AI in the U.S. Congress

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Welcome to another episode of the Practical AI Podcast. This is Daniel Whitenack. I'm the founder and CEO at Prediction Guard, where we're safeguarding private AI models and I'm joined as always by my co host Chris Benson, who is a principal AI Research engineer at Lockheed Martin. How you doing Chris?

Doing great today, Daniel. Excited about today's guest.

I'm thankful in this particular weekend that the government has given us a holiday. So we're about to have a long weekend here in the US and to kick in the long weekend, we've got with us the current Congressman from Northern Virginia who is Don Byer.

Welcome Don, Daniel and Chris, thanks so much for inviting me to be on the show.

Yeah, well, we are super excited to have you join us. I think Chris and I were talking before the show. It's just encouraging to see how you're engaging with the subject of AI and encouraging that there are people like yourself in our government and I'm sure governments around the world as well. Thinking very deeply about this topic, could you give us a little bit of your background in terms of particularly how you started getting more and more interested in kind of science and AI policy?

Well, not to go too deeply, but when I was in high school and grade school, I just loved math and math puzzles. When I was a kid Scientific American, there was a guy named Martin Gardner who had a big puzzle at the back of Scientific American every month that I loved doing. And when I was in high school, I thought I was going to be a physicist. My dream was to be work at the Niels Bohr Institute of Theoretical Physicists in Copenhagen. And then I figured out in college I wasn't smart enough to do that. So I became a car dealer instead and spent most of my professional career, although my mom didn't think it was a profession using math that I knew in fourth grade. Once I had division down, I was okay for the business world, but I was always interested in it. And when I got to Congress, I was privileged enough to be appointed to the science committee. You know, science committee is not everyone's first choice. Like nobody gives you money because you're on the science committee. But I found it fascinating, not only because of climate change, because we got to do the oversight of NASA, we were the first ones to see the pictures of the black hole. When they figured out gravitational waves, we had those scientists come talk to us. And it was just really fun and interesting. So I got to do a lot more science than I'd ever done before. Some years ago, well, actually like 40 years ago, I'd heard a graduation speech by one of those 60 Minute guys, Eric Severode, and this is like 19, late 1970s. And he talked about how much information we were generating every year and our inability to see the patterns in that information. It was just too much. And there's always been in the back of my mind that it would be that we were being overwhelmed by information. So maybe eight, 10 years ago, when AI was really starting to catch gear, there was a Coursera course. I'd taken a couple of Coursera courses. My first one was on gamification, which I thought was pretty cool. And so I took an AI course on AI. At the first three weeks I just found totally fascinating the idea that you could do signal amplification, that you can use mathematical formulas and linear algebra to progressively get closer to actual connections and relationships. But then I got to the first exam and literally I say I got a zero on it. It was worse than that. I didn't turn it in because I couldn't answer a single question because I didn't know Python, I didn't know Java, I had never taken linear algebra. And I just put it aside. And then two and a half years ago, our local university, George Mason University, you know, one of the things you do in politics is you tour all the new sites. Any place you can cut a ribbon, right? So we were cutting the ribbon on their new Innovation center in Arlington, and I was just really intrigued and actually jealous. And so as a throwaway, I said, could I ever take courses here? They looked at me sort of funny, didn't really answer me, but I got the lady's card and I wrote her later that night and said I'd really like to sign up. And I was late for the filing deadline, the application deadline, but they waived it for me and I ended up taking pre calculus in that spring. And again, I'd taken all that 50 years ago, 56 years ago, but it was, it was like taking it all over again. And so in the meantime, I just finished my sixth course on object Oriented programming and I'm coding now for the first time in both Python and Java. And getting ready for the other 11 courses, which as long as I live long enough, will give me a Master's in machine learning.

That's fantastic. Ironically, before I move on to a question for you, I took that same Coursera Certificate course and it was hard. It was a hard course.

You did much better than I. I'm sure.

