April 25, 2024 • 29 mins
Newt discusses the transformative potential of Artificial Intelligence (AI) with Neil Chilson, the leader of AI policy at the Abundance Institute. Chilson explains that while AI has the potential to revolutionize various sectors, including healthcare and creative fields, there is a pervasive fear and pessimism surrounding the technology. He argues that this fear-based approach could hinder the full potential of AI. Chilson also discusses the Abundance Institute's focus on Artificial Intelligence and energy, emphasizing the need for regulatory changes to foster innovation in these areas. He invites those interested in a positive technological future to get involved with the Abundance Institute.
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Speaker 1 (00:04):
On this episode of news World, we're living in a
time of transformation that may rival any other time in
human history. We have a big problem. The narrative that
surrounds the biggest and most groundbreaking technologies of today is
one of pessimism and fear. While technology holds the power
(00:25):
to transform our economy and our lives, we often throttle
technological breakthroughs before they can fulfill their life changing potential.
Fueled by a mix of cultural anxieties and policy changes.
This fear based approach risk denying humans and abundant future.
My guest today says quote, our leaders are unprepared to
(00:48):
address the rapid pace of technological change in a positive way.
The Abundance Institute is a new, mission driven nonprofit organization
that is folks on creating space for emerging technologies to grow, thrive,
and have a chance to reach their full potential. Here
(01:09):
to discuss his new organization, I'm really pleased to welcome
my guest, Neil Chilson. He is the new leader of
Artificial Intelligence policy at the Abundance Institute. Neil, welcome and
(01:33):
thank you for joining me on Newtsworld.
Speaker 2 (01:35):
Thanks so much for having me. It's great to be here.
Speaker 1 (01:38):
Can you talk about your background and artificial intelligence?
Speaker 2 (01:43):
Sure? So I am a Master's agree in computer science
undergrad and master's agree in computer science. And when I
was in grad school, I focused on a couple different things,
but did some work on it what are known as
agent based systems. Now, when I was in grad school
in the early two thousands, none of the huge breakthroughs
that have happened in machine learning were Some of them
(02:05):
were maybe on the threshold, but nothing had broken the
way that it has, and certainly hadn't broken into the
public consciousness like it did with the release of chat
GPT two years ago. And so my focus was on
another type of artificial intelligence. And maybe we'll get into
this a little bit, but there have been many, many
waves and many different types of what computer scientists have
(02:26):
called it artificial intelligence over time, and so I've since
translated that expertise into the policy space. After grad school,
went to law school, spent some time doing telecommunications law,
spent a lot of time at the Federal Trade Commission,
where I was the chief technologist at one point, and
(02:47):
my job there was to engage with new technologies and
figure out how do they affect consumers? How can government
set policies so that consumers are getting the best benefits
out of it? And how can we drive innovation through
good policy making? And so that's what I bring to
this conversation.
Speaker 1 (03:03):
You wrote a paper Getting out of Control Emergent Leadership
in a Complex World, which looks at different leadership paradigms.
Is that published? Is it available?
Speaker 2 (03:14):
Yeah, it's actually a book that's available on Amazon, and
I also have a substack out of Control dot substack
dot com where you can learn more about the book.
Speaker 1 (03:22):
What led you to write it?
Speaker 2 (03:24):
Well, you know there's two paradigms, Well there's at least
two paradigms, but there's one that's very dominant in DC
in particular, and that's the idea that in a world
of increasing complexity and what might even look like chaos,
what we really need is more control. We need people
with more authority who take strong positions to jump into
the fray and get things under control. And I think
(03:46):
a lot of people often when they look at how
complex the world is, they're sort of wishing for that
that somebody will just take control and tell the world
how to run. But the problem is that complexity brings
so many benefits when we think about the ability of
our ecosystem and our economy to adapt and to create
new products and benefits for individuals, and those benefits rely
(04:12):
on a complex network where nobody is really in control,
but where many people have influence. And so I wrote
that book to say that both in your personal life
and in the policy space, we should be really focused
on not trying to seize control, which can have some
really negative side effects and get rid of a lot
of the benefits, but in trying to increase our influence
(04:35):
and also to understand the complex systems that we're in
better so that we can resist this impulse to grasp
for control.
Speaker 1 (04:44):
It's interesting. It's a little bit like Adam Smith's description
of the hidden hand, which enables us, through the market mechanism,
to move resources and increase innovation in a way that
nobody can control, and if you try to control it,
you actually kill it.
Speaker 2 (05:00):
The invisible hand is an emergent phenomenon. It's something that
happens when all of us trying to apply the information
that we know in front of us, are trying to
solve the problems that we face, and then over time
you get something that looks quite orderly from the outside,
but which no one person designed, and that's very powerful
and it says a lot about our capabilities as humans
(05:23):
to solve really complex problems as a group, even when
there's nobody setting out a design ahead of time.
Speaker 1 (05:30):
The number of different things that have to come together
for major breakthroughs are beyond the ability for bureaucratic planning.
They almost occur serendipitously.
Speaker 2 (05:42):
Yeah, and you can actually see often they occur in parallel,
so you might have multiple people who are doing similar
work on similar tracks, and there's a sort of threshold
where all of the enabling technologies enable a lot of
new people who are trying to solve lots of different
problems to solve a very similar problem all at once.
The light bulb this happened, you know, Calculus was kind
(06:05):
of parallel invented between two very very smart people, and
so I think this happens a lot, and I think
it's happening in the artificial intelligence space right now, where
you have a lot of people are seeing like these
new capabilities are possible and trying to solve new problems,
and there's just so much going on right now.
Speaker 1 (06:22):
You saw that with the Wright brothers, where there were
really many people in Europe and America trying to figure
out how to fly, and they happened to be first,
but there were a lot of other people working the problem,
and in fact they often would write letters back and forth.
