[Senate Hearing 118-118]
[From the U.S. Government Publishing Office]


                                                          S. Hrg. 118-118
                                                          

                  OPEN HEARING: ADVANCING INTELLIGENCE IN 
                   THE ERA OF ARTIFICIAL INTELLIGENCE: AD-
                   DRESSING THE NATIONAL SECURITY IMPLICA-
                   TIONS OF AI

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                                HEARING

                               BEFORE THE

                    SELECT COMMITTEE ON INTELLIGENCE

                                 OF THE

                          UNITED STATES SENATE

                    ONE HUNDRED EIGHTEENTH CONGRESS

                             FIRST SESSION

                               __________

                           SEPTEMBER 19, 2023

                               __________

      Printed for the use of the Select Committee on Intelligence
      
[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]      


        Available via the World Wide Web: http://www.govinfo.gov
        
                              __________

                   U.S. GOVERNMENT PUBLISHING OFFICE                    
53-570                    WASHINGTON : 2024                    
          
-----------------------------------------------------------------------------------     
                    
                    
                    SELECT COMMITTEE ON INTELLIGENCE

           (Established by S. Res. 400, 94th Cong. 2d Sess.)

                   MARK R. WARNER, Virginia, Chairman
                  MARCO RUBIO, Florida, Vice Chairman

DIANNE FEINSTEIN, California         JAMES E. RISCH, Idaho
RON WYDEN, Oregon                    SUSAN M. COLLINS, Maine
MARTIN HEINRICH, New Mexico          TOM COTTON, Arkansas
ANGUS S. KING, Jr., Maine            JOHN CORNYN, Texas
MICHAEL F. BENNET, Colorado          JERRY MORAN, Kansas
ROBERT P. CASEY, Jr., Pennsylvania   JAMES LANKFORD, Oklahoma
KIRSTEN E. GILLIBRAND, New York      MIKE ROUNDS, South Dakota
JON OSSOFF, Georgia

                CHARLES E. SCHUMER, New York, Ex Officio
                 MITCH McCONNELL, Kentucky, Ex Officio
                  JACK REED, Rhode Island, Ex Officio
                ROGER F. WICKER, Mississippi, Ex Officio
                              ----------                              

                       William Wu, Staff Director
                  Brian Walsh, Minority Staff Director
                   Kelsey Stroud Bailey, Chief Clerk
                           
                           
                           C O N T E N T S

                              ----------                              

                           SEPTEMBER 19, 2023
                           OPENING STATEMENTS

                                                                   Page
Mark R. Warner, U.S. Senator from Virginia.......................     1
Marco Rubio, U.S. Senator from Florida...........................    28

                               WITNESSES

Benjamin Jensen, Ph.D., Senior Fellow, Center for Strategic and 
  International Studies..........................................     3
    Prepared Statement...........................................     6
Yann LeCun, Ph.D., Vice President and Chief AI Scientist, Meta 
  Platforms, Inc.................................................    11
    Prepared Statement...........................................    13
Jeffrey Ding, Ph.D., Assistant Professor of Political Science, 
  George Washington University...................................    18
    Prepared Statement...........................................    20

                         SUPPLEMENTAL MATERIAL

Response from Meta Platforms, Inc., dated January 12, 2024, to 
  questions for the record from the Committee....................    58

 
     OPEN HEARING: ADVANCING INTELLIGENCE IN THE ERA OF ARTIFICIAL 
   INTELLIGENCE: ADDRESSING THE NATIONAL SECURITY IMPLICATIONS OF AI

                              ----------                              


                      TUESDAY, SEPTEMBER 19, 2023

                                       U.S. Senate,
                          Select Committee on Intelligence,
                                                    Washington, DC.
    The Committee met, pursuant to notice, at 2:32 p.m., in 
Room SH-216 in the Hart Senate Office Building, Hon. Mark R. 
Warner, Chairman of the Committee, presiding.
    Present: Senators Warner (presiding), Rubio, Wyden, 
Heinrich, King, Bennet, Casey, Gillibrand, Ossoff, Cotton, 
Cornyn, Moran, Lankford, and Rounds.

 OPENING STATEMENT OF HON. MARK R. WARNER, A U.S. SENATOR FROM 
                            VIRGINIA

    Chariman Warner. I want to welcome our witnesses:
    Dr. Yann LeCun, who is the chief AI scientist at Meta, and, 
as I've learned, one of the real pioneers of machine learning. 
Dr. LeCun, it's really great to have you.
    Dr. Benjamin Jensen, who is senior fellow at the Center for 
Strategic and International Studies and a professor at the 
Marine Corps University School of Advanced Warfighting. 
Welcome, Dr. Jensen.
    And Dr. Jeffrey Ding, who is professor of Political Science 
at my alma mater, George Washington University, and author of 
the influential ``ChinAI Newsletter'' on China's AI landscape.
    I also want to thank all of my colleagues who've been 
interested in this, but particularly Senator Rounds and Senator 
Heinrich, who have been working with Leader Schumer on a series 
of other AI forums, closed and open.
    In many ways, the opportunity and risk of this technology 
that has kind of captured everyone's attention, AI, is 
obviously not new for this Committee. The agencies that we 
oversee have been some of the most innovative developers and 
avid adopters of advanced machine learning capabilities, 
working with large language models and computer vision systems, 
long before those terms entered the public vocabulary.
    The ability to sift through and make sense of enormous 
amounts of data has been a hallmark of the American 
Intelligence Community since its inception. And the use of data 
science and advanced computation has been one of the core 
competencies of the IC for the last half century. Our Committee 
has been engaged on all of those topics for as long as I've 
been on this Committee. What has dramatically changed, however, 
are the potential social, political, and national security 
implications of this technology. And it's driven in large part 
by the proliferation of generative models that are both 
publicly accessible and incredibly capable due to a combination 
of unprecedented scale and breakthrough in training methods. 
Rapid advancements in this field have the potential to unlock 
enormous innovation and public benefit in areas as diverse as 
drug discovery, creative arts, and software programming. But as 
Congress evaluates the scope and significance of those 
transformations, we must equally grapple with the disruptions, 
ethical dilemmas, and potential dangers of this technology.
    In both the wider Senate and through a series of 
roundtables I've hosted the last several months, we as a body 
are seeking to rise to that risk. And candidly, we were not 
able to do so on social media, and again, I think our consensus 
is we can't repeat that here. As we discuss in today's hearing, 
the proliferation of these technologies has dramatically 
lowered the barrier of entry for foreign governments to apply 
these tools to their own military and intelligence domains.
    The public release of technical details from trained model 
weights to code bases of highly capable models is a boost to 
foreign governments, just as it is to startups, university 
researchers, and hobbyists. While the United States 
Intelligence Community has benefited from AI innovation for 
signal processing, sensing, machine translation, and more, so 
too should we now anticipate that a wider set of foreign 
governments will be able to also harness these tools, many of 
them developed and actually released by U.S. companies. But 
they will take those products, candidly, for their own military 
and intelligence uses.
    Our witnesses are well positioned to describe the current 
posture of our Nation's most strategic rival, the People's 
Republic of China, as it pertains to AI. I look forward to 
hearing from our experts where leading PRC research labs and 
technology vendors are in their efforts to build cutting-edge 
AI models, development tools, and innovation ecosystems. In 
today's hearing, this Committee will focus on maintaining the 
U.S. Intelligence Community's edge, including how the IC can 
better adapt and even holistically adapt for these 
technologies.
    I hope today's discussion can better identify some of the 
organizational, contracting, and technical barriers to 
achieving these objectives. I'm also eager to hear where these 
tools currently fall short of some of the loftiest claims about 
their capabilities--something Dr. LeCun and I have talked 
about. For instance, the propensity of even the most advanced 
language models to hallucinate raises serious questions about 
their fitness in mission-critical and other sensitive areas. In 
the intelligence domain, mistakes can impact our Nation's 
security, the privacy of Americans, and the clarity of pivotal 
foreign policy discussions.
    To be sure, as well, generative models can improve 
cybersecurity, helping programmers identify coding errors and 
contributing towards safer coding practices. But with that 
potential upside, there's also a downside since these same 
models can just as readily assist malicious actors. I hope this 
hearing will explore the ways in which generative models alter 
the cyber landscape, lowering the barrier to entry for formerly 
second-tier cyber powers, and how in the cyber domain, AI can 
advance the capabilities of more advanced state actors. Lots to 
discuss.
    Now, as the leading body in Congress in tracking 
disinformation, market manipulation, and election influence 
efforts by our Nation's adversaries, our Committee is also 
deeply interested in the ways in which AI expands and 
exacerbates the threat of foreign malign influence. The ability 
for foreign actors to generate hyper-realistic images, audio, 
and videos will undeniably make it harder for Americans to 
navigate our ever more complex, fraught, and fast paced media 
environment.
    We must also contend with the ways in which bad actors will 
use these tools to undermine trust in markets, public 
institutions, and public health systems. These tools will 
greatly challenge our society's ability to agree on baseline 
facts and our already impaired ability to develop consensus. 
The last several years have amply demonstrated the ways in 
which speed, scale, and excitement associated with new 
technologies have frequently obscured the shortcomings of their 
creators in anticipating the harmful effects of their use.
    I hope we will also queue up a discussion of how the U.S. 
can best harness and govern these technologies to avoid the 
same mistakes we made in failing to foresee vulnerabilities in 
other global-scale technologies like social media. To that end, 
I hope we'll also touch upon how other countries, both allies 
and rivals, are coping with potential disruptions, risks, and 
economic dislocations of AI technologies through their own 
regulatory proposals. And I know I hope I hear from Dr. Ding on 
what actually the PRC is doing in this field. AI capabilities, 
I think we all know, hold enormous potential. However, we must 
make sure that we think about that potential, gain the upside, 
but where appropriate, put safeguards in place. I look forward 
to today's discussion.
    I did an extra-long opening today because the Vice Chairman 
has been held up for a moment. He will join, and when he joins 
us, after the presentations, to allow him to make an opening 
comment. And because, in our tradition, in open hearings, we 
will go by rank of seniority for five-minute rounds.
    With that, I'm not sure who drew the long straw or the 
short straw in terms of opening comments, but I'll turn it over 
to our panel.
    Thank you.

 STATEMENT OF BENJAMIN JENSEN, PhD, SENIOR FELLOW, CENTER FOR 
  STRATEGIC AND INTERNATIONAL STUDIES, AND PROFESSOR, MARINE 
        CORPS UNIVERSITY, SCHOOL OF ADVANCED WARFIGHTING