It was tough. It was a tough course, so. And I also had to bone up on some skills that I had long since lost or not touched on. It's a fantastic story. Just to back up for one second, I actually first saw this story that you're telling us here when Ian Bremmer had interviewed you. And I was following his social media releases and was very inspired by that, especially because it's so common for the public just to assume congress doesn't understand science, doesn't understand technology and AI. And there you were doing it, and especially leaping, unabashed at the age you're at, which in my view is a plus here. And we have a lot of folks, I'm in my 50s, that are my age and older, that follow the show. And I really wanted to bring this out. I'm kind of curious. Were you nervous about it, you know, in terms of diving into this? You know, there's this perception that even I experience at my age that AIML is a young man, young woman's game and we're all kind of a little bit on the older side for it and stuff. Did you have any fear of diving into this topic? How was your head at in terms of. Of making that step, Chris?

I should have been more afraid than I was. You know, I had Originally graduated in 1972, degree in Economics and I worked for a year or so and then I decided that I didn't know what I wanted to do. I was wandering around, so I thought, well, I'll go to med school. But I didn't have any of the prereqs. So I went back and did the whole pre med thing in about 12 months. And at the time I had figured out that it was just sailing. I was competing against 18, 19, 20 year olds and I was 23 and I'd worked for a year and was married and, and I crushed. You know, I just felt like I dominated. So I thought maybe I would again that all these kids were, you know, they're too busy trying to figure out who their next romantic liaison might be and how much they could drink tonight. And boy, was I wrong. The kids that I've been in school with are so serious and they have These great technical backgrounds from high school and they were the ones crushing me. So it was really a great exercise in humility for me. And I honestly say I did well in college and taken courses here and there over the years. I've never worked harder in a course than I did on this last Java course. I typed 193 pages of notes.

Wow. It's funny you say that. I'm trying to teach my daughter right now to start taking notes in class and instructions. Oh, dad. And stuff like that. But I'm like the good students. That's what they do.

Well, I still, I have two packed notebooks from the Python course. I look around the classroom with the other 80 kids. I'm the only one with a notebook. A lot have laptops open and some have their phones and some are asleep. The whole range.

And as you've been diving into this subject at a more hands on level, I'm wondering, on the one side you're a part of and participating in conversations about AI and increasing conversations about AI on the government side and policymaker side. And then on, on this side you're kind of in the weeds, so to speak, of, you know, creating, literally programming and in Python or whatever, whatever it is. What's been maybe surprising to you about like, how AI or machine learning is developed at a hands on level at a practitioner level, versus the perception at the policymaker level. Is there anything that's stood out to or things that have surprised you?

Before answering that, though, you touched on something that has always fascinated me. I don't know if you've read Kim Stanley Robinson's Mars trilogy. It's a wonderful book. It got me through a gubernatorial campaign, 15 pages a night. And one of the things in it that I really took away from it was the idea of both leadership from the balcony and leadership from the field. That one of the lead characters in it would spend a year or two managing the planet Mars from like a general manager level, a presidential level. And then he'd go out in the field for two years and live in a tent by himself, looking at Mars biology. And when I was in the car business, just to be mundane once again, I'd always spend two weeks every summer working as a technician in the shop, as a mechanic. By the end of the third day, I was cursing as bad as they went. And I'd hate management by then, by the third day. And, you know, but I come away from it at the end of those hot summer days with cut hands and a real appreciation for what it was like to work on cars all day long, replacing water pumps and trying to figure out where that obnoxious knocking noise is. And now I'm finding the exact same parallel with artificial intelligence and serving on the AI Task force and the AI Caucus, the various committees that we have. That is really fun to see. And like, I'm still very much a rookie, a baby in this field, but I can imagine what two or three years later a senior software engineer is doing. The people that are building these wonderful models right now that understand how the neural networks come together, on the other hand, trying to figure out what do we do about deep fakes, what do we do about hallucinations, what do we do to protect our electoral systems? You know, to look at the policy side also. And I'm blessed to have people like, I have a wonderful tech fellow, which means some other foundation is paying for him for a year, who's an MIT computer scientist who worked in AI. He's actually knows the math and the computer science and the hardware of it to help with me on the policy side.

That's great.

