[House Hearing, 118 Congress]
[From the U.S. Government Publishing Office]
ADVANCES IN AI: ARE WE READY
FOR A TECH REVOLUTION?
=======================================================================
HEARING
before the
SUBCOMMITTEE ON CYBERSECURITY, INFORMATION
TECHNOLOGY, AND GOVERNMENT INNOVATION
of the
COMMITTEE ON OVERSIGHT
AND ACCOUNTABILITY
HOUSE OF REPRESENTATIVES
ONE HUNDRED EIGHTEENTH CONGRESS
FIRST SESSION
__________
MARCH 8, 2023
__________
Serial No. 118-7
__________
Printed for the use of the Committee on Oversight and Accountability
Available on: govinfo.gov
oversight.house.gov or
docs.house.gov
_________
U.S. GOVERNMENT PUBLISHING OFFICE
51-473 PDF WASHINGTON : 2023
COMMITTEE ON OVERSIGHT AND ACCOUNTABILITY
JAMES COMER, Kentucky, Chairman
Jim Jordan, Ohio Jamie Raskin, Maryland, Ranking
Mike Turner, Ohio Minority Member
Paul Gosar, Arizona Eleanor Holmes Norton, District of
Virginia Foxx, North Carolina Columbia
Glenn Grothman, Wisconsin Stephen F. Lynch, Massachusetts
Gary Palmer, Alabama Gerald E. Connolly, Virginia
Clay Higgins, Louisiana Raja Krishnamoorthi, Illinois
Pete Sessions, Texas Ro Khanna, California
Andy Biggs, Arizona Kweisi Mfume, Maryland
Nancy Mace, South Carolina Alexandria Ocasio-Cortez, New York
Jake LaTurner, Kansas Katie Porter, California
Pat Fallon, Texas Cori Bush, Missouri
Byron Donalds, Florida Shontel Brown, Ohio
Kelly Armstrong, North Dakota Jimmy Gomez, California
Scott Perry, Pennsylvania Melanie Stansbury, New Mexico
William Timmons, South Carolina Robert Garcia, California
Tim Burchett, Tennessee Maxwell Frost, Florida
Marjorie Taylor Greene, Georgia Becca Balint, Vermont
Lisa McClain, Michigan Summer Lee, Pennsylvania
Lauren Boebert, Colorado Greg Casar, Texas
Russell Fry, South Carolina Jasmine Crockett, Texas
Anna Paulina Luna, Florida Dan Goldman, New York
Chuck Edwards, North Carolina Jared Moskowitz, Florida
Nick Langworthy, New York
Eric Burlison, Missouri
Mark Marin, Staff Director
Jessica Donlon, Deputy Staff Director and General Counsel
Raj Bharwani, Senior Professional Staff Member
Lauren Lombardo, Senior Policy Analyst
Peter Warren, Senior Advisor
Mallory Cogar, Deputy Director of Operations and Chief Clerk
Contact Number: 202-225-5074
Julie Tagen, Minority Staff Director
Contact Number: 202-225-5051
------
Subcommittee on Cybersecurity, Information Technology, and Government
Innovation
Nancy Mace, South Carolina, Chairwoman
William Timmons, South Carolina Gerald E. Connolly, Virginia
Tim Burchett, Tennessee Ranking Minority Member
Marjorie Taylor Greene, Georgia Ro Khanna, California
Anna Paulina Luna, Florida Stephen F. Lynch, Massachusetts
Chuck Edwards, North Carolina Kweisi Mfume, Maryland
Nick Langworthy, New York Jimmy Gomez, California
Eric Burlison, Missouri Jared Moskowitz, Florida
C O N T E N T S
----------
Page
Hearing held on March 8, 2023.................................... 1
Witnesses
Dr. Eric Schmidt, Chair, Special Competitive Studies Project
Oral Statement................................................... 6
Dr. Aleksander Mafdry, Director & Cadence Design Systems
Professor of Computing, MIT Center for Deployable Machine
Learning & Massachusetts
Institute of Technology
Oral Statement................................................... 7
Dr. Scott Crowder, Vice President & CTO, IBM Quantum/IBM Systems,
Technical Strategy, and Transformation
Oral Statement................................................... 9
Ms. Merve Hickok, Chair and Research Director, Center for AI and
Digital Policy
Oral Statement................................................... 11
Written opening statements and statements for the witnesses are
available on the U.S. House of Representatives Document
Repository at: docs.house.gov.
Index of Documents
----------
* Questions for the Record: to Dr. Crowder; submitted by Rep.
Mace.
* Questions for the Record: to Dr. Crowder; submitted by Rep.
Connolly.
* Questions for the Record: to Dr. Schmidt; submitted by Rep.
Mace.
* Questions for the Record: to Dr. Schmidt; submitted by Rep.
Connolly.
* Questions for the Record: to Ms. Hickok; submitted by Rep.
Connolly.
* Questions for the Record: to Dr. Mafdry; submitted by Rep.
Mace.
* Questions for the Record: to Dr. Mafdry; submitted by Rep.
Connolly.
ADVANCES IN AI: ARE WE READY
FOR A TECH REVOLUTION?
----------
Wednesday, March 8, 2023
House of Representatives
Committee on Oversight and Accountability
Subcommittee on Cybersecurity, Information Technology, and Government
Innovation
Washington, D.C.
The Subcommittee met, pursuant to notice, at 2:19 p.m., in
room 2154, Rayburn House Office Building, Hon. Nancy Mace
[Chairwoman of the Subcommittee] presiding.
Present: Representatives Mace, Timmons, Burchett, Greene,
Luna, Edwards, Langworthy, Burlison, Connolly, Lynch, Khanna,
Mfume, and Gomez.
Ms. Mace. All right. Good afternoon, everyone. The
Subcommittee on Cybersecurity, Information Technology, and
Government Innovation will come to order.
Welcome and good afternoon to everyone who is here on both
sides of the aisle. Without objection, the Chair may declare a
recess at any time. I recognize myself for the purpose of
making an opening statement, if I may.
Thank you all for being here today, the time and the effort
and commitment to this congressional hearing on our artificial
intelligence. As Chair of this committee, I recognize myself
for five minutes to provide an opening statement on this very
important topic which many of us here today are extremely
passionate about.
The field of artificial intelligence is rapidly evolving,
and one of the most exciting developments in recent years has
been the emergence of generative models. These models have
shown the ability to produce human-like language and even
generate images, videos, and music. While the potential
applications of generative models are vast and impressive,
there are also serious concerns about the ethical implications
of their use.
As we explore the potential of AI and generative models, it
is essential that we consider the impact they may have on
society. We must work together to ensure that AI is developed
and used in a way that is ethical, transparent, and beneficial
to all of society. This will require collaboration between
government, industry, and academia to ensure that the AI we
develop is reliable, trustworthy, and aligned with public
policy goals.
Moreover, we must consider the operational legal
responsibilities of companies that use these models. AI can
help us make better decisions, but we must also ensure that
those decisions are ethical, unbiased, and transparent. To
achieve this, we need to establish guidelines for AI
development and use. We need to establish a clear legal
framework to hold companies accountable for the consequences of
their AI systems.
The Federal Government has an important role to play in the
development and deployment of AI. As the largest employer in
the United States, the government can use AI to improve
operations and provide better services to the public. AI can
help reduce costs, improve efficiency, and enhance the accuracy
of decision-making, for example. AI can be used to analyze vast
amounts of data to identify patterns and make predictions which
can help government agencies make more informed decisions.
As we move forward, we must also ensure that AI is used for
the benefit of society as a whole. While AI has the potential
to improve efficiency, increase productivity, and enhance the
quality of life, it can also be used to automate jobs, invade
privacy, and perpetuate inequality. We must also work together
to ensure that AI is used in a way that benefits everyone, not
just a privileged few.
In conclusion, the emergence of generative models
represents a significant step forward in the development of
artificial intelligence. However, with the progress comes
responsibility. We must ensure that AI is developed and used in
a way that is ethical, transparent, and beneficial to society,
and the Federal Government has an important role in this
effort.
I look forward to working with my colleagues on both sides
of the aisle on this committee to ensure that the U.S. remains
a leader in the development of AI technologies. Thank you for
your time and attention.
Now before I yield back, I'd like to note that everything I
just said in my opening statement was, you guessed it, written
by ChatGPT in AI.
The advances that have been made just in the last few weeks
and months have been radical, they've been amazing, and show
the technology is rapidly evolving. Every single word up until
this sentence was generated entirely by ChatGPT. And perhaps
for the first time in a committee hearing--I know Jake
Auchincloss said a statement on the floor a couple weeks ago,
but I believe this is the first opening statement of a hearing
generated by ChatGPT or other AI models.
I now yield to the distinguished Ranking Member, Mr.
Connolly, for your opening statement.
Mr. Connolly. Thank you, Madam Chairwoman. And let me first
thank you for reaching out on a bipartisan basis to talk about
this Subcommittee and our agenda. I really appreciate that, and
I wish more committees and subcommittees operated that way. And
I think we had fruitful conversation. We actually had a meeting
with certain cyber officials of the executive branch while we
were in Munich at the Security Conference. And, again, I just
appreciate your approach, and hope we can collaborate and make
music together over the next two years.
The Cybersecurity, Information Technology, and Government
Innovation Subcommittee has dedicated its first hearing to
examining advances in artificial intelligence and its
revolutionary impact on society. This decision reflects our
membership's interest in commitment of exploring,
understanding, and implementing emergent technologies.
Last Congress, Chairwoman Nancy Mace, Representative Ro
Khanna, and I introduced the Quantum Computing and
Cybersecurity Preparedness Act, which encourages Federal
agencies to adopt post-quantum cryptography. I'm also pleased
the bill was signed into law just a few months ago. I look
forward to future bipartisan collaboration as we define the
problem sets associated with AI design solutions and that
promote innovation while simultaneously mitigating the dangers
and risks inherent in AI technology.