    Dr. Jensen. Short straw, Senator.
    Chairman Warner, Vice Chairman Rubio, distinguished Members 
of the Committee, I really am honored today to sit with you and 
share my thoughts on what I think you all agree, from reading 
all the work that you've done on it, is probably the most 
important question facing our Nation from a technological 
perspective.
    The magnitude of the moment is clear, right? Both the 
Senate and the House are very much cultivating a national 
dialogue, and I just want to open as a citizen by thanking you 
for that. You have a powerful role in that, and so doing this 
here right now is key. And so, I have to be very blunt and 
clear with you that I'm going to talk to you less about the 
threat outside, Sir. I'm going to talk to you more about how I 
think we could get it wrong.
    Today, as part of that ongoing dialogue. I really want to 
look at the often-invisible center of gravity behind any 
technology: people, bureaucracy, and data, and in particular, 
data infrastructure and architecture. Put simply, you get the 
right people in place, with permissive policies and 
computational power at scale, and you gain a position of 
advantage in modern competition. I'll just put it bluntly. In 
the twenty-first century, the general or spy who doesn't have a 
model by their side is basically as helpless as a blind man in 
a bar fight.
    So, let's start with people. Imagine a future analyst 
working alongside a generative AI model to monitor enemy cyber 
capabilities. The model shows the analyst signs of new 
adversary malware in targeting U.S. critical infrastructure. 
The analyst disagrees. The challenge we have is today our 
analysts can't explain why they disagree because they haven't 
been trained in basic data science and statistics. They don't 
know how to balance causal inference and decades of thinking 
about human judgment and intuition. And sadly, I'll be honest, 
our modern analytical tradecraft and even something close to 
me, professional military education, tends to focus on discrete 
case studies more than statistical patterns or trend analysis. 
In other words, if we unleash a new suite of machine learning 
capabilities without the requisite workforce to understand how 
to use them, we're throwing good money after bad. And we really 
have to be careful about this. I can't stress this enough. If 
you don't actually make sure that people understand how to use 
the technology, it's just a magic box.
    Let's turn to the bureaucracy.
    Now, I want you to imagine what the Cuban Missile Crisis 
would look like in 2030: all sides with a wide range of machine 
learning capabilities. There would be an utter tension as 
machines wanted to speed up decision-making in the crisis. But 
senior decision-makers needed to slow it down to the pace of 
interagency collaboration. Even worse, you would be overwhelmed 
by deep fakes and computational propaganda pressuring you as 
elected officials and any senior leader to act. And pressure to 
act at a moment of crisis doesn't necessarily lead for sound 
decision-making. Unfortunately, neither our modern national 
security enterprise nor the bureaucracy surrounding government 
innovation/experimentation are ready for this world. If the 
analyst and military planner struggles to understand 
prediction, inference, and judgment through algorithms, the 
challenge is even more acute with senior decision-makers.
    At this level, most international relations and diplomatic 
history tells us that the essence of decision is as much 
emotion, flawed analogies, and bias as it is rational 
interests. What happens when irrational humans collide with 
rational algorithms during a crisis? Confusion could easily 
eclipse certainty, unleashing escalation and chaos.
    There are even larger challenges associated with creating a 
bureaucracy capable of adapting algorithms during a crisis. 
Because of complexity and uncertainty, all models require a 
constant stream of data to the moment at hand, not just the 
moment of the past. But crises are different than necessarily 
what preceded them. Unfortunately, slow adapters will succumb 
to quick deaths on that future battlefield. As a result, a 
culture of experimentation and constant model refinement will 
be the key to gaining and sustaining relative advantage. Now, 
ask yourself, do we have that bureaucracy?
    Last, consider data, architecture, and infrastructure, how 
we put the pieces of the puzzle together. I want you to imagine 
we're almost back to the old days of the SCUD hunt, right? 
Imagine the hunt for a mobile missile launcher in a future 
crisis. A clever adversary, knowing they were being watched, 
could easily poison the data used to support intelligence 
analysis and targeting. They could trick every computer model 
into thinking a school bus was a missile launcher, causing 
decision-makers to quickly lose confidence in otherwise 
accurate data. Even when you were right 99 percent of the time, 
the consequences of being wrong once are still adding unique 
human elements to crisis decision-making. Artificial 
intelligence and machine learning, therefore, are only as 
powerful as the underlying data. Yet to collect, process, and 
store that data is going to produce significant costs going 
forward. This is not going to be cheap. Furthermore, bad 
bureaucracy and policy can kill great models if they limit the 
flow of data.
    Last, let's talk about the fact that Prometheus has already 
shared the fire, and I think you all know that even from your 
opening comments, Chairman. Adversaries now into the 
foreseeable future can attack us at machine speed through a 
constant barrage of cyber operations and more disconcerting, 
mis-, dis- and mal-information, alongside entirely new forms of 
swarming attacks that could hold not just our military, but our 
civilian population at risk. Unless the United States is able 
to get the right mix of people, bureaucratic reform, and data 
infrastructure in place, those attacks could test the very 
foundation of our Republic.
    Now, I'm an optimist, so I'm going to be honest with you. 
I'm confident the United States can get it right. In fact, the 
future is ours to lose. Authoritarian regimes are subject to 
contradictions that make them rigid, brittle, and closed to new 
information. Look no further than regulations about adherence 
to socialist thought in data sets. These regimes are afraid to 
have the type of open, honest dialogue this Committee is 
promoting. And that fear is our opportunity.
    Thank you for the opportunity to testify.
    [The prepared statement of the witness follows:]
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
   STATEMENT OF YANN LeCUN, PhD, VICE PRESIDENT AND CHIEF AI 
  SCIENTIST, META PLATFORMS, AND SILVER PROFESSOR OF COMPUTER 
         SCIENCE AND DATA SCIENCE, NEW YORK UNIVERSITY

    Dr. LeCun. Chairman Warner, Vice Chairman Rubio, and 
distinguished Members of the Committee. Thank you for the 
opportunity to appear before you today to discuss important 
issues regarding AI.
    My name is Yann LeCun. I'm currently the Silver Professor 
of Computer Science and Data Science at New York University. 
I'm also Meta's Chief AI Scientist and co-founder of Meta's 
Fundamental AI Research Lab. At Meta, I focus on AI research, 
development strategy, and scientific leadership.
    AI has progressed leaps and bounds since I began my 
research career in the 1980s. Today, we are witnessing the 
development of generative AI, and in particular, large language 
models. These systems are trained through self-supervised 
learning. Or more simply, they are trained to fill in the 
blanks. In the process of doing so, those AI models learn to 
represent text or images--including the meaning, style, and 
syntax--in multiple languages. The internal representation can 
then be applied to downstream tasks such as translation, topic 
classification, et cetera. It can also be used to predict the 
next words in a text, which allow LLMs to answer questions or 
write essays, and write code as well. It is important not to 
undervalue the far-reaching potential opportunities they 
present. The development of AI is as foundational as the 
creation of the microprocessor, the personal computer, the 
Internet, and the mobile device. Like all foundational 
technologies, there will be a multitude of uses of AI. And like 
every technology, AI will be used by people for good and bad 
ends.
    As AI systems continue to develop, I'd like to highlight 
two defining issues. The first one is safety, and the second 
one is access. One way to start to address both of these issues 
is through the open sharing of current technology and 
scientific information. The free exchange of scientific papers, 
code, and trained models in the case of AI has enabled American 
leadership in science and technology. This concept is not new. 
It started a long time ago. Open sourcing technology has 
spurred rapid progress in systems we now consider basic 
infrastructure, such as the Internet and mobile communication 
networks.
    This doesn't mean that every model can or should be open. 
There is a role for both proprietary and open-source AI models. 
But an open-source basic model should be the foundation on 
which industry can build a vibrant ecosystem. An open-source 
model creates an industry standard, much like the model of the 
Internet in the mid '90s. Through this collaborative effort, AI 
technology will progress faster, more reliably, and more 
securely.
    Open sourcing also gives businesses and researchers access 
to tools that they could not otherwise build by themselves, 
which helps create a vast social and economic set of 
opportunities. In other words, open sourcing democratizes 
access. It gives more people and businesses the power to build 
upon state-of-the-art technology and to remedy potential 
weaknesses. This also helps promote democratic values and 
institutions, minimize social disparities, and improve 
competition. We want to ensure that the United States and 
American companies, together with other democracies, lead in AI 
development ahead of our adversaries, so that the foundational 
models are developed here and represent and share our values. 
By open sourcing current AI tools, we can develop our research 
and development ecosystem faster than our adversary.
    As AI technology progresses, there is an urgent need for 
governments to work together, especially democracies, to set 
common AI standards and governance models. This is another 
valuable area where we welcome working with regulators to set 
appropriate transparency requirements, red teaming standards, 
and safety mitigations to help ensure those codes of practice, 
standards, and guardrails are consistent across the world. The 
White House's voluntary commitment is a critical step in 
ensuring responsible guardrails, and they create a model for 
other governments to follow. Continued U.S. leadership by 
Congress and the White House is important in ensuring that 
society can benefit from innovation in AI while striking the 
right balance with protecting rights and freedom, preserving 
national security interests, and mitigating risks where those 
arise.
    I'd like to close by thanking Chairman Warner, Vice 
Chairman Rubio, and the other Members of the Committee for your 
leadership. At the end of the day, our job is to work 
collaboratively with you, with Congress, with other nations, 
and with other companies in order to drive innovation and 
progress in a manner that is safe and secure and consistent 
with our national security interests.
    Thank you. I look forward to your questions.
    [The prepared statement of the witness follows:]
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    STATEMENT OF JEFFREY DING, PhD, ASSISTANT PROFESSOR OF 
        POLITICAL SCIENCE, GEORGE WASHINGTON UNIVERSITY

    Dr. Ding. Chairman Warner, Vice Chairman Rubio, and Members 
of the Committee. I am honored by the opportunity to brief this 
Committee on the National Security Implications of AI. In all 
honesty, I also have a selfish reason for attending today. I 
teach political science at GW, and my students all really look 
up to the Committee Members in this room and also all the staff 
who are working behind the scenes to put this hearing together. 
So, when I got to tell the class this morning that I was doing 
this testimony, they all got the most excited I've ever seen 
them get excited this semester. And so, hopefully, that will 
cause them to do more of the required readings in class. In all 
seriousness, I have great students, I'm very grateful to be 
here.
    Today, in my opening remarks, I want to make three main 
points from my written testimony. The first is when it comes to 
the national security of implications of AI, the main driver 
and the main vector is which country will be able to sustain 
productivity growth at higher levels than their rivals. And for 
this vector, the distinction between innovation capacity and 
diffusion capacity is central to thinking about technological 
leadership in AI. Today, when various groups--whether that be 
experts, policymakers, the Intelligence Community--when they 
try to assess technological leadership, they are overly 
preoccupied with innovation capacity. Which state is going to 
be the first to generate new-to-the-world breakthroughs, the 
first to generate that next leap in large language models. They 
neglect diffusion capacity. A state's ability to spread and 
adopt innovations after their initial introduction across 
productive processes.
    And that process of diffusion throughout the entire economy 
is really important for technologies like AI. If we were 
talking about a sector like automobiles, or even a sector like 
clean energy, we might not be talking as much about the effects 
of spreading technologies across all different productive 
processes throughout the entire economy. AI is a general-
purpose technology, like electricity, like the computer, like 
my fellow panelists just mentioned in his testimony. And 
general-purpose technologies historically precede waves of 
productivity growth because they can have pervasive effects 
throughout the entire economy. So, the U.S. in the late 19th 
century became the leading economic power before it translated 
that influence into military and geopolitical leadership, 
because it was better at adopting general purpose technologies 
at scale, like electricity, like the American system of 
interchangeable manufacture, at a better and a more effective 
rate than its rivals.
    Point number two is when we assess China's technological 
leadership and use this framework of innovation capacity versus 
diffusion capacity, my research finds that China faces a 
diffusion deficit. Its ability to diffuse innovations like AI 
across the entire economy lags far behind its ability to 
pioneer initial innovations or make fundamental breakthroughs.
    And so, when you've heard from other people in the past or 
in the briefing memos you are reading, you are probably getting 
a lot of innovation-centric indicators of China's scientific 
and technological prowess: its advances in R&D spending, 
headline numbers on patents and publications. In my research, 
I've presented evidence about China's diffusion deficit by 
looking at how is China actually adopting other information and 
communications technologies at scale? What are its adoption 
rates in cloud computing, industrial software, related 
technologies that would all be in a similar category to AI? And 
those rates lag far behind the U.S.
    Another indicator would be how is China's ability to widen 
the pool of average AI engineers? I'm not talking about Nobel 
Prize of computing winners like my fellow panelists here, but 
just average AI engineers who can take existing models and 
adapt them in particular sectors or industries or specific 
applications. And based on my data, China has only 29 
universities that meet a baseline quality metric for AI 
engineering, whereas the U.S. has 159. So, there's a large gap 
in terms of China's diffusion capacity compared to its 
innovation capacity in AI.
    I'll close with the third point, which is some recent 
trends in Chinese labs' large language models. China has built 
large language models similar to OpenAI's ChatGPT, as well as 
OpenAI's text-to-image models like DALL-E. But there's still a 
large gap in terms of Chinese performance on these models. And, 
in fact, on benchmarks and leaderboards where U.S. models are 
compared to Chinese models on Chinese language prompts, models 
like ChatGPT still perform better than Chinese counterparts. 
Some of these bottlenecks relate to a reliance on Western 
companies to open up new paradigms, China's censorship regime, 
which Dr. Jensen talked about, and computing power bottlenecks, 
which I'm happy to expand on further.
    I'll close by saying I submitted three specific policy 
recommendations to the Committee. But I want to emphasize one, 
which is keep calm and avoid overhyping China's AI 
capabilities. In the paper that forms the basis for this 
testimony, I called attention to a 1969 CIA assessment of the 
Soviet Union's technological capabilities. It was remarkable 
because it went against the dominant narrative of the time of a 
Soviet Union close to overtaking the U.S. in technological 
leadership. The report concluded that the technological gap was 
actually widening between the U.S. as the leader and the Soviet 
Union because of the U.S.'s superior mechanisms to spread 
technologies and diffuse technologies. Fifty years later, we 
know why this assessment was right, and we know we have to 
focus on diffusion capacity when it comes to scientific and 
technological leadership.
    Thanks for your time.
    [The prepared statement of the witness follows:]
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    Chairman Warner. Thank you all very much, gentlemen. I'm 
going to ask Vice Chairman Rubio to make any opening comments 
he wants. Then we'll go to a question round.