There was certainly not an isolation.
Speaker 2 (06:41):
One of the great things about that example is that
the Wright brothers were not scientists, right. They were bicycle
mechanics who were trying to solve a problem that lots
of scientists had tried to solve, and many scientists at
the time, if you read the contemporary history, were quite
skeptical that it was possible from the equations that they
had worked out, and the Right brothers, through trial and
(07:02):
error and practical experience, were able to show that no,
it actually was possible. And once they made that break through,
all of a sudden there was a flood of even
bigger flood of people tried to solve that problem once
they could see that it was possible to do.
Speaker 1 (07:16):
The Smithsonian got fifty thousand dollars to try to build
a heavier than air vehicle and failed, and did it
with a very elegant design using a very powerful German
motor which required a very large structure to be able
to hold the motor, and it was just way too big,
and they didn't know what they were doing. And it
actually cleverly decided to launch it off of a ship
(07:39):
in the Potomac, so when it crashed, it went straight
into the water and they couldn't figure well went wrong. Meanwhile,
the Right Brothers, for about a dollar per flight, are
operating out of Kitty Hawk, doing ten, twelve, fifteen flights
a day, and they weren't breaking their plane, so they
could say, gee, that didn't quite work, let's do this
and that. But when the Right Brothers broke through shortly
(08:00):
after the Smithsonian failed, the Smithsonian was so angry that
for years they wouldn't deal with the Wright brothers. That's
one of those great examples where human nature transcended the
scientific impulse. I am interested by the way. You have
a bachelor's in computer science from Harting and a master's
in computer science from the University of Illinois, and you
(08:23):
have a law degree from George warshingtam Law School, So
you really have a combination of the law and public
policy with the science and technology. Do you think that
gives you a different outlook than most of the people
in the field.
Speaker 2 (08:38):
I think it does in two ways. It's very interesting
because when I was in grad school, there wasn't a
path really for computer scientists to work on public policy
in the way that there is now that's much more common,
and so I went to law school. What law school
taught me is that the engineering paradigm that I think
we typically think about the engineers try to solve problems with,
(09:02):
is not the same as the paradigm for lawmaking. Unfortunately,
we do see a lot of people who have tech
expertise come to DC with a sort of engineering mindset
that says, well, law is like code, and once we
write it, of course it will work the way that
it's intended, just like when I write code in a computer,
(09:22):
it runs that way. But humans and the law, as
you well know, are a complex system with lots of
feedback loops. The things that you write don't run the
way that you think that they should, and so I
often have to temper some of my enthusiastic engineering background
to friends about what is possible in law and when
(09:47):
it's an appropriate approach to solve problems and when it isn't.
And so I think having window into both of those.
Lets me speak across that chasm that I think exists
between engineering and law.
Speaker 1 (10:13):
I mean, you're studying of artificial intelligence. You indicate that
it's evolved already in less than a decade to about
one hundred billion dollar industry, but you think in the
next decade it could easily grow to something like a trillion,
three hundred billion dollars as an industry. The analysts at
Pricewater Scoopers estimate that AI will add fifteen point seven
(10:35):
trillion dollars to the global economy by twenty thirty. First
of all, why is it accelerating that rapidly and what
is the nature of its impact?
Speaker 2 (10:47):
So this is a difficult question to answer, and I
think everybody's trying to predict the future on AI comes
up against a single difficult problem, which is that defining
what exactly is artificial intelligence is quite difficult. This current
wave of deep learning, large language models, I think is
what most people sort of settle on as what they're
(11:08):
trying to project off of right now. And the reason
it's moving so fast right now is that there is
this real confluence of capability in these new chips, these
new techniques in the transformer model that was developed at Google,
but quickly spread beyond that company to lots of researchers,
and this demonstrated almost in the Right Brothers manner. This
(11:31):
demonstrated potential in something like chat GPT, where people are like,
I can't believe this works as well as it does.
I don't think that pre chat GPT being released, that
people were really aware, even people in the computer science field,
that you could have something that was as useful as
it turns out these large language models are turning out
(11:53):
to be. And so I think once that was demonstrated,
there's been such a flood of energy into this space.
The impacts I think are still pretty unknown. But when
you think of what these large language models and other
deep learning models can do, what they can do is
they can take a big bunch of unstructured data and
(12:13):
they can pull essential patterns out of it in a
way that reveals new things that wasn't easy to identify
in that data previously. And so I think it has
the biggest potential in spaces where we have a lot
of data but we don't know what to do with
that data. And so healthcare to me is one of
(12:35):
the biggest potential impacts. Here we can collect a lot
of data about an individual's basic bodily functions, their heart rate,
they're breathing, their brain waves. We can see that data,
but we don't quite understand what it means. And so
I think using these types of techniques of deep learning
(12:55):
to pull out meaning from those large data is going
to help us do things like personalized medicine where we
no longer treat people as just essentially an average human,
but we can look at what are the specific conditions
and functions of their body, and we can design treatments,
including potentially medicines that focus specifically on their particular body.
(13:20):
And so I think there's a lot of potential there.
There's obviously lots of potential in the creative fields because
you can use these techniques to generate well written pros,
to translate between lots of different languages, to create even
pictures and videos now in a way that's quite impressive,
and so I think it brings a lot of powerful
(13:42):
content creation tools down to the average person in a
way that is going to mean that it's much easier
to create high quality content even if you're an individual
or a small team. And so I think we'll see
an explosion of creativity using these tools in the very
near freet as well.
Speaker 1 (14:01):
The process could be very, very positive, almost certainly will be.
But at the same time, there's a cultural aura that
views all of it with fear. Why do you think
we have drifted into this fear based response to technological opportunity?