The Federal Government has a historic, necessary, and
appropriate role guiding and investing research development for
new and emerging technologies. The Defense Advanced Research
Projects Agency, DARPA, the well-known research and development
agency of the United States Department of Defense, is
responsible for the development of myriad emerging
technologies.
One of the most famous successes includes the ARPANET,
which eventually evolved into the internet which we know today.
Other innovations include microelectronics, global positioning
systems, infrared--inferred night imaging, unmanned vehicles,
and what eventually became cloud technology.
AI will require similar Federal investment and engagement.
As stated in the January 2023 final report from the National
Artificial Intelligence Research Task Force, the recent CHIPS
and Science Act reinforces the importance of democratizing
access to a national AI research cyber infrastructure. U.S.
talent and frontier science and engineering, including AI, in
the report calls for 2.6 billion over the next six years for
the purpose of funding national AI research infrastructure.
While government certainly plays a role in R&D, a very
important role, it also has a regulatory role. Congress has the
responsibility to posture careful and thoughtful discussions to
balance the benefits of innovation with the potential risks of
emerging technology.
A recent National Bureau of Economic Research report found
that AI could save the United States healthcare industry more
than $360 billion a year and be used as a powerful tool to
detect health risks. A GAO report predicts AI could help
identify and patch vulnerabilities and defend against cyber
attacks, automate arduous tasks, and expand jobs within the
industry.
As with all technologies, in the wrong hands, AI could be
used to hack financial data, steal national intelligence, and
create deep fakes, blurring people's abilities to certify
reality, and sow further distress within our democracy. AI can
cause unintentional harms. GAO found that certain groups, such
as workers with no college education, tended to hold jobs
susceptible to automation and eventually unemployment.
Another concern relates to machine learning and data. ML,
machine learning, uses data samples to learn and recognize
patterns, such as scanning hundreds or thousands of pictures of
lungs to better understand pulmonary fibrosis and revolutionize
medical care. But what happens if those lung samples only come
from a homogeneous portion of the population? And that medical
breakthrough is inaccurately applied. When it comes to data,
equity is accuracy and must ensure datasets include as much and
as comprehensive a universe of data as possible.
It is paramount that during this hearing we begin to create
a flexible and robust framework, particularly for government's
use of AI to protect democratic values and preemptively address
social, economic, and moral dilemmas AI might raise.
During the last Congress, this committee voted to pass the
AI Training Act and the AI in Counterterrorism Oversight
Enhancement Act, with bipartisan support. The committee is not
entirely new to the AI space, and we look forward to continuing
efforts to support transformative research. We also look
forward to building on the Biden Administration's efforts such
as the National Artificial Intelligence Resource Task Force.
Just over a month ago, that task force released its report,
providing a roadmap to stand up a national research
infrastructure that would broaden access to the resources
essential to AI.
AI is already integrated within the world around us, and
its growing use throughout society will continue to drive
advancements. America must implement an aggressive, research-
forward Federal AI policy to spur competition with other
countries that have already established nationwide strategies,
and additional supporting policy strategies might also include
promoting open data, policies, or outcome-based strategies when
assessing algorithms.
Finally, and more importantly, our country needs the work
force to properly develop, test, understand, and deploy AI.
This work force of the future will include technologists who
will help govern AI responsibly.
I look forward to hearing from our witnesses today. I look
forward to collaborating with you, Madam Chairwoman, on any
subsequent legislation we might want to develop.
I yield back.
Ms. Mace. Thank you, Congressman Connolly. And I, too,
agree, I hope and I believe we will make music together,
continue to do that. Cybersecurity has been one of the few
places in Congress where we have been able to be bipartisan and
not crazy. And so, I appreciate the ability to work with folks
on both sides of the aisle.
I'm pleased to introduce our four witnesses today for this
Subcommittee's inaugural hearing of the 118th Congress. Our
first witness is Dr. Eric Schmidt, Chair of the Special
Competitive Studies Project. Dr. Schmidt is a former Google
executive, where he held multiple senior-level positions,
working alongside founders Sergey Brin and Larry Page.
Google literally changed the world, and it's a huge honor
to have one of the godfathers of modern day technology here
with us today talking about the advent of AI and what comes
next, because I believe this will be one of the greatest
technological revolutions of our lifetime and around the world.
Dr. Schmidt is an accomplished technologist, entrepreneur,
and philanthropist. Dr. Schmidt founded SCSP in 2021. This is a
bipartisan, nonprofit initiative that works on issues relating
to AI and other emerging technologies. Dr. Schmidt also co-
authored a book in 2021 with Dr. Henry Kissinger and MIT dean,
Dr. Daniel Huttenlocher, titled, ``The Age of AI: And Our Human
Future.'' The book attempts to explain artificial intelligence
while raising thought-provoking questions about the role of AI
in topics such as security and world order. And there is a Wall
Street Journal article that was an excerpt from the book that
folks should pick up and read, ``ChatGPT Heralds an
Intellectual Revolution.'' I'm going to encourage folks in this
space to read it.
Our second witness is Dr. Aleksander Mafdry, director of
the MIT Center for Deployable Machine Learning. Dr. Mafdry is
also a member of the MIT Computer Science and Artificial
Intelligence Laboratory, Cadence Design Systems professor of
computing, and co-lead of the MIT AI Policy Forum. Dr. Mafdry's
research interests span algorithms, continuous optimization,
the science of deep learning, and developing reliable,
trustworthy, and secure machine learning systems.
We look forward to hearing from you about the policy
challenges and moral and ethical questions surrounding AI.
Our third witness is Dr. Scott Crowder, vice president of
Quantum Computing and IBM, and chief technology officer, IBM
Systems, Technical Strategy and Transformation. Dr. Crowder's
responsibilities include leading the commercialization effort
for quantum computers and accelerating innovation within
development through special projects.
The Subcommittee is very interested in learning more about
quantum AI and how quantum computing may some day change the
way AI models can store, process, and even report data.
Our fourth witness is Ms. Merve Hickok, Chair and research
director for the Center for AI and Digital Policy.
We welcome everyone who is here today, and we are so
pleased to have all of you here this afternoon.
Pursuant to committee rule 9(g), the witnesses, if you will
please, stand up and raise your right hands.
Do you solemnly swear or affirm that the testimony you are
about to give is the truth, the whole truth, and nothing but
the truth, so help you God?
Let the record show that the witnesses all answered in the
affirmative.
Thank you, and you may be seated.
We appreciate all of you being here today and look forward
to your testimony. I want to remind the witnesses that we have
read your written statements, and they will appear in full in
the hearing record. Please limit your oral arguments to five
minutes, initially. As a reminder, please press the button on
the microphone in front of you so we can all hear you when you
are speaking.
When you speak--begin to speak, the light in front of you
will turn green. And after four minutes, the light will turn
yellow. And then the light--red light comes on after your five
minutes has expired. And we would ask that you please try to
wrap up your comments at that time so that all the Members who
are here today as part of this Subcommittee will get a chance
to speak and ask you all questions.
I would like to first recognize our first witness, Dr.
Schmidt, to please begin your testimony.
STATEMENT OF DR. ERIC SCHMIDT, CHAIR
SPECIAL COMPETITIVE STUDIES PROJECT
Mr. Schmidt. Chairwoman and Ranking Member, thank you so
much, all of you, for spending some time on this incredibly
important issue.
I've been doing this for 50 years, and I have never seen
something happen as fast as this round. It took five days for
ChatGPT to get to a million users, and now we have it being
used here in Congress. And, if you look throughout the country,
throughout America, throughout the world I live in, machine
learning in the broad form has taken it by storm. I'm used to
hype cycles, but this one is real in the sense that enormous
amounts of money are being raised to implement and build these
systems.
The sense to me is that this moment is a clear demarcation:
A before and an after. And in our book, ``Age of AI,'' which
you kindly mentioned, we actually talk about this is actually
more than just an industrial strategy, it is actually a new
epic in human experience. The last epic, of course, was the age
of reason 400 years ago which came from the century of the
printing press and the Reformation and things like that.
The ability to have nonhuman intelligences that we work
with and occasionally have to deal with is a major change in
human history and not one that we will go back to. And you can
imagine, if you speculate 10, 20, 30 years from now, at the
rate at which this innovation is going, what it would be like
to having these nonhuman intelligences in the midst, right? A
topic for another day.
The two most interesting things that have emerged in the
last year have been large language models. Large language
models can be understood as a system that was originally built
to predict the next word, the next sentence, the next
paragraph. But if you make them big enough--and when I say big,
I mean huge--to the cost of a hundred million dollars, 200
million to build them, they appear to have emergent properties.
They have what is technically known as capability overhang. In
other words, we don't know exactly what they know. Although we
do know they know an awful lot of things that are wrong, but we
also know that they have a lot of insights.
This has spurred enormous industry and a set of competitors
that will be emerging in the next month or two. It's literally
that fast. So, boom, boom, boom.
The other one is the term ``generative AI,'' which for me
is largely focused on the ability to generate new language, new
pictures, new videos, and so forth. It's reasonable to expect
that, in the next few years, a great deal of the content that
we consume will be generated for us.
Now, these are very, very, very powerful technologies. And
the impact on society is going to be profound, and I don't
think any of us understand how broad and how deep it will go.
When I look at some of the issues that you all should face,
I think the most obvious one is, what do you do about how
people interact with the platforms? And I'll offer three
principles.
One is the platforms need to know where the content came
from and they need to be able to tell you--this is to avoid
misinformation, Russian actors, that sort of thing. You need to
know who the users are. Even if you don't tell the end user who
they are, there needs to be some notion of who they are and
where they came from. True anonymity hidden behind a paywall
would allow nation-state attacks. And the third is that these
systems have to publish how their algorithms work, and then
they have to be held to how their algorithms work. Those simple
principles, I think, will help us manage the extreme cases
here.
We all, everyone in this room, wants the U.S. to win in
this. And, again, Ranking Member, you mentioned--Connolly, you
mentioned this issue around the national resource. My colleague
to the left can speak about what it's like to be in a
university where you don't have access to these models. We need
that, and we need the computing capability as it transforms,
not just language, but also every aspect of science and health
and biology and material science.
