Speaker 1: And we are back with another edition of the Federalist
Speaker 1: Radio Hour. I'm Matt Kittle, Senior Elections correspondent at the Federalist,
Speaker 1: and your experience sharpa on today's quest for Knowledge. As always,
Speaker 1: you can email the show at radio at the Federalist
Speaker 1: dot com, follow us on ex at FDRLST, make sure
Speaker 1: to subscribe wherever you download your podcast, and of course
Speaker 1: to the premium version of our website as well. Our
Speaker 1: guest today is Neil Chilson, former FTC Chief Technologist and
Speaker 1: currently head of aipolicy with the Abundance Institute. Where are
Speaker 1: we taking artificial intelligence? Where is artificial intelligence taking us? Well?
Speaker 1: We ask those questions and many more on this edition
Speaker 1: of the Federalist Radio Hour. Neil, thank you so much
Speaker 1: for joining us. It's great to be here speaking of here.
Speaker 1: Is this really you? Or is this AI? Or as
Speaker 1: they used to say when I was much younger dating
Speaker 1: myself here, is it live or is it memorys? We've
Speaker 1: come a long way since cassette tapes, certainly in America.
Speaker 1: I jest, of course, this is the real Neil Chilsen,
Speaker 1: but it does raise the question of just how much
Speaker 1: we are inundated with AI and the difficulty sometimes of
Speaker 1: telling artificial intelligence from reality. I think this thing is
Speaker 1: only going to intensify, of course, as we move forward.
Speaker 1: Where are we going with all of this?
Speaker 2: Well, as much as my wife and my kids might
Speaker 2: appreciate an AI upgraded dad and husband, they're stuck with me,
Speaker 2: and so are you guys, the real me.
Speaker 3: But you know that's I say that sort of jokingly.
Speaker 2: But where we are right now with AI is continuing
Speaker 2: a long trend of adding new capabilities to computers. And
Speaker 2: they're surprising in this case, and they're surprising in some ways.
Speaker 2: But the history of artificial intelligence, a term that was
Speaker 2: coined in the nineteen fifties, has been exactly this, surprising
Speaker 2: new things everybody gets excited about. We figure out, oh,
Speaker 2: this is not actually the same thing as human intelligence,
Speaker 2: not exactly. It's powerful, and then we sort of it
Speaker 2: gets adapted. It's in our phones, it's in our computers,
Speaker 2: and we move on. So, you know, artificial intelligence. The
Speaker 2: cutting edge of artificial intelligence technology when I was first
Speaker 2: getting into computers in the.
Speaker 3: Nineties was chess playing.
Speaker 2: And now everybody's phone can play chess better than ninety
Speaker 2: nine point nine percent of humans on the planet, and
Speaker 2: we don't think of that as somehow you know, terminator
Speaker 2: style artificial intelligence.
Speaker 3: And so so.
Speaker 2: Where we are now is we have these really powerful
Speaker 2: tools called large language models that can be used for many,
Speaker 2: many different types of things. But one of the ways
Speaker 2: they're being used is in a sort of chatbot form
Speaker 2: where you can ask questions and get really comprehensive, detailed,
Speaker 2: sometimes made up answers, and that's that's really powerful. It
Speaker 2: teaches us that there's a lot to learn about humans
Speaker 2: that you can that you can collect and gather into
Speaker 2: computers in these in these formats and then query in
Speaker 2: a way that gets you know, very persuasive, very interesting,
Speaker 2: often very entertaining content, whether it be text, video, audio.
Speaker 2: I love making songs. Actually, my little girls love making
Speaker 2: up new songs using some of the apps out there
Speaker 2: where you can just type in a prompt and get like,
Speaker 2: you know, a song about unicorns or a song about
Speaker 2: you know, their kids club running around the neighborhoods as
Speaker 2: kids spies or something like that, and so like we
Speaker 2: love that stuff. And so yeah, there's a lot of entertainment.
Speaker 2: There's a lot of power in these tools, and there's
Speaker 2: some risks and people need to be aware of that,
Speaker 2: and policymakers do as well.
Speaker 1: Some of the uses that you mentioned, you know, are
Speaker 1: fun to play around with. Certainly, like you said making
Speaker 1: up songs, I've seen that in play and the technology
Speaker 1: is really quite good and it takes you literally seconds
Speaker 1: to do what the Beatles spent, you know, the better
Speaker 1: part of a year doing to make Sergeant Pepper's Lonely
Speaker 1: Hearts Club band. I'm not sure saying that the artistry
Speaker 1: is the same, but I'm you know, the production value
Speaker 1: is certainly there. Yeah, the risk that you talk about
Speaker 1: in this arena copyright infringement, Where are we with that today?
Speaker 1: And where are we heading in terms of intellectual property?
Speaker 2: So there's two concerns on the copyright front. One is
Speaker 2: on the training side, So when a company is building
Speaker 2: these models and they use a bunch of different content
Speaker 2: in order to train on, what are the legal restrictions
Speaker 2: on that? And then the other side of it is
Speaker 2: on the output side. So when a user types in
Speaker 2: a prompt and says, you know, give me a picture
Speaker 2: of Mickey Mouse, you know, battling the Marvel team or
Speaker 2: something like that, and the model puts out something that
Speaker 2: includes you know, maybe some sort of intellectual property protection
Speaker 2: protect and content.
Speaker 3: Who's responsible for that? Is it the user, is it
Speaker 3: the model? And so on.