  OPENING STATEMENT OF HON. MARCO RUBIO, A U.S. SENATOR FROM 
                            FLORIDA

    Vice Chairman Rubio. And I'll be brief, and I apologize. I 
was wrapped up in a call that started late and ended late. So, 
I'll be very brief.
    This whole issue is fascinating to me because the story of 
humanity is the story of technological advances from the very 
beginning in every civilization and culture. And there are 
positives in every technological advance, and there are 
negatives that come embedded in it. And generally, 
technological advances have for the most part--what they've 
allowed is human beings to do what humans do, but faster, more 
efficiently, more productively, more accurately. In essence, 
technology and technological advances have, in essence, allowed 
humans to be better at what humans do.
    I think what scares people about this technology is the 
belief that it not simply holds the promise of making us 
better, but the threat of potentially replacing the human, 
being able to do what humans do without the human. I think one 
of the things that's interesting is we've been interacting with 
AI, or at least models of AI and applications of it, in ways we 
don't fully understand. Whether it's estimating how long it's 
going to take from point A to point B and which is the fastest 
route, based on their predicted traffic patterns at that time 
of day to every time you say Alexa or Siri, all of these are 
somewhat built into learning models.
    But now we get into the application of machine learning, 
where you're basically taking data and you're now issuing 
recommendations of a predictive nature. So, that's sort of what 
we understand now. But then the deeper learning that actually 
seeks to mimic the way the human brain works. Not only can it 
take in things beyond text-to-images, but in essence, learn 
from itself and continue to take on a life of its own. All of 
that is happening, and frankly, I don't know how we hold it 
back.
    So, really, the three fundamental questions that we have 
from the perspective of national security is first and 
foremost, which I think this is a broader topic that involves 
national security, but beyond national security, is how do you 
regulate a technology that is transnational, that knows no 
borders, and that we don't have a monopoly on? We may have a 
lead on it, but some of the applications of AI are going to be 
pretty common, and for purposes of what some nefarious actor 
might use and some of its applications as well.
    The second is, will we ever reach a point--and this is the 
one that, to me, is most troubling--where we can afford to 
limit it. So, as an example, we are in a war, God forbid, with 
an AI general on the other side. Can we afford to limit 
ourselves in a way that keeps pace with the speed and 
potentially the accuracy of the decision-making of a machine on 
that end and our limits on ours? And the same is true in the 
business world when we get AI CEOs making decisions about where 
companies invest. There comes a point where you start asking 
yourself, can we afford to limit ourselves despite the downside 
of some of this. It's something we haven't thought through, and 
one that I think you've addressed a lot or are going to be 
addressing a lot are the national security implications.
    But here's the one that I think is related to national 
security. We have seen that globalization and automation has 
been deeply disruptive to societies and cultures all over the 
world. We have seen what that means in displacing people from 
work and what it does to society and the resentments it 
creates. I think this has the potential to do that by times-
infinity. In essence, how disruptive this will be, the 
industries that it will fundamentally change, the jobs it will 
destroy and perhaps replace with different jobs, but the 
displacement it could create. And that has national security 
implications, and what happens in the rest of the world and in 
some of the geopolitical trends that we see. And I think it has 
the threat of reaching professions that up to this point have 
been either insulated or protected from technological advances 
because their education level. So, again, not a national 
security matter.
    Look at the strike in Hollywood. A part of that is driven 
by the fear that screenwriters, and maybe even the actors, will 
be replaced by artificial intelligence. And imagine that 
applied to multiple industries and what that would mean for the 
world and for its economics. There's a lot to unpack here. But 
the one point that I really want to focus in on is we may want 
to place these limits, and we may very well be in favor of them 
from a moral standpoint, but can we ever find ourselves at a 
disadvantage facing an adversary who did not place those limits 
and is therefore able to make decisions in real time, at a 
speed and precision that we simply cannot match because of the 
limits we put on ourselves? That may still be the right thing 
to do, but I do worry about those implications.
    Thank you.
    Chairman Warner. Well, thank you, Marco. I think, again, we 
are all trying to grapple with this in a variety of ways. I 
remember a year and a half ago, as I was trying to get self-
educated a bit and thought at first, well, should we even find 
a definition? How about now? Not worth that. Last week we had 
22, and it was really kind of the who's who, from all the tech 
side and a variety of figures from civil society. A year and a 
half later, and no one still started with the definition of 
what the terms we're even using mean.
    And yet we've seen, I would argue, I think about most 
things I spend time on. There's some linear progression, I 
feel, in terms of the amount of time versus the amount I've 
learned. In this topic area, I get sometimes more confounded. I 
also think, for example, the economics, the economics of last 
November, whenever you said whether it was China in terms of 
scale, amount of data compute, et cetera, or entities like 
Microsoft or Google, the gating cost to come into this would be 
so high. We've had the director of the OSTP recently say, 
potentially because of release of things like LAMA, that you 
can now get in on a variation of large language model for 
pennies on the dollar. So, this is moving so quickly.
    Dr. LeCun, I'm going to start with you, and I warned you I 
was going to come at you on this. I worry a little bit when we 
talk about democratizing access. Then it sounds like some of 
your colleagues from social media in the late '90s. We're going 
to democratize access, and we'll figure out the guardrails 
after the fact. But in that democratization of access, I don't 
think we ever put our guardrails in place. How do we 
democratize access with AI tools and yet still put some level 
of guardrails? And obviously, bearing in mind what Senator 
Rubio said, is you don't want to act and unilaterally disarm, 
but the notion of not putting some guardrails in place in a 
field that's changing so quickly. Can you speak to that?
    Dr. LeCun. Senator, this is a very important question that, 
of course, we've devoted a lot of thought to. I think the best 
way to think about this is to think about the type of AI 
systems that have been released so far as being basic 
infrastructure. In themselves, those systems are not 
particularly useful. To be useful, they need to be customized 
for a particular vertical application. And a good example of 
this is the infrastructure of the Internet, which is open 
source. It didn't start out as being open source, it started 
out as being commercial.
    And then the open-source platforms won because they are 
more secure, easier to customize, safer. There are all kinds of 
advantages. AI is going to become a common platform, and 
because it's a common platform, it needs to be open source if 
you want it to be a platform on top of which a whole ecosystem 
can be built. And the reason why we need to work in that mode 
is that this is the best way to make progress as fast as we 
can.
    I really like the argument of Professor Ding about the fact 
that, in the U.S., we're extremely good at seizing the 
opportunity when a new innovation comes in or a new scientific 
advance, it diffuses very quickly into the local industry. This 
is why Silicon Valley is Silicon Valley. It's geographically 
concentrated because information flows very, very quickly. 
Other countries that are somewhat isolated ecosystems 
intellectually do not have the same kind of effect. And so, it 
favors us to have open platforms.
    Chairman Warner. I would respectfully say, I think the two 
most immediate potential harms with AI tools that have already 
been released is the ability for massive undermining of trust 
in our elections and massive underlying of trust in our public 
markets. Come back to that later.
    Dr. Ding, one of the things you said, and I think you're 
accurate about the fact that the PRC as a state has not done a 
good job of diffusing technological innovation. But I have to 
believe if we do open source, and there are geopolitical harms, 
and there is the ability for the PRC, at least its intel and 
military, to gain this knowledge from things that are released 
into the wild. Can you speak to that?
    Dr. Ding. Yeah, I think it's a tough debate and tough 
discussion point. I think I agree that open source is important 
to spur diffusion and in terms of fundamental architecture. So, 
going back to this Internet example, the protocols for how IP 
addresses work, it makes sense that open source might be a way 
we spread that at scale. I'm less convinced that open source is 
the best model for reducing the harms that you identified, in 
terms of specific, powerful models that might not be as close 
to this infrastructure layer.
    So, something like ChatGPT is, I think, took a good stance 
in terms of setting up an application programming interface 
which makes it not open source, it's closed source. And 
developers can impose rules on how the model can be used. So, 
Chinese developers cannot use ChatGPT today, to your point. And 
so, I think this provides a balance in terms of the research 
community can still play around with the model by using the API 
system. But OpenAI and potentially government actors can use 
this API to implement rules to govern how these models are used 
by potentially malicious actors.
    Chairman Warner. Let me get one question quickly to Dr. 
Jensen, because the notion that scale was going to be the 
determinant factor seemed to be the argument that most large 
language models launched from November till about May, and then 
it switched. If scale is not the largest determinant of who 
will be most successful, what will be the determinant?
    Dr. Jensen. The people. So, you're worried about the 
guardrails. I'm not even sure you have the railroad engineers 
to get the train out the station. And what I mean by that is 
you're going to have to have hard decisions, first, like you're 
seeing of what data is open and closed. Because you're right, 
if you want an innovative ecosystem, the exchange of ideas, 
we're built on that as a nation. Obviously not all ideas are 
meant to be shared. Even George Washington had secrets.
    So, what do you hide in terms of data will become really 
important. And how do you aggressively corrupt your 
adversaries' data through poisoning it as well--a digital 
Terracotta Army to confuse them. And scale will matter because 
the larger amounts of data inputs you have, the more likely you 
are to be able to detect adversary manipulation or those 
markets that we should worry about. And to do that, it's not 
just the data infrastructure. Again, you have to have the 
people who know what they're doing. And I work with these 
folks. I mean, I've served in uniform for 20 years. I teach 
warriors, and I watch them when we tinker with this in the 
classroom. And honestly, some part of me goes back to classical 
reasoning. You want to really teach someone how to work with a 
large language model? Make them think like Socrates, learn how 
to ask questions anew. Otherwise, it'll just be confirmation 
bias. You made them to understand how a sequential prediction 
that a large language model is just basically predicting how 
they're going to finish their sentence. There's no magic there. 
And so, it's not just the scale of the data, as you point out, 
Sir. It's how do we actually educate that workforce so that we 
can out-cycle any adversary.
    Chairman Warner. Thank you. I'm going to Senator Rubio. And 
again, I remind my colleagues in our open hearings, we go by 
seniority.
    Vice Chairman Rubio. I understand, we want to talk about 
sort of the commercial and broader scientific applications of 
this. It'd be great to be the world leader, industry standard, 
top of the line. But for purposes of this Committee, how it 
would be used as a nation-state? What I think is important to 
reiterate is you don't need the state of the art for it to be 
adopted internally for your decision-making. Every conflict in 
human history has involved an analysis. At some point, someone 
who started the war made an analysis based on their data that 
was in their brain, their understanding of history, their 
beliefs, their preconceived notions and the like, that they 
could be successful and that this was really important and now 
is the time to do it. And that's the part that worries me the 
most, particularly when it comes to how it applies to 
authoritarian regimes. And here's why.
    At least in our system, for the most part, we encourage 
people to come forward and, as policymakers, make an analysis 
and give us accurate information even if it may not be the one 
we want to hear. In authoritarian systems, you usually get 
promoted and rewarded for telling leaders what they want to 
hear and not for reporting bad news and the like. And so, I 
don't know if anyone can answer this question, but I wanted to 
pose it to you.
    Isn't one of the real risks as we move forward that some 
nation with some existing capabilities will conduct analysis on 
the basis of their version of AI, which will be flawed to some 
extent by some of the data sets, and those data sets and the 
analytic functions, they reach the conclusion that this is the 
moment to take this step. Now is the time to invade, now is the 
time to move because our system is telling us that now is the 
time to do it. And that system may be wrong. It may be based on 
flawed data. It may be based on data that people fed in there 
on purpose because that's the data that their analysts are 
generating.
    That's the part that I worry about. Because even if it's 
not the top-of-the-line or the state-of-the-art data, it will 
be what influences their decision-making and could very well 
lead to twenty-first century conflicts started not by simply a 
human mind, but how a human mind used technology to reach a 
conclusion that ends up being deeply destructive. Is that a 
real risk is the question?
    Dr. Jensen. I'm happy to talk about war anytime, Senator. I 
think you're hitting on a fundamental of human history, as 
you're saying, right? Every leader, usually not alone, as part 
of a small group, is calculating risk at any moment. And having 
models incorrectly or correctly add to their judgment about 
that is a real concern. There will be windows of vulnerability 
and the possibility of inadvertent escalation that could make 
even the early Cold War look more secure than it actually was.
    And so, I think that's the type of discussion you have to 
have. That's where we hopefully will have back-channel 
conversations with those authoritarian regimes. And frankly, it 
just bodes well for what we know works for America: a strong 
military, where your model finds it really hard to interpret 
anything but our strength. So, I think that there are ways that 
you can try to make sure that the right information is 
circulating, but you can never fundamentally get away from 
those hard, weird moments, those irrational people with 
rational models. So, you see the model as rational or flawed 
because it collects just the skewed data. I worry more about 
what we just saw happen in Russia where a dictator living in 