I'm looking forward to diving into the policy side. But before we go there, how have other members of Congress received your dive into this topic with such an intensity? Do they tend to come to you? Do they look to you? Are you seen that way? And what hopes might you have of members of Congress really digging in, whether they're in the House or the Senate, digging in to this topic with some sense of willingness to recognize that it is a huge topic for our future. Do you have any hope for that, or do you think it's going to kind of stay this way?

Now, Chris, I'm embarrassed to say this, but I think the primary reaction has been amusement. You know, especially when I was doing, you know, single variable multivariable calculus, I'd bring my the homework to the floor because sometimes we'd have long vote sessions, especially during COVID when we had to vote by proxy. So people would come on over and say, what are you doing? You know, and then they'd look at it and then get a little anxious and walk away from me. So no, I don't expect many other people to be going back and taking undergraduate and graduate courses in it. But I do think many, many members of Congress are trying to read everything they can about it. There's an abundance of AI books out. You see them all over the place, and people are trying to read every article they can. We have had just a myriad visits on the Hill from people that are experts in the field Just this last week we had a number of people on the AI safety side. People from NIST came, for example.

That's very encouraging to hear.

And we get people from industry and people from academia. Stuart Russell was there a week or two ago from Berkeley who wrote apparently the classic textbook on it. Marc Andreessen came a couple of weeks ago to give us his Techno Optimist manifesto. So we've been listening to as many people as we can in order to try to develop good policies.

Well, Don, I think some of us who are observing our US Government with things coming out like the executive order and you mentioned nist. I know. I've read a bit of some of the guidelines, best practices, some of what they're digging into. Could you help those of us who aren't well versed in the things that policymakers are involved with around the topic of AI? How would you categorize those? What are the main focuses and what are the main activities that policymakers, congressmen like yourself, what are those main activities that are going on? And how should we kind of be expecting some of that to trickle down to us as practitioners or kind of hit our desk, so to speak?

I think there's a tidal wave coming at us right now because it seems like every, every group, every committee, every caucus wants to have a little AI specialty. So for example, I'm on the AI caucus, which is pretty large right now, bipartisan, almost all of it, which is really encouraging. And so we're doing the education piece for other members and their staffs, especially.

Their staffs, and for those maybe in an international context, because we do have international listeners. What is a caucus in terms of the AI caucus that you just mentioned?

Well, the first AI caucus is open to all members of the House and Senate. And literally in earlier years we'd have 10 or 12 members and six would show up for lunch. Now 150 show up.

Wow.

A lot of them staff, but really interested in the speakers and what they can learn and then the individual. We have smaller groups, like half of the Democrats belong to the New Democrat Caucus, which sort of defines itself as being pro innovation, pro business, pro trade. And so they have their own AI caucus. The Progressives, I don't think have one yet, but they're really interested in it. And then probably the most important one is the Speaker, Mike Johnson, and the Democratic leader, Hakeem Jeffries appointed a 24 person task force for this year to try to look at the 200 different pieces of legislation that have been introduced on AI and focus it down to the handful that we should pass this year. That would be building blocks for the years to come. You can probably elegantly put it in four or five buckets. But clearly the deepfake problem. Not just deepfake, but the whole copyright plus problem from music and in illustrations and photographer and text and, and voice, obviously Scarlett Johansson, that piece. Then there's the whole piece about generative AI. And what can we expect and what can we rely on from the large language models that are springing up the whole safety concern, which one of the I think most encouraging things, by the way, all this is on the background of social media and the fact that we've done Nothing in the 25 plus years of social media except make it impossible to sue them. Section 230 and no one's been able to come up with an agreement on how to modify 230 to allow people to be held accountable without crashing the whole Internet, making it just endless lawsuits. So we're trying to get ahead of that. First of all, humbly, Congress will never be ahead of the American people, but we want to get ahead of where Congress typically is by looking at significant legislation.