Speaker 2 (14:22):
Humankind always has had a sort of technopanic curve where
you have the early adopters who are really excited, then
you have a sort of peak moment where people are
talking about the potential downsides, and then it gets accepted
into the community and people forget that they ever debated,
like oh, that novels were good or that bicycles were good.
(14:43):
And so I think it's part that. But I think
AI in particular raises because it's such a vague technology.
So much of what we call AI artificial intelligence. There's
actually dozens and dozens of artificial intelligence algorithms on everybody's phone, right.
It does all the things like helping you search for
your photo collection for a particular individual. Those are artificial
(15:08):
intelligence algorithms. But people when they hear the term artificial intelligence,
they think of sci fi movies, right, They think of
the Terminator, or they think of two thousand and one
and How and in those movies, almost universally, AI is
projected as an evil entity or an entity that's gone
wrong somehow that is threatening human safety. And so I
(15:31):
think there's that sort of cultural piece. But more generally,
I think the US has shifted from a frontier mindset,
one where we're trying to push the edges we're trying
to explore. We want grand adventures, we want to be
the next Right Brothers. We hold up those people as
icons for what it means to contribute to society, and
(15:55):
we're afraid of that now. I worry that we're now
in a time where maybe our kids' greatest adventures will
be figuring out what particular trauma they are trying to
deal with in their life, when their great adventures should
be like trying to come up with the next big invention,
or exploring a new space of science, or maybe outer space.
(16:16):
And so I think that cultural shift not one hundred
percent sure why it's happened. I think in part it
might be we've gotten comfortable as a country, maybe, and
so we're focusing on problems that are smaller when we
should be looking to opportunities and problems that are bigger.
But it really is a sort of new phenomenon. When
I think of the late nineties, we were a country
(16:38):
that was excited to be on the cutting edge of technology,
and now I think we're often, at least at the
elite levels, people talk about technology as if it's primarily
a threat rather than an enormous opportunity For the United States.
Speaker 1 (16:55):
Somebody said that the Europeans had decided they regulation over innovation,
and the result was in almost every new innovative area,
the US was just rapidly pulling away from Europe. And
isn't there a real danger that some of our politicians
would like to introduce a European style bureaucratic over control.
Speaker 2 (17:18):
Absolutely, and in fact almost expressly. When you look at
what California has done around some of its approaches to
software development and privacy, they're borrowed directly from the European Union.
And the European Union has a different mindset around technology,
whereas in the US we generally think people have the
(17:40):
right to build new things and then we'll see what
the effects are and they might have to temper their solutions.
Both the market might discipline them but then also there
might be real consumer harms aos are possible, Whereas in
Europe they have a mindset that basically, until the government
sort of authorizes an innovation in a particular space, that
nobody is really allowed to do it. And so that
(18:03):
mindset is very chilling because inevitably it's not the regulators
who are the best at trying to figure out what
future technologies might come about. It's the people who are
practically trying to make those things happen. And if they
live in a culture that says until you get the
okay you can't try something new, you know, it's just
(18:25):
a very difficult place to innovate in. So the US
does and maintains quite a good edge in that space,
but we are at risk of, at least at the
policy level, giving that up by trying to adopt these
precautionary approaches to innovation.
Speaker 1 (18:41):
Doesn't there seem to be a pretty big partisan split
over regulation versus innovation, with people like send them a
Joy Leader Chuck Schumer really pushing for all out federal regulation.
Speaker 2 (18:55):
I don't feel like artificial intelligence has been particularly politicized yet,
but we have seen many other of cutting edge technologies,
especially in the software space really do have a political
valance to them. The default, I think would be in
the partisan space would be that Democrats tend to be
much more precautionary in approaches to technology and Republicans tend
(19:19):
to be more permissionless let people build things. That's not
one hundred percent across the board, and there are interesting opportunities,
I think in the AI space to think about that.
On AI in particular, the Biden administration has very much
taken a whole of government approach, one that says, hey,
we as a government had to figure this thing out,
(19:40):
and we need to get all our ducks.
Speaker 1 (19:41):
In a row.
Speaker 2 (19:42):
And not all of that is bad. Government uses of
AI should certainly should be thoughtful. But when we're trying
to set up an environment in which we are making
sure that innovators have both the incentive and the freedom
to develop new things, a lot of government action can
be pretty chilling to that. And so I do think
the Biden administration has not really set a very positive vision.
(20:06):
In contrast, in the late nineties, there was a very
clear vision set for the Internet that the commercial development
of the Internet, that it was going to be market
driven rather than government driven. And we have a very
different mindset about artificial intelligence right now coming out of
this administration.
Speaker 1 (20:40):
You've sort of triggered a couple of thoughts on my part.
One is you could do a very interesting survey of
the artificial intelligence that's already around us. I mean your
whole point, for example, about your cell phone and how
many different versions of AI you're relating to, And I
think people would be shocked realized that artificial intelligence isn't
(21:02):
the future. Artificial intelligence is the present and is going
to expand into the future. They're an amazing number of
places where we've actually been using a variation of artificial
intelligence many many years ago. I went out to San
Diego to the Navy's labs and looked at how they
had designed a carrier battlegroup defense system, and it was
(21:25):
clearly what we would now call artificial intelligence. But it
was thirty five years ago. So in that sense, we're
already surrounded by a large amount of artificial intelligence.
Speaker 2 (21:36):
Absolutely. There's a great quote by AI pioneer John McCarthy,
which is that as soon as it works, nobody calls
it AI anymore. There was a time at which chess
playing was cutting edge artificial intelligence or speech recognition, or
recommendation algorithms like what you should watch next on Netflix.
(21:56):
There was a point at which these were cutting edge
AI research. But now because as they're working, we just
call that computers and we don't call it artificial intelligence anymore.