We want democratic partners, that is other countries. This
is something where the West can do this together, and we can
beat China, who is my primary focus. And, obviously, we need
more AI and software talent in the government. And we wrote a
long report for you all called the NSCAI that goes into that in
great detail.
What I want you to do is imagine the alternative. China
announced a couple of years ago that they are going to be the
dominant force in AI in 2030. Can you imagine the technology
that imbues how we think, how we teach, how we entertain, and
how we interact with each other imbued with Chinese values, not
American values, not the values and rules that we have in our
democracy? It's chilling.
The military consequences are also profound, as are the
biological, which we can talk about if you're interested. But
the most important thing to understand is that we need to win
because we want America to win, and this is our best, great
opportunity to create trillions of dollars of wealth for
American firms and American partners.
Thank you.
Ms. Mace. Thank you, Dr. Schmidt.
I would now like to recognize our second witness, Dr.
Mafdry, for his opening statement.
STATEMENT OF DR. ALEKSANDER MAfDRY, DIRECTOR, MIT CENTER FOR
DEPLOYABLE MACHINE LEARNING, AND CADENCE DESIGN SYSTEMS
PROFESSOR OF COMPUTING, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Mr. Mafdry. Chairwoman Mace, Ranking Member Connolly,
Members of the committee, thank you for inviting me to testify.
Today, I want to make three points: First, AI is no longer
a matter of science fiction, nor is confined to research labs.
The genie is out of the bottle. AI is being deployed, broadly
adopted, as we speak.
The key factor that made recent AI tools so popular is the
accessibility. Tools like ChatGPT can be directed using simple
language commands. We can ask it to draft us a memo or a speech
or summarize a movie in much the same way we would ask any
human. No AI expertise required.
As the barrier to adopting AI gets lower and lower, AI will
spread across our economy and our society. It will assist us in
mental and creative tasks, such as writing, visual design, and
coding. It will bolster and expand our capabilities. It can
even help us integrate our accumulative knowledge; for example,
in healthcare, in science, and engineering.
But along with these opportunities, AI also brings risks.
OK. Its lack of reliability; its propensity for promoting bias
and enhancing social inequities; its undermining of
accountability; its facilitation of deep fakes and manipulated
media; its ability to fuel personalized, online phishing and
harassment at scale.
It's critical we proactively identify these emerging risks
and develop clear and actionable ways to mitigate them. While
doing that, we need to recognize, though, all the positives of
AI and balance them against the negatives. In the end, the
impact of AI is not a foregone conclusion as much as rapid
progress of AI might suggest otherwise.
This brings me to my second point. As we engage with AI
more directly, we expose ourselves to interactions that go
against our intuition. Because AI exploits our cognitive
biases, we are often too likely to accept its results as
gospel. Indeed, as we are able to communicate with AI so
easily, so seamlessly, it's natural for us to think of them as
human, but this is a mistake. These tools aren't human. They're
a simple computation executed at impressive scale.
By creating--by treating them as human, we fool ourselves
into thinking that we understand how AI tools behave. We fool
ourselves into thinking that we can straightforwardly adapt
policies designed for humans to work in AI-driven contexts.
Indeed, our intuition often fails us. Take ChatGPT. Given a
question, it can write a really convincing answer, even if
everything it is writing is factually incorrect. It can trick
us into thinking the answer is critical by using prose that
sounds like human experts. Therefore, an unmitigated reliance
on such tools in our day-to-day lives, or even worse in our
education, can have disastrous consequences. It can erode our
analytical and reasoning capabilities.
The final point I want to make is that we also need to pay
attention to how AI is deployed. That is, if we focus solely on
what I have discussed so far, we'll have a major blindspot. A
key feature of measuring AI systems is that they can be used as
foundation on top of which other systems are being built,
forming what I would call AI supply chain.
The upstream of this chain are organizations that create
the foundation AI tools, like ChatGPT. And here, very few
players will be able to compete, given the highly specialized
skills and enormous capital investments the building of such
systems requires. In contrast, we should expect an almost
Cambrian explosion of startups and new use cases downstream of
the supply chain, all leveraging the capabilities of upstream
AI systems.
This leads to a couple of policy-relevant observations.
First, the limited number of large upstream systems may
introduce new challenges, such as hidden systemic fragilities
or structural biases. Imagine, for instance, if one of these
upstream models goes suddenly offline. What happens downstream?
Second, AI system won't be developed by a single entity.
They will be products of multiple AI systems grouped together
each from a different place. These composite systems will
become even harder to predict, harder to audit, harder to
regulate. For instance, who will be responsible and legally
liable when something goes wrong?
Third, this AI supply chain can redistribute power, control
over where, when, and how AI is used. This factor will be
paramount from a societal standpoint, from a geopolitical
standpoint, from a national security standpoint.
To conclude, let me say, we are at an inflection point in
terms of what future AI will bring. Seizing this opportunity
means discussing the role of AI, what exactly we want it to do
for us, and how to ensure it benefits at all. This will be a
difficult conversation, but we do need to have it and have it
now.
Thank you for the opportunity to speak with the
Subcommittee. I look forward to the questions.
Ms. Mace. Thank you.
And I would like to recognize our third witness, Dr.
Crowder, for your opening statement.
STATEMENT OF DR. SCOTT CROWDER, VICE PRESIDENT, IBM QUANTUM,
AND CTO, IBM SYSTEMS, TECHNICAL STRATEGY AND TRANSFORMATION,
IBM
Mr. Crowder. Chairwoman Mace, Ranking Member Connolly, and
distinguished Members of the Subcommittee, thank you for this
opportunity to testify before you today.
Today, I represent IBM Quantum where we have two goals: to
bring usable quantum computing to industry and research and to
make our digital infrastructure quantum safe. We have a network
of over 200 industry and research partners exploring the use of
quantum computing for business and science, and have developed
technology to make the transition to quantum safe cryptography
easier.
There is a common perception that classical computers can
solve any problem if they're just big enough. That is not the
case. There is a whole class of problems that classical
computers are not good at and never really will be.
When I talk to leading U.S. companies about their unsolved
problems that if solved could bring them huge economic
benefit--these types of problems turn up everywhere. Some of
these longstanding problems could be solved with a combination
of quantum computing and artificial intelligence.
Quantum computing is a rapidly advancing and radically
different computing paradigm which could launch a new age of
human discovery. Just seven years ago, the notion of a quantum
developer didn't exist. IBM was the first to put a real quantum
computer on the cloud; at the time it was just five qubits.
Today, IBM has systems over 400 qubits. And if we continue on
this technology roadmap, by the middle of this decade, we'll
have 4,000 qubit systems and will demonstrate the first
practical use of quantum computing.
IBM alone has deployed over 60 systems, and our 500,000
registered users have published over 2,000 research papers. One
key thread in this research is the application of quantum
computation within artificial intelligence. Many of our
partners have published research results using quantum machine
learning techniques. Examples include financial institutions
exploring quantum algorithms for improved fraud detection;
Boeing exploring optimization of composite materials for better
airplane wings; and CERN exploring applications in high-energy
physics.
One primary reason quantum computing has benefit for
artificial intelligence is because it uses a different method
to find patterns in data. For example, in fraud detection, a
quantum algorithm may be better at detecting true fraud and
reducing false positives. A data scientist may choose to use
either a quantum fraud model or a classical AI fraud model or a
combination for the best results. Put simply, quantum will be
another computational tool to use to improve AI results.
Generally, we see the future of computing as a combination
of classical, specialized AI, and quantum computing resources.
It will not be based solely on classical bits, but rather built
upon bits and neurons and quantum bits, or qubits. This will
enable the next generation of intelligent, mission-critical
systems and accelerate the rate of science-driven discovery.
Researchers, companies, and governments that leverage this
technology will have a distinct competitive advantage.
That leads to a critical point: When one examines the
financial commitment other countries are making in quantum
computing, our belief is the U.S. Government investment in
driving this critical technology is insufficient to stay
competitive. At its inception in 2018, the $1.7 billion
National Quantum Initiative stood as a leading public
investment. Today, the planned global public investment in
quantum technology is estimated to exceed $30 billion, with
China at $15 billion. It is critical that we not only
reauthorize the NQI, but also increase its investment in the
critical area of research of use of quantum computers for
mission-critical applications.
The same importance for ethical and trustworthy AI applies
whether classical compute or quantum compute underpins the
solution. We know that trustworthiness is key to AI adoption,
and the first step in promoting trust is effective risk
management policies and practices. Companies must have strong
internal governance processes, including, among other things,
designating a lead AI ethics official responsible for its
trustworthy AI strategy, and standing up an AI ethics board as
a centralized clearinghouse for resources to help guide that
strategy. IBM has implemented both, and we continue to advocate
others in the industry to do likewise.
Additionally, it's important to establish best practices
for AI bias mitigation, similar to BSA's framework published in
2021.
It's difficult to pinpoint the precise benefits and
possible challenges presented by any new emerging technology.
Quantum computing is no different. However, those countries
that make investments in this transformative technology today
will reap benefits in the years to come. Those countries that
do not will be at a competitive disadvantage in the future. At
the same time, countries will also need to invest time and
energy in developing an appropriate regulatory environment that
supports the adoption of trustworthy AI regardless of the
underlying compute technology.
Thank you again for inviting me to testify, and I look
forward to today's discussion.
Ms. Mace. Thank you.
And I would like to recognize our fourth witness, Ms.
Hickok, for your opening statement.
STATEMENT OF MS. MERVE HICKOK, CHAIR AND RESEARCH DIRECTOR,
CENTER FOR AI AND DIGITAL POLICY
Ms. Hickok. Thank you so much.
Good afternoon, Chairwoman Mace and distinguished Members
of the committee. I'm Merve Hickok, Chair and research director
for Center for AI and Digital Policy. It's an honor to be here
today, and thank you for the opportunity to testify.