Speaker 2: The first thing about like the sort of ingestion of
Speaker 2: this content, the use of this content. We've had some
Speaker 2: court decisions and there is a category of this that
Speaker 2: judges are thinking of as fair use right. And so
Speaker 2: you're training these models the way you might train you know,
Speaker 2: anybody reading a book, and so the content of the
Speaker 2: model or the content isn't copied into the model, It
Speaker 2: is used to train the model, and then the model
Speaker 2: understands that content in the context of you know, lots
Speaker 2: of other content. And so there are ways in which
Speaker 2: the courts are saying that that is fair use. There
Speaker 2: was an anthropic settlement in this space where the big
Speaker 2: problem the anthropic had was that they hadn't paid for
Speaker 2: the original content, right, and so they hadn't bought the
Speaker 2: books that they were scanning. They had just downloaded pirated
Speaker 2: copies of the book. And the court said, no, no, no,
Speaker 2: you can't do that. You need to buy the content.
Speaker 2: You need to buy you the copy of the content
Speaker 2: and use that to train. You can't just use pirated content.
Speaker 2: But overall, that doesn't that doesn't say that much about
Speaker 2: you know, I think that still leaves the door open
Speaker 2: for companies to train on a lot of this data,
Speaker 2: which I think is probably the right decision. I think
Speaker 2: it is very analogous to training to reading a book.
Speaker 2: And therefore, you know, we don't have copyright that says
Speaker 2: you can't learn from a book without permission from the
Speaker 2: copyright holder, and so on the other side, we don't know.
Speaker 2: We don't know yet. Those cases are still ongoing. We
Speaker 2: don't know how courts are going to partition liability for
Speaker 2: copyright infringement between the user who writes the prompt and
Speaker 2: the company who is running the model or trained the model.
Speaker 2: There's some really complicated questions there about what is infringement
Speaker 2: even on that side, but then splitting it up between
Speaker 2: the user who's asking for something and the model who's
Speaker 2: generating it, it gets pretty complicated there. You know, we
Speaker 2: don't we don't sue pencil companies because people draw images
Speaker 2: of of you know, Mickey Mouse, sure with using a pencil,
Speaker 2: and that wouldn't.
Speaker 3: Make a ton of sense.
Speaker 2: And so we're just trying to figure out the right
Speaker 2: analogies here and what makes sense economically.
Speaker 1: Still, that's very complicated in the same legal neighborhood. What
Speaker 1: about reputational damage. We've already seen some cases on that front.
Speaker 1: And listen, there are AI companies you know that are
Speaker 1: I think very responsible AI companies. They're doing what they
Speaker 1: can and they're investing a lot of money to make
Speaker 1: sure that these systems are working right and they don't
Speaker 1: cause damage. But sometimes they do reputational damage. What about
Speaker 1: the liability issue on that front? Where are the courts
Speaker 1: on that particular matter?
Speaker 2: So the big, the big challenge. The courts have looked
Speaker 2: at this a couple of ways. There's a bunch of
Speaker 2: challenges when you're talking about defamation or libel. The biggest
Speaker 2: challenge is that often this involves public figures, and public
Speaker 2: figure liability requires showing that there was a sort of
Speaker 2: malicious intent.
Speaker 3: By the party.
Speaker 2: And the question again, the same question comes up here
Speaker 2: that comes up in the generation who is the party
Speaker 2: at fault here? Is it the person who typed the
Speaker 2: prompt who said, like, tell me everything you know about
Speaker 2: you know X public figure? Or is it the model generation?
Speaker 2: And then on top of that, unless it's made public,
Speaker 2: this type of generation by itself is not. I don't
Speaker 2: think it's usually subject to defamation law. So the question
Speaker 2: is if I generate, If I type a prompt into
Speaker 2: chat GPT and I get inaccurate information back, but then
Speaker 2: I publish it on my social media website or in
Speaker 2: a newspaper article or something, it's the publication of that
Speaker 2: that is the real problem. It's not the fact that
Speaker 2: the chat GPT came up with it, because nobody sees
Speaker 2: that except the person who asks for it. And so
Speaker 2: I do think that these defamation cases against the models
Speaker 2: are going to be complicated by that factor. I think
Speaker 2: that it's difficult to say that the model is that
Speaker 2: fault for the distribution of a falsehood when the model
Speaker 2: just generates content and then the person has to make
Speaker 2: a choice to then.
Speaker 3: Put it out into the world. And so I think
Speaker 3: that's a complicating factor. And I think that.
Speaker 2: Means while the models are trying very hard to be
Speaker 2: accurate on this sort of stuff and users, it's still
Speaker 2: really on the users to verify that the content that
Speaker 2: they're posting, that they're taking from that and using in
Speaker 2: the real world is accurate. And I think that's where
Speaker 2: the liability probably should sit.
Speaker 1: Yeah, tied end to that. We have some very interesting
Speaker 1: cases on free speech. We have some and AI. We
Speaker 1: have some some concerning moves by politicians, by lawmakers. I'm
Speaker 1: thinking of a guy who would like to be president
Speaker 1: of the United States in twenty twenty eight. There's no
Speaker 1: doubt about that. Gavin Newsom in California and the battles
Speaker 1: that have gone on there legally speaking in terms of
Speaker 1: content that is driven for political advertising political parody. Really,
Speaker 1: that's the interesting part about this, the brave new world
Speaker 1: of political advertising or political parody where, for instance, you
Speaker 1: have a famous AI video that came out last year
Speaker 1: that had Kamala Harris, then the Vice President of the
Speaker 1: United States, and a candidate, the candidate the Democrats candidate
Speaker 1: for president. The AI technology made her say some things
Speaker 1: that she certainly did not say. It was very amusing
Speaker 1: to about half of the country. Wasn't so amusing to
Speaker 1: the other half. So Gavin Newsom and crew in California
Speaker 1: took that and some other instances and used that as.
Speaker 3: Kind of a red flag law for or a red flag.
Speaker 1: Incidance for AI and communication and put limits on what
Speaker 1: you could do or penalties on what you could do
Speaker 1: if you did this. Kind of thing. The courts have
Speaker 1: entered into this case. Where does all of that stand today?