corrupt mansions, reading ancient imperial history of Russia, 
decided to make one of the largest gambles in the twenty-first 
century. And so, I don't think that's going to leave us. I 
think that's a fundamental of human history. And I actually 
think, in some senses, the ability of models to bring data 
could steady that a bit. And we can make sure that we show the 
right type of strength, that it steadies it further.
    Vice Chairman Rubio. Let me ask this question related to 
that one, and it has to do with the work of this Committee in 
general. At the core of intelligence work is the analysis. In 
essence, you can collect all the raw bits of data you want, but 
someone has to interpret and tell a policymaker this is what we 
think it means, in light of what we know about those people, 
what we know about historical trends, what we know about 
cultures, common sense, whatever you name. And there's an 
analytical product. And then you have to make policy decisions, 
either with high confidence in the analysis, moderate 
confidence, low confidence, whatever it may be.
    Given that, what suggestions, if it's possible at this 
point, could you provide us as to what that analysis should 
include or look like if applied to the way we analyze data 
sets, so that not only are we reaching the most accurate 
results and the ones that are true, but ones that provide our 
policymakers a basis upon which to make the best decisions 
possible? Weighing all the equities, including human 
consideration, not just simply the cost benefit analysis from 
an economic or military standpoint.
    Dr. Ding. So, let me start with your earlier question, 
which I take as, what is the risk of AI in terms of 
contributing to military accidents? And so, I would say that an 
authoritarian regime might be a contributing factor to a state 
having a higher risk of military accidents. I think when we 
talk about these information analysis systems, think about the 
Aegis, right? The U.S. Aegis system that collects information 
and analyzes it and issues what this target is, whether it's 
friend or foe, and then whether we should fire a missile 
towards the target. In the 1990s, the U.S. accidentally fired 
upon an Iranian civilian airliner, killing 300 people. So, 
military accidents can happen in democratic countries.
    But I think it's an interesting research question, right? 
One of the things that I'm interested in studying is, how has 
China, as an authoritarian state, actually demonstrated a 
decent safety record with civil nuclear power plants and 
aviation safety? How does that happen in a closed authoritarian 
system? What is the role of international actors? And a 
military accident anywhere, whether it's caused by AI or any 
other technology, is a threat to peace everywhere. Right? To 
your point, so we should all be working to try to reduce the 
risks of these systems, sort of accidents in military AI 
systems.
    To your second point, one of my recommendations would be to 
keep a human in the loop, regardless of whatever AI system we 
adopt in terms of intelligence, surveillance, and 
reconnaissance. And hopefully that will make these systems more 
robust.
    Chairman Warner. Senator Wyden.
    Senator Wyden. Thank you, Mr. Chairman. Let me start with 
you, Dr. LeCun.
    I have proposed the Algorithm Accountability Act that would 
require that companies test their AI for harmful bias, such as 
biases that can affect where you buy a house or what healthcare 
you have access to. Now, reviewing your testimony, Dr. LeCun, 
you stress commitment to privacy, transparency, and mitigating 
bias--all important values. It's important to me also that 
there not be an uneven playing field that advantages the 
companies cutting corners over companies that do the right 
thing.
    Could your company support this legislation?
    Dr. LeCun. Senator, this is a very important question. 
Thank you for raising this point. I'm not familiar with the 
details of the legislation in question. Certainly, in the basic 
principles, they align with my personal thinking and those of 
Meta. And I'll be happy to put you in touch with relevant 
people within the company who take care of legislation.
    Senator Wyden. Hearing you agree with the principles is a 
good way to start. We'll follow up.
    Now, the use of AI by the U.S. Intelligence Community 
raises many issues, starting with accountability. Now, if AI is 
going to inform the Intelligence Community's surveillance 
decisions, one question I would have is if an American is spied 
on in violation of the law, who is responsible?
    Dr. LeCun. Senator, again, this goes very much outside of 
my expertise, not being a lawyer or legislator. Privacy, 
security, and safety are on top of our list in terms of 
priorities. They're very good principles to follow. We try to 
follow them as much as we can. I think an important point to 
realize, as it relates to current AI technologies such as large 
language models, they are trained on public data, publicly-
available data, not on private user data. So, there's no 
possibility of any kind of privacy invasion from that.
    Senator Wyden. Let me ask it this way. Your testimony 
stresses the importance of accountability in the private sector 
for its use of AI. How might these principles apply to the 
government?
    Dr. LeCun. Senator, I think the White House commitments, 
voluntary commitments, are a good start to specify guidelines 
according to which the AI industry should conform, including 
questions related to your point.
    Senator Wyden. So, what was it about the guidelines in your 
view that are responsive to my question about how this 
accountability in the private sector, the principles, would 
apply to government?
    Dr. LeCun. Senator, again, this is very much outside my 
expertise, and I'd be happy to put you in touch with the right 
people.
    Senator Wyden. Let me move on, then.
    Dr. Jensen, your testimony describes the need for 
intelligence analysts who understand the AI that's informing 
their analysis. Now, I don't know how realistic it is to add 
advanced computer science expertise to the job requirements of 
intelligence analysts. It seems to me, though, you're raising a 
very important issue. What are the consequences of 
disseminating intelligence analysis derived from processes that 
nobody understands?
    Dr. Jensen. You hit the nail on the head, Senator. And I 
think it's less about making sure every analyst has a PhD in 
computer science. We can't afford that. No offense, Meta. But 
what we can do is make sure they understand the basics of 
reasoning, causal inference. Sometimes we throw around the term 
critical thinking as a blanket statement. But almost going back 
to how would Aristotle teach Alexander the Great to interpret a 
model. I know that's a weird thought experiment but think about 
that. Would that be a square of syllogism? Would it be about 
the logic? Would it be about contradictions? I think it's 
realistic that we can go back to some basic philosophical 
reasoning and not necessarily have to have high degrees of 
computational understanding. And I think by doing that, you 
start to get--you hone the ability of the person to ask a 
question. And as you all know, asking the great question is 
what produces real dialogue.
    Senator Wyden. So, as a general proposition, who would be 
accountable when the intelligence analysis turns out to be 
wrong?
    Dr. Jensen. This is a great question, and it actually 
dovetails with the question you were just asking. I'm going to 
summon my inner Senator King and say, you still need one throat 
to choke, right? So, that means ultimately, you have to have 
people accountable in terms of how they certify the assurance 
of their AI model. And that means, just like we had to struggle 
with this in the financial sector, you're going to have 
entirely new positions created on how people certify the actual 
model. And does the model address a certain set of data, a 
certain set of questions? And back to what Senator Rubio was 
talking about, even assign confidence levels to that. And I 
don't think we can even imagine what that's going to look like 
in five years. But I can tell you it's going to be a growth 
industry and an important one.
    Senator Wyden. My time has expired.
    Chairman Warner. Thank you, Senator Wyden. Senator Cornyn.
    Senator Cornyn. A simple computer or Internet search tells 
me that the AI was basically--the roots of AI go back to 1956. 
So, maybe you could explain to me why we've gone all these 
years and haven't talked much about AI. And today we can't talk 
about anything else. Anybody want to take a stab?
    Dr. LeCun. I think I have to answer that question, Senator. 
Thank you for asking it.
    Generally, the problem of making a machine act 
intelligently has been much more complex than people initially 
realized. And the history of AI has been a succession of new 
ideas, with people thinking this new idea was going to lead us 
to machines that are as intelligent as humans. And over and 
over again, the answer has been no. Those new ideas that you 
had have solved a number of problems. But human level 
intelligence is still out of reach, and this is still the case 
today. So, despite the fact that we have incredibly powerful 
systems that are very fluent, that seem to manipulate language 
at least as well as humans, those systems are very far from 
having human intelligence.
    Now, to directly answer your question, the reason why we 
hear about AI today, so much over the last ten years, roughly, 
is because of a new set of techniques called deep learning that 
has allowed machines to not be programmed directly, but to be 
trained for a particular task. And that's been incredibly 
successful for relatively narrow tasks where we can train 
machines to have superhuman performance. But so far, we still 
do not have a way to train a machine that is as efficient as 
the way humans, or even animals, can train themselves. This is 
why we don't have self-driving cars. We don't have domestic 
robots that can clear up the dinner table and fill up the 
dishwasher.
    Senator Cornyn. Thank you. I'm going to try to get two more 
questions in.
    One. Dr. Ding, you mentioned that artificial intelligence 
is a general-purpose technology. Which leads me to ask, why is 
it that we feel the need to regulate AI as opposed to 
regulating the sectors where AI is actually used, which the 
government already does?
    Dr. Ding. It's a great question. I do think that a sector-
specific regulatory model is a good starting point, because AI 
will have different risks across different sectors. In fact, 
the European Union is a good model for this. In their EU/AI 
regulations, they have identified certain high-risk 
applications, where there are more stringent regulations. So, I 
think the crucial work to be done is identifying which sectors 
or which specific type of applications would be considered more 
high risk than others.
    Obviously, the use of AI in a nuclear power plant, for 
example, or a chemical processing plant, might be higher risk. 
And then there are other applications that are in a murky area, 
such as large language models and content generation models. 
It's more difficult to assess how you would compare the safety 
risks of those against more traditional industries.
    Senator Cornyn. Thank you. I just have about a minute. 
Georgetown Center for Security and Emerging Technology 
documents that American investors have provided roughly $40 
billion, or 37 percent of the capital, to PRC AI companies from 
2015 to 2021. This has been an area of emerging concerns. 
Subject of an Executive Order by the Administration. Senator 
Casey and I have in the Defense Authorization Bill an Outbound 
Investment Transparency Bill because it occurs to us that we 
are helping to finance our chief competitor globally, the PRC.
    So, can you maybe--I'll stick with you, Dr. Ding. Can you 
speak to how effective those export controls, the outbound 
transparency issues, how are we doing in terms of slowing down 
our principal competitor while we try to run faster?
    Dr. Ding. It's a great question. I think, first of all, the 
transparency measures are a good starting point, because we 
need to have a better sense of how much outbound investment is 
going to China. What's the nature of that outbound investment, 
and what are the national security risks of that investment? I 
think oftentimes we think anything that helps China is going to 
hurt the U.S. when it comes to this space. I think it's an 
interesting question. Right? ByteDance, companies like Alibaba, 
Baidu--these giants in China's AI industry, they have a lot of 
foreign investment, but a lot of the profits that they make 
come back to the U.S. economy, and it hopefully gets reinvested 
into American productivity.
    So, I think it's a more difficult question than just any 
investment in China's AI sector means it's harmful to U.S. 
national security. So, hopefully the transparency measures will 
help us get a better 360 degree view of the situation.
    Senator Cornyn. Thank you.
    Chairman Warner. Senator Heinrich.
    Senator Heinrich. Thank you, Chairman.
    Dr. Jensen, you've spoken a little bit about the importance 
of data, how should we leverage our unique U.S. government data 
sets to our advantage?
    Dr. Jensen. Thank you, Senator. First, make them actually 
interoperable. One of the big challenges we have is that 
because of just antiquated bureaucratic procedures and stove 
piping, this AI scientist would probably not be that successful 
in the U.S. government, because he couldn't make any of the 
data--. It's not that it's just heterogeneous, it's spread out 
over bureaucracies with random officials, each exerting 
authority they don't have to limit the ability to share. So, if 
you need more data to make smarter models, and you have people 
limiting the exchange of data, the train's not going to leave 
the station.
    So, I think the first thing--and this is where I think the 
Congress has a very important role. How do you actually--
whether it's through testimony, whether it's through the NDA, 
whether it's through hearings like this--how do you get 
bureaucrats to be accountable to actually exchange the data? 
And it actually goes deeper into actually government 
procurement and contracting. Right now, we could insert basic 
blanket language that require all vendors, because the 
sensors--the Intelligence Community, we buy it, right? So, why 
shouldn't it be that they're required to produce the 
information in standardized formats that we could ensure are 
interoperable. So, we lower the actually bureaucratic and 
engineering friction, and we could make use of it. It's just a 
boring issue, so sadly people don't pay attention to it.
    Senator Heinrich. But it's probably the most foundational 
issue from----
    Dr. Jensen. Yes.
    Senator Heinrich. Dr. Ding, you talked a lot about the 
fusion capacity. Are there any things that you consider threats 
to our diffusion capacity that we should be concerned with?
    Dr. Ding. Yes, I would say the main things to improve in 
terms of the U.S. diffusion capacity is investing more in the 
STEM workforce, in terms of not just attracting or building up 
the best and the brightest but widening the base of average 
engineers in software engineering or artificial intelligence. 
The U.S. government has some proposals on that, and the CHIPS 
and Science Act made some steps in that direction, but I would 
say we have overly weighted towards investing in R&D as sort of 
like the end all be all of science and technology policy. A 
more diffusion-oriented policy would look at things like 
broadening the workforce, investing in applied technology 
service centers, and dedicated field services. There are 
different voucher schemes that could encourage the adoption of 
AI techniques by small businesses as well. So, all of those 
things would help resolve some gaps in our diffusion capacity.
    Senator Heinrich. Very helpful.
    Dr. Jensen, the current DOD directive governing lethal 
autonomous weapons systems appears from my read to permit the 
potential development, even deployment, of fully autonomous 