And so I do think that people don't realize that,
and I think in part it's because these new technologies
came out in a sort of chatbot form, right, and
so like you're talking and it sort of seems like
you're talking to something. I think that feels a little
(22:20):
different to people. But ultimately, I do think it is
helpful to point out that AI is around us. It's
pretty ubiquitous.
Speaker 1 (22:27):
In fact. The other side of that is the notion
of getting across how many different ways AI helps us.
I mean, I'm saying about this the other night because
I was trying to go somewhere, but I realized I
don't care anymore because I just plug in the address
and the GPS system, which is artificial intelligence, knows where
I am, knows where I am going, and has a
(22:50):
sense of which route will be best. Now, if you
think about it, that's an astonishing level of data in
real time at no cost. We thought about the number
of ways the AI is already helping us, and then
you could actually build out the potential over the next decade.
I think, particularly for example of helping Alzheimer's patients and
(23:12):
parkinson patients, and people have much better richer lives and
their families having much better richer lives as new artificial
intelligence systems are developed.
Speaker 2 (23:23):
Yeah. Absolutely, And you know I was thinking. I had
a bit of a health scare with my twenty month
old last week where she had a seizure, and one
of the things that doctors kept asking me was how
long did it last? And I was literally able to
look at my fit bit heart rate monitor and see
when my heart rate shot through the roof to see
(23:44):
when it started, and I could figure out from that. Now,
it would have been even better if I could just
ask the Alexa that was in my room like hey,
you hurt us, yelling like how long did that last?
Speaker 1 (23:54):
Right?
Speaker 2 (23:54):
But I couldn't do that, in part because I think
that people worry about enabling those types of devices to
monitor all the time, and sure that can have some downsides.
But all I could think was, Man, how great it
would have been if I could have pulled that data
that I know was available even if we weren't capturing it,
and I think AI opens the potential to do that
(24:14):
sort of really powerful empowerment of people to make the
most of the information that's around them, and so I'm
excited about it.
Speaker 1 (24:24):
I really want to ask you a little more about
the Abundance in City because it's absolutely what I believe
in and I'm fascinated that you're doing it. But I
noticed that you have focused on artificial intelligence and energy
as your two major emphasis. Why is that?
Speaker 2 (24:38):
Thank you so much for your interest in what we're
doing at the Abundance Institute. As you said, we just
launched recently launched this week, actually, although we've been in
soft launch for a little while. Right now we're very
focused on AI and energy because artificial intelligence is not
only a hot topic, but this issue is not going away.
The way people are talking about AI right now, the
(25:00):
cultural and the policy decisions that we make right now,
we'll have ramifications for the next fifty years, if not longer,
and in a space that's moving this fast, those are
really huge impacts. You already mentioned some of the numbers
about why AI could drive such tremendous growth across the
US economy and across the world. But if we don't
(25:20):
get this right, we're going to see the lead that
the US has to other nations such as China, and
I think it is really important that we get this
right right now, which is part of why we're focusing
on it. Energy. Similarly, there's enormous opportunity. There's new technologies
in this space that could really drive us into a
time of energy abundance. We've been operating in the US
(25:42):
since the seventies under a sort of scarcity mindset around energy,
the idea being that there's a limited amount of it
and that we should conserve it. Our goal should be
to conserve energy. Whereas we know that the wealthiest countries
are those that produce the most energy, and so here
in the US we need to get better at producing energy.
We need to do it in a way that's sustainable
(26:03):
and creates a good environment for our people. But there's
no reason that we are stagnating when it comes to
the amount of energy that we're creating. We have the technology,
we have the capabilities. The main things that are holding
us back are regulatory barriers, and we need to get
those out of the way so that we can do
things like bring nuclear back to the US in a
(26:24):
way that produces a huge abundance of energy that we're
going to need for many different things, including artificial intelligence.
And so that's why we're very focused in those two areas.
I will say that in the longer term, we are
also very interested in the biology space. The biotech space.
There is a huge amount of potential there, especially as
some of these artificial intelligence therapies come online, to really
(26:48):
just a total step change and the way that we
deal with health and healthcare in this country and around
the world. And so we're very excited about the potential
in that space as well.
Speaker 1 (26:57):
So if somebody wanted to get involved with the Abundance Institute,
what would they do? What could they do?
Speaker 2 (27:03):
You can reach out to us. Our website is abundance
dot Institute and we have an email at Hello at
abundance dot Institute. What we're trying to do is invest
in talent and talent assembly. We're also trying to build
a community of optimists, founders, and inventors to combat this
very pessimistic mindset about the future of technology that I
(27:23):
think resonates. That's very prevalent in DC, but I think
there's lots of people talking that way other places as well,
and so we welcome all people of similar mindset who
are excited about the future, who think that humans have
the great potential to create solutions to big problems, and
who want to get involved. We would love to hear
from you.
Speaker 1 (27:44):
We will certainly put the connections on our show page,
and we will encourage people to look at the Abundance
Institute and to get involved in helping think about a
positive future. Now, I want to thank you for joining me.
I'm looking forward to seeing what the Abundance Institute can accomplish.
I think it's exactly the right direction. I know you're
(28:05):
just getting started, so I hope in a few months
you'll come back and join us again and report on
what you're working on.
Speaker 2 (28:12):
I would love to and in a few months the
AI technology will probably be even different than it is
now and we'll have plenty to talk about. So very
much welcome the opportunity.
Speaker 1 (28:24):
Thank you to my guest Neil Chilson. You can learn
more about the Abundance Institute on our show page at
newtsworld dot com. Newsworld is produced by Gingrid three sixty
and iHeartMedia. Our executive producer is Guarnsey Sloan. Our researcher
is Rachel Peterson. The artwork for the show was created
by Steve Penley. Special thanks to the team at Gingrid
(28:45):
three sixty. If you've been enjoying Newtsworld, I hope you'll
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sign up from my three free weekly columns at Gingrich
three sixty dot com slash newsletter. I'm Newt Gingrich. This
(29:06):
is Nutsworld.