Speaker 2: So yeah, So California and some other states have looked
Speaker 2: at how to govern the use of AI generated content
Speaker 2: in advertising, in political advertising in particular, and they faced
Speaker 2: some real constitutional challenges here. Political speeches right at the
Speaker 2: core of First Amendment rights. Lying in political speech is
Speaker 2: not policed by the courts as because they often say, essentially,
Speaker 2: now you might be able to bring a defamation case possibly,
Speaker 2: but again we're talking about public figures, and even in
Speaker 2: the political context that gets even harder. Courts tend to
Speaker 2: really say, like, if you're going to do political ads,
Speaker 2: it's going to be up to the voters to decide
Speaker 2: who's telling the truth. And and AI doesn't really change
Speaker 2: that that much. It's always been easy to create false
Speaker 2: content right now. What you can do maybe with AI
Speaker 2: is the types of deep fakes that you're talking about,
Speaker 2: the types of putting words in somebody's mouth in a
Speaker 2: very convincing way.
Speaker 3: I think deceptive content.
Speaker 2: Like that is still it's right for you know, competitors
Speaker 2: to call it out. I think that companies are trying
Speaker 2: to figure out how to balance at but political parity
Speaker 2: is highly protected free speech, and I think any type
Speaker 2: of you know, government thumb on the scale about what
Speaker 2: people can and can't say is just ripe for abuse.
Speaker 2: You'll end up getting police, you know, from one political
Speaker 2: perspective or another. One party will will bear the brunt
Speaker 2: of this more than the other. And I just think
Speaker 2: that it's a super risky road to walk down. The
Speaker 2: better the better options here are more speech, not censoring
Speaker 2: the ability to create content in the first place.
Speaker 3: And I think that some of the companies.
Speaker 2: Have had some early experiences in trying to shape the
Speaker 2: content that was coming out of their models, and I
Speaker 2: think they've dialed that back to say, like, hey, we're
Speaker 2: going to largely try to lean on the side of
Speaker 2: generating what the user is asking for and protects protect
Speaker 2: speech there as long as they're not generating illegal content.
Speaker 2: I think that's the better that's the better frame.
Speaker 3: I hope that more and more companies move towards that direction.
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Speaker 1: Well, let's face it, if the courts really did punish
Speaker 1: politicians for lying, our correctional system would be just overbroad.
Speaker 2: Yeah, political speech is full of half to true misframeans
Speaker 2: and courts just don't want to get into that. I
Speaker 2: think that's that's just fraught territory, and I think I
Speaker 2: think it's right to leave it to the people to
Speaker 2: make the decisions about who they trust.
Speaker 1: Yeah, that is America, and that's the bottom line. The information,
Speaker 1: of course is it's a different age for all of that.
Speaker 1: But we've had the same issues and the same problems
Speaker 1: over the two hundred and fifty years of this one.
Speaker 1: Let's turn our attention to the emotional aspects of AI.
Speaker 1: Harvard Business School has an interesting piece up came out recently.
Speaker 1: Feeling lonely an attentive listener is an AI prompt away,
Speaker 1: and it delves into the brave new world of companionship
Speaker 1: with AI. There are well, I think what we're finding out, Neil,
Speaker 1: over the last year or so is that there are
Speaker 1: a lot of lonely people out there, and they have
Speaker 1: turned AI to solve that loneliness problem. Where does all
Speaker 1: of that stand today, and where do you think that's
Speaker 1: all going?
Speaker 2: Yeah, I mean, I think you're totally right. I mean,
Speaker 2: we do have an epidemic of sort of people isolating
Speaker 2: themselves from others, and I think this is an outgrowth of,
Speaker 2: you know, some really bad policies that we had around
Speaker 2: the COVID pandemic, as well as just some general fracturing
Speaker 2: of American you know, socialization institutions that we've you know,
Speaker 2: historically relied on to bring us together with people.
Speaker 3: And so I.
Speaker 2: Think that trying to fill this gap with AI chatbots
Speaker 2: is at best a sort of temporary measure.
Speaker 3: I think that.
Speaker 2: I don't know how to exactly measure this. You know,
Speaker 2: these chatbots are general purpose. Most of them are general purpose.
Speaker 2: They're not aimed at this sort of function. Now, there
Speaker 2: are some companies who are offering specifically this type of function,
Speaker 2: but most of the chatbots that people are using our
Speaker 2: general purpose. People are using them for a wide range
Speaker 2: of things, from academic research to generating funny videos, and
Speaker 2: some people use them occasionally to talk to about like
Speaker 2: personal problems or you know, relationship problems or things like that.
Speaker 3: I think some of that.
Speaker 2: Can be very useful and very helpful, but certainly would
Speaker 2: want to keep an eye out on people replacing time
Speaker 2: with other humans with these these chatbots. It's just it's
Speaker 2: just not it's not the same obviously, and it doesn't
Speaker 2: create the same types of deep connections with community that
Speaker 2: I think are essential to human flourishing.
Speaker 3: And so.
Speaker 2: My hope would be that these systems, as they build out,
Speaker 2: are aimed at improving people's ability to engage with other people,
Speaker 2: because there certainly are people who are not as practice
Speaker 2: at that who maybe spent you know, two years, especially
Speaker 2: when we get into the kids sector, who spent maybe
Speaker 2: two years like doing zoomed school and need to get
Speaker 2: back into the swing of, you know, talking to other people.
Speaker 2: And I think these tools could can help give tips
Speaker 2: on that sort of thing.
Speaker 3: But I hope they don't.
Speaker 2: I hope people don't rely on them as a substitute
Speaker 2: for reaching out, being brave, talking to people they haven't
Speaker 2: talked to before, and getting to know people in their community.