systems--not just in the defensive--that could select or engage 
targets without a human in the loop.
    Talk to me about what are the risks there, how we should be 
further developing that policy, especially with regard to 
potential escalation?
    Dr. Jensen. Sure, great question, Senator.
    I think the real issue here is not whether or not you 
should do it. It's here, right? It's how, again, you get the 
assurance and the model and why you have to constantly train 
and experiment to understand those edge cases, that I think was 
where also Senator Rubio was getting. Where is there that 
moment of high escalation risk that's actually irrational in 
the grand scheme of things, but it makes perfect statistical 
sense at the moment?
    You won't find those weird cases until you do large-scale 
war gaming and constant experimentation. That's not just fine-
tuning the model. This is where I think we get it wrong: we 
think it's like, well, I'm just going to calibrate the model, 
I'm going to fine tune the model. No, it's fine tuning the men 
and women who will use the model to make some of the most 
difficult decisions about taking life. Because eventually, 
there's still someone flipping that switch, right. And so, we 
need to make sure that those people--those men and women in 
uniform and the elected officials granting them the ability to 
use lethal force--have actually done tabletop exercises and 
experiments where they thought this through. Don't let your 
first moment of unleashing your robotic swarm be the first time 
you thought about it. And that's going to require a larger 
national security dialog and even tabletop exercises that help 
them see those moments.
    Senator Heinrich. Dr. LeCun, last question.
    Before systems get released into the wild, as it were, 
there are a lot of ethical and other questions that need to be 
asked. Do you think that Meta's AI and Trust Safety Team, or 
for that matter the team for any AI developer, have the 
capability to really understand what the potential risks and 
benefits are? To be able to know whether or not making a 
system, putting into the wild, or making it open is a good 
idea?
    Dr. LeCun. Senator, thank you for your question.
    I can describe the process that we went through for the 
LAMA and LAMA-2 system. So, first of all, the LAMA system was 
not made open source. There are two parts to an open source 
package: there is the code, and the code frankly is very simple 
and not particularly innovative. What is interesting for the 
community to release is the train model, the weights. This is 
what's expensive, this is what only large companies can do at 
the moment. And we released it in a way that did not authorize 
commercial use. We vetted the people who could download the 
model. It was reserved for researchers and academics. This was 
a way for us to test what the system could be used for. Of 
course, there was a long history of three years of open source 
LLMs that were available before, and the harm has not 
materialized so far. So, there was a history we could based 
ourselves on.
    For LAMA-2 we had a very thorough process. First, the data 
set was curated in such a way that the most controversial, 
toxic content was eliminated from it so that we would get a 
high-quality model. Second, there was a lot of red-teaming and 
so people basically tried to break the system and get it to 
produce dangerous toxic content. Thousands of hours were spent 
doing this by a group that is independent from the group that 
actually designed and trained the system. We have an entire 
group called Responsible AI, whose responsibility, among 
others, is to do this kind of thing.
    And then we distributed in a limited way the model to 
Whitehat hackers at the DefCon Conference, for example slightly 
bigger community of people who are really expert at trying to 
break systems of this type. And I got some assurance that those 
systems were good. We fine-tuned them so that whatever was bad 
was fixed.
    And then we instituted a bug-bounty policy, so that there 
would be an incentive for people who find weaknesses in our 
system to tell us. And in fact, the enthusiasm from the open 
source community, after the release of LAMA-2, has been so 
enormous that we are getting feedback absolutely all the time 
and make those systems safer.
    Chairman Warner. Senator Moran.
    Senator Moran. Chairman, thank you.
    Dr. Jensen, you say that kids in China aren't rushing to 
join the Army and that Russian tech workers fled the country to 
avoid fighting the unjust war in Ukraine. Senator Warner and I 
have introduced over the years, a start-up act designed to 
create greater entrepreneurial environment in the United States 
but includes the creation of a STEM visa that would allow 
immigrants with advanced STEM degrees to stay in the U.S. as 
long as they remain engaged in the STEM field, with the 
indication is workforce is hugely important.
    Let me ask though, when you say what you said, give me some 
ideas of what you think about how opening the pathway for 
bright minds would benefit the U.S. in development of AI, and 
at the same time, depending on where they come from, perhaps 
actually hindering our adversaries?
    Dr. Jensen. Any time you bring people in, as you well know, 
right? When you bring someone into the SCIF, you always assume 
risk. So, the question becomes: what procedures do you put in 
place to analyze the risk versus the tradeoff of exchanging 
that information and honing someone's ability to make a time-
sensitive decision.
    I tend to view immigration, especially with people with 
high STEM backgrounds, as an outstanding American tradition and 
don't just give us your tired and your poor and you're sick, 
give us your brilliant people who want to come here and make 
the world a better place. Now, how we integrate them into 
national service, I think, is a really an extension to what 
you're talking about. How do we make them see that we're a 
country that believes in service and it believes in service 
from the local to the national level and encouraging that? Now, 
does that mean everyone who maybe has a cousin in the PLA gets 
a top secret clearance? No, but there's still a lot of ways 
people can serve, whether in uniform or whether in the 
government or our society writ large. And frankly, the smarter 
people we have to look at this critical moment in history, 
bring 'em.
    Senator Moran: Thank you.
    Dr. LeCun, I think I'll address this to you. I'm the lead 
Republican on a Subcommittee that appropriates money for the 
Departments of Commerce, Justice, and Science. And one of the 
efforts that the NSF has is the National Artificial 
Intelligence Research Institute Program. I'd be glad to hear 
from you, or any of our panelists, critique or praise for the 
outcomes of the capabilities of that program. And how do we 
work to see that that program fits in with the majority of 
research which is done in the private sector?
    Dr. LeCun. Senator, this is a great question.
    As a person who has one foot in academia and one in 
industry, what we're observing today is that academia, when it 
comes to AI research, is in a bit of a bind because of the lack 
of competing resources. So, one thing I believe is in this bill 
is to provide infrastructure--computing infrastructure--for 
academics and other non-commercial scientists to make progress, 
which I think is probably the best use of money you can have. 
Another one would possibly be favoring the free exchange of 
information and ideas to basically improve the diffusion 
process between industry and academia.
    In some European countries, there are programs that allow 
PhD students who have residency in industry, not just an 
internship, but spend a significant amount of time, like two or 
three years during their PhD--. And we actually at Meta have 
established programs of this type, with bilateral agreements 
with various universities across the U.S., because it was so 
successful in Europe that we tried to translate it here. If 
there was some help from the government for this, that would be 
absolutely wonderful.
    Last thing is access to data, so this is something that Dr. 
Jensen mentioned in a different context; but certainly research 
in healthcare, for example, in medicine, could be greatly 
improved if researchers had better access to data, which is 
currently mostly kept private for various reasons--complicated 
legal reasons, that perhaps Congress could help resolve.
    Senator Moran. Thank you. I'm going to try to get one more 
question in.
    Tell me about how custom AI models, and off-the-shelf 
models. How do they fit together? How can the government best 
combine commercial off-the-shelf and custom AI technologies to 
ensure that government is fully leveraging AI capabilities, 
particularly in the Intelligence Community?
    Dr. LeCun. Senator, I think there is the current state of 
affairs in the AI marketplace; and there is where I think it's 
going, which is a bit of a guess. And where I think it's going 
is that it's going to be a little bit like the Internet, in the 
sense that there's going to be common platforms that are 
essentially open source produced by companies like Meta and 
others possibly, with contributions from academia, et cetera. 
On top of those platforms, commercial products will be 
produced, which may not necessarily be open source. They may be 
commercial in various ways, and there will be customized, fine-
tuned for particular applications, whether it is in government, 
in education, in industry, manufacturing, services, 
entertainment, you name it.
    So that's, I think, the model of the future. This is the 
type of model we're observing today in various domains in 
software, certainly on the Internet. That's where I think it's 
going.
    Senator Moran. Thank you. Thank you all.
    Chairman Warner. And now, to the author of the Choke Theory 
as Dr. Jensen indicated, Senator King.
    Senator King. Well, Mr. Chairman, a couple of minutes ago I 
went to ChatGPT and asked them to write a poem about the Senate 
Intelligence Committee in the style of Dr. Seuss. I want to 
share it with you. This was written in about four seconds, I 
timed it: four seconds.
    [Senator King read the following text into the record:]
          In a chamber up high, beneath the Capitol's dome, 
        there's a Committee that seeks knowledge, they call it 
        their home.
          The Senate Intelligence, wise as an owl, in the 
        pursuit of truth, they go the extra mile.
          With hearings and briefings, they gather the facts, 
        no secrets too deep, no hidden contracts.
          They investigate, inquire, and they never back down 
        in their quest for transparency throughout the town.
          They question the spies, they question the chiefs, to 
        uncover the truth, they provide us relief.
          In a world full of secrets where lies often play, the 
        Senate Intelligence lights up the way.
          They protect our nation, our values they hold with 
        their wisdom and courage, stories untold.
          So, here's to the Senate Intelligence steadfast and 
        true.
          In the style of Dr. Seuss, we salute you.
    Four seconds. I mean, you've got to experience this to 
realize the implications and the power that this has.
    Dr. Jensen, first. Coincidentally, we had our third report 
from the Solarium Commission this morning, and I talked about 
the brilliant work of the staff, and you were a leader in that, 
and I just want to thank you. The work that you did was 
absolutely extraordinary. We're now up to about 70 percent 
implementation of the Solarium recommendations. So, thank you 
for that.
    The word productivity has been mentioned. One person's 
productivity is another person's job loss.
    Dr. Ding, talk to me about that. Are we in 1811 and the 
Luddites--is this something that is a serious concern? Or is 
this just the march of human history where tools have been 
enabling more productivity since the invention of the hammer 
improved upon the rock?
    Dr. Ding. It's a great question. I think it also aligns 
with some of the statements that Vice Chair Rubio was talking 
about in terms of job displacement.
    When I have my national security cap on, the reason why I 
emphasize productivity and diffusion capacity is because 
historically great powers have been able to rise and fall based 
on whether they've been able to leverage and exploit new 
technological advances to achieve economy-wide productivity 
growth.
    Senator King. The Manhattan Project would be an example of 
that.
    Dr. Ding. Yes. I think for me, it's less about the 
moonshot's singular technological achievement. It's more about 
who can diffuse electricity at scale or who can 
``intelligentize'' the entire economy at scale. And to your 
point, there are debates among economists about whether the 
future job displacement by AI and robots would be greater than 
the jobs that would be created by some of these new 
technologies. I will defer to those works by folks like Daron 
Acemoglu in terms of job displacement. I think it gets 
complicated because if job displacement is so severe that it 
causes internal political cleavages, that could also then 
become a national security issue.
    Senator King. And it's also hard to project what the gains 
will be because it's a new area. You don't know how many new 
jobs will be created in wholly different areas.
    Dr. Ding. Yes. I also agree with that, and Erik 
Brynjolfsson at the Digital Economy Team at Stanford has talked 
about it's very hard to measure the productivity gains from 
digital technologies in particular. And I think another key 
point is these general purpose technologies, their effect on 
productivity takes decades to materialize. So, we might not see 
the computer in the productivity statistics as economist Solow 
once said, but eventually, we will see that come.
    Senator King. One of the major spurs of the economic boom, 
if you will, of the '90s and early part of the century was the 
integration of the computer into the workforce. That enabled a 
great economic upsurge of----
    Dr. Ding. Exactly. And a key is that took more decades to 
happen than we predicted initially.
    Senator King. Now, Dr. LeCun. A very practical question: 
how feasible is watermarking of AI-generated images? This is of 
concern to us, frankly, because we are very likely to be 
subject to AI-generated false disinformation. Very skillful. 
Our face, our voice, our gestures--but completely false.
    How feasible is it to require that AI-generated images on 
Facebook or on TikTok or Instagram all be watermarked or 
labeled in such a way so that the consumer will know that 
they're looking at something that isn't real? Is that 
technically feasible? And is that something we should be 
thinking about here as we're thinking about regulation?
    Dr. LeCun. Senator, this is a very timely question. Of 
course, it is technically feasible. The main issue is for a 
common standard to be adopted industry wide. So there needs to 
be a common way of watermarking, invisible or invisible by 
using steganography.
    The fact that the process by which a piece of information 
has been produced, this can be done with images and video and 
audio, such that a computer can detect whether the system has 
been generated by a generative AI system. But the user will 
have to, not counteract it--will have to use the products to 
produce it that obey that standard. And so, that needs to be 
adopted industry wide.
    The problem is much more complicated for text. There is no 
easy way to hide a watermark inside of a text. People have 
tried to do this by manipulating the frequency of different 
words, but it's very far from perfect. But for text, is 
produced by humans. It's not like a photograph, which you can 
take anywhere. In the end, the person posting the text has to 
be responsible for that content.
    Senator King. So, we should not have liability protection 
like Section 230? Publishers should be responsible for what 
they produce?
    Dr. LeCun. Senator, I'm not a lawyer. I know that Section 
