. There's a bit of 
ambiguity right now going back to the conversation earlier 
about licensing versus registration. It's still the wild west. 
Companies don't know--they need to train their models. They 
don't know exactly if they would be doing something illegal or 
doing something unethical by using certain datasets. If the 
government were to make data available and instruct companies 
and researchers to use that data source and remove this 
ambiguity, I think that would be--to Clement's point, I think 
that will be a huge step forward for these researchers.
    Mrs. Foushee. Thank you, Mr. Chairman. I yield back.
    Chairman Lucas. The gentlelady yields back.
    The Chair now recognizes the gentlelady from Colorado, Ms. 
Caraveo, for five minutes.
    Ms. Caraveo. Thank you, Chairman Lucas, and to you and 
Ranking Member Lofgren for this very exciting hearing on AI, 
which I think we've all been following closely. And as a doctor 
as well, as Dr. McCormick mentioned earlier, I'm following 
advancements that are happening in the healthcare sector. New 
forms of biomedical technology and data are transforming the 
way that we research, diagnose, and treat health issues. And 
there was a lot of enthusiasm using AI to combine different 
forms of data such as those from genomics, healthcare records, 
medical imaging to provide clinicians with critical insights to 
make clinical decisions for individual patients.
    So, Dr. Matheny, can you describe what you think the 
technological benchmarks are needed to realize benefits, 
especially in the medical field in the next two, five, 10 years 
and beyond?
    Dr. Matheny. Thank you for the question. I think 
applications to medical diagnostics, to personalized medicine, 
to home healthcare are profound. And I think that establishing 
the sort of evaluative framework that we're going to need in 
order to assess the added value of these technologies over 
current practice is one that's really important for the FDA 
(Food and Drug Administration), for Medicare, for Medicaid, to 
be able to evaluate what advantages these technologies bring.
    I really liked the expression that with brakes, we can 
drive faster, and this is the history of technology innovation 
in the United States is that we have had government testing and 
evaluation as a propellant for innovation because when 
consumers can trust that technologies are safe, they use them 
more. And that allowed the United States to lead in 
pharmaceuticals and in other health technologies because of 
that framework.
    Ms. Caraveo. I'm going to kind of expand on your notion of 
trust. Dr. McCormick also mentioned an article in The New York 
Times talking about how doctors are using ChatGPT to 
communicate with their patients and looking for a more 
empathetic way of responding to them. As somebody that trained 
in medicine, that at first made me chuckle, then it made me 
worried a little bit. And then as I read the article, I must 
admit that the answers that it came up with were actually quite 
good and in keeping with some of the training around 
communication that we use. But I think if I was a patient and 
realized that my doctor was not answering something themselves 
but using a separate technology to create more empathy and 
communication between us, I'd also be a little bit concerned.
    So looking at that, and then also realizing in the article 
that--in my thoughts, something like AI would be very good at 
the radiology aspect, right, making sure that it was reading 
mammograms faster, for example. But it also pointed out that it 
was leading to a lot of false positives that were leading to 
unnecessary tests. So how in the future, as patients, as 
providers, can we ensure confidence in the different kinds of 
applications that AI and these technologies are going to be 
used for in medicine? And that's really for anybody, starting 
with Dr. Matheny.
    Dr. Matheny. I do think that more testing in which the 
comparison arm is current practice so that we can evaluate does 
it have the same precision recall as existing, for example, 
diagnostic practices, is going to be essential. I think society 
as a whole is going to be working to adapt on what we sort of 
recognize as being sincere communication when more and more 
communication will be generated by language models.
    But I do think that one benefit of some of these models is 
that they don't get tired, they don't get stressed. So if 
you're a care provider and you're working under sleep 
deprivation and time pressure, you might not be giving as much 
explanation as is really needed for a patient, so there could 
be real benefits there.
    Dr. Murdick. Just to add to that, I think this concept of 
human machine teaming and trust is really important here. I had 
the fortune of working with the armed forces for the first part 
of my career, and I watched the training culture that they had. 
They knew--they trained well with their teammates, and they 
knew what their colleague was going to do and not do because 
they had spent that time training. And I think there's a lot of 
interesting measures and metrics for designing systems that 
maximize that trust. So imagine a doctor or actually part of 
their training, learning how to work with these systems, 
spending those hours so that they know when they can rely on 
it, when they can't rely on it. I just think it's an important 
framing for the future of AI to make sure that that's part of 
the team and is calibrated correctly, that trust, and we can 
use it appropriately.
    Ms. Caraveo. I really appreciate your answers. In 
particular, I think, Dr. Matheny, I thought about burnout, as 
well as we've come particular out of a pandemic where we're 
going to be facing more physician shortage. What are the 
applications to read over patient charts, to compile 
information, to write long notes that doctors don't necessarily 
need to spend hours on, and so very, very much appreciate those 
comments.
    Chairman Lucas. The Member's time has expired.
    The Chair now recognizes the gentlelady from Pennsylvania, 
Ms. Lee, for five minutes.