Speaker 1: We certainly have learned over the last few years just
Speaker 1: how dangerous, how much peril lockdown policies have put our
Speaker 1: children in especially, but society in general, when it comes
Speaker 1: to picking up with communication and relationships and all of
Speaker 1: those sorts of things. You know, Neil, though, on this topic,
Speaker 1: they always say the heart wants what the heart wants,
Speaker 1: The market wants what the market wants. And there are
Speaker 1: some strange areas in the marketplace for AI. Not to
Speaker 1: say that, you know, there isn't a market for it,
Speaker 1: but the question maybe should there be a market for it.
Speaker 1: One of those areas is connecting with the dead, and
Speaker 1: this has become an interesting subject of late. But the
Speaker 1: AI products that offer people the ability to chat with
Speaker 1: or hear the voice again of a loved one who's
Speaker 1: passed away, what about all of that?
Speaker 2: I mean, grief is a grief is a crazy thing, right,
Speaker 2: and so I think that people try to deal with
Speaker 2: it in a lot of different ways, some of it
Speaker 2: healthy and some of it not healthy.
Speaker 3: What I would be worried about. Here would be.
Speaker 2: Apps that are you know, claiming some sort of therapeutic
Speaker 2: effect on the heart, like misclaiming that, fault claiming that,
Speaker 2: or falsely claiming that you know, they're going to provide
Speaker 2: some sort of resolution. I think people, like I said,
Speaker 2: people might use these as tools to engage with maybe
Speaker 2: the content that somebody left behind. And I think that
Speaker 2: could be interesting if done in a healthy way. Right,
Speaker 2: if you are able to look at like air quote
Speaker 2: talk to you know, the all the letters that your
Speaker 2: your father or your grandfather left behind, that might be
Speaker 2: a really engaging way to learn more about their life
Speaker 2: and to think about like what they meant to you.
Speaker 2: But I don't think there are healthy ways to do that,
Speaker 2: and there are unhealthy ways to do that, And I
Speaker 2: would be worried about, you know, apps that are claiming
Speaker 2: to provide some sort of therapeutic effect when they're not
Speaker 2: doing that, rather than maybe ones that are talking about
Speaker 2: how they can help you understand you know, your past
Speaker 2: and understand your connection to your family better.
Speaker 3: Some of that could be very interesting and positive.
Speaker 1: I can't wait to get an AI letter the kinds
Speaker 1: of letters I get from some members of family and friends,
Speaker 1: particularly on the older spectrum of family and friends, the
Speaker 1: old merry medical Christmas letters where they tell me about
Speaker 1: all of their health ailments in the space of two
Speaker 1: very long pages.
Speaker 3: I suspect, I suspect, I suspect.
Speaker 2: If you get Christmas cards this you know, this season,
Speaker 2: there's there's a good chance a chunk of them were
Speaker 2: helped in being written by AI. But can I give
Speaker 2: you one example. My in laws recently came across a
Speaker 2: handwritten letter from you know, my father in law's dad.
Speaker 2: It was a very it was a terrific piece consoling
Speaker 2: somebody else in the loss of their their spouse. But
Speaker 2: some of it was actually quite difficult to read because
Speaker 2: it was handwritten. I used, you know, I scanned it
Speaker 2: into chat GPT really quickly and asked it if it
Speaker 2: could create a transcript of it. And that was extremely helpful.
Speaker 2: Actually it decrypted some of the words that we had
Speaker 2: we were really struggling with and uh, and we were
Speaker 2: able to enjoy this this letter in a way that
Speaker 2: I don't think we would have struggled more to figure
Speaker 2: out what the handwriting said had we had we not
Speaker 2: use that.
Speaker 3: So there are really positive uses to this technology. But there.
Speaker 2: You know, people shouldn't be substituting, you know, moving through
Speaker 2: their grief process by you know, pretending that they could
Speaker 2: still talk to their their their past relatives.
Speaker 1: I think, yeah, I don't I don't doubt that that
Speaker 1: kind of positive application and the implications of that. You know,
Speaker 1: you talk about, let's face it, cursive writing as a
Speaker 1: lost art in America anyway, But there's some beautiful cursive writing,
Speaker 1: uh in you know the history of family letters and
Speaker 1: those sorts of things. But I think about those, you know,
Speaker 1: as a history buff those important historical documents, you know,
Speaker 1: the letters from Lincoln that I can't always you know,
Speaker 1: I'm looking at the penmanship, and he's got pretty good penmanship,
Speaker 1: but there are areas where I can't find it. I
Speaker 1: think that's interesting. That's a very interesting application. We're going
Speaker 1: to talk more about some of you know, those very
Speaker 1: useful applications for AI coming up. Our guest today in
Speaker 1: this edition of The Federalist Radio Hour Neil Chilson, former
Speaker 1: FDC Chief technologist and currently head of AI policy with
Speaker 1: the Abundance Institute. I want to get to the jobs
Speaker 1: question though, because that is a huge concern, a growing
Speaker 1: concern for a lot of Americans what is fact? What
Speaker 1: is fiction? About what AI is doing and is about
Speaker 1: to do to the job market.
Speaker 2: Well, sorting out the facts can be challenging in sort
Speaker 2: of the effects of new technology on jobs. What we
Speaker 2: do know is that companies are spending a bunch of
Speaker 2: time on trying to figure.
Speaker 3: Out how to use AI, these new AI.
Speaker 2: Tools in their systems, but the actual implementation is actually
Speaker 2: still in the very early stages for almost all companies,
Speaker 2: and so while they're spending time and money on it,
Speaker 2: figuring out how to use it in their environments is
Speaker 2: less clear. I do think that there are lots of
Speaker 2: individuals who are figuring out how to use it, and
Speaker 2: that is moving faster because they can iterate within their company.
Speaker 2: They can try lots of different things. They can try
Speaker 2: to see how, hey, how does this help me write emails?
Speaker 2: How does it help me draft? How does it help
Speaker 2: me brainstorm? How does it help me take notes?