On this episode of news World, we're living in a
time of transformation that may rival any other time in
human history. We have a big problem. The narrative that
surrounds the biggest and most groundbreaking technologies of today is
one of pessimism and fear. While technology holds the power
(00:25):
to transform our economy and our lives, we often throttle
technological breakthroughs before they can fulfill their life changing potential.
Fueled by a mix of cultural anxieties and policy changes.
This fear based approach risk denying humans and abundant future.
My guest today says quote, our leaders are unprepared to
(00:48):
address the rapid pace of technological change in a positive way.
The Abundance Institute is a new, mission driven nonprofit organization
that is folks on creating space for emerging technologies to grow, thrive,
and have a chance to reach their full potential. Here
(01:09):
to discuss his new organization, I'm really pleased to welcome
my guest, Neil Chilson. He is the new leader of
Artificial Intelligence policy at the Abundance Institute. Neil, welcome and
(01:33):
thank you for joining me on Newtsworld.
Speaker 2 (01:35):
Thanks so much for having me. It's great to be here.
Speaker 1 (01:38):
Can you talk about your background and artificial intelligence?
Speaker 2 (01:43):
Sure? So I am a Master's agree in computer science
undergrad and master's agree in computer science. And when I
was in grad school, I focused on a couple different things,
but did some work on it what are known as
agent based systems. Now, when I was in grad school
in the early two thousands, none of the huge breakthroughs
that have happened in machine learning were Some of them
(02:05):
were maybe on the threshold, but nothing had broken the
way that it has, and certainly hadn't broken into the
public consciousness like it did with the release of chat
GPT two years ago. And so my focus was on
another type of artificial intelligence. And maybe we'll get into
this a little bit, but there have been many, many
waves and many different types of what computer scientists have
(02:26):
called it artificial intelligence over time, and so I've since
translated that expertise into the policy space. After grad school,
went to law school, spent some time doing telecommunications law,
spent a lot of time at the Federal Trade Commission,
where I was the chief technologist at one point, and
(02:47):
my job there was to engage with new technologies and
figure out how do they affect consumers? How can government
set policies so that consumers are getting the best benefits
out of it? And how can we drive innovation through
good policy making? And so that's what I bring to
this conversation.
Speaker 1 (03:03):
You wrote a paper Getting out of Control Emergent Leadership
in a Complex World, which looks at different leadership paradigms.
Is that published? Is it available?
Speaker 2 (03:14):
Yeah, it's actually a book that's available on Amazon, and
I also have a substack out of Control dot substack
dot com where you can learn more about the book.
Speaker 1 (03:22):
What led you to write it?
Speaker 2 (03:24):
Well, you know there's two paradigms, Well there's at least
two paradigms, but there's one that's very dominant in DC
in particular, and that's the idea that in a world
of increasing complexity and what might even look like chaos,
what we really need is more control. We need people
with more authority who take strong positions to jump into
the fray and get things under control. And I think
(03:46):
a lot of people often when they look at how
complex the world is, they're sort of wishing for that
that somebody will just take control and tell the world
how to run. But the problem is that complexity brings
so many benefits when we think about the ability of
our ecosystem and our economy to adapt and to create
new products and benefits for individuals, and those benefits rely
(04:12):
on a complex network where nobody is really in control,
but where many people have influence. And so I wrote
that book to say that both in your personal life
and in the policy space, we should be really focused
on not trying to seize control, which can have some
really negative side effects and get rid of a lot
of the benefits, but in trying to increase our influence
(04:35):
and also to understand the complex systems that we're in
better so that we can resist this impulse to grasp
for control.
Speaker 1 (04:44):
It's interesting. It's a little bit like Adam Smith's description
of the hidden hand, which enables us, through the market mechanism,
to move resources and increase innovation in a way that
nobody can control, and if you try to control it,
you actually kill it.
Speaker 2 (05:00):
The invisible hand is an emergent phenomenon. It's something that
happens when all of us trying to apply the information
that we know in front of us, are trying to
solve the problems that we face, and then over time
you get something that looks quite orderly from the outside,
but which no one person designed, and that's very powerful
and it says a lot about our capabilities as humans
(05:23):
to solve really complex problems as a group, even when
there's nobody setting out a design ahead of time.
Speaker 1 (05:30):
The number of different things that have to come together
for major breakthroughs are beyond the ability for bureaucratic planning.
They almost occur serendipitously.
Speaker 2 (05:42):
Yeah, and you can actually see often they occur in parallel,
so you might have multiple people who are doing similar
work on similar tracks, and there's a sort of threshold
where all of the enabling technologies enable a lot of
new people who are trying to solve lots of different
problems to solve a very similar problem all at once.
The light bulb this happened, you know, Calculus was kind
(06:05):
of parallel invented between two very very smart people, and
so I think this happens a lot, and I think
it's happening in the artificial intelligence space right now, where
you have a lot of people are seeing like these
new capabilities are possible and trying to solve new problems,
and there's just so much going on right now.
Speaker 1 (06:22):
You saw that with the Wright brothers, where there were
really many people in Europe and America trying to figure
out how to fly, and they happened to be first,
but there were a lot of other people working the problem,
and in fact they often would write letters back and forth.
There was certainly not an isolation.
Speaker 2 (06:41):
One of the great things about that example is that
the Wright brothers were not scientists, right. They were bicycle
mechanics who were trying to solve a problem that lots
of scientists had tried to solve, and many scientists at
the time, if you read the contemporary history, were quite
skeptical that it was possible from the equations that they
had worked out, and the Right brothers, through trial and
(07:02):
error and practical experience, were able to show that no,
it actually was possible. And once they made that break through,
all of a sudden there was a flood of even
bigger flood of people tried to solve that problem once
they could see that it was possible to do.