CAIDP is a global research organization based in D.C. We
educate and train future AI policy leaders, collaborate with AI
policy experts around the world. We also publish AI and
Democratic Values index, analyzing AI policies and practices
across 75 countries.
I also train and build capacity in organizations on
responsible AI development and governance. And prior to CAIDP,
I was in the corporate world as a senior leader at Bank of
America Merrill Lynch, responsible for recruitment technologies
internationally.
I provide this background because we believe in the promise
of AI. However, we also know that AI systems, if not developed
and governed with safeguards, have negative impacts on
individuals and society. We believe that AI should first and
foremost serve members of the society, their rights, their
freedoms; our social, moral, and ethical values.
The title of the hearing today asks if you are ready for a
tech revolution. My brief answer is no. We don't have the
guardrails in place, the laws that we need, the public
education, or the expertise in the government to manage the
consequences of the rapid technological changes.
Internationally, we are losing AI post leadership.
Domestically, Americans say they are more concerned about--
concerned than excited by AI making important life decisions
about them, knowing their behavior. AI systems now produce
results we cannot assess or replicate. Opaque systems put
governments, companies, and individuals at risk. AI expands our
research and innovation capabilities; however, it also
replicates existing biases in the datasets and biases in the
choices of the developers, resulting in disadvantaging people
with disabilities in hiring, for example; inaccurate health
predictions for patients of color; offering women lower credit,
lower home valuations; innocent people being arrested due to
biased facial recognition.
We are now debating generative systems which produce
synthetic text, image, video, and audio. The systems will
certainly add to our creativity, there is no doubt about it,
but they're already impacting the original creators. They will
also be used by malicious actors to fabricate events, people,
speeches, and news for disinformation, cyber fraud,
blackmailing, and propaganda purposes.
I give this testimony on International Women's Day, when
unregulated opaque AI systems deepen discrimination and online
harassment against women.
Both governments and private companies know that public
trust is a must-have for further innovation, investment,
adoption, and expansion. Companies, large and small, are
calling for regulatory guidance.
Administrations of both parties have called for trust for
the AI. President Trump's Executive Order 13960 explained that
ongoing adoption and acceptance of AI will depend significantly
on public trust, and AI should be worthy of people's trust, and
that this order signals to the world U.S. commitment to develop
and use AI underpins by democratic values. The order
characterized trustworthy AI as being lawful, respective of
civil rights, accurate, reliable, safe, understandable,
responsible, transparent, accountable, and regularly monitored.
Office of Science and Technology has recently published
AI--Blueprint for an AI Bill of Rights, a critical policy
framework underscoring similar qualities for AI, emphasizing
democratic values and civil rights.
President Biden has called for bipartisan legislation to
keep companies accountable, and reiterated the same principles:
transparency, accountability, and safeguarding our values.
We very much support this committee and its bipartisan
nature, but there are real challenges ahead, and I will
conclude with a few recommendations toward those.
We really need the Congress to hold more hearings like
this, explore the challenges, the risks and benefits, and hear
from the public and those impacted. We need the Office of
Management and Budget to move forward with the long-delayed
rulemaking for the use of AI in Federal agencies as part of the
executive order. We need to build multidisciplinary capacity in
Federal Government to ensure the work force understands the
benefits and risks of AI. We need the wider work force to
understand benefits and risks of AI as well. We need R&D
capabilities expanded beyond a handful of companies, campuses,
and labs, and demand trustworthy AI with our research agenda. I
urge you to act now to enact the legislation reflecting the
bipartisan nature.
Absent a legislative agenda or implementation of AI policy,
American people, American companies, and allies are lost about
U.S. AI policy objectives.
Thank you.
Ms. Mace. And thank you.
And I think one of the things that sticks out to me today
is, actually, this is the first AI hearing this Congress in the
U.S. House of Representatives. But also, this same day, the
U.S. Senate had their first hearing on AI on this subject
matter; they beat us by four hours this morning at 10 a.m.
But I would now like to recognize myself for five minutes
for a few questions of our panelists.
Thank you, Dr. Schmidt, for painting what I would describe
as a very vivid picture of what is happening in this space,
because I agree with you, it's been rapid and, in your words,
epic. And I'm not sure that the world is ready for what is to
come in the next few months, years, et cetera. And so, it
reminds me of Einstein. He said: ``I never think of the future.
It comes soon enough.'' And it is here. And it is moving faster
than the speed of light.
So, my first question today is for Dr. Schmidt. How can we
ensure that AI technology is developed in a way that is safe,
transparent, and beneficial for society without stifling
innovation?
Mr. Schmidt. I'm always worried about AI conversations,
because everyone believes the AI that we are building is what
they see in the Terminator movies. And we are precisely not
working on those things. So we are clear, we are not doing----
Ms. Mace. Not yet.
Mr. Schmidt. We are not doing it yet, and we are not likely
to. But what we are doing is working on systems that will
affect the way people perceive their world. And I think the
best thing for America to do is to follow American values,
which include robust competition with government funding of
basic research, and using the innovators, including the folks
to my left, to actually deliver on this.
I think that one of the things that is not appreciated in
the last 30 or 40 years of tech companies is--speaking as a
person who is associated with a number of them--is how good
they are as American exports of our values. So, I come back to
a much simpler formulation that American ingenuity, American
scientists, the American government, and American corporations
invent this future and will get something pretty close to what
we want. And then you guys can work on the edges where you have
misuse.
The alternative is, think about if it comes from somewhere
else which doesn't have our values. And I really believe that.
Everything that you can do to finish, to support that
innovation cycle, the universities, the graduate students,
getting foreigners who are high-skilled in to help us, building
those corporations, creating shareholder wealth, hiring lots of
people--it's the American formula, and it will work here too.
Ms. Mace. And then, on that note, in terms of the
personnel, the resources, training folks in the technology so
that it can advance, having that innovation. And lot of it is
on the software side, but how does hardware figure into that?
CHIPS, for example.
Mr. Schmidt. So, in our AI commission that you all
commissioned a while ago, we spent a lot of time on this. We
felt it was very important for America to retain its
leadership, which of course we didn't have, we gave it to
Taiwan. The best result was to basically get the Taiwanese
firms, primarily TSMC, and Korean firms, primarily Samsung, to
locate plants in the United States, which has occurred.
The Trump and Biden administrations have done a good job of
restricting some of the sales and access to these tools to the
Chinese. But, fundamentally, this is a race, it's a
competition, and we're not that far ahead. So, we have to keep
innovating, which is why your support for the CHIPS Act was so
helpful. And so, on behalf of the whole industry I'll thank all
of you for doing that. That's a good example of the government
getting ahead of a real problem.
Ms. Mace. Thank you.
And, Dr. Mafdry, my next question for you, do we need to be
worried about too much advancement too fast in AI? Are we
capable of developing AI that could pose a danger to humanity's
existence all over the world, some of the things that people
talk about out of fear in this conversation because of a lack
of knowledge, or is that just science fiction?
Mr. Mafdry. Well, it really depends on what do we view as
this kind of catastrophic risk. So, a Terminator-style
scenario, I'm not too worried about this, as Dr. Schmidt just
said. What I am worried is about something more mundane but
essentially very, very corrosive, right. So, we see how this
works out in social media where, essentially, AI also runs in
social media. That's what decides what we see. And we are
seeing the effect of that. Well, this is kind of not really
transparent, not really aligned with the societal goal.
So, that is--now think about things like this new
generative models developed essentially in a way when we just
maximize the profit, we just try to get maximum adoption. I'm
worried about that.
Having said that, I do think we can figure it out how to
not stifle innovation, just moderate it so we still can
progress. But just, again, ensure that the companies that we
talk to, they are not only--only driven by profit, but realize
they have some responsibilities, and they need to acknowledge
them.
Ms. Mace. And I would agree. And I think, you know, we've
talked about algorithms for years, like on social media, and
the use of divisiveness of politics today, and each side
getting the extreme of their side and getting fed more of that
information. I sort of feel like it would be--we were putting
it on steroids of the future, immediate future of what the
advances in AI might be. What are your concerns there?
Mr. Mafdry. Yes. So, essentially, I think saying that this
might be like social media on steroids is very much--is very
much justified. So, again, now I told you that ChatGPT will be
so much more pervasive than social media. And, essentially, we
don't exactly know what will be the effects on our thinking
here or like the way our children learn to think. Like, do they
just fully trust what ChatGPT tells them or do they learn how
to reason?
So, again, I'm really worried about this, but I think--and
that is where the government really needs to step up. We can--
you know, with enough involvement with government, which I
think might not be too much in context of social media, but
here we have to do it differently, and I think that we will do
it well.
Ms. Mace. Thank you.
I would now like to recognize my esteemed colleague, Mr.
Connolly, for questions.
Mr. Connolly. Thank you so much, Madam Chairwoman.
Listening to Ms. Hickok and the potential of AI is actually
really positive in terms of how it can complement the quality
of life for humans and make things better and promote peace and
harmony. But we know that technology can be used for good and
evil.
And I'm listening to what you just said, Dr. Mafdry, in
terms of your hope for the government's role. And yet, if you
look at social media and you look at technology in general,
Congress has been very reluctant to get into the game of
regulation. And as a result, awesome power has been developed
by and deployed by entrepreneurs who became billionaires in,
largely, Silicone Valley without any interference by the
government. They make all kinds of massive decisions in terms
of content, in terms of what will or won't be allowed, in terms
of who gets to use it, et cetera.
And so, why should we believe that AI would be much
different in terms of its future in the hands of the Federal
Government?
Mr. Mafdry. Well, again, the hope here is that we will--
don't play the same playbook we played for social media. And in
particular, I think the point of start here is before we go--
so, first of all, I want to say that I strongly believe that
regulation is a very important tool to make sure that, you
know, just certain technologies are aligned with like broad
societal benefits, and they need to be used.
Having said that, before we go to premature regulation and
we kind of rush regulation, first of all, even the rush
regulation might not be fast enough for AI because AI is a very
fast moving target. But even we forget, I think what we need to
start, we need to start ask questions. And, in particular,
government needs to ask questions of this company saying, what
are you doing? Why are you doing this? What are the objectives
of the algorithms that you are developing? Why is there no
objectives? How will we know that you are accomplishing these
objectives? How can we have some input into what these
objectives showed.