Speaker 3: But we're still not.
Speaker 2: Seeing like a huge productivity boost yet that would suggest
Speaker 2: that this is sort of replacing a bunch of time
Speaker 2: that people are spent, or that it's enhancing and allowing
Speaker 2: them to move into other areas.
Speaker 3: Not yet.
Speaker 2: I think some of that's just because we're still at
Speaker 2: the very early stages of this stuff working its way
Speaker 2: into the job sector. I think, you know, we've heard
Speaker 2: some talk about how, you know, early career people are
Speaker 2: struggling to get jobs, and the finger is being pointed
Speaker 2: at AI. I think that it's it's more about uncertainty
Speaker 2: in the economy. And some of that uncertainty, for sure,
Speaker 2: is being driven by not knowing what jobs are going
Speaker 2: to be affected by AI in the future. And so
Speaker 2: I don't want to say that it's not about AI
Speaker 2: at all, but there are lots of other factors in
Speaker 2: the economy that suggest uncertainty, and so I think teasing
Speaker 2: that out is very complicated. We'll have to see a
Speaker 2: little bit more. One other area of research has been
Speaker 2: about the types of tasks that these large language models
Speaker 2: are good at and where they're not good.
Speaker 3: And what we know is right now is that for.
Speaker 2: Certain types of jobs, AI has this effect of leveling
Speaker 2: up quickly people who are earlier or less experienced, and
Speaker 2: so in like a call center job, for example, where
Speaker 2: you're offering you know, your troubleshooting for customers over and
Speaker 2: over and over, these tools can really make somebody who
Speaker 2: is new to this job perform at a very much
Speaker 2: higher level, much more quickly, But it doesn't help the
Speaker 2: people at the top of that experience trend very much.
Speaker 2: In those types of jobs and other types of jobs,
Speaker 2: say where you're running a scientific laboratory and you're using
Speaker 2: these types of models to help you brainstorm. There, it
Speaker 2: seems like it makes the highest performers perform even better,
Speaker 2: and so the distribution of effect is different. So there
Speaker 2: the highest performers get a huge boost and the lower
Speaker 2: performers don't, because the highest performers can tell, like sort
Speaker 2: of intuitively, which threads of this brainstorming process makes some
Speaker 2: sense and which ones don't, and so they get a
Speaker 2: productivity boost and the lower skilled people don't. And so
Speaker 2: I think it really is job dependent that's going to
Speaker 2: be the case. I think for general purpose technologies overall,
Speaker 2: we don't know all the applications of AI. We don't
Speaker 2: know exactly how it's going to help, but there's lots
Speaker 2: of different ways that people are figuring it out.
Speaker 3: I actually saw the demo the other.
Speaker 2: Day of how people are using AI and what they
Speaker 2: call augmented reality headsets on construction sites to be able
Speaker 2: to much more quickly and more safely figure out where
Speaker 2: they should put pieces, what's the next step that they
Speaker 2: need to take. And you can help people coordinate in
Speaker 2: a much more safe way using both AI and this
Speaker 2: VR together. But this is all still very early days.
Speaker 2: I don't think we really know the overall job impacts
Speaker 2: of this technology. What we do know is that intelligence,
Speaker 2: artificial intelligence is valuable because intelligence is valuable, and so
Speaker 2: to the extent that we can use AI to amplify
Speaker 2: our intellectual endeavors the way that we used you know,
Speaker 2: the steam engine and the combustion engine to amplify our
Speaker 2: our physical uh capabilities we had. There is huge potential
Speaker 2: here for a lot of productivity and that means that
Speaker 2: means change in the types of jobs.
Speaker 3: And but we don't.
Speaker 2: Really know how fast or how that's going to work
Speaker 2: out yet, so there's some uncertainty for sure.
Speaker 1: You note that AI can sharpen skills of individuals. Can
Speaker 1: it steal the courage of members of Congress? Is that
Speaker 1: yet part of the technology suite?
Speaker 2: Uh? Wow, I hadn't thought of that as an application.
Speaker 2: Somebody should train an app to do that? Uh, you know,
Speaker 2: train a model to do that. The question would be
Speaker 2: could we and get members of Congress to use it.
Speaker 2: I did hear that Josh Holly recently used chat GPT
Speaker 2: to explore some sixteen hundred's Puritan history and was quite
Speaker 2: impressed with the response that he got back. But I
Speaker 2: think that was a very early use for him. So
Speaker 2: I hope he digs in, and I hope member of
Speaker 2: members of Congress do as well. I think understanding how
Speaker 2: this technology works and doesn't work is really valuable, and
Speaker 2: it's it's not the type of technology that sits off
Speaker 2: somewhere else and you would have to like plan a
Speaker 2: trip to go do it. Anybody can try it out,
Speaker 2: see how it works, and I think that that experience
Speaker 2: is worth doing just to understand, you know, what exactly
Speaker 2: we're dealing with here.
Speaker 1: Well, I mean, you know, it's always as a computer programmer,
Speaker 1: you know the model here, I guess the mantra more so,
Speaker 1: and that is garbage in, garbage out. It's what you
Speaker 1: put into the system, what you can expect to get
Speaker 1: out of it. And I think that pretty much described
Speaker 1: the sixteen nineteen projects, speaking of Puritan.
Speaker 3: Travel, all of.
Speaker 1: That sort of thing we've talked about. You know, some
Speaker 1: of the concerns obviously in this emerging technology that's been
Speaker 1: emerging by the way for the last seventy years, as
Speaker 1: you note, But there are some really powerful applications. I
Speaker 1: think about the healthcare arena, what about AI and healthcare?
Speaker 1: What about you know, what we're really seeing in terms
Speaker 1: of AI really driving positive change in different facets of
Speaker 1: our lives.
Speaker 2: So I think the healthcare arena is a great example.