Speaker 1 (07:16):
The Smithsonian got fifty thousand dollars to try to build
a heavier than air vehicle and failed, and did it
with a very elegant design using a very powerful German
motor which required a very large structure to be able
to hold the motor, and it was just way too big,
and they didn't know what they were doing. And it
actually cleverly decided to launch it off of a ship
(07:39):
in the Potomac, so when it crashed, it went straight
into the water and they couldn't figure well went wrong. Meanwhile,
the Right Brothers, for about a dollar per flight, are
operating out of Kitty Hawk, doing ten, twelve, fifteen flights
a day, and they weren't breaking their plane, so they
could say, gee, that didn't quite work, let's do this
and that. But when the Right Brothers broke through shortly
(08:00):
after the Smithsonian failed, the Smithsonian was so angry that
for years they wouldn't deal with the Wright brothers. That's
one of those great examples where human nature transcended the
scientific impulse. I am interested by the way. You have
a bachelor's in computer science from Harting and a master's
in computer science from the University of Illinois, and you
(08:23):
have a law degree from George warshingtam Law School, So
you really have a combination of the law and public
policy with the science and technology. Do you think that
gives you a different outlook than most of the people
in the field.
Speaker 2 (08:38):
I think it does in two ways. It's very interesting
because when I was in grad school, there wasn't a
path really for computer scientists to work on public policy
in the way that there is now that's much more common,
and so I went to law school. What law school
taught me is that the engineering paradigm that I think
we typically think about the engineers try to solve problems with,
(09:02):
is not the same as the paradigm for lawmaking. Unfortunately,
we do see a lot of people who have tech
expertise come to DC with a sort of engineering mindset
that says, well, law is like code, and once we
write it, of course it will work the way that
it's intended, just like when I write code in a computer,
(09:22):
it runs that way. But humans and the law, as
you well know, are a complex system with lots of
feedback loops. The things that you write don't run the
way that you think that they should, and so I
often have to temper some of my enthusiastic engineering background
to friends about what is possible in law and when
(09:47):
it's an appropriate approach to solve problems and when it isn't.
And so I think having window into both of those.
Lets me speak across that chasm that I think exists
between engineering and law.
Speaker 1 (10:13):
I mean, you're studying of artificial intelligence. You indicate that
it's evolved already in less than a decade to about
one hundred billion dollar industry, but you think in the
next decade it could easily grow to something like a trillion,
three hundred billion dollars as an industry. The analysts at
Pricewater Scoopers estimate that AI will add fifteen point seven
(10:35):
trillion dollars to the global economy by twenty thirty. First
of all, why is it accelerating that rapidly and what
is the nature of its impact?
Speaker 2 (10:47):
So this is a difficult question to answer, and I
think everybody's trying to predict the future on AI comes
up against a single difficult problem, which is that defining
what exactly is artificial intelligence is quite difficult. This current
wave of deep learning, large language models, I think is
what most people sort of settle on as what they're
(11:08):
trying to project off of right now. And the reason
it's moving so fast right now is that there is
this real confluence of capability in these new chips, these
new techniques in the transformer model that was developed at Google,
but quickly spread beyond that company to lots of researchers,
and this demonstrated almost in the Right Brothers manner. This
(11:31):
demonstrated potential in something like chat GPT, where people are like,
I can't believe this works as well as it does.
I don't think that pre chat GPT being released, that
people were really aware, even people in the computer science field,
that you could have something that was as useful as
it turns out these large language models are turning out
(11:53):
to be. And so I think once that was demonstrated,
there's been such a flood of energy into this space.
The impacts I think are still pretty unknown. But when
you think of what these large language models and other
deep learning models can do, what they can do is
they can take a big bunch of unstructured data and
(12:13):
they can pull essential patterns out of it in a
way that reveals new things that wasn't easy to identify
in that data previously. And so I think it has
the biggest potential in spaces where we have a lot
of data but we don't know what to do with
that data. And so healthcare to me is one of
(12:35):
the biggest potential impacts. Here we can collect a lot
of data about an individual's basic bodily functions, their heart rate,
they're breathing, their brain waves. We can see that data,
but we don't quite understand what it means. And so
I think using these types of techniques of deep learning
(12:55):
to pull out meaning from those large data is going
to help us do things like personalized medicine where we
no longer treat people as just essentially an average human,
but we can look at what are the specific conditions
and functions of their body, and we can design treatments,
including potentially medicines that focus specifically on their particular body.
(13:20):
And so I think there's a lot of potential there.
There's obviously lots of potential in the creative fields because
you can use these techniques to generate well written pros,
to translate between lots of different languages, to create even
pictures and videos now in a way that's quite impressive,
and so I think it brings a lot of powerful
(13:42):
content creation tools down to the average person in a
way that is going to mean that it's much easier
to create high quality content even if you're an individual
or a small team. And so I think we'll see
an explosion of creativity using these tools in the very
near freet as well.
Speaker 1 (14:01):
The process could be very, very positive, almost certainly will be.
But at the same time, there's a cultural aura that
views all of it with fear. Why do you think
we have drifted into this fear based response to technological opportunity?
Speaker 2 (14:22):
Humankind always has had a sort of technopanic curve where
you have the early adopters who are really excited, then
you have a sort of peak moment where people are
talking about the potential downsides, and then it gets accepted
into the community and people forget that they ever debated,
like oh, that novels were good or that bicycles were good.
(14:43):
And so I think it's part that. But I think
AI in particular raises because it's such a vague technology.