I think this change of tone, together with the government
recognizing that you cannot abdicate AI to the big tech, as
capable as they are, that they have different use cases. They
have different priorities. Like, that's what needs to change.
If this doesn't change, I'm extremely worried.
Mr. Connolly. Well, I just think, if we look at the past
and we look at social media, I wouldn't bet the farm on any
kind of rapid regulatory regime coming from the Federal
Government.
Mr. Mafdry. And just to clarify, that's what I--that's
exactly what I'm worried about. So, let's have conversations we
can have now.
Mr. Connolly. Right.
Mr. Mafdry. Hopefully, we'll learn from the mistakes.
Mr. Connolly. Thank you.
Dr. Schmidt, you want to see the United States get ahead in
this lane of technology and to compete successfully against the
Chinese. Can you talk a little bit about what is the nature of
that threat? How well are they doing in this sphere, and what
do we need to be concerned about?
Mr. Schmidt. There are four or five companies, Mr. Leader,
that are American or British firms that have extremely large
language models. There's also at least one large one in Baidu
in China. I was interested to note that the largest
noncorporate such example in the world that is not owned by a
corporation is also in Tsinghua University in Beijing.
So, there's every reason to believe that the Chinese
understand everything that we're talking about now extremely
well. They've published their intent, so we can read about it.
And I view it as a national emergency. This technology is so
powerful in terms of its ability to transform science,
innovation, biology, semiconductors, you name it--and along
with quantum, I should add--that we need to get our act
together to win and to win a competition.
If we don't--let me give you some examples. AI can be used
to generate good things in biology, but also lots of bad
viruses. You all have created a Bioterror Commission, which I'm
fortunate to serve on, to take a look at this and the impact of
that. That's another example of national security.
The issues of misinformation of the nation-state could be
very significant. Think about the progress of war and conflict
where decision-making can be done faster than the OODA loop or
faster than human decision-making. These are all challenges,
and our government is behind where it needs to be in the
adoption of these technologies for national security as well.
Mr. Connolly. I just would end by saying, I couldn't agree
with you more. And I think really we need to be looking at sort
of like, you know, the race to the moon kind of shot in, you
know, quantum computing, AI, cyber, 5G. Because if the Chinese
dominate those areas, the future is theirs.
I yield back. Thank you, Madam Chair.
Ms. Mace. Thank you, Mr. Connolly.
I would now like to recognize a fellow South Carolinian,
Congressman Timmons.
Mr. Timmons. Thank you, Madam Chair. That's great to say:
Madam Chairwoman. Congratulations on being the Chair.
Ms. Mace. Thank you.
Mr. Timmons. First up, thank you so much for your
attendance here today. You all are experts in your field, and
we really appreciate you taking the time to come and share your
thoughts on this important topic.
Congress is grappling with technology. Our country's
grappling with technology. And we're doing our best to try to
figure out a regulatory environment that fosters innovation and
allows economic growth, while managing the potential adverse
impacts that technology can have.
Obviously, we are working on cryptocurrency and digital
assets, and that's a major challenge for us. Congress is not
the youngest, most tech savvy part of our society, and we are
doing our best.
But I do want to talk about AI's potential impact on our
work force, particularly how tech can be leveraged to further
individual efficiency rather than possibly displace workers.
So, Mr. Crowder, I want to start with you. What are the
most promising use cases for AI as a tool in the work force,
and how do you anticipate AI will be--will influence industries
such as the financial services sector?
Mr. Crowder. Yes, I think it's going to be pretty broad.
And one of the exciting things that we didn't really talk about
is that leveraging some of the underlying technologies like
base or foundation models can be applied to things, not just
writing a haiku or coming up with a speech, but also, you know,
looking at language-like things in other fields, like tabular
data and finance, et cetera, et cetera.
So, in addition to the kind of things I talked about fraud
detection, I think we've all experienced, you know, maybe
traveling abroad and having your credit card be declined, and
that's bad for banks because they want that credit card money.
So, even a small percentage improvement in false positives is a
lot of money for our financial institutions. So, there's lots
and lots of applications.
But to your point, I mean, I think we need to look at AI as
augmenting what humans can do as opposed to replace. And I
think good utilization of AI is to make it--make our work force
more efficient. And I would argue one of the things that we do
a good job in the United States is funding basic science. But
we also need to look at how do we encourage our work force to
be able to use the technology as opposed to just develop the
technology. Because I think the use of AI is going to be a
differentiating factor on, you know, making the U.S. Government
as well as our companies more effective and more competitive.
Mr. Timmons. As businesses try to compete in the free
market, they're inevitably going to try to cut costs and
replace work force with technology. What--how are we going to
manage that challenge?
Mr. Crowder. That's a good question. I don't know if I have
a perfect answer for it. But I think having a more productive
work force that is focusing on value creation, I think at the
end of the day is what really drives success in business. And
the more that you can automate tasks that aren't really value
creation so you can free up your workforce to create value, I
think that is good. And I think that is a more positive way of
driving additional productivity as opposed to thinking about it
as removal of cost.
Mr. Timmons. Sure, sure. Dr. Schmidt, what jobs do you
think will be created in the wake of AI and what jobs do you
think will be threatened?
Mr. Schmidt. I think one of the general comments to make is
I've spent 20 years listening to the theme that jobs will be
displaced or lost because of technology. And today we have a
huge shortage of workers to fill the jobs in America. The
biggest category is truck drivers. Remember how truck drivers
are going to be replaced by automation.
So, it looks to me like the most likely scenario in the
next 20 years or so is not enough people to fill these jobs.
And the best way to get people who can fill those jobs, to have
them have better tools, better education, better knowledge, and
a partner, if you will--all of the evidence that I've studied
indicates that having a digital partner increases your wage,
right? Literally, when you are using a computer to help do the
job, the job has a higher salary.
So, it looks to me like as we get more diffusion of this
technology, on average, jobs go up. There are jobs that are
lost, there are jobs that are created.
Mr. Timmons. Sure. I couldn't imagine my life without
Google, Apple, and Amazon. I feel like I'm attached to my
phone. And I haven't been to the grocery store in three years,
and it is great. So, I'm sure that this is going to create
additional opportunities to make my life more efficient and
make me more capable of having a greater impact. So, I
appreciate that.
And thank you, Madam Chair. I yield back.
Ms. Mace. Thank you.
I now recognize Congressman Lynch for five minutes.
Mr. Lynch. Thank you, Madam Chair. And congratulations to
you and to the Ranking Member.
Dr. Schmidt, in March 2021, the National Security
Commission on Artificial Intelligence released its
comprehensive, I think it was like 800 pages. It actually
defended itself against the risk of being read by its sheer
thickness. But right after your report came out, I was the
Chair of the National Security Subcommittee, and we invited you
to testify regarding that. I see your staff all nodding. They
have painful memories of this, I'm sure. But we invited you in,
and we went over the report. It had 16 major recommendations,
and then probably another 20 other subsidiary ancillary
recommendations.
I'm wondering if you could talk about the progress, or the
lack of progress, we've made over these two years now since you
last testified before this committee about this issue.
You had some very pointed recommendations, you know, for
DARPA. You had recommendations and action items for Congress,
for the executive, for this interagency task force that you
envisioned.
Can you talk a little bit about where you think--how much
progress do you think we have made? And, you know, would you
give us--what kind of grade would you give all of us.
Mr. Schmidt. Well, in the first place, you guys give us the
grade, and we are happy to serve. I would say about half of our
recommendations have been adopted through the NDAA and other
processes. We were kind enough to write the legislation for you
as a hint, and you all were able to adopt it fairly quickly,
and it worked.
The area that I'm most focused on right now is basically
the training problem. And I just don't see the progress in the
government to reform the way it hires and promotes technical
people. As part of the--part of the AI report, we proposed a
civilian, essentially, trading academy for digital skills. And
there are various different forms of this. But I don't think
the government is going to get what it needs unless it has a
program which is modeled on the military academies but for
civilians, where civilians get trained in technology in return
for serving in the government in the civilian capacity to help
the government out.
This is a young person's game. These technologies are too
new. You need new students, new ideas, new invention. I think
that is going to be the fastest way to get it. I don't think
the government is going get there without it. That'd be my
highest--I think that's the largest omission.
Mr. Lynch. Is there a way--so I actually was confronted
with this same problem in my district, where a lot of high-tech
firms, biotech firms moving into the district, and I grew up in
the local public housing projects and those kids--our kids
weren't getting those jobs. So, I started a--I founded a
charter school that focuses on STEM, you know, math, science,
technology. And it's doing really, really well. But it's one
school, you know, out of a hundred.
And is there a way to--I'm not so sure if it is efficacious
to try to take somebody who is coming out of high school or in
college and then make them a tech person. I think it's a much
longer runway and better chances of success if we start at a
very early age. I mean, is there any thoughts about, you know--
I mean, you know, we are having problems with our public
education system anyway. But is there a way to amp that up at
an early age in early grades to produce the type of workers
that you envision will be necessary to maintain our edge, not
only in artificial intelligence, but everything else we have
got to do.
Mr. Schmidt. Israel does something interesting in this
area. If you are 15 or 16 and a math prodigy, they actually put
you in a special school that is combined with their mandatory
military training. I'm not suggesting a mandatory military
training for our math people. God knows how they would do. But
the important thing is identifying the talent early and then
getting it into the right track. And, again, the educators to
my left can talk about this at more length.
But I think that at a Federal level, the easiest thing to
do is to come up with some program that is administered by the
states or by leading universities. Every state has a big land
grant university that is interested in this. And getting them
money so that they can build these programs, and then they get
paid back for that with service. I like those models, and that
is a model that takes your idea and scales it. There is a lack
of money to build these systems at scale, and that idea or some
variant of it would do it.
Aleksander?