Speaker 2: The way that these tools work is they take large
Speaker 2: amounts of data that human as humans, we can't see
Speaker 2: complicated patterns in, and they help expose those patterns. And
Speaker 2: healthcare is full of this type of data where we
Speaker 2: have a lot of data about you know, we might
Speaker 2: have millions of CT scans of breast cancer, for example,
Speaker 2: but we still have a very manual process for identifying that. Well,
Speaker 2: these tools can can do that type of analysis faster
Speaker 2: and more accurately, and often they can identify, you know,
Speaker 2: risk factors earlier than doctors could, in particular in the
Speaker 2: breast cancer context. That's just one area. One of the
Speaker 2: other big applications it's possible here is you know, we
Speaker 2: treat and we research healthcare in this country and around
Speaker 2: the world, sort of ash in a very generic way.
Speaker 2: We sort of treat people as like an average human.
Speaker 2: But the truth is there's so much variation in the
Speaker 2: human body and in the human health health system. They're
Speaker 2: there that being able to diagnose at a much more
Speaker 2: personalized level what is going on in your body is
Speaker 2: something that AI is getting better and better at, and
Speaker 2: I think that that raises just huge potential benefits for
Speaker 2: customized medicine that is directed exactly at the problems that
Speaker 2: you have and the cluster of problems that you or
Speaker 2: your family member might have that won't look like the
Speaker 2: vast majority of.
Speaker 3: Issues that other people have.
Speaker 2: And so we've already seen that in what are called
Speaker 2: like orphan diseases.
Speaker 3: These are diseases.
Speaker 2: That might affect, you know, a thousand people across the
Speaker 2: world at any time, and there are millions of people
Speaker 2: who are suffering from these types of diseases that are
Speaker 2: so small and so targeted that it's very hard just
Speaker 2: from a business perspective or an economic incentive perspective, to
Speaker 2: create treatments for them. If you're only going to solve
Speaker 2: problems for a thousand people, it's hard to know. But
Speaker 2: what we're learning and there's some great research. I believe
Speaker 2: it's at the University of Washington that is taking existing drugs,
Speaker 2: existing treatments and identifying where those treatments could apply to
Speaker 2: some of these orphan orphan diseases. And they're really they're
Speaker 2: bringing like real benefit, like life saving treatments to people
Speaker 2: by applying existing approved drugs that otherwise wouldn't have been
Speaker 2: used in that context to these orphan diseases. And that's
Speaker 2: the sort of thing that you could really only do
Speaker 2: with AI because it can see you can enter in
Speaker 2: all this data and you can get these patterns out
Speaker 2: that identify, hey, maybe we should try this technique over
Speaker 2: on this thing where we've never thought to try it before.
Speaker 2: And so I'm super excited about those types of treatments.
Speaker 2: I think that's going to make our lives healthier and longer.
Speaker 2: And it's one of the areas where we need to
Speaker 2: get the policy right because the way we treat healthcare
Speaker 2: data in this country is makes it very hard to
Speaker 2: do some of these types of things, and so we
Speaker 2: need to get policy right. But if we get that right,
Speaker 2: the health benefits here are really really exciting.
Speaker 1: Well, speaking of palas, see again you know the big
Speaker 1: beautiful bill has some interesting things in it about AI.
Speaker 1: It aims in many ways to stem the wave of
Speaker 1: state AI laws you know that that create this patchwork
Speaker 1: of you know, different boundary lines for AI, which you
Speaker 1: know is problematic to save the least, I mean, there
Speaker 1: has to be clearly some regulation in this area. It's
Speaker 1: just what that is now that did not make it
Speaker 1: through the you know, the process. Right Where where does
Speaker 1: all of that stand?
Speaker 2: Now?
Speaker 1: Where do you see that going? Because you know, it's
Speaker 1: it's understandable that AI companies have some concerns about trying
Speaker 1: to navigate so many different laws in this arena.
Speaker 3: Yeah, there's two real concerns here. One is the one
Speaker 3: that you said, which is.
Speaker 2: A sort of patchwork of you know, there were over
Speaker 2: one thousand state laws that related to AI that were
Speaker 2: introduced in in twenty twenty five so far, and you know,
Speaker 2: most of those were not problematic, but you still have
Speaker 2: to pay attention to them a little bit. Some of
Speaker 2: them were deeply problematic and didn't pass. Some of them
Speaker 2: were deeply problematic and did pass. And so I think
Speaker 2: California passed sixteen different AI laws this session.
Speaker 3: And so so one of the.
Speaker 2: Concerns is just this patchwork compliance, right, Like, how do
Speaker 2: I know what? What if I offer this product and
Speaker 2: there's somebody who used it in Missouri and somebody who
Speaker 2: used it in California, am I going to have to
Speaker 2: comply with two different sets of laws? How do I
Speaker 2: do That's that's that's a real problem. That's especially a
Speaker 2: problem for startups. Bigger companies can kind of afford to
Speaker 2: pay like a huge legal shop to try to figure
Speaker 2: out how to do this, but smaller companies are just
Speaker 2: going to struggle to do that. And so that's a
Speaker 2: problem for competition in this space, which I think is
Speaker 2: an important dimension. The second problem is what I call
Speaker 2: extra territoriality. And this is an old problem in our
Speaker 2: federalist system. It's in fact, it's what drove the founders
Speaker 2: to move away from the Articles of Confederation and to
Speaker 2: move towards the Constitution to have a more centralized government
Speaker 2: with limited powers, and then you know, the states to
Speaker 2: have you know, other powers, the police powers primarily. And
Speaker 2: so what that means is if California passes a law,
Speaker 2: say dictating what you know political advertising can look like,
Speaker 2: it embeds you know the values of the California legislature.