It's really good to hear some of this, and I don't think, I'm sure that that's accessible to people if they know where to look. But I think Daniel and I are learning a lot from you here today in terms of how this works. Compared to previous technologies that we've seen over the decades, this is a bit of a different beast. AI, it's going much faster. It is likely to have a much more profound impact on work, certainly in the industry I'm in. Warfare across the board, even what it means to be a human as you go forward in time. To some degree with these changes happening, we're getting big news in the AI space every week. A couple of weeks ago, less than two weeks ago, as we record this, OpenAI announced ChatGPT4 omni. And that alone, relative to its previous version of the model, changed how people are using AI in day to day life. You have the entire open source arena with hugging face. There are over a million models there. This thing is happening so fast. How do you envision Congress trying to get an appropriate, you know, handle on that, you know, whatever that means to you on such a fast moving, expansive topic? I, I struggle as a citizen to envision how that even happens. And I'm really, I've been waiting to ask you that question for ever since we agreed to do this.

Chris, you're not suggesting that Congress act slowly, are you?

I would never do that to a congressman.

That is a really hard question because as I've discovered in my nine and a half years there, it moves glacially.

Yes.

You know that our founding mothers and fathers, by building in a Senate and a House, the competing chambers, and you add a filibuster in and a one person hold and a Senate that I respect as part of the founding compromise. But a very small fraction of Americans elect a big fraction of the senators. Like 30% of Americans elect 7% of the senators. And it's amazing we get anything done. And then you almost sometimes need what they call a trifecta where that one party controls the House, the Senate and the presidency to do any major legislation like the Affordable Care act or the Inflation Reduction act, or Donald Trump's Tax Cut and Jobs Act. Those happen under trifecta. So all we can do is keep this as bipartisan as possible and then probably deal with whatever emerges as the largest downsides. We're not really having any big downsides yet. We talk about the threats. I mean, there are very obvious things, the whole CSAM issue, you know, making an undressed Taylor Swift or sex videos for underage teenage girls that never participated in them, that those are very real threats. And we struggle to know who to hold accountable for them. But in general, the hope is that by talking about it, looking at these 200 plus bills, looking at the plethora of bills that have been introduced at state levels, I understand more than 50 just in California, that we figure out the handful that actually make a difference and actually protect people. And by the way, I'm very impressed by the President's executive order, Biden's. Turns out it's the largest executive order in American history. They've hit all of their benchmarks so far, their timelines. And maybe the most important thing is they set up the Safety Institute at NIST now led by Elizabeth Kelly, that's staffing up. So finally, at the federal government level, there is a group that is specifically charged with dealing with the safety and trust issues.

So we've seen that executive order. I think we had a previous show where we talked through certain pieces of that. Really encouraging to see some leadership there. How would you view kind of the more international side of this in terms of how the US and our policymakers are proceeding forward versus policymakers across the world? How do you view that from your perspective and what conversations are going on as related to kind of our positioning within that and the role that AI plays globally?

Daniel, I think that's it's a really important issue and I think there's lots and lots of conversations. I think I've had no fewer than eight meetings with actual European parliamentarians who have been putting together their EU AI Act. And just in the last three weeks, you know, one big dinner and a long session with, during the day with those same people and their, their lead technical staff explaining how the eu, their act is working and how it differs from ours. You know, the, the shorthand that's an oversimplification is they describe themselves as a regulatory superpower and we are all committed to innovation, you know, so we're not licensing algorithms or giving permission to do certain things, but we also know that we have to be there in the UK when they talk about their Bletchley doctrine. We're going to Japan for their stuff. Ultimately, in the middle run, we need to have something like a Geneva Convention on AI. This is especially true to the extent that we can engage China in it. We know China is concerned, obviously they're investing hugely in it, but they also concerned about the safety parts of it. And we all have to come together. Right?

That's exactly what I was about to ask next is, and we often bring it up on the podcast is kind of the safety. You know, you have all these things pulling against each other with tension. You have, you know, the innovation just driving forward constantly. As we've talked about the understandable safety concerns that we all have, which we have increasingly over the last few years been talking about and which our audience is also, you know, you know, demanding. There's a lot of concern out there is with looking at the international balance, intentions. You know, we have, we have Russia doing what Russia is doing with Ukraine. We have China and Taiwan and, and all of these things. You know, Ukraine is the first war that is becoming increasingly AI driven in terms of the technologies being used. China is an AI superpower, as you know, along with the US and we're all talking about this need for, you know, us and the Europeans to, to work together. But there's always the, the concern about bringing everybody on board because you're. I love the idea of the Geneva Convention that you just mentioned. If all the major powers can get on board, how do you envision all these different tensions pulling against each other, possibly working out and getting the motivations of all of the Western countries led by the US With Russia and its sphere of influence in China and its sphere of influence coming together. Do you have any either aspiration or expectation on how you see that coming Together over the next few years, Chris.