Mr. Lynch. Thank you.
Yes. Mr. Mafdry.
Mr. Mafdry. If I can just add, is that AI's technology is
to learn best by applying it to used cases. And government--so
I actually was discussing exactly this problem with DOD because
they have exactly suffered from this--and instead of thinking
of this is a weakness, this could be a strength. Once it is
tried to apply AI internally to your problems, that's where
people will learn. And this way actually people come, let's say
to DOD, or to government programs for three, five years, and
they come back to the civilian sector. And really like, also,
well, we are lacking this talent also in our civilian economy
too. So, I think that is the way to go, and the government
could play a big role here.
Mr. Lynch. Thank you.
Madam Chair, I know I have another one witness need to
answer the question, but I think I've run out of time.
Mr. Burchett. Chairlady, why don't you let him go. I'm
next, but go ahead.
Mr. Lynch. OK. Ms. Hickok?
Ms. Hickok. I just wanted to follow up with the last remark
as well in terms of education. I echo the task force
nationally. I researched task force reports and recommendations
on democratizing the research and development capabilities
within the Federal Government for the government as well.
Sometimes our brightest minds are forced to go to a handful
of companies and labs and campuses to do their research in
areas that they are interested. But if you have this capacity
within the government's infrastructure as well, that would also
be another way to attract this work force.
And I will expand also the education piece from the schools
to consumers, and expand the education need from technology
jobs to all the jobs. We need lawyers who understand these
concepts. We need sociologists, anthropologists, ethicists,
policymakers. We need understanding and capacity building in
this topic across the whole domain and industry.
Mr. Lynch. Thank you.
Madam Chair, I yield back. And I thank you for the
courtesy, and I thank the gentleman as well. Thank you.
Ms. Mace. Thank you.
I would now like to yield to Congressman Burchett from
Tennessee.
Mr. Burchett. I'd tell my friend across the aisle, you'll
get nowhere calling me a gentleman; I just want you to know
that. And I didn't miss my thought process when I came in here,
and we have a lady who is chairman and it is international day
of the woman. And I think that is pretty cool that you Chair.
If my momma were alive, she would think that is very cool, too,
because she was a pretty cool lady.
Thank you all for being here. I'm probably the least
qualified person of ever asking y'all questions, but as the
435th most powerful Member of Congress, I feel very empowered
today, and I'm kind of digging this subject matter. And I'll
try to go through these quick.
Mr. Schmidt, I did Google, brother. And I don't know what
it is. I hit that button--you know, my mom and daddy would
always say look it up. Now I tell my daughter, Google it,
honey. You know, so I think it is pretty cool.
But the development of AI, how will that impact our
international relations specifically with China? I fear what
they will do if they get control of it, as you have stated
there. I think you mentioned the date that they said they were
going to control it, and I would say they probably be doing
that five years ahead of time.
Go ahead, brother.
Mr. Schmidt. Thank you, Congressman. I worry about the
following scenario: In the future, there's a war. It's an
attack by North Korea on the U.S. Sorry. China stops the war
between North Korea and the U.S., and the entire war took one
millisecond.
And the reason I worry about that is I can't figure out how
we are going to build offensive and defensive systems that are
reliable enough to put them in charge of a war that occurs
faster than human decision-making. That, to me, is the ultimate
threat of this technology, that the things occur faster than
humans can judge them. I don't have a good solution for that.
My second observation is that China is very smart, and they
have identified these areas as these underlying technologies to
provide leadership that dominates industries. A good example is
synthetic biology, which is an area which was imbedded in the
United States, likely to be, again, trillions of dollars of
wealth. China has now maximized its investment in this area.
Not only is it good for their national security, but it's good
for their businesses.
So, when you have got a nation-state that's smart,
technocratic, focused on its own defense and innovation, and
proposing its own companies in the form of civil military
fusion, we have got a serious competitor. That's why we have to
act.
Mr. Burchett. Are you aware--or maybe you are not. I'm
not--of the Chinese infiltrating any of our AI companies?
Mr. Schmidt. I am not. You must assume the Chinese have
operatives pretty much everywhere, based on their history.
Mr. Burchett. OK. Is there any way that we could
proactively protect against AI-generated cyber attacks?
Mr. Schmidt. Well, you defend against them.
Mr. Burchett. Right.
Mr. Schmidt. Technology--we looked a lot at this. The
question is could you create the equivalent of a Manhattan
Project that was secret and you'd keep it all in one place, in
one location, New Mexico, what have you. The knowledge is
moving too quickly. There's too many people globally. We are
going to have to win by staying ahead, which means building
powerful defensive systems.
Mr. Burchett. OK. Thank you.
Dr. Mafdry, what are some of the personal risks to personal
privacy that are associated with the use of AI?
Mr. Mafdry. Sir, could you clarify what kind of risk?
Mr. Burchett. Well, I guess I should ask my research
person. As I stated, it is a little out of my league.
Mr. Mafdry. No, I just didn't hear. I just didn't hear.
Mr. Burchett. No, I just said what are some of the
potential risks?
Mr. Mafdry. I see. So, again, there is many, and they
really depend which sector you look at because there are
different levels of credibility. So, one of them and like the
one big risk, and that's something I research myself so I'm
intimately familiar with, this technology is not fully
reliable. It works most of the time, but not always.
Mr. Burchett. It's not fully what? I didn't----
Mr. Mafdry. It's not fully reliable. So essentially, it
works most of the time but not always. And then what is worse,
you might not realize when it's not working. OK. So, for
instance, in ChatGPT they hallucinate things sometimes, and you
might not realize they are hallucinating things because it
looks very convincing.
The other aspect of this is, essentially, as the systems
ingest our data, they can really essentially know us better
than we do. And again, that was true also of Google, also of
the social media, but I think with this next generation of
models, this will become even more so.
And then the third level of risk is exactly the one that
Dr. Schmidt talked about. I'm actually really worried about
that. Not even about the actual war happening, but us preparing
for the war like something can go wrong. And it becomes like
when things are happening within a millisecond, like, we have
no good intuition or no good ways to actually figure out how to
make it safe.
Mr. Burchett. OK. Thank you.
Running out of time but, Dr. Crowder, real quickly, how
will the quantum computing impact the security of encrypted
data?
Mr. Crowder. In the long-term, quantum computers, someone
proved on a blackboard, that a lot of our current cryptography,
how we basically send keys around and how we digitally sign
things, eventually will get broken by a quantum computer. The
good news is that people have come up with algorithms that
quantum computers are not good at solving and classical
computers are not good at solving.
So, our challenge is really transitioning from the
cryptography we use today to that new form of cryptography. And
we want to do that as quickly as possible once we have got
really safe standards because we're worried that people will
take all the data today and decrypt it later. So, for some
applications, that doesn't matter, but for a lot of
applications that makes a big deal.
Mr. Burchett. OK. Thank you. I've run out of time.
Chairlady, thank you, ma'am, very much.
Ms. Mace. You did a great job.
I would now like to recognize Congressman Mfume.
Mr. Mfume. Thank you very much, Madam Chair. Again, my real
thanks to you and the Ranking Member for having discussions
that led us to this point.
This, for lack of a better term, has scared the hell out of
me. And I thought I knew something about AI. I'm bopping around
on the campus talking to students in a classroom now and then
teaching them, but what I have heard today is unlike anything I
have ever heard, particularly in terms of our national
security.
I think the Chairwoman mentioned or quoted Einstein a few
minutes ago. He also said that great minds have always
encountered violent opposition from mediocre spirits. And I
don't know if you are encountering violent opposition, but I
think you are encountering a great deal of inattentive or
unattentive population groups who just, for whatever reason,
are not paying attention to what is going on. It is very scary,
and I would strongly support, Madam Chair, any future hearings
on this. I just don't that think we have much of a choice. It
is that imperative.
Dr. Schmidt, you said it was a national imperative, almost
a national emergency. That got my attention, and it will keep
my attention.
I don't know that we can do enough to ring the bell on this
so that our institutions, whether it's government or business
or academia, all start paying the kind of attention that we
really, really need.
Dr. Mafdry, you, in your testimony, talked about the
overarching points. And the fourth one you talked about was
that we pay attention, critical attention, to the artificial
intelligence supply chain, that it will structure the
distribution of power in a new AI world. Could you take just a
moment to explain that?
Mr. Mafdry. Of course. So, essentially the way AI is being
deployed right now is no longer just one entity who gathers the
data, trains the model, and applies it and deploys it to a
given task. The way things happen is that there is a supply
chain, in particular with this new generative models, like,
they are very expensive to train, but they are very capable.
And essentially what happens is that, you know, one of these
companies--there is very few companies that can afford training
such a model--they essentially develop it and then let other
companies build on top of it.
So, think of this as just like having initial capability
and just adjusting it. For instance, you have a model like
ChatGPT. It is able to summarize texts and understand to some
extent the texts. So, maybe you have a hiring tool that then
you build on top of it that essentially uses this capability to
screen resumes and something. There is many risks there, but
this is just an example. So, we are heading this kind of supply
chain of interdependencies.
And again, we have upstream where there is very few
players. There is very few critical important models on which a
lot of the economy depends, and then there are all these
interactions between these other things. So, this is something
that now we have to think. There's an ecosystem.
Mr. Mfume. I see. I see.
This sort of boasting that China has been doing that, by
2030, they will be the dominant player is scary also. And the
fact that the universities in Beijing and elsewhere are openly
sort of trying to develop this and develop thinking that way,
and that it's only seven years from now makes me, again, very
concerned.
I want to talk about risk for just a moment, and then my
time will have expired. This whole notion of a war and
decisions being made with the use of AI in a millisecond that
counter and then counter-counter the decision. I don't know to
what extent the military establishment--I assume they're
looking at this as much as you are, but it is interesting.
Now, I'd like to ask also, there are, as you know, fallible
algorithms. You know better than I. They are just misleading or
they are incorrect. What happens in consumerism, in business,
in law enforcement, in military context that frighten you the
most as risk as a result of an infallible algorithm? Any of
you?