230 has been crucial for the success and the development of the 
Internet. But I would certainly be happy to put you in touch 
with experts.
    Senator King. Chairman, I hope the panel will help us on 
this watermarking question, because I think that's something we 
really need to understand and that may well be part of whatever 
legislation we're developing. We need your expertise on that. 
The consumer should know what they're looking at. Thank you.
    Thank you, Mr. Chairman.
    Chairman Warner. I concur, and I think the notion that 
there could be seven or eight or ten different standards that 
each platform chose may not get us there.
    With apologies to my friend Mike Rounds, Senator Lankford.
    Senator Lankford. Well, thanks for apologizing to Mike 
Rounds that I'm going to be up next. I appreciate that.
    So, thanks for your testimony, and thanks for the research 
and the work that you've already done on this. Part of the 
challenge, on whether it be watermarking, whatever it may be, 
is that obviously there's open platforms there that both Meta 
has produced and that also the Emiratis have produced. And 
obviously, we don't have authority to be able to tell the 
Emiratis what they can actually produce for watermarking. So, 
this becomes their text in that sense, so it becomes much more 
difficult in this process. Go back decades ago. We're 
researching how to build rockets and to be more effective, both 
for space and for military use, and we understand that other 
countries are working on the same thing--for both space and 
military use--and to be able to determine the differences. And 
so, we try to set limitations for that.
    We're in a unique position now, where the PRC is also very 
interested in partnering, and there are current research hubs 
in the PRC on Open AI that Google and Microsoft both have at 
this point. The question is, how far does that go and what do 
we engage with and at what point does that become facilitating 
someone who may be an economic adversary, that we hope is never 
a military adversary in that kind of partnership of that kind 
of research?
    So, at Meta, as you all are dealing with this and trying to 
be able to think through partnerships, whether it be cloud-
basing with Alibaba or whether it be actually partnerships with 
PRC entities for research in the area of AI, how should we 
approach that from the Intelligence Committee and as just a 
national security issue?
    Dr. LeCun. Thank you, Senator. This is a question I think 
that Dr. Ding is much more expert at than I am. Meta does not 
operate in the PRC for two reasons: because the regulations in 
the PRC about user privacy are incompatible with Meta's privacy 
principles, and also because the PRC basically wants to control 
what information circulates. So, Meta does not operate in the 
PRC.
    Senator Lankford. Alibaba is a Cloud partner, though, with 
Meta, aren't they?
    Dr. LeCun. So, Alibaba installed Llama 2 and provides it as 
a service on its Cloud services outside of the PRC, and so I 
don't know.
    Senator Lankford. Dr. Ding, you want to be able to help us 
unravel this? Because this will be a larger policy issue that 
we've got to be able to resolve.
    Dr. Ding. Yes, I think it goes back to our conversation 
about U.S. investment flows into Chinese AI companies. A 
similar story, a similar debate is happening around--should 
U.S. multinational technology giants have R&D labs in China, if 
I were to rephrase your question. Like Microsoft Research Asia 
in Beijing, which is----
    Senator Lankford. Or have PRC partnerships that are here in 
the United States actually, and they do research together on 
it, knowing full well where that research goes.
    Dr. Ding. Yes, I think my take is one of the Senators 
earlier mentioned this idea of running faster. My take is the 
U.S. can adopt one of two approaches.
    One is this Fortress America approach, where we can't let 
any technological secrets leak to China.
    The second is this run faster approach, where we're going 
to take the bet that our open economy, our open system of 
innovation--there are going to be some leaks, there might be 
some partnership that might allow China to get a little bit 
further in AI than they otherwise would have--but that 
partnership might also help U.S. companies run faster. So, 
being able to access global innovation networks and keep 
abreast of what's happening, not just in China, but the UAE or 
Israel--I think the advantages of that, and continuing to 
maintain the openness of those global innovation networks, is 
always going to favor the U.S. in the long run, in terms of our 
ability to run faster.
    Senator Lankford. So, it's going to always favor us in the 
long run based on what?
    Dr. Ding. I think there's a couple of historical examples 
in this space. So, we had similar debates about satellites and, 
previously, we had a lot of export controls on satellite 
technology. But over time, we relaxed those controls, because 
we realized, first of all, this technology is so commercially 
based and being driven by the commercial sector that Chinese 
companies were just getting satellite parts from European 
suppliers or other hubs in this global innovation network of 
satellites.
    Senator Lankford. But that's already developed technology. 
The challenge that we have with the AI side is that we're in 
the process of still developing in so many areas. When you form 
a partnership and they're getting it near simultaneous--, 
that's a very different issue. If you've got a satellite part, 
piece, or satellite as a whole, it's already been developed, 
already used. We're already seeing copies of it. We're already 
seeing other innovation commercially on that. That's different 
than ground zero.
    If we're going to remain a competitive edge, having someone 
at the table that may be then exporting that in real time out, 
becomes a real challenge for us on the intel side of things in 
a relationship issue. It's the reason that we partner with 
Russia with NASA on technology, on the Space Station. But we're 
not going to partner with the Soviet Union in the earliest days 
of all of our work, because that's the innovation side of 
things. Now, while we're always innovating, it's trying to be 
able to protect what's first generation. Does that make sense? 
So, that's an issue long term that we've just got to be able to 
determine what's the best way to be able to do that. Where do 
you put limitations, and how do you develop those?
    Thank you, Mr. Chairman.
    Chairman Warner. Senator Rounds, he actually asked pretty 
damned good questions.
    Senator Bennet.
    Senator Bennet. Thank you, Mr. Chairman.
    And I want to thank my colleague Senator Lankford for his 
questions. I think it's an important thought exercise to 
consider where we were with space technology ten years ago, 
when it was ground zero--when it was zero hour--for that 
technology. I think, you know, at least from my perspective, I 
think it's very, very clear that our complete lack of export 
controls, our complete lack of paying attention to the 
protection of our IP, has allowed China to build something in 
outer space that's our near-peer competitor, or even worse than 
that--without the expense that we went through to develop this, 
without the expertise, and without the society that Dr. Ding 
has talked about, which I never would bet against either. But I 
do think that it is a serious problem that we have just spent 
the last ten years doing--serious issue with respect to space, 
and I hope we find a way to avoid it here, which I think is 
your point. I definitely want us to avoid it here.
    Dr. Jensen, something you said at the very beginning of the 
hearing caught my attention and I thought it was worth more 
elaboration. So, what if you find yourselves in the Cuban 
missile crisis again, or something equivalent to that? You've 
obviously thought about that. So, let's talk about that.
    What would that look like today, versus what that would 
have looked like in the early 1960s when President Kennedy was 
trying to reach the decision that he was trying to make? 
Khrushchev was trying to reach the decisions he was trying to 
make. And at least both people were making fundamental mistakes 
of judgement along the way. In the end, it resolved itself in 
the best resolution possible for humanity.
    What does that look like in an AI-charged situation?
    Dr. Jensen. So, after this meeting, Senator, I'm going to 
send you the generative AI artwork we did on this to have it 
imagine Salvador Dali paint an Edward Murrow type news 
broadcast of that moment, and it is both beautiful and deeply 
disturbing.
    Senator Bennet. I'm going to hang it up right next to 
Angus's poem.
    Dr. Jensen. Yes. Dr. Seuss meets nuclear war.
    Senator Bennet. Exactly.
    Dr. Jensen. So, this came out of actually a series of 
tabletop games we did for the Defense Threat Reduction Agency.
    Senator Bennet. Actually, we should ask ChatGPT, if you're 
listening, we should ask them to run the scenario for us.
    Dr. Jensen. Just don't ask----
    Senator Bennet. [continuing]. But you're going to do it for 
us, so go ahead.
    Dr. Jensen. I'll do it for you, but----
    Senator Bennet. Yes.
    Dr. Jensen. We did this in a tabletop for DTRA--Defense 
Threat Reduction Agency, thinking about what exactly would a 
critical crisis moment look like. And one of the most 
interesting things is there's such a tendency to move faster. 
But faster isn't better, even when you're analyzing data. And 
what we found is a lot of the discussion was about how--just as 
actually Senator Rubio was talking about--you're getting 
information correct or incorrect, and now it's triggering these 
very human instincts, right? We're all of a sudden getting 
afraid, we're nervous, your heart's racing. It's the person 
interacting with the algorithm, there is no pure machine. What 
we found is actually maybe our earlier generation of statesmen 
and women were brilliant by slowing down crisis decision-
making.
    If you've ever seen the red phone, it's not a phone, it's a 
telex for a reason, because it deliberately slows down and 
makes you deliberate. So, what we walked away with is you're 
going to be fine in the crisis if you know when to slow down 
and not let the machines speed you up.
    Now, how do we get here? Again, this is why at CSIS and the 
Futures Lab and the ISP program, we're running tons of 
tabletop--actually, I've seen some staffers here; we invite 
Congressional staffers out--because if we don't actually think 
about those moments now in a very human sense--this isn't 
necessarily ``fine tune the algorithm''--it's those 
interactions.
    Senator Bennet. How about if you had replayed on a day-to-
day basis the decisions that were made about our nuclear 
arsenal or the Soviet Union's nuclear arsenal? Have you guys 
thought about that at all? What that would look like?
    Dr. Jensen. Sure. And so, at the Office of Net Assessment 
in the late Seventies, early `Eighties, when they funded the 
Rand RASP program, where they used old school, expert-system AI 
to actually do large-scale modeling of what strategic 
competition looked like, the military balance, and actually to 
fight entire simulated campaigns.
    So, what Senator Cornyn's saying is true. We've been 
experimenting with variations of the science and the 
artificial--the intelligence isn't quite there--for 
generations. And I think we'll continue to do so, because 
ultimately, crisis decision-making is about people, it's about 
politics and it's about emotion even more than the models in 
front of you.
    Senator Bennet. You know, we've had this discussion today 
about a race, in effect, and can we win the race or is China 
going to win the race? We haven't had a discussion about how do 
we get to an end state here where AI reflects the values that 
this democracy supports in terms of freedom, in terms of 
rights, in terms of free speech and other kinds of things. So 
that, if there are people that are unfortunate enough on planet 
earth to live in a totalitarian society where the authoritarian 
is rolling out that sort of AI system, there is something 
available to the rest of humanity that is not that lowest 
common denominator or uncommon denominator.
    I don't know the answer to that, but I suspect that has a 
lot less to do with some sort of race with China than ensuring 
that as we think about the implementation here, we're doing it 
in a way that actually is true to those core values that we 
have. And that, among other things, is going to look a lot 
different than the rollout of social media over the last 15 
years or so, as you said at the outset of this hearing.
    Thank you, Mr. Chairman.
    Chairman Warner. In a brilliant lead up to Senator Rounds.
    Senator Rounds. Thank you, Mr. Chairman.
    Listening to Senator King's poem, I wondered if it was a 
hallucination on the part of ChatGPT at that point as they 
kindly talked about our Committee, and I'm wondering if it 
would have been a different poem if it would have been a House 
member requesting that type of a message on a Senate Committee.
    Dr. Jensen, I want to begin just with a question to you 
about our current state of play. Loitering munitions. I'm 
thinking back to the Nagorno-Karabakh War between Armenia and 
Azerbaijan, September of 2020. Azerbaijan was very, very 
successful in a very short time period, using loitering 
munitions to literally identify, using AI--actually, an Israeli 
drone system that was there in the marketplace, they could buy 
it.
    Can you talk a little bit about just how widely spread and 
how difficult the battlefield situations are right now with 
regard to the use of AI?
    Dr. Jensen. I'm going to try to be short, because I could 
talk to you about this all day, Senator.
    I think, actually, Ukrainian President Zelensky summed it 
up best when he said it's trench warfare with drones. And so, 
one of the reasons this spreads is because really cost matters. 
So, if I can get low cost ability to actually move a munition 
closer to its intended target with a good circular air 
probability to strike, I'm going to do it. That's just human 
instincts. If we're in a fight, you got to win.
    And why I think you're going to see something really 
interesting on the horizon, and why we need to get our house 
order in the United States, is that the only way to make that 
work is if you allow people to actually tailor their model from 
the bottom up. We've actually seen this in Ukraine, where non-
profits and tech workers had been able to actually train 
imagery recognition on the fly. If they would have waited for a 
standard U.S. government certified algorithm and piece of 
equipment, it would not look the same, right? So, they're able 
to actually take all that drone footage, quickly to retrain 
their model in the battlefield, and then revector the attack. 
And that's why honestly, we're going to have to be honest about 
how expensive this is going to be. The intelligence collection 
cost to get a picture of every tank at every time of day, at 
every angle, just so you reduce the risk, means that you are 
now going to have constant collection going on to train that 
model. And if you don't have the people who know how to use it 
and interpret it, you're going to have the training and 
calibration up at such a high level, that the bureaucratic time 
to use will not be there.
    Senator Rounds. But the point being, it exists today, the 
cat's out of the bag. And our adversaries or other countries 
are utilizing it today, and the United States is probably in a 
position to use it today as well.
    Dr. Jensen. We are in a position to use it, and in the best 
of American traditions, let's scale it and do it better and 
more just than the other guy.
    Senator Rounds. Dr. LeCun, Europe has developed a model 
right now, or at least, they're in the process through their 
European Parliament, develop a model to regulate AI. They've 
identified high-risk categories, along with two other 
categories of lesser risk. Have you had an opportunity to look 
at that? And what is your thought about the approach they're 