Is probably more aspiration than expectation. And with you coming from the defense industrial base, you know how important our war fighters think this is.

Indeed I do.

The chairs of our intelligence committees, the chairs of our armed services committees, they very much do not want us to be behind China. There's a lot of debate about how much human agency should there be in the use of kinetic force. Can you have machines deciding who to target rather than an Air Force pilot, even if he's sitting in a room with levers in Colorado Springs? At least that's a human being saying, let's take out that car, take out that building, rather than letting a drone decide who to attack and the like. And then there's space and the weaponization of space and the role that AI plays in that. My hope is that we will have some renewed arms control in the days ahead. It's been pretty sad the last couple of years as Russia has progressively withdrawn from arms control agreements and China has been unwilling even to sit down and talk to us. But sooner or later, if we're going to make the world a safer place, we need to talk about all the nuclear weapons on the planet and at the same time, talk about artificial intelligence, too. One of the very first pieces of legislation introduced was Ted Lieu, Ken Buck and I on prohibiting letting an artificial intelligence algorithm make the decision to launch a nuclear attack on another country. That has to be the President of the United States and the Chairman of the Joint Chiefs of Staff and the Secretary of State. Human beings making decisions of that magnitude. And yes, they can use all the data they want, but a machine can't decide.

I totally get that 100%. But I'm. I'm often kind of shocked at how many people don't understand that, you know, the idea of nuclear weapons. A few years ago, I was with the CTO of Lockheed Martin at the time, who since left, and I was doing an event in London and I was on the stage, and I actually had an audience member say, could you talk to us about. With an assumption in the question, could you talk to us about the fact that the US has AI controlling its nuclear weapons and what you think the implications are? And I laughed it off and said, that's not the case, obviously. But that was one of those first moments where I realized how much misinformation about these topics was out there. And obviously since then, that's just gotten more and more. There's so many variations, deep fakes, constant misinformation that AI enables in nefarious intent. Could you talk a little bit about kind of the safety of how to approach that from regulation? And you're obviously deeply into the topic, kind of give us a little bit of guidance. Things I can tell my family who are not into AI because when we sit around Christmas time and holidays and the extended family, they don't know either. And I'm always shocked that my own family doesn't know. And so would love to hear your thoughts on just kind of how to approach some of these incredible misconceptions that people have.

Number one is safety issue is how about, how about job elimination? We know that it's going to replace many, many jobs. One of the exciting things is what they call ambient clinical documentation. You know, doctors and nurses say 25, 30, 50% of their time is filling out job data on the clinical visit they just finished or in the middle of it now there's software hardware that listens to the conversation between Chris and his doctor and writes it all down. And by the for the time the doctor or the patient leaves, the doctor can read it and check it. And yep, that's what we talked about, saving immense amount of time. But then, so job elimination and what do we do? We know that that's happened in every revolution, agricultural, industrial information. But we also know that will probably happen much more quickly now than it ever has before. So much less time to react and for people to adapt to that change. You know, second level is all the misinformation, whether coming out of a large language model, it's unintentional, or the intentional stuff, how do we protect against that? Then there's the whole notion and some people take this seriously, other people will say, nah, that with desktop ability to generate DNA, to synthesize DNA, and the ability to look up what's a smallpox vaccine, what's the DNA of that? Or go from COVID 19, let's make Covid 27. The whole notion of bioweapons or others things that can be used based on the information that comes from large language models and AI and then all the way to the existential threat. It's interesting that so many of the computer scientists I talk to just really say no, no, no, we're nowhere near artificial general intelligence. And even if we were at AGI, that's not going to be conscious, it's not going to have will, it's not going to plan its own things. I tend to be on the humbler side, which is we don't know where consciousness comes from and we don't know when and where it's an emergent property from what? If we're building machines that can think some things thousands or millions of times faster than we can, why are we so sure that there won't be an emergent consciousness coming from this? In my conversations with Elizabeth Kelly at the NIST Safety Institute, my plea is that there's at least some subset of that group that always is keeping the existential threat at the top of mind.