Mr. Schmidt. Quickly, the biggest issue, as I mentioned, is
the compression of time. Let's assume you have time. Then the
question is, who gets to decide between the system and the
human? And I'm very concerned about a misalignment of interests
where the human has one set of incentives, the computer has
been trained against a different set of outcomes, and the whole
society wants an even different goal.
And I spent lots of time with the military, who I'm really,
really proud of and fond of, and they all want systems that
help them automate their decisions. In practice, their use of
technology will be largely to replace boring and uninteresting
jobs, like watching TVs and things like that. These are things
like Project Maven and its successors and so forth. So, I think
at the moment, the government at the military level is going to
use these more for sensing and analysis and not decision-
making. Just to make it very clear, I think we would all agree
it is not ready to make a final life critical decision. It may
never be, but it is certainly not now.
Mr. Mfume. Yes. Thank you. My time is expired.
Thank you, Madam Chair, appreciate it.
Ms. Mace. Thank you. Great questions.
I would now like to recognize Congressman Burlison.
Mr. Burlison. Thank you, Madam Chair.
I, for one, am not afraid of the advent of AI. In fact, I
want to welcome our future overlords. But I will say, I do see
a lot of promise. You know, working in healthcare technology,
we see an amazing opportunity to be able to comb the data
records of patients, be able to use--be able to take that data
and be able to accurately diagnose better than probably any
medical professional possibly ever could to a greater degree of
accuracy what you might be facing. To me, there is tremendous
opportunity, but I also do recognize some of the threats,
obviously.
To that end, my question for you, Dr. Schmidt, first is
that, given the state--the size and scope of the equipment
that's necessary today, we're limited to what actors do have
the ability to use AI, right? So, at least we know who has
access to it, who's utilizing it. It's not like we have people
in a Nigerian criminal syndicate using AI at this point. Is
that correct?
Mr. Schmidt. I can assure you it is coming because of
diffusion. Basically, the models are trained very expensively,
but when they're used for inference, which is where they answer
questions, it's quite simple. So, I would expect us to see
terrorists, other bad actors begin to use this technology in
ways that we should anticipate.
Mr. Burlison. And they--but at this point, they would have
to access it on another platform. Someone would have to spend
the resources to develop the tech--to house the data, et
cetera?
Mr. Schmidt. And Aleksander can--Professor Mafdry can help
me here because we work together. The simplest way to
understand it is the training part is really expensive, but you
can take the trained information and put it on a laptop, and
then it can be used. So unfortunately, in this scenario, all
you need is a computer or a phone to do your evil acts.
Mr. Burlison. OK. Dr. Crowder, my question to you relates
to the quantum computers. These too, these are machines that
you just couldn't walk around, handheld devices, right? Can you
walk us through what it takes to--what the environment
requirements are, what it takes to have a quantum computer?
Mr. Crowder. Yes. I mean, the--we deploy them right now in
regular data centers, but they are not laptops. They are not
mobile phones. They are large, complex systems, and they are
very, very hard to calibrate and manage, and that is a major
trade secret of how to keep them up and running. That's
probably going to be true for quite a while.
So, you know, right now, we don't actually sell systems. We
sell cloud access, because there's a small number of people who
know how to actually keep them running, operating, et cetera,
et cetera. So, you know, obviously that has some benefits from
an IP protection security point of view as well.
Mr. Burlison. And so, this is being used at what scale? How
many businesses or----
Mr. Crowder. So, we have over 200 partners, and we
carefully select what regions of the world we do business in
and carefully select who we partner with. But we have got over
200 industry and academic and research partners who are
leveraging--we've got about 26 computers right now that are
accessible via the cloud.
Mr. Burlison. To that end, I know that from other testimony
from other hearings, we have been hearing that the Chinese
Government has a pattern of sending students to American
universities, who then are able to glean data in working in
cooperation on some of these projects.
Are you aware of that activity?
Mr. Crowder. Not personally aware of that activity. We
obviously are very--for business reasons for our, whatever you
want to call them, crown jewels or most protected IP, we are
very careful not to--we are very careful who is part of that
work.
Mr. Burlison. And then again, my last question to you is,
in the subject of quantum entanglement, has that had any real
world applications or potential real world applications?
Mr. Crowder. Right now, nobody from our perspective have
proved that there is practical use, which means it is better
than simulating it or just using classical computers. But we
think that is going to change in the next very soon amount of
time. The computers are getting rapidly advancing, and we think
by the middle of this decade, there will be practical use.
We are working with a lot of, you know, U.S. companies on
applications today. I had mentioned a couple of them before,
like Boeing on looking at better airplane wing optimization
materials, fraud detection for banks, looking at medical health
records and trying to predict more efficient treatment for
patients with healthcare, like sciences companies. We have a
big partnership with Cleveland Clinic looking more broadly
across five sciences. But it is not practical today to be
better than what we have classical. But we think that's going
to come in the next couple of years.
Mr. Burlison. Thank you.
Ms. Mace. Thank you.
I would now like to recognize Congressman Gomez for five
minutes.
Mr. Gomez. Thank you, Madam Chair.
Before we begin, I want to--I was thinking about this: How
do we rank AI in the history of development in humankind? It is
something that I believe is--could be extremely startling. It's
one issue that I have random people bring up to me on the
streets. Some people compare it to the invention of the
television or computer, or the internet, and I think it's
beyond that, because this is something that--it makes it hard
to discern something that you are looking at, a photo, a video,
or even words on a piece of paper if it was actually written or
developed by a human.
And that is something that I think most people are trying
to wrap their minds around. How is this revolutionary
technology going to fundamentally change the way we live our
lives, the way we interact with one another, the way we
interpret information that is coming in? Because when you can't
discern what is actually created by a person and what is
developed by a computer program, then people start questioning
all sorts of things.
And that's one of the challenges. Maybe that is a
philosophical challenge. Maybe it is a real life government
regulatory challenge, but it is something that really, I think,
is at the heart of it, when people start to question what is
real and what is not.
But I recognize it has--AI has a lot of great potential.
Everything from predicting new variants of COVID to detecting
certain types of cancers that doctors miss. The potential is
staggering. But people want guardrails on this new technology.
If not, it can and will be misused.
When I first got elected, one of the--somebody ran a test
of Members of Congress, and there was about--I think it was
about 28 of us that got matched with people that had committed
crimes. And this was under the best circumstances. They were
using our photos from our websites that were taken with the
best lighting and the best quality. So, AI has a potential also
to have inherent bias built into it, and it often
disproportionately impacts people of color, women, and that is
a concern.
So, how do we address those limitations on AI? How do we
safeguard against the violation of people's civil liberties?
And this is something that even my--when Mark Meadows was on
this committee, him and I and others agreed that this was a
problem. We just couldn't figure out a solution.
So, Ms. Hickok, how can Congress best help address AI's
racial bias? What can we do as a body and the Federal
Government do to protect individual's civil liberties and, at
times, their right to privacy as well?
Ms. Hickok. Thank you for the question. And I'm really
thankful that you mentioned the civil liberties, because with
AI systems, as I mentioned earlier, you're talking about every
single industry and domain that is going to be--is going to be
even further impacted by this.
You talk about civil liberties and access to resources and,
unfortunately, that spans from anything from housing to
employment to education to insurance loan to policing and
criminal justice decisions and judiciary decisions. My concern
is, as AI also, if you don't have the guardrails now and these
systems are imbedded in the public sector services as well as
private services, that they are also going to eventually
connected.
So, one erroneous decision from one system is going to be
the input to another system, and we are going to completely
lock people out of resources and opportunities, and that is
going to widen the gap between haves and have nots. And it is
also going to widen the gap within the society that we are all
trying to narrow.
How can we narrow that? How can we keep the systems
accountable? It is really about the people and organizations
and how we use them that we should be focusing on. Putting the
civil liberty is putting the freedoms and rights at the center
of it, and making sure that these systems--the systems that we
use, especially that impact the resources and the rights, are
built accountably, transparently, replicable. We heard from my
co-panelists and witnesses that a lot of the times the systems
are opaque. We don't know how they work, and we also cannot
replicate the decisions.
So, you might be denied a credit. You might be denied
insurance or a job. It might not be--if you are trying to keep
the organization accountable, we will not be able to trace back
and keep them liable as well. So, we need to make sure from the
start, from the very start, from the design data and design
stages, that we put those guardrails in place, and we keep
organizations and the users accountable.
Mr. Gomez. Thank you. And my time has expired, but the
question is: Is it too late to put those guardrails on?
Ms. Hickok. It is not at all. In fact, at CAIDP, our
students, especially our law students, asked that question last
week, is it too late, is it inevitable, has the ship sailed?
No, it is not. The humans, organizations, lawmakers, the
humans, the users behind it hold the power.
Mr. Gomez. Thank you so much. I yield back.
Ms. Mace. Thank you, Congressman.
I would now like to recognize Congresswoman Greene.
Ms. Greene. Thank you. I think this is a very important
hearing to have as AI is progressing and working in a lot of
different sectors. And I really appreciate each of you being
here.
I'm definitely not an expert in AI, but I would like to
talk about the fears and concerns that people in my district
and people all over the country have when it comes to AI.
We certainly don't like the idea of AI replacing humans and
replacing people, especially when it comes to jobs. And so,
when there's headlines like Alphabet announcing 12,000 job cuts
globally while chief executive officer singled out AI as the
key investment area, that is what people start to think about.
Or when Microsoft announces its $10 billion investment in
OpenAI just days after saying it would lay off 10,000
employees, those are the kinds of things people think about.
Now, there is a difference between robotics and AI.
Obviously, robotics are a good thing. For example, tightening
bolts or moving heavy objects like in manufacturing, we really
appreciate that. But when it comes to AI being able to be
smarter than humans or replace humans on the job, I think that
is a major concern, especially for a country that's over $30
trillion in debt and an economy that is struggling.