Speaker 2: But because California is such a large market, and because
Speaker 2: companies are going to want to sell into that market,
Speaker 2: that means that you know, probably the people in Oklahoma
Speaker 2: and Missouri and Iowa and you know, North Carolina, they're
Speaker 2: probably going to be opera within the same system that
Speaker 2: California has dictated. And I think that that is a
Speaker 2: real problem, especially when we get into some more of
Speaker 2: the political and speech.
Speaker 3: Based concerns in this area.
Speaker 2: We don't want California to be setting the national standard
Speaker 2: for what AI looks like. That's that's Congress's job. This
Speaker 2: is a national technology, and so you know, the big,
Speaker 2: the you know, big beautiful Bill had there was an
Speaker 2: opportunity there for Congress to do something. They tried, couldn't
Speaker 2: get it across the line on that particular one. But
Speaker 2: there's building interest in this having a federal approach to
Speaker 2: this technology, which is a national technology that has both
Speaker 2: like nationally economic importance but also just national security importance
Speaker 2: as well when we talk about, you know, competing with
Speaker 2: China in this area, and so I think there is
Speaker 2: building interest in doing something at the federal level. We
Speaker 2: just had another opportunity. There was an exploration of whether
Speaker 2: or not we could get this into you know, Congress
Speaker 2: was going to put this into something like the the
Speaker 2: the NDAA, which is the big appropriations bill for National defense.
Speaker 2: Ultimately that that didn't It wasn't the right vehicle. It's
Speaker 2: a consensus document. It was just challenging to get bipartisan
Speaker 2: support there. But I think Congress continues to be interested
Speaker 2: in this. I think people recognize that national technology should
Speaker 2: be you know, regulated at the national level, and so
Speaker 2: I think Congress is going to continue to explore that.
Speaker 2: The White House has been pushing pretty hard on this.
Speaker 2: President Trump has come out vocally and said that we
Speaker 2: need a federal framework in this space. We cannot subject
Speaker 2: to this national technology to you know, fifty state patchwork
Speaker 2: of laws, and we certainly can't let California dictate what
Speaker 2: our technology looks like when we're competing with China.
Speaker 1: So well, just just to ask the hard producers of
Speaker 1: Iowa about the impact California laws can have on their marketplace,
Speaker 1: you know.
Speaker 2: Or anybody who's bought a pickup truck right absolutely and
Speaker 2: had to meet you know, California cafe like fuel efficiency standards.
Speaker 3: Yeah, yeah, exactly.
Speaker 1: All right, Well, we're quickly running out of time. I
Speaker 1: just have a couple of questions left. The first is
Speaker 1: the resources involved in this. Obviously, AI, this technology requires
Speaker 1: a great deal of a particular natural resource. Where do
Speaker 1: you see all of that going ahead?
Speaker 2: Yeah, so AI requires a lot of energy. The current
Speaker 2: models and the current chips that are used to run
Speaker 2: them require energy, and so we are. Unfortunately in this
Speaker 2: country we have been operating since the nineteen seventies under
Speaker 2: a sort of scarcity mindset when it comes to energy,
Speaker 2: This idea that we need to conserve and recycle and
Speaker 2: all of these are Efficiency is good, recycling is good.
Speaker 2: Using things the best we can is good.
Speaker 3: But we have.
Speaker 2: A wealth of resources in this country. We should be
Speaker 2: using them. Prosperity and energy intensity are directly related. It's
Speaker 2: hard to be a wealthy country without using a lot
Speaker 2: of energy, and we shouldn't think of that as a
Speaker 2: bad thing. We should be trying to build more energy
Speaker 2: in this country. And I think it's finally AI has
Speaker 2: sort of woken people up to the fact that, hey,
Speaker 2: this scarcity mindset needs to go away. We need an
Speaker 2: abundance mindset when it comes to energy. Building more energy
Speaker 2: is a good thing, and providing more energy is good
Speaker 2: and that's the sort of thing that over time will
Speaker 2: drive down prices and we'll drive up new solutions, and
Speaker 2: so these data centers are.
Speaker 3: Sort of the spark for that. But I think we could.
Speaker 2: All benefit from having more energy abundance in this country.
Speaker 2: And so the balance is going to be like, where
Speaker 2: we build this, how can we build it, how do
Speaker 2: we deal with the concerns of you know, local communities
Speaker 2: about energy prices. All of that means that we should
Speaker 2: aim towards, you know, not subsidizing these types of projects,
Speaker 2: but finding a way to enable them to help give
Speaker 2: back both on the energy production side, but also on
Speaker 2: the you know, the the amazing tools that they're building.
Speaker 2: We talk about data centers, I have to think we
Speaker 2: should just say supercomputers, like people should be like excited
Speaker 2: to have a new supercomputer in their backyard. I well,
Speaker 2: maybe that just makes me super nerdy. I am excited
Speaker 2: by such things.
Speaker 1: These are not everybody, not everybody who's excited as you
Speaker 1: are about this.
Speaker 2: But I think you're right, and I need to I
Speaker 2: need to step out of my own bubble sometimes. And
Speaker 2: there certainly are trade offs that come from from this,
Speaker 2: But I think overall, the benefits are enormous. There are
Speaker 2: communities that are very interested in the level of investment
Speaker 2: that come with these types of data centers. You know,
Speaker 2: Texas is building a bunch of them, Louisiana has is
Speaker 2: building some massive ones. These are creating you know, hundreds
Speaker 2: of thousands of construction jobs, and then you know the
Speaker 2: ongoing like, uh, you know, maintenance of the data centers
Speaker 2: is you know, less job intensive, but is you know,
Speaker 2: creates prosperity and creates a hub for technology in the
Speaker 2: in the nearby area. And so I think there's there
Speaker 2: are lots of benefits. I think we need to keep
Speaker 2: those in mind, and we need to deal with some
Speaker 2: of the myths. One of the biggest myths is around
Speaker 2: this water use issue, which is just totally made up.