Well, Don, I'm always thinking of my practical day to day work in using models, building models, applying things in a sort of enterprise context. From your perspective, now that you have both a view from kind of the hands on granular level, but also from the kind of global and policymaker perspective. If you could say something to everyday practitioners in the AI space who are building these systems, what should we have in mind or what should we be thinking about kind of moving to the future? You said policy and regulations and all of those things will catch up. But there's people building these systems now. So from your perspective, what are some things to keep in mind from a practitioner level?

Daniel, I love your question. I get a blast email, I think it's once a week, it might be twice from something called AI Tangle. And in every one there's like 15 or something new ideas about AI and the new companies that just sprung up. And I'm always reading and say, well, what are they going to do? And they all sound really similar. They're going to help you manage your enterprise and they're going to coordinate, blah, blah, blah, and it's never very inspiring. Instead, if I were in that mode, if I could quit my job now and start an AI company, the first thing I'd probably try to do is say, what are the real big challenges in our lives that we're not fixing? How can I use AI to improve our climate change posture? How can I use AI to lift all the people that are food insecure in America out of that food insecurity or something that's very close to my heart. Ten years ago, with a noble Republican, we started a suicide prevention task force. And if I ever get past my master's, what I'd love to do is based on where history is, is use AI to work on a predictive model on who's at risk for suicide. We lost one of our beloved Capitol police officers three days ago who took his own life, died by suicide. And I used to say good morning to him every morning. Big smile, sweet guy. I talked to a bunch of his fellow officers yesterday. Nobody had any idea this was coming. And if we lose 50,000 people a year, that's just about where we were last year to death by suicide. Wouldn't it be great to be able to use AI to figure out ahead of time that a thousand of them were at risk and intervene and save those lives or make even more? And that's just one small example. But there's so many ways that I'd love for us to use AI. The generative part, yes, but especially the predictive part to see if we couldn't make the world just a really better place. You've all probably read, was it the Minority Report?

Yeah.

Philip K. Dick. Right. Well, they actually used science fiction so they could look ahead, you know, Chris could look ahead and figure out who's going to commit a crime and they throw him in jail ahead of time. Well, we can't do that. But if you can use artificial intelligence to figure out who's most at risk of committing a crime and intervene in a positive way to change their lives, maybe we can have a safer, happier world.

I really love this line of thinking. It's Daniel and I focus very much on the show about kind of AI for good and not everything being about making a profit in a business, but kind of how does it affect society? It's a big theme in the show and I run a nonprofit when I'm not working at Lockheed. It's a pure public service nonprofit and it's turned into another full time job that I don't get paid for. When you think about how I can do good in these ways, obviously taking into account the safety and privacy issues that are there, you mentioned climate change, you mentioned food insecurity and suicide prevention, which kind of ties into a mental health theme. And as we have these AI agents, as we combine generative AI with some other tools and kind of there's an agent for everything in the future, there's some agents that can handle many tasks and there are many specialized. And everyone talks about that coming. How do you know? You mentioned about kind of suicide prevention and I think that's a fantastic idea. If you have that agent, that personal assistant that's always there and can kind of take care of you. Beautiful idea. Also in education, I have a daughter who just turned 12 and I'm trying really hard and I know education is a big thing for you. How do we see our children today growing up in this world? How can AI help them with education? How can it make their world better? There's the scary things like job loss that we always worry about, but there's also these amazing potentials for good as well. What are your thoughts about. About AI and education and where that goes into the future?