    Ms. Lee. Thank you, Mr. Chairman. And thank you to our 
witnesses for your time and expertise on this critical area of 
technological innovation.
    Advancing innovation in western Pennsylvania have become 
synonymous when we view the technological developments we're 
providing to the Nation. For example, Pittsburgh is America's 
hub for autonomous vehicles research and development, evidenced 
by the numerous self-driving vehicles you'll find on our 
streets. Carnegie Mellon University in my district brought home 
$20 million from NSF to develop AI tools specifically to 
address the design and utilization of ethical humancentric AI 
tools that will help improve disaster response and aid public 
health.
    As an environmental justice advocate, I've seen how ethical 
AI has been used to monitor, predict, and combat environmental 
conditions that are ravaged by corporate polluters. AI can mean 
new possibilities for clean air and water and livable 
communities. We know that smart city initiatives, innovations 
that leverage AI and data analytics, will help to improve the 
quality of life for citizens and enhance sustainability.
    However, despite the numerous possibilities for AI 
integration into every facet of the American economy and 
everyday life, there also exists serious concerns that I don't 
take lightly. As some of my colleagues have mentioned this 
morning, we're really discussing privacy rights, the ethical 
implications of AI technology, the continuing war against 
disinformation and misinformation. As a proud Representative 
from western Pennsylvania, I'd be remiss to not discuss the 
implications AI will have on our labor and our workforce. AI is 
exciting, sure, but we must exercise caution to ensure that we 
provide access and opportunities for skills training and 
education to every single worker so they're not left behind 
throughout and by this revolution.
    Last week, I introduced an amendment that encapsulates my 
legitimate concerns on the disparate impacts these technologies 
have on people that look like me. I would like to commend 
Chairman Lucas and this Committee for their express commitment 
to ensure that the advancement of AI technology in our society 
does not result in Black folks and otherwise marginalized 
communities being used as sacrificial lambs. We can all agree, 
striking a balance between harnessing the benefits of AI and 
addressing its challenges is crucial to ensuring AI truly has a 
positive impact. Striking that balance begins here, of course.
    So, Mr. Delangue, what are the untapped potentials of AI 
that could substantially improve combating environmental 
injustices and daily living standards of ordinary citizens?
    Mr. Delangue. So first, I really appreciate your points. I 
think one of the important things that we need to realize today 
is that AI needs to be more inclusive to everyone, more 
transparent. Something that we've seen, for example, is that by 
sharing models, datasets, you actually allow everyone to be 
able to discuss them, contribute to them, report biases that 
maybe the initial model builders wouldn't see. So I think it's 
really, really important to invest a lot in more equity, more 
inclusiveness for AI.
    To your second question on the environment, I've been 
really interested in all the research that has been done around 
carbon emission from AI because that's a very important problem 
that we'll have to deal with in the future. So, for example, 
there's this model called Bloom that has been developed by big 
science that HuggingFace participated in that published 
actually the carbon emissions generated by the training of the 
model. And that's something that also I think we should 
incentivize more in order to see the potential impact on carbon 
emissions of AI.
    Ms. Lee. Thank you. And I agree with your first point, 
obviously.
    Just last year, Shudu, a Black AI-generated model was 
featured in campaigns for Balmain, for Elise, and even Vogue. 
The creator said the model was inspired by human models like 
Naomi Campbell and Alex Weck. We know the fashion industry has 
long been discriminatory toward Black women and other women of 
color.
    My question, Dr. Chowdhury, what needs to be done to ensure 
that AI technologies are not taking away work from Black folks 
in industries that are already White-dominated?
    Dr. Chowdhury. I think investing in retraining programs and 
compensation programs for individuals whose jobs will be taken 
away is key and critical here. We can't just leave people at 
the whims of massive companies that don't care or don't think 
or don't even know anything about them.
    Ms. Lee. Do you have an opinion or an idea of how we can 
create safeguards to establish the rights of individuals over 
the use of their image, their voice, and their likeness in AI 
technologies?
    Dr. Chowdhury. I think this is something that Congress 
should take very seriously and think through. But I do--for 
example, myself, I am concerned about my image being on the 
internet and being part of training data or, you know, an image 
being generated that looks something like me but maybe isn't 
me, or, more seriously, the ability to create deepfakes that 
look like people, so we need these protections for all 
individuals. And in particular, I want to say that women and 
people of color are the primary targets of deepfake-generated 
photos.
    Ms. Lee. Thank you. I could go on and on, but that is my 
time, so I yield back, Mr. Chairman.
    Chairman Lucas. The gentlelady has expired--time has 
expired.
    The gentleman from California, Mr. Lieu, is recognized for 
five minutes.
    Mr. Lieu. Thank you, Chairman Lucas. Thank you for holding 
this important hearing, and it's been very informative.
    Generalized large language models are incredibly expensive 
to create and to operate. There was an article earlier this 
month in the Washington Post. The title of it was ``AI Chatbots 
Lose Money Every Time You Use Them.'' And it goes on to say the 
cost of operating the systems is so high that companies aren't 
deploying their best versions to the public. Estimates are that 
OpenAI lost over $500 million last year.