taking in terms of trying to regulate AI based on that 
approach?
    Dr. LeCun. Senator, there are principles that are in that 
bill that are probably a good idea, although, I don't know the 
details, frankly. I think the Startup and Industry Committee in 
Europe has been quite unified in opposing that regulation, not 
because of the points that you're making, which I think are 
probably good ones, but because of the details of the 
regulation. Frankly, my knowledge of it is too superficial to 
make more comments about it.
    Senator Rounds. Very good.
    Dr. Ding, I'm just curious. Weapon systems on call today, 
as indicated earlier in the conversations, right now we have 
our weapon systems that, once they're armed, whether it be on 
one of our ships near our coastlines and so forth--once we've 
armed a weapon system and they can identify an incoming as 
being a threat, we currently utilize that, because in some 
cases, there is no way a human could make the decision as 
quickly as that machine could.
    Is it fair to say that not just us but our adversaries are 
also using that same weapon system and it's being incorporated 
in the battlefield today?
    Dr. Ding. I'll defer mostly to Dr. Jensen's answer to this. 
I think my slightly different take is loitering munitions, to 
the best of my knowledge, are not using cutting-edge deep 
learning, neural-net deep learning advances that Dr. LeCun is 
talking about.
    So, I guess it's AI if you define the term very, very 
loosely. But like the algorithms that are underlying, the 
fundamental breakthroughs in the civilian AI space today, to 
the best of my knowledge, are not being deployed at scale in 
any military, whether it be the U.S. or any other leading 
militaries. And I think that speaks to--these technologies take 
a very long time to diffuse and become adopted throughout 
different militaries. So, I guess the optimistic view from that 
is we do have time to hopefully figure some of these things 
out. That might be a different view than you hold or others in 
this room hold, though.
    Senator Rounds. Thank you. Thank you, Mr. Chairman.
    Chairman Warner. Senator Ossoff.
    Senator Ossoff. Thank you, Mr. Chairman. And thank you to 
the panel.
    So, Dr. Ding, as I understand the argument you're making, 
and it's a compelling one, the U.S. has an advantage because of 
the structure of our market economy and our R&D enterprise, 
that this technology can be diffused, adopted across sectors 
more rapidly, enabling us to realize productivity gains more 
rapidly and so on.
    I guess a question for you is, is it about maximizing the 
rate of diffusion in your terms? Or is it about achieving the 
optimal rate of diffusion? And what's the difference between 
those two?
    Dr. Ding. Yes, it's a great question. I think there is a 
difference. I can imagine a world where a country diffuses a 
technology very quickly in the initial years, maybe a 
technology that's unsafe or harmful and sort of--there's a 
backlash to that technology, so it doesn't reach that optimal 
state or that optimal level of diffusion that you're talking 
about in the long run.
    So, for me, when we're having discussions about AI 
regulations, oftentimes it's framed as regulation will hamper 
diffusion and maybe in the short term, it might reduce the 
speed of diffusion. But I think smart, sensible regulation that 
ensures more trustworthy AI systems, safer AI systems, in the 
long run will get us to that more optimal rate of diffusion 
that you're talking about.
    Senator Ossoff. And by the way, I think the conversation is 
a little bit overweight, risked to the point where we are in 
jeopardy of unduly restraining the diffusion of some 
productivity-enhancing or research-advancing capabilities. But 
in addition to the risks associated with economic displacement, 
which you spoke to earlier, Senator Rubio spoke to earlier, 
what are the other specific risks that you anticipate could 
emerge from too-fast diffusion and adoption?
    Dr. Ding. Some of the ones we've talked about today with 
regards to the use of these large language models; for example, 
for disinformation, misinformation, to enable propaganda at 
scale. I think some of the risks that we haven't talked about 
today, we've seen examples of algorithms that have mis-
specified reward systems.
    So, there's an example of an open AI system that is about 
like this boat trying to go through this course as fast as 
possible, and it's driven by an AI model based on reinforcement 
learning. Because the programmers have mis-specified how the 
model should learn, the model learns that the best way to 
accumulate the most points in the fastest amount of time is 
just to crash the boat immediately. And that kind of creates 
some sort of--
    Senator Ossoff. So, short-run, long-run incentives, for 
example.
    Dr. Jensen, what are some specific potential applications 
of this technology in the conflict avoidance/risk mitigation--? 
We've talked a lot about how it can make militaries faster, 
better informed, more lethal. What are the applications that 
are applicable in institutional structures analogous to the red 
phone, the hotline, or the now moribund U.N. Security Council, 
or the kinds of verification regimes that were in place around 
the Test Ban Treaty and so on? Open Skies?
    Dr. Jensen. Yes. Before we get to that hard intelligence 
problem set, I know that you have an interest in human rights 
in this as well. So, think about what a peacekeeping mission 
would look like if I could actually tailor my messages when I'm 
doing key leader engagements and meeting with different 
stakeholders.
    I think that first, at that most basic level, it's not 
just--. You know, the best wars you win are the ones that you 
never fight, without ceding the advantage. And usually, that 
requires a degree of actually managing crises all over the 
world. So, I think there's actually a whole way we could use 
those in peace building and development as well. When we go to 
the harder targets that you're talking about, which deal with 
hard-target problems where an adversary is deliberately hiding 
something--so I'm using Open Skies--you can see me looking, but 
we're playing this kind of compliance game. I have to expend 
energy to hide.
    I think there will be very clever ways of building models 
that simulate some of that, or even help you analyze some of 
the data. But again, I would even be okay if we could have the 
simple models Dr. Ding is talking about. I would be okay if we 
could even just have some basic imagery recognition and 
accelerate it faster than we currently have, and then, get to 
the other stuff.
    The most beautiful AI thing I've seen is watching a 
military officer--when we introduce this in the classroom--try 
to write their Commander's Intent. The most personal thing any 
military officer will do--because you are responsible for your 
actions--is to write your Commander's Intent. Well, why 
wouldn't I want to have a dialog with the corpus of military 
history and look at different ways it was worded and different 
ways I could hone my own voice as I took responsibility for 
using force?
    Senator Ossoff. Mr. Chairman, one more question, if that's 
all right.
    So, Dr. LeCun, I'm not entirely sure what exactly you're 
proposing in your discussion of the merits of open source 
systems for the development and diffusion of the technology.
    Are you suggesting that it should be mandated that models 
are based upon and licensed on open source principles?
    Are you suggesting that it's simply preferable if 
developers use open source ethics and guidelines in the 
development and licensing of their models?
    What exactly do you mean when you advise us that this is 
desirable?
    Dr. LeCun. Senator, thank you for your question, which I'm 
personally very, very interested in.
    I think it should certainly not be mandated. It should not 
be regulated out of existence. There are people who are arguing 
that AI technology, particularly in the future, will be too 
dangerous to be accessible. And what I'm arguing for personally 
and also Meta's policy, is, on the contrary, the way to make 
the technology safe is to actually make it open, at least the 
basic technology, not the products that are built on top of it, 
to ensure American leadership. Because this is the only way we 
know to promote progress as fast as we can and stay ahead of 
our competitors. So that's the first point.
    Then there is imagining a future in which AI systems reach 
the level of human intelligence; for example, let's say a 
decade or two from now--the number may be wrong--every one of 
our interactions with the digital world will be mediated by an 
AI system. All of us will have an AI assistant helping us in 
our daily lives all the time. You're familiar with the 
situation because you have staff working for you. So, this 
would be like having a staff of artificial people, basically, 
who are smarter than you, possibly. I'm familiar with having 
people who are smarter than me working with me.
    Senator King. What about the impact on job loss?
    Dr. LeCun. What I learned about this is from Daron 
Acemoglu, whose name was mentioned by Dr. Ding before.
    I think we have no idea what the major jobs that we'll hire 
in 20 years will be. There will be new jobs; we can't imagine 
them today. But to continue on this picture, everyone's 
information and interaction with the digital world will be 
mediated by one of those AI system, which basically will 
constitute the repository of all human knowledge. That cannot 
be proprietary; it's too dangerous. It has to be open. It has 
to be open and contributed to by very wide population the way, 
for example, Wikipedia is produced through crowdsourcing. 
That's the only way to have enough of a diverse set of views to 
train those AI systems. They need to be able to speak all of 
our languages, know about all the world cultures. And this 
cannot be done by a single private entity. It will have to be 
open.
    And it will occur, as long as it's not regulated out of 
existence, because it's the most natural way things will 
evolve, the same way they've evolved with the Internet. 
Internet has become open source because it's the most efficient 
model.
    Senator Ossoff. Thank you.
    Chairman Warner. I've got a couple more questions, but 
Senator King's got one. I've got one, Jon, I'd like you to stay 
for.
    Senator King. As you know, we've been doing a lot of work 
on AI. We had the big forum last week with some amazing folks.
    I want to thank you all. This has been a very informative 
panel.
    Here's my question: we're in the legislation business. 
What's the problem? Everybody's talking about legislation, 
we've got to do something with AI. I would like to ask you to 
do some homework and give us: here are four things that the 
Congress should address with AI. Is it watermarking? Is it job 
displacement? Is it copyright? What I'm searching for--the 
problem that we're trying to solve. Because until we know 
exactly what we're trying to solve, we can't begin to write 
legislation.
    So, you're in a position to tell us or to suggest to us 
what you think the major problems we should be addressing are. 
That's my questions.
    Thank you.
    Chairman Warner. Let me echo what everybody said. Very 
informative. I first of all, I've got to take a couple of quick 
shots, fair or unfair, but Dr. LeCun, when you were talking 
about all of the steps that Meta went through before they 
released their large language model, it seemed appropriate, but 
I think back from that point, long before you were with Meta, 
if we use that same testing model the initial Facebook product, 
you know, I'm not sure you could have known the downside 
implications.
    Facebook was going to be tested. Does it bring people 
together in a social setting? Yes, we did pretty good. But I 
don't think there was a malicious understanding that it might 
also lead to people's mental health issues that exponentially 
come out of being so dependent upon this social connection. So, 
I don't know how you fully test everything on the front end. I 
think about the fact of this connectivity, much of which this 
Committee exposed when foreign entities, Russia in particular, 
used these platforms that did not have that intent, through the 
use of bots and other things to create huge downsides.
    I think about fact that--and this is in one of the closed 
models and I'm wide open on this open/closed analogy--but I'm 
really concerned that we've already seen, say with ChatGPT, 
that with very little push and pull, all of the guardrails of 
protections that were put in place didn't stand the test and 
you were very quickly starting to have a ChatGPT model give out 
answers that were way beyond its scope.
    Now, that in a sense, I would argue a reason for open, 
because you have much more of the testing with the white hat 
hackers. And so, I'm really worried--and this is one of the 
areas where, and understanding Dr. LeCun's comment, you don't 
want to regulate out of existence. But do we really want to 
trust that the risk-benefit analysis should only be done by the 
vendors who may or may not have the same long-term societal 
goals that are part of our responsibility?
    I'm not sure people fully appreciated Dr. Ding's comment, 
which I've been briefed on by much smarter people than myself, 
on the boat example. The programmer's goal was to create a tool 
that could show how this boat could destroy as many bad guys as 
possible. Well, the model soft tested in a way that thought 
about the problem in a way that obviously a programmer never 
assumed, and it figured out that the best way to score the most 
points was actually being self-destructive--ramming directly 
in, not avoiding the adversary, but ramming directly into it.
    And I don't think this is too big of a stretch, but it's a 
little bit like how in ``2001: A Space Odyssey'', that if you 
don't program it right and how smart can any of the programmers 
be--I'm not saying, to Dr. Jensen's point, that assuming the 
government bureaucrats and politicians are going to be smart 
or--may not be the right presumption either. But I do think 
there is an underlying question here: should we simply trust 
the vendors alone to make these determinations?
    And I'd like everybody to address this, and then I'll have 
one other wrap-up question.
    And at the same time, if we presume that here may be some 
level of outside trusting or outside scrutiny, some available 
to have routinizing, enforceable approach to make sure that 
these models have been tested, should we have that?
    And frankly, it goes back to my initial questions about 
definition. If we said that the tools that fell into the AI 
category had to go through this testing, you might have a whole 
bunch of these AI tools that are simply just advanced computing 
that are using AI now as a marketing tool to redefine 
themselves.
    So, it's a long way around the kind of the base question, 
which was what Senator King is asking, of--should we just trust 
the companies to do all the testing on their own? Because if we 
don't get the right questions asked, I think Dr. Ding's 
suggestion of the boat self-destructing is only one step away 
from HAL in ``2001: A Space Odyssey.''
    You want to go down the line or how do you want to----
    Take it away, Dr. LeCun.
    Dr. LeCun. Thank you, Senator for your question. I think 
there are three or four questions, if I understood correctly, 
in your remarks.
    The first about should vendors be trusted. I mean, that's 
why we have regulations. When a product is put on the market, 
for example, a driving assistance system for a car or a medical 
image analysis system that uses AI----
    Chairman Warner. But you should know that, at least in 
terms of social media, Congress has done nothing, even though 
your companies and others have said they'd be willing. But we 
have done nothing.
    Dr. LeCun. I'm well aware of that. So, what's happened is 
an interesting history that's happened with social networks, 
which is that some side effects of enabling people to 
communicate with each other on social networks that occurred, 