I'm very excited about it. By the way, let me just before that, one of the. That ties in some of the last couple of questions. One of the things that fascinated me about my last meeting with the European Union people was apparently their AI act banned the use of AI for emotional recognition. And they didn't want not just facial recognition, but reading faces to see how, you know, Chris is feeling today. And I was concerned about that and pushing back on it because you think that that might be a really helpful thing as you look at somebody who might be at risk. There are a couple of linguistic professors at Georgetown University who are trying to use people's writing and texts to see language that jumps out, that suggests suicide ideation once again, for a predictive sense. But moving to education, I'm sure you guys had the same experience. I know my wonderful Jason Langhorn, tech fellow must have had many boring, boring hours in high school while you sat there while everyone else tried to catch up. I can't tell you how many plays and poems I wrote while other people were trying to figure out the physics problem. You know, the fact that you can use technology in general and artificial intelligence in particular, to let people go at their own pace and learn as much as they can, as fast as they can. But then on the other side, for the kids that can't read at grade level in second and third and fourth grade, I know that you can use technology in a way that can help them learn these reading skills early on and improve it. It should be the kind of thing that applying artificial intelligence to education should make the teacher's task easier. Personalized education. I've never had to teach a classroom full of 30 kids, but, you know, 30 kids have 30 different levels of ability, and that's gonna be really challenging.

As you mentioned, that the emotional detection thing, I think if you can get through the privacy concerns and who's in control of that data, I would imagine that that could be a huge plus across mental health education and stuff to do that. And so I'm very encouraged to hear you say, you know, indicate that assuming that the context is right, assuming that we can find the right constraints and barriers around it, that that would be a plus going forward, rather than just saying, nope, we're not going to do that. Leading into this, I tend to take my daughter Rogue a little bit on her homework. The teachers are telling her don't use any AI on any of your stuff. And the teachers are bound by the policies of the school board. So I'm not lashing out at teachers at all in that way. I just wanted to be clear on that. But policy right now is very much against that in the school systems. And I tell her, no, no, I'd much rather you learn the material, but let's use the technologies to help that learning happen. Do you think that that will prevail and that we are going to have these technologies in a really beneficial way, very personalized for each student, as you mentioned? Do you think we can get through the politics and the lobbying against that that currently exists?

I think so. I think it's natural there's going to be resistance in the short run. But already I've talked to a lot of college professors who are like, don't worry about it. You know, I'm thrilled that they're using it because they're learning the material and they're asking deeper questions, and the AI is often pushing back and asking what they know. You know, it's. I think it's fine. And worst case, we can go back to bluebooks for the exams where you hand write the whole thing.

Yeah, I definitely remember my fair share of all those tests where you have to fill in the little. The little dots. Scantron with your pencil. Yeah, Scantrons. Those are interesting closing out here, Don. We're getting near to the end of our conversation, and we've talked a bit about education policy, international approaches to. And how AI is influencing kind of global relations and those sorts of things. I thought it might be fun to end here with just asking you how AI is influencing your life personally. What have been some things that have been helpful for you? And as a congressman working in our government, how is or are you thinking AI will shape your job?

I confess at the beginning, I'm a huge AI optimist. Maybe not as far as Marc Andreessen, but still a big AI optimist. And where I see it most meaningfully is in health care. The fact that we can now in some cases, diagnose pancreatic cancer three or four years ahead of when we could otherwise. I met with a bunch of radiation oncologists the other night. And the difference in getting radiation treatment for cancer 20 years ago and today, because of artificial intelligence, is night and day. They could exactly pick out your tumor to the micron externally and put that protein beam or neutron beam on and make it dissolve and go away. It's just remarkable. There's a wonderful new book out called why We Die on the science of longevity that argues that the first person to live to be 150 years old has already been born. He or she is among us today because of the difference that artificial intelligence, just the applied knowledge of this extraordinary amount of data that we have. You know, we have some good, pretty good ideas about physics and some on chemistry. We know very little about biology, very little about the human brain. But artificial intelligence is going to open up a lot of those doors for us.

Well, thank you for taking time today to give us a bit of that optimism, but also help us understand kind of how, how government is thinking about some of the more difficult and safety related issues with AI. We're very encouraged to have you in those conversations and taking time to join us and speak to practitioners directly in this conversation. So thank you so much, John. It was great to talk to you.

Thanks a lot, Don. We really appreciate it.

Thank you. Daniel and Chris, good luck.

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