    So my first question is to Dr. Farshchi. Do you think 
OpenAI can be profitable?
    Dr. Farshchi. So I am not close to OpenAI, so I don't know 
what their commercialization plans are.
    To the comments that were made earlier regarding the 
productivity of AI, I believe AI can eventually become 
productive and enhance human output and create a net positive 
economic impact on society. But then as it relates to OpenAI--
--
    Mr. Lieu. I'm not asking that.
    Dr. Farshchi. I don't have an answer for you, 
unfortunately.
    Mr. Lieu. In any of these large language models, could they 
actually commercially be profitable, given how much it costs to 
develop them and how much it costs to operate them on a daily 
basis?
    Mr. Delangue. So I think it's important to realize that 
large language models are just like a small portion of what AI 
is. What we're seeing as--in terms of like usage from, you 
know, companies is more the use of smaller, more specialized, 
customized models that are both kind of like cheaper to use and 
more environmentally friendly. And that's what I see the future 
of AI is, more than one large language model to rule them all. 
An example of that is when you build a customer chatbot for a 
bank, you don't need the model to tell you about the meaning of 
life, so you can use a smaller, more specialized model that is 
going to be able to answer your question.
    Mr. Lieu. Thank you. So my next question is for you. I'm a 
recovering computer science major. I, on balance, generally 
support open source. Some of your testimony was about having 
America continue to be a leader in this area. I'm just curious 
how America is a leader if it's all open source where our peer 
competitors simply copy it.
    Mr. Delangue. So you look at the past, I think the reason 
why America is the leader today is because of open science and 
open source. If there wasn't open science and open source, the 
United States probably wouldn't be the leader. And I think 
actually in the past few years the fact that it got less open 
is leading to, you know, the leadership of the United States 
diminishing.
    Mr. Lieu. But we don't quite have open science, right? We 
have an entire patent system where you can't copy that science.
    Mr. Delangue. I would argue that most of today's progress 
is powered by the open research papers that have been published 
in the past that I mentioned like the Attention Is All You Need 
paper. And actually most of the commercial companies today 
exploiting AI are actually based on these papers, right?
    Mr. Lieu. So--right----
    Mr. Delangue. The ChatGPT, the T is transformers. It's the 
famous----
    Mr. Lieu. Right.
    Mr. Delangue [continuing]. Open----
    Mr. Lieu. No, I got that. I'm fine with research and 
development and so on being open.
    So I'll just give you a story. Last year, I talked to a CEO 
in my district who was creating a new chip that's faster. He's 
an immigrant. And at some point, I said, how fast is this chip? 
And he says 50,000 times faster. I was like, whoa. And it 
occurred to me that he never would have gone to Moscow, right? 
No one wakes up and says I want to go to Moscow. No one really 
wakes up and says I want to go to Beijing, where if you say 
something bad about President Xi Jinping or you get too 
powerful, they kidnap you and reeducate you. But they'll come 
to United States, not just because we have talent and 
resources, but because we have a whole vast set of intellectual 
property laws that we enforce, and we have the rule of law, and 
we don't let people copy this.
    So I'm just so interested in how you can have America be 
the leader and then have--I mean, if we just sort of say to all 
these AI companies, just make everything open source, I don't 
even know how they monetize that. And if people can just copy 
it--it's just--it is sort of interesting to me, so I would like 
to hear more about that later. I do want to move to another 
topic.
    And this is to Jason. You mentioned in one of your 
recommendations, additional funding for NIST to make sure they 
have the capacity to do their AI risk framework. So I assume 
you think it is a good framework? I think it's a good 
framework. I looked through it and thought about it quite a 
bit. It's pretty generalized, so you don't--it's not that 
prescriptive, so any company could actually adopt it. What is 
your view of just forcing companies to think about AI by, let's 
say, anytime the Federal Government contracts with someone, we 
require them to go through the NIST AI framework?
    Dr. Matheny. Thank you. I do think that we need an approach 
in which the safety and reliability of systems is assessed 
before they're broadly deployed. And right now, we don't have 
such a system. And there are a few ways for the Federal 
Government to help. One is not only requiring that for direct 
Federal contractors but also making it a term and condition 
that any compute provider that has a Federal contract would be 
required to place those conditions on any company that's using 
its computing infrastructure. So sort of similar to the Common 
Rule in which a Federal contract with a research organization 
requires them that any organization that they're working with, 
even if it's not on a Federal contract, follows the Common 
Rule, say, for human subjects research. We could require the 
same for safety testing for AI.
    Mr. Lieu. Thank you. I yield back.
    Chairman Lucas. The gentleman yields back.
    Seeing no other Members present, I want to thank the 
witnesses for your valuable and in some ways exceptional 
testimony and the Members, of course, for their questions.
    The record will remain open for 10 days for additional 
comments and written questions from Members. This hearing is 
adjourned.
    [Whereupon, at 12:08 p.m., the Committee was adjourned.]

                                Appendix

                              ----------                              

                   Answers to Post-Hearing Questions
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]

                                 [all]