So much of what we call AI artificial intelligence. There's
actually dozens and dozens of artificial intelligence algorithms on everybody's phone, right.
It does all the things like helping you search for
your photo collection for a particular individual. Those are artificial
(15:08):
intelligence algorithms. But people when they hear the term artificial intelligence,
they think of sci fi movies, right, They think of
the Terminator, or they think of two thousand and one
and How and in those movies, almost universally, AI is
projected as an evil entity or an entity that's gone
wrong somehow that is threatening human safety. And so I
(15:31):
think there's that sort of cultural piece. But more generally,
I think the US has shifted from a frontier mindset,
one where we're trying to push the edges we're trying
to explore. We want grand adventures, we want to be
the next Right Brothers. We hold up those people as
icons for what it means to contribute to society, and
(15:55):
we're afraid of that now. I worry that we're now
in a time where maybe our kids' greatest adventures will
be figuring out what particular trauma they are trying to
deal with in their life, when their great adventures should
be like trying to come up with the next big invention,
or exploring a new space of science, or maybe outer space.
(16:16):
And so I think that cultural shift not one hundred
percent sure why it's happened. I think in part it
might be we've gotten comfortable as a country, maybe, and
so we're focusing on problems that are smaller when we
should be looking to opportunities and problems that are bigger.
But it really is a sort of new phenomenon. When
I think of the late nineties, we were a country
(16:38):
that was excited to be on the cutting edge of technology,
and now I think we're often, at least at the
elite levels, people talk about technology as if it's primarily
a threat rather than an enormous opportunity For the United States.
Speaker 1 (16:55):
Somebody said that the Europeans had decided they regulation over innovation,
and the result was in almost every new innovative area,
the US was just rapidly pulling away from Europe. And
isn't there a real danger that some of our politicians
would like to introduce a European style bureaucratic over control.
Speaker 2 (17:18):
Absolutely, and in fact almost expressly. When you look at
what California has done around some of its approaches to
software development and privacy, they're borrowed directly from the European Union.
And the European Union has a different mindset around technology,
whereas in the US we generally think people have the
(17:40):
right to build new things and then we'll see what
the effects are and they might have to temper their solutions.
Both the market might discipline them but then also there
might be real consumer harms aos are possible, Whereas in
Europe they have a mindset that basically, until the government
sort of authorizes an innovation in a particular space, that
nobody is really allowed to do it. And so that
(18:03):
mindset is very chilling because inevitably it's not the regulators
who are the best at trying to figure out what
future technologies might come about. It's the people who are
practically trying to make those things happen. And if they
live in a culture that says until you get the
okay you can't try something new, you know, it's just
(18:25):
a very difficult place to innovate in. So the US
does and maintains quite a good edge in that space,
but we are at risk of, at least at the
policy level, giving that up by trying to adopt these
precautionary approaches to innovation.
Speaker 1 (18:41):
Doesn't there seem to be a pretty big partisan split
over regulation versus innovation, with people like send them a
Joy Leader Chuck Schumer really pushing for all out federal regulation.
Speaker 2 (18:55):
I don't feel like artificial intelligence has been particularly politicized yet,
but we have seen many other of cutting edge technologies,
especially in the software space really do have a political
valance to them. The default, I think would be in
the partisan space would be that Democrats tend to be
much more precautionary in approaches to technology and Republicans tend
(19:19):
to be more permissionless let people build things. That's not
one hundred percent across the board, and there are interesting opportunities,
I think in the AI space to think about that.
On AI in particular, the Biden administration has very much
taken a whole of government approach, one that says, hey,
we as a government had to figure this thing out,
(19:40):
and we need to get all our ducks.
Speaker 1 (19:41):
In a row.
Speaker 2 (19:42):
And not all of that is bad. Government uses of
AI should certainly should be thoughtful. But when we're trying
to set up an environment in which we are making
sure that innovators have both the incentive and the freedom
to develop new things, a lot of government action can
be pretty chilling to that. And so I do think
the Biden administration has not really set a very positive vision.
(20:06):
In contrast, in the late nineties, there was a very
clear vision set for the Internet that the commercial development
of the Internet, that it was going to be market
driven rather than government driven. And we have a very
different mindset about artificial intelligence right now coming out of
this administration.
Speaker 1 (20:40):
You've sort of triggered a couple of thoughts on my part.
One is you could do a very interesting survey of
the artificial intelligence that's already around us. I mean your
whole point, for example, about your cell phone and how
many different versions of AI you're relating to, And I
think people would be shocked realized that artificial intelligence isn't
(21:02):
the future. Artificial intelligence is the present and is going
to expand into the future. They're an amazing number of
places where we've actually been using a variation of artificial
intelligence many many years ago. I went out to San
Diego to the Navy's labs and looked at how they
had designed a carrier battlegroup defense system, and it was
(21:25):
clearly what we would now call artificial intelligence. But it
was thirty five years ago. So in that sense, we're
already surrounded by a large amount of artificial intelligence.
Speaker 2 (21:36):
Absolutely. There's a great quote by AI pioneer John McCarthy,
which is that as soon as it works, nobody calls
it AI anymore. There was a time at which chess
playing was cutting edge artificial intelligence or speech recognition, or
recommendation algorithms like what you should watch next on Netflix.
(21:56):
There was a point at which these were cutting edge
AI research. But now because as they're working, we just
call that computers and we don't call it artificial intelligence anymore.
And so I do think that people don't realize that,
and I think in part it's because these new technologies
came out in a sort of chatbot form, right, and
so like you're talking and it sort of seems like
you're talking to something. I think that feels a little
(22:20):
different to people. But ultimately, I do think it is
helpful to point out that AI is around us. It's
pretty ubiquitous.
Speaker 1 (22:27):
In fact. The other side of that is the notion
of getting across how many different ways AI helps us.