were not predicted, perhaps because of some level of naivete or 
perhaps other reasons. But they were not predicted.
    But for most of them that were predicted, they were fixed 
as soon as possible. And so, for every attack, for example, 
attempt to corrupt elections, there's a countermeasure. 
Attempts to distribute CSAM, child sexual abuse material 
content, there's a countermeasure. Attempts to misinform 
dangerous misinformation, there are countermeasures, deep 
fakes, et cetera. All of those countermeasures make massive use 
of AI today. So, this is an example where AI is not actually 
particularly the problem, it's really the solution. Taking down 
objectionable content, for example, has made enormous progress 
over the last five years--terrorist propaganda and things of 
that type--because of progress in AI.
    And so, as long as--again, recycle the old jokes--of the 
good guy with AI is better than the bad guy with AI, but there 
are considerably more good people more educated with more 
resources with AI than there are bad people, and AI is the 
countermeasure against AI attacks. So, that was the second 
point.
    Third point is I think it would be a mistake to extrapolate 
the limitation so of current AI systems--current LLMs--LLMs are 
really good for producing poems. They're not very good for 
producing things that are factually correct. They're not good 
as a replacement for . . .
    Senator King. But it can be entertaining.
    Dr. LeCun. It can be entertaining, that's for sure, but 
factually correct is different. So, in fact, I don't think 
current AI technology, LLMs in particular, could be useful in 
the kind of applications that Dr. Jensen was talking about, 
because it's just too unreliable at this time.
    Now, this technology is going to make progress. One of the 
things that I've been working on personally, and various other 
people, is AI systems are capable of planning and reasoning. 
Current LLMs are not capable of planning. They're not capable 
of reasoning. You don't want to use them for defense 
applications because they can't plan. They can retrieve 
existing plans that they've been trained on, and adapt them to 
the current situation, but that's not really planning. It's 
more kind of memory retrieval.
    So, until we have technology that is actually capable of 
planning in real situations--currently, we have such technology 
only for games. So, a system, for example that can play 
``Diplomacy''--we were just talking about this with Dr. 
Jensen--or play poker or play Go, things like that, those 
systems can plan, but currently we don't have systems that can 
deal with the real world that can plan.
    That progress will occur over the next decade, probably. 
I've been calling this objective ``driven AI.'' So, these are 
AI systems that do not just produce one word after the other 
like LLMs, but AI systems that plan their answers so that it 
satisfies a number of constraints and guardrails. These are the 
AI systems of the future. They're going to be very different 
from the ones that currently exist. They're going to be more 
controllable, more secure, more useful, smarter. I can't tell 
you exactly when they will appear. That's the topic of 
research.
    Chairman Warner. Well, let me make sure, because I got one 
other, but I want to hear from Dr. Jensen and Dr. Ding, because 
I think Dr. LeCun said well, maybe you got to have some 
preclearance. But I think you're still more saying leave it to 
the enterprises to decide, not----
    Dr. Jensen. So, I think this is the first major 
disagreement we had amongst my new friends. I think a lot of 
people underestimate how important knowledge retrieval is and a 
dialogue in actual military planning, because so much of good 
military planning--and I'd actually say that's a good thing 
about good intelligence analysis, too--is that creative spark 
and how you're able to start thinking about defining the 
problem, right?
    The whole point is defining a problem in a way that it 
lends itself to a solution to be transparent. So don't discount 
even just basic LLMs can augment some very critical parts of 
military planning right now, before we get to higher-order 
reasoning.
    Senator, as you were talking, to answer your question, I 
had a thought experiment of the FDA, right? What would this 
hearing have looked like, thinking about Food and Drug 
Administration and how you would go about actually regulating 
what we put into our body? And if it's true that we're going to 
have most of our interactions--and I think you're right--
mediated through digital assistance of varying degrees of being 
artificial and intelligent, I think the hard regulatory 
question on you is, like, what counts as things that the 
companies who produce these products have to report, have to 
account for, have to be transparent, and then can be actually 
inspected and certified via some whole new form of AI 
assurance? And I think it's going to take, sadly, a generation 
at least to work out that balance of what actually is reported, 
how you study it, what that looks like. But the analogy I come 
back to is the FDA. And it's going to be just as important as 
that, frankly.
    Chairman Warner. And Dr. Ding, and I should make clear I 
was reminded by my guys, your boat analogy was about a boat 
race, not about one boat taking out the other. I think the 
premise, again, that if you didn't ask the right question, the 
boat took an action that none of the original programmers would 
have thought, that not winning the race but crashing the boat 
was a way to score more points.
    Dr. Ding. I don't think we should expect vendors to be the 
sole solution to regulating and ensuring safe and robust AI 
systems. I think even if there are, like, more good guys or 
good people, oftentimes the boat example is an example of 
everybody was trying to do the right thing, but you still had 
these accidents that can occur.
    I think I would cite my former colleague at the Center for 
the Governance of AI, Toby Shevlane. He wrote a lot about 
structured access to very powerful AI models. He now works at 
DeepMind, one of the other leading AI labs. And he was 
proposing ideas about how labs like Meta's AI Lab could open up 
checkpoints corresponding to earlier stages in the training of 
these models, to allow outside researchers to study how the 
model's capabilities and behaviors are evolving throughout 
training. So obviously, Meta's doing great work on that, in 
terms of all of these red teaming exercises, but having ways to 
involve outside researchers might help check against----
    Chairman Warner. We want you to take Angus's task to bear, 
in terms of what--. But last one. Then if Jon wants to jump 
back in. It's this question of, with this tool, do we need to 
over-weigh. Because, you know, I've been spending a lot of time 
thinking about where is the most immediate threat coming from 
existing AI tools? And I make the case that two of our 
institutions that are most immediately threatened are faith in 
our public elections and faith in our public markets.
    Public elections, because First Amendment what have you 
gets more challenging to think through. But faith in our public 
markets, there are already laws in place about deception and 
manipulation of stocks.
    What I think, in regard to Angus's question, which was a 
good one, you know, where's the problem? Well, the problem is 
we now have tools with AI that allow those manipulation actions 
to take place at an exponentially, almost unlimited volume that 
never before took place. Somebody had to cheat on an individual 
basis or manipulate on an individual basis. But the volume of 
things that could take place from deep fakes to product 
misrepresentation to false SEC claims, just the litany--. 
Because I would argue that there was an overweighed risk of AI 
taking something that's already wrong and doing it on steroids 
exponentially.
    And if you would agree with that, then the question is: 
does it need a new law? Does it need a lower standard of proof 
because the damage could be so great? Is it a higher penalty? 
Am I going at least directionally the right way?
    And I will do it in reverse order this time. We'll do Dr. 
Ding and back up the row, and then I'll turn it back over to 
Jon if you want.
    Dr. Ding. Yeah, I think in terms of the specific 
legislative proposals, let me take some time and get back to 
you on that. I would say one of the things here is we often 
overweigh the role of technology in this. So, you want to 
protect public markets, you want to protect public democracy. 
Should we spend all of the time thinking about how AI is going 
to impose risks on all of these different things? Or should we 
think about just overall procedures or policies that would 
incentivize and create a more robust and trustworthy media 
system, that would then be able to fact check any sort of AI-
generated false information?
    So, I will get back to you on the specific legislative 
proposals on the AI side, but I would also emphasize that it's 
not just about technology, it's about how do we provide the 
better-surrounding society that can ensure that this technology 
doesn't undermine those two public goods that you mentioned.
    Chairman Warner. Dr. LeCun.
    Dr. LeCun. Thank you, Senator, for the question.
    I'll speak to the technological aspects, or the scientific 
aspects, rather than the legislative ones.
    So, this electronic astroturfing that you are describing, 
which, if I understand correctly, for markets or the political 
scene, I think what would help with this would be a systematic 
way of tracing the origin of a piece of content that would be 
in the hands of, that would be visible for, users. So, 
watermarking is an example of this for pictorial content. 
Unfortunately, that doesn't work for text. So, for text, you 
have to make people who post the thing responsible for the 
content they produce and for its deceptive character. In the 
U.S., of course, we have the First Amendment, so you can't stop 
people from seeing what they want, and you shouldn't. But there 
is an interesting point there, which is that the main issue 
with misinformation is not at the creation of misinformation 
but at the dissemination level.
    There are interesting economic studies on this by Arvind 
Narayanan from Princeton University, who has studied those 
questions. He seems to think that the problem is that 
dissemination--and certainly I admit I will agree with this, 
because we have systems in place to take down dangerous 
misinformation to limit its impact, on things like vaccines, 
for example, or any misinformation that endangers the public or 
the integrity of elections.
    So, I think that of all the measures of dissemination of 
all the measures that can be taken technologically, 
watermarking for pictorial content and audio content, not for 
text, and then perhaps some regulatory help.
    Chairman Warner. I'm going to go to Dr. Jensen but I'm just 
going to quickly add here. I think the tool itself--the 
distribution of the tool, I understand. But at some point, we 
have to think about whether the tool--. You know, the world 
decided that the use of chemical gas was going to carry a 
bigger penalty than shooting somebody. Both ways, you're going 
to be dead. And do we need to overweigh the risk because 
somebody giving a false tip could manipulate the market. But 
the volume of tools that could be used to mess with the market 
using these AI tools seems to me to be of a--what's the 
analogy--but the gas versus bullets, but there's something 
in--. So, Dr. Jensen, bring us home.
    Dr. Jensen. All right. Task accepted, Senator. Thank you. 
Your larger question is really a philosophical question about 
what it means to govern in the twenty-first century. And 
honestly, answering----
    Chairman Warner. I'm not sure that's going to bring us 
home.
    Dr. Jensen. Well, we can do it quickly, but honestly, it's 
important because I don't think necessarily you have to--. 
We're not going to throw out the beauty of our Constitution. 
So, we have to think about how we have maintained our values 
and standards and execute those laws. And you're probably going 
to have to--you already have. I mean, going through the work 
you've already been doing, we are going to add acts, we are 
going to add laws. But I think it needs to be done in open 
settings like this, an open dialogue that allows you to 
calibrate how far that goes.
    And to be really clear that, obviously, this is not 
libertarian paradise; there will be regulation and there needs 
to be, because it helps create common standards, and standards 
are strategy. They allow everyone to have foresight and think 
about what's going to happen. How to make business decisions 
about how to actually tool their algorithms and send them to 
the marketplace.
    So, I think you're probably going to have to create 
something. I'll definitely get back to Senator King and you and 
the Committee. I think you're definitely going to have to 
create something like the FDA, but for AI assurance. I don't 
know what that is. I'm not usually a fan of creating new 
bureaucracies in our government. So maybe it's an existing body 
that does that. Maybe it's an extension of NIST. But I think 
that we have to all be patient. This has happened faster than 
we anticipated and we're going to have to work it out over the 
next generation.
    Chairman Warner. Well, if you could, spend a little time on 
it, because I think it is a clear--We spent a lot of time on 
public elections. I don't think we spent near enough time on 
disruption of public markets. And I think we are an event or 
two away from what then Congress would potentially overdo, 
which is overreact. But because I think the threat could be so 
great and the tools could be so unprecedented, you know----
    A question I pose for you: do you need a new law? Do you 
simply need a different standard approved? Do you need a higher 
penalty? And is the potential downside of robbing broad-based 
faith across the whole market, how do you preempt? Most of 
these laws and penalties and proofs are after the fact, and I 
don't know how in some of this you----
    Dr. Jensen. We've already had this in a flash crash in a 
sense, right? So, if two algorithms are trying to place trades 
and each of them is trying to anticipate--we've already seen 
flash crashes. What you're getting at is more the question of 
intent, when an individual or a network of individuals actively 
uses these tools, which can be for good, to do broad-based 
market deceptions. And that's really a straightforward legal 
question. You hold them accountable, and you prosecute them, 
and you throw them in jail. And you throw them in jail in a way 
that still is keeping to the rule of law but that definitely 
sets an example.
    Chairman Warner. But is there--and I keep coming back to my 
example I didn't want to use--but there have been times when 
society have said we've got to so overweigh the risk that we're 
going to really throw the entire buyer book at you. And I'm not 
saying we have to go there, but I do worry that the arguments--
and I'm not saying from Dr. LeCun--but I have heard this from 
many: Oh gosh, you're going to regulate away innovation; you're 
going to kill the golden goose!
    And that argument, which was very much the argument that 
was launched by--and again, I say to my friends from Meta, 
we've had these discussions many times--but was launched by 
many of the social media platforms in the late '90s, early 
2000s. I don't think there's very many members of Congress, 
either side, that wouldn't say, you know, gosh, we ought to 
have some guardrails. Now, there still would be not lack of 
agreement on what those guardrails are, so I rest my case. But 
the upside threat and potential, as, Dr. LeCun, you just laid 
out with all of the AI assistants we're going to have. Boy oh 
boy, we've got to get it right.
    Gentlemen, it was a very stimulating conversation and a 
good hearing. I very much appreciate it.
    We're adjourned.
    [Whereupon the hearing was adjourned at 4:48 p.m.]
    
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