Speaker 3: I mean, there is.
Speaker 2: Why data centers use less water than the typical like
Speaker 2: mid size brewery. It does, and so or like a
Speaker 2: small manufacturer often uses more water than a data center.
Speaker 3: They are energy intensive.
Speaker 2: But water is not really an issue in this space,
Speaker 2: and it's I don't know why that's become such a story,
Speaker 2: but I think people just feel, you know, they're there.
Speaker 2: They don't want their bodily fluids polluted. We learned that
Speaker 2: from uh uh, you know from you know, uh what
Speaker 2: what is that movie? I'm blanking on it right now,
Speaker 2: But Soil and Green?
Speaker 3: That wasn't Soiling Green, it was Blanken. But but you know, people.
Speaker 1: I'm trying to remember a movie with bodily fluids as
Speaker 1: it's core principle. Actually, I'm sure they're Strange love, doctor Strange.
Speaker 1: Now you got it, yes, yeah.
Speaker 3: Doctor Strange love.
Speaker 2: And so I don't know why the water talking point
Speaker 2: has been so viral, but it's just not true.
Speaker 1: It really has been. That's I mean, that's what that's
Speaker 1: really what I was getting at is that's the that's
Speaker 1: been the topic of conversation. Now. Of course, it's been
Speaker 1: the topic of a conversation that's in no small part
Speaker 1: driven by environmentalist and extreme environmentalist of course. And they
Speaker 1: have certainly never led us astray before right now.
Speaker 2: They they I don't, And honestly, there's it's such a
Speaker 2: self defeating mindset that that the environmentalists are have been
Speaker 2: the primary driver of that mindset I said before, of
Speaker 2: like energy scarcity, that basically that that humans using the
Speaker 2: resources that God put here on our planet are is
Speaker 2: a bad thing. More or less and so I think
Speaker 2: they certainly have led us astray before I think on
Speaker 2: this water thing, they very much are leading us astray.
Speaker 2: You know, electrical use is a energy use is a challenge,
Speaker 2: and we need to make sure that we we get
Speaker 2: that balance right. And to me, the right solution there
Speaker 2: is to build more. We have the ability, we have
Speaker 2: the technical capability, we have the deep resources in this
Speaker 2: country to do it.
Speaker 3: We need to do it, or you know.
Speaker 2: These data centers will be being built in China and
Speaker 2: in you know, Saudi Arabia and in places where they'll
Speaker 2: be outside of the sort of US cultural influence, and
Speaker 2: then they won't be serving our national interest. And so
Speaker 2: I worry about that as a challenge. I think it's
Speaker 2: what we can tackle and we should.
Speaker 1: Well. If my AOC clock is right, we have about
Speaker 1: two and a half years of existence left. If I'm
Speaker 1: not missing because I was out twelve years at one point,
Speaker 1: I haven't checked in. I probably should. Final question for you,
Speaker 1: and this is it. You know, I've made no secret
Speaker 1: to you as we've talked about these issues in the past.
Speaker 1: AI technology scares the hell out of me. Yeah, it's
Speaker 1: probably because I didn't. I'm old enough to live in
Speaker 1: a time where I didn't envision the technology could possibly
Speaker 1: get beyond the Atari twenty six hundred and frogger. So,
Speaker 1: you know, maybe I'm a ludite on that front. But
Speaker 1: here's the question, plain and simple. Will AI eventually become
Speaker 1: our overlords and slave us and our progeny?
Speaker 2: No, no, no, these are these are advanced computers. They
Speaker 2: don't have motives, They don't have initiative.
Speaker 3: What they have right now.
Speaker 2: What these systems have right now is they have the
Speaker 2: ability to identify patterns in a wide range of data
Speaker 2: and collate it in a response to a query, and
Speaker 2: we can do really amazing things. It turns out we
Speaker 2: can do really amazing things with that. But there is
Speaker 2: not They do not have initiative or drive. I worry
Speaker 2: more about how humans will misuse them than I do
Speaker 2: about whether or not the AIS will somehow become independent.
Speaker 2: Right now, there is not a clear pathway to that
Speaker 2: sort of like autonomy, and so.
Speaker 3: I don't worry about that at all. I worry.
Speaker 2: I worry about not being able to take advantage of
Speaker 2: all the huge benefits that these technologies bring because because
Speaker 2: we have people who are too worried about you.
Speaker 3: Know, science fiction scenarios.
Speaker 1: So well it is. It is a brave new world
Speaker 1: in many facets. It's a very interesting new world, and
Speaker 1: it's a new world filled with all kinds of possibilities,
Speaker 1: as you say, many of them extremely positive. I'm not,
Speaker 1: you know, too much of a Luddite to understand that,
Speaker 1: but we need to stop every once in a while
Speaker 1: and discuss the impacts thus far and get a sense
Speaker 1: of where we're going. And you helped us do exactly that.
Speaker 2: I appreciate it absolutely, and I should point out that,
Speaker 2: you know, it's not just the ludd Eights that like Froger.
Speaker 2: My six year old daughter plays Froger all the time,
Speaker 2: so it has a long life. Some of these things
Speaker 2: stick with us. Great technology can bring joy to people,
Speaker 2: you know, for decades. So that's what I'm hoping AI does.
Speaker 1: Introducer to dig Doug and you may never see her again.
Speaker 3: That might be right.
Speaker 1: Thanks to my guest today, Neil Chilse, head of AI
Speaker 1: policy with the Abundance Institute, you've been listening to another
Speaker 1: edition of the Federalist Radio Hour. I'm Matt Kittle, Senior
Speaker 1: Elections correspondent at the Federalist. You'll be back soon. With more.
Speaker 1: Until then, stay lovers of freedom and anxious for the fray.
Speaker 2: I heard the fame, voice the reason, and then it
Speaker 2: faded away.