I mean, I'm saying about this the other night because
I was trying to go somewhere, but I realized I
don't care anymore because I just plug in the address
and the GPS system, which is artificial intelligence, knows where
I am, knows where I am going, and has a
(22:50):
sense of which route will be best. Now, if you
think about it, that's an astonishing level of data in
real time at no cost. We thought about the number
of ways the AI is already helping us, and then
you could actually build out the potential over the next decade.
I think, particularly for example of helping Alzheimer's patients and
(23:12):
parkinson patients, and people have much better richer lives and
their families having much better richer lives as new artificial
intelligence systems are developed.
Speaker 2 (23:23):
Yeah. Absolutely, And you know I was thinking. I had
a bit of a health scare with my twenty month
old last week where she had a seizure, and one
of the things that doctors kept asking me was how
long did it last? And I was literally able to
look at my fit bit heart rate monitor and see
when my heart rate shot through the roof to see
(23:44):
when it started, and I could figure out from that. Now,
it would have been even better if I could just
ask the Alexa that was in my room like hey,
you hurt us, yelling like how long did that last?
Speaker 1 (23:54):
Right?
Speaker 2 (23:54):
But I couldn't do that, in part because I think
that people worry about enabling those types of devices to
monitor all the time, and sure that can have some downsides.
But all I could think was, Man, how great it
would have been if I could have pulled that data
that I know was available even if we weren't capturing it,
and I think AI opens the potential to do that
(24:14):
sort of really powerful empowerment of people to make the
most of the information that's around them, and so I'm
excited about it.
Speaker 1 (24:24):
I really want to ask you a little more about
the Abundance in City because it's absolutely what I believe
in and I'm fascinated that you're doing it. But I
noticed that you have focused on artificial intelligence and energy
as your two major emphasis. Why is that?
Speaker 2 (24:38):
Thank you so much for your interest in what we're
doing at the Abundance Institute. As you said, we just
launched recently launched this week, actually, although we've been in
soft launch for a little while. Right now we're very
focused on AI and energy because artificial intelligence is not
only a hot topic, but this issue is not going away.
The way people are talking about AI right now, the
(25:00):
cultural and the policy decisions that we make right now,
we'll have ramifications for the next fifty years, if not longer,
and in a space that's moving this fast, those are
really huge impacts. You already mentioned some of the numbers
about why AI could drive such tremendous growth across the
US economy and across the world. But if we don't
(25:20):
get this right, we're going to see the lead that
the US has to other nations such as China, and
I think it is really important that we get this
right right now, which is part of why we're focusing
on it. Energy. Similarly, there's enormous opportunity. There's new technologies
in this space that could really drive us into a
time of energy abundance. We've been operating in the US
(25:42):
since the seventies under a sort of scarcity mindset around energy,
the idea being that there's a limited amount of it
and that we should conserve it. Our goal should be
to conserve energy. Whereas we know that the wealthiest countries
are those that produce the most energy, and so here
in the US we need to get better at producing energy.
We need to do it in a way that's sustainable
(26:03):
and creates a good environment for our people. But there's
no reason that we are stagnating when it comes to
the amount of energy that we're creating. We have the technology,
we have the capabilities. The main things that are holding
us back are regulatory barriers, and we need to get
those out of the way so that we can do
things like bring nuclear back to the US in a
(26:24):
way that produces a huge abundance of energy that we're
going to need for many different things, including artificial intelligence.
And so that's why we're very focused in those two areas.
I will say that in the longer term, we are
also very interested in the biology space. The biotech space.
There is a huge amount of potential there, especially as
some of these artificial intelligence therapies come online, to really
(26:48):
just a total step change and the way that we
deal with health and healthcare in this country and around
the world. And so we're very excited about the potential
in that space as well.
Speaker 1 (26:57):
So if somebody wanted to get involved with the Abundance Institute,
what would they do? What could they do?
Speaker 2 (27:03):
You can reach out to us. Our website is abundance
dot Institute and we have an email at Hello at
abundance dot Institute. What we're trying to do is invest
in talent and talent assembly. We're also trying to build
a community of optimists, founders, and inventors to combat this
very pessimistic mindset about the future of technology that I
(27:23):
think resonates. That's very prevalent in DC, but I think
there's lots of people talking that way other places as well,
and so we welcome all people of similar mindset who
are excited about the future, who think that humans have
the great potential to create solutions to big problems, and
who want to get involved. We would love to hear
from you.
Speaker 1 (27:44):
We will certainly put the connections on our show page,
and we will encourage people to look at the Abundance
Institute and to get involved in helping think about a
positive future. Now, I want to thank you for joining me.
I'm looking forward to seeing what the Abundance Institute can accomplish.
I think it's exactly the right direction. I know you're
(28:05):
just getting started, so I hope in a few months
you'll come back and join us again and report on
what you're working on.
Speaker 2 (28:12):
I would love to and in a few months the
AI technology will probably be even different than it is
now and we'll have plenty to talk about. So very
much welcome the opportunity.
Speaker 1 (28:24):
Thank you to my guest Neil Chilson. You can learn
more about the Abundance Institute on our show page at
newtsworld dot com. Newsworld is produced by Gingrid three sixty
and iHeartMedia. Our executive producer is Guarnsey Sloan. Our researcher
is Rachel Peterson. The artwork for the show was created
by Steve Penley. Special thanks to the team at Gingrid
(28:45):
three sixty. If you've been enjoying Newtsworld, I hope you'll
go to Apple Podcast and both rate us a five
stars and give us a review so others can learn
what it's all about. Right now, listeners of Newtsworld can
sign up from my three free weekly columns at Gingrich
three sixty dot com slash newsletter. I'm Newt Gingrich. This
(29:06):
is Nutsworld.
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