This is something also concerning for education, learning
that ChatGPT scored higher than many people on the MBA exam
that was administered at Penn's elite Wharton School. That's
definitely concerning, especially when thinking about how that
could affect education. Just recently, ChatGPT was--is
currently banned in New York City schools over cheating
concerns, but then you think about what would this look like if
AI became teachers, especially after the devastation caused to
children's education levels, but also more importantly, kids
being taught at home on computers. That was more devastating to
them.
The idea that AI could replace software engineers,
journalists, graphic designers, that is also extremely
concerning. So, I think these are important conversations to
have.
But something that happened, I just learned about in
researching for this hearing, was that there are scams that
happen to people where AI is so intelligent, it is able to
imitate people's voices and images. And there is been people
taken advantage of in horrible ways where they have gotten
phone calls from who they thought was their loved ones but was
not. And their loved one, which was really an artificial
intelligence voice, mimicking their loved one was calling for
help in serious distress, and then they got scammed out of a
lot of money. That's terrifying and concerning that that can
happen.
But another thing that happened recently was when San
Francisco officials voted in December against a controversial
measure that would have allowed police to deploy robots to use
lethal force in extreme situations, but this happened after the
San Francisco Board of Supervisors came a week--it was a week
after the board voted to approve the policy to allow what
people called killer robots. But this is what people think
about when they think of AI. They think of a robot that has the
artificial intelligence to replace the police officer.
But then the application to the military is where I thought
was pretty concerning.
Dr. Schmidt, I wanted to ask you, because I took to Google
on this issue. And I wanted--I saw a headline that said ``AI's
impact on warfare will be as big as nuclear weapons.'' And I
also saw another headline that said ``Eric Schmidt Is Building
the Perfect AI War-Fighting Machine.'' So, I thought you would
be the perfect person to ask about this. Could you explain a
little bit?
Mr. Schmidt. Let me be clear, that's for the benefit of the
United States.
Ms. Greene. Only if it is in the United States' hands,
though, Dr. Schmidt.
Mr. Schmidt. And it will be.
The trends in the military are fundamentally autonomy and
drones and intelligence sensing gathering. The military spends
most of its time looking at things and trying to analyze it.
So, in the near term, the benefits to the military are
profound. It allows the service people who we have trained
exquisitely who are watching dumb screens to use their higher
factory skills and have the computer say, hey, look, this tank
moved or, hey, this thing happened over here, can you analyze
it.
I think a better framing for your constituents' fear is to
say that AI will make the people much more successful in what
they do, and that will drive higher incomes, higher jobs. And I
think that that's the best, at least in the next 20 years,
narrative about AI. It is true in the military. It is true in
civilians as well.
Ms. Greene. One more question. My time is expired, but how
do we--with China and their ability to constantly spy on us and
steal our technology and information, how could we prevent
China from stealing this type of artificial intelligence with
our military? And thank you.
Mr. Schmidt. Of course. The bad news is that these research
ideas are in the public domain and international, so we can't
prevent China from getting it. The Trump and Biden
administrations have done a good job of restricting access to
hardware, which is helpful. So good job, all of you.
With respect to software, the biggest answer is more
software people, trained in the West, trained under our values,
building systems that you as our Representative have some level
of regulatory control over. When they do it in China, you can't
pass a law to change that, but you can in the United States.
Ms. Mace. Thank you.
All right. I would like to now toss it over to Congressman
Khanna for five minutes.
Mr. Khanna. Thank you, Madam Chair, and thank you for your
leadership, for your bipartisan cooperation and collaboration
on the quantum bill, and the approach you have taken to work
across the aisle.
Dr. Schmidt, I respect your leadership in Silicon Valley.
There's a paradox in my mind that I would love your insight. On
the one hand, DARPA in the Department of Defense gave us the
internet, as you know, with Vinton Cerf, gave us GPS, gave us
the drone, gave us the mouse, probably the most innovation in
the history of the 20th century, defense technology. And yet,
now it seems there is this problem of the adoption of
innovative technology.
Why is the model that gave us all of this revolutionary
technology not working?
Mr. Schmidt. Thank you, Congressman. And you have really
helped in a lot of these areas.
If you go back to Vannevar Bush, the National Science
Foundation, and DARPA, those are the engines that got us all
here. We are all here fundamentally because of early decisions
made by the Federal Government to invest in researchers who
then we built on top of. So, I'm incredibly grateful to them.
In the case of the government, and particularly the
military, those innovations go into a bureaucracy that is not
organized in a way to take them. And a simple example is
software. The military is organized around procurements of a
15-year cycle and complicated bidding among a small number of
contractors. That is not how software works. And a number of us
have worked hard to get software treated more as a continuous
process. But the military, for example, would benefit by a
large expansion of a number of more software people, just
fixing stuff, making things work, making them smarter. That is
a simple thing that you could do.
Mr. Khanna. To that end, what do you think about an actual
service academy around technology, cyber, AI?
Mr. Schmidt. We looked hard at creating a military service
academy when I was doing the AI commission. And the military
has really, really good people in their academies. And what
they do is, because of the way military promotions work, you
take some brilliant person, you make them go stand guard duty
for a while, which is stupid. Sorry to be blunt. It is much
better to change the HR policies, which the military is trying
to do now. In particular, Secretary Brown in the Air Force is
trying to create a technical path to keep these people. That is
how you solve that problem. And let me give the rest of my time
to Aleksander.
Mr. Mafdry. Yes. So, I just wanted to add because it is a
very important question that you ask. So, I actually happen to
co-lead and codevelop at MIT an executive education class, AI
for national security leaders, which essentially hosts a number
of general offices from Pentagon and other places to come and
learn about AI. And this is a three-day program. Half of this
program is not about AI; it's about organizational management
aspects.
So, this is what you recognize. There's a lot of
frustration in DOD in your top military leaders that the
technology is developed. DARPA did their part, although they
should do more particularly in generative languages. But then
we hit the bureaucracy. And there is just a lot of
organizational problems that are kind of silly that the DOD is
completely crippled in terms of adoption of AI. So, that is
where we need the attention.
Mr. Khanna. Well, I look forward to working with you
[inaudible] with Representative Mace and Representative
Gallagher.
One other question--I mean, I'm back home in Silicon
Valley. It seems the new thing there is everyone is doing AI.
I'd be curious, Dr. Schmidt and Dr. Mafdry, how do you see--
will Silicon Valley lead the world in AI? How are we doing
compared to China?
And then one comment from my own version of American
exceptionalism, it drives me crazy when Europeans are lecturing
us about AI and technology. You know, I don't see Google,
Apple, Tesla. I get they say they are going to innovate in
policy, they are going to also innovate in technology. How are
we compared to Europe as well?
Mr. Schmidt. My cynical answer about Europe is that Europe
is going to lead in regulation and, therefore, not lead in
anything else. Their efforts do not appear to be successful, as
you have pointed out.
The reason we are so excited about AI is that anything that
makes humans smarter and makes algorithms smarter and makes
discoveries quicker is a horizontal technology that is
transformative. The opportunities to make basic advancements
outside of language models, right, are profound in terms of
science, materials, plastics, every kind of logistics, every
kind of analytical problem, as has been summarized by the
panel.
So, I think that AI is here to stay. It is the next big
wave. I don't know when it will end, but we are still very
early. Remember, we still don't understand exactly how these
algorithms work. We also don't understand how big the models
have to be. At some point, we'll know. But we are not anywhere
close to being able to answer those questions.
Mr. Mafdry. If I can just add very quickly because you
asked the question about Silicon Valley. Silicon Valley is
doing great. They will do great job. They are clearly
harnessing this progress, but we as a country should not
abdicate the progress on the strategically important technology
just for Silicon Valley. Again, they will do great, but we
should be doing more, and the U.S. Government should be doing
more.
Mr. Schmidt. Speaking as a professor at MIT.
Mr. Mafdry. Yes. But I like Silicon Valley.
Ms. Mace. In closing this afternoon, first of all, I just
want to thank you all, all of our panelists for your time and
your talent and everything that you have shared with us today.
This will be, Congressman Mfume, the first of a series of
hearings that I hope that we'll have on AI. I don't think that
we are ready for what is going to happen in a very short period
of time. And I think, if it is not happening already, it will
be in the next five years where AI will be programming AI, and
then what's next?
And so, this was a great first discussion to start this
conversation about what needs--what we need to be talking about
in regards to this.
So, in closing, I want to thank all of our panelists once
again for your insightful testimony today. You have given us a
tremendous amount to think about, and AI was created by humans,
but it doesn't mean that it is going to be easy for all of us,
especially up here on the Hill, to grasp what is before us and
what is imminently coming. We appreciate the panel's expertise
and ability to shed light on the state of the science and the
broader societal implications that policymakers must consider.
And I would like to yield to the Ranking Member,
Congressman Connolly, for your closing remarks.
Mr. Connolly. Thank you so much, Madam Chairwoman.
And I found this an intriguing conversation, but wanting
more. And like Mr. Mfume, I think we have opened the door to a
lot further in-depth exploration, hopefully by this
Subcommittee and by the Congress, because there are lots of
issues we have to face.
And while you may be right, Dr. Schmidt, about dismissing
the Europeans as regulators but not innovators, on the other
hand, given what we heard from Ms. Hickok and Dr. Mafdry about
the need for some Federal intervention here, there have to be
guidelines and guideposts so that we are off on the right foot
and not facing profound issues later on where the technology is
advanced and we never either anticipated it or addressed it.
Maybe there are things we can learn from the Europeans in the
regulatory guidelines, either things not to do or things to do.
But any rate, I just think there is a lot more for us to
explore, and I really appreciate this being the first of a
series of hearings.
Thank you, Madam Chairwoman. I yield back.
Ms. Mace. Thank you. And I look forward to working with
everyone on both sides of the aisle on this issue. It is very
important.
With that and without objection, all Members will have five
legislative days within which to submit materials and to submit
additional written questions for the witnesses which will be
forwarded to the witnesses for their response.
If there is no further business, without objection, my
first Subcommittee stands adjourned.
[Whereupon, at 3:57 p.m., the Subcommittee was adjourned.]
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