Here is Lillian Corral, who is the Vice President of Technology and Democracy here at New America, and we're going to consider all the future of AI accountability look like. So I'm going to hand it over to Lillian, and she will introduce one of our keynotes. Thank you. Hello, everyone. Hi, Alan. Hi. It's my great pleasure today to have this wonderful conversation. Thank you for joining us. Today is Alan Davidson, the Assistant Secretary for the Department of Commerce for Communication and Information, and he's also the head administrator of the NTIA.  But Alan has over 20 years of experience at the intersection of public interest, advocacy, and technology, and the law. Obviously now, as the administrator of NTIA, you oversee a lot of the issues that are at the forefront of discussion in the American public debate, AI being one of the most important ones that we'll be talking about today, but other critical ones like our Internet infrastructure and so on, which we care a lot about at the Open Technology Institute at New America. Prior to being at NTIA, Alan was the senior advisor for the Mozilla Foundation, which is a global nonprofit that also promotes openness, innovation, and participation on the Internet. And then obviously Alan has a long history of being here at New America, where he was once the director of the Open Technology Institute and also vice president of technology policy. So thank you again for joining us. Oh, it's so great to be back here. Thank you. So today is an interesting day to talk about AI. I know it's somewhere in D.C. There is also a strong cohort of individuals, leaders in the industry, having an important conversation as well on the future of AI and AI governance as part of Leader Schumer's Insights Forum. Can we start the conversation here just talking a little bit about how you at NTIA think about balancing the need to police AI and optimize AI? Obviously, a lot of the debate is sort of what feels like extreme polarization around is it really great for our society or will it lead to our extinction? How do you kind of set the tone or lay the ground for your own thinking around this? Excellent question. Well, actually, let me first, by the way, start by saying it is really, truly great to be back here, back at New America, back at this forum. And I have to say congratulations to the leaders who have been, I very vividly remember the, I think it was the first forum that we did in this partnership between New America and ASU and admiring the quality of the conversation. I felt in some ways it was sort of the best of what this community can bring to furthering our thinking on the hard policy issues in front of us. So I just say congratulations to Peter, to our colleagues at ASU and for the continuation of the forum. And it's an honor to be here today for me. So, terrific question. And I think the answer is going to be yes and both, right? Like that the truth is that I think the administration is committed to the idea that responsible innovation in AI is going to bring enormous benefits to humanity, to people. At the same time, we know that there are very real risks and that those risks need to be dealt with, need to be dealt with today, real risks that we are seeing today if we're going to realize that promise of AI. And, you know, I think just unpacked that the president said it himself very clearly. I think he said we have to be clear eyed about and vigilant about the threats emerging technologies can pose, but there will be enormous, enormous potential upsides as well. And I think that encapsulates our approach, right? You know, the rise of these machine learning techniques has been pretty incredible. It's been coming and building for years. I think many of us have been surprised by how fast some of the most recent developments have been. And we see this is going to transform our society in many, many ways. And the benefits are very clear. You look at something like medicine, disease detection, drug discovery, access to healthcare information by sets of people who might never have had that access. All of that is not just on the horizon. It's happening now because of these techniques. And that's just one area, precision, agriculture, climate change. All of these things will benefit from these new machine learning tools. At the same time, we see real risk, right? And it's long-term risk and it's also immediate risk. And that we need to address that risk if we're going to realize those promises. Do you think that some of this notion that it will lead to our, you know, extinction, does that sort of obfuscate or kind of make it difficult to really address the concerns and the regulations that need to be in place? Well, it's a great question because I think we see really what should be best thought of as a spectrum of risk, right? And in some ways, it might be easy to try to bifurcate it between the ends of the spectrum. There are longer-term risks. And we've heard pretty clearly from fairly senior people in the field about their worries about some of those longer-term risks. And I think we've taken an approach in the administration to say we need to address those. They're real national security concerns, safety concerns, and we should look at those in the long-term. But I think we benefit also from bifurcating that and saying, but there are also, that should not hide the fact that there are immediate risks too. And it's a different track, the immediate risk category. And I think we are seeing real potential harms to privacy, to security, concerns about human rights and civil rights, concerns about civic discourse. And those do need bias, you know, bias in these systems and equity. And those do need to be addressed. You may have already answered it in that remark, but are there specific issues with how NTIA and the administration are thinking about the concerns around the large language models? Is there a nature or already some framework that you're thinking about in terms of categorizing these issues or risks? Yeah, it's interesting because the, you know, it's funny, I'll just say the large language model, for those of us who've been following this space for a while, right, it's not even clear that the large language models are going to be the most impactful or economically consequential, you know, developments in this space. But they've really captured the public's imagination. And they're being used already today. Certainly my kids are using chatGPT. I hope it's not on all their homework, but they're using it. And so it's behooves us to be thinking in a clear-eyed way about what those risks are. And that work has already begun. And it's been in progress, you know, from the administration point of view, OSTP, the Office of Science Technology Policy, put out a blueprint for an AI Bill of Rights. We have worked with companies, talked more about it, to get commitments from them to address some of the harms that we're seeing today. And I do think that there are these really immediate questions that need to be answered about the large language models and how they're being used and how they're impacting our society. And I'll just say at NTIA, we're thinking a lot about accountability in that space. That's a piece that we've, we launched a project about a year ago to talk more about it, but to think about how we make sure that models are accountable and do what they say they're going to do. Yeah. I mean, it's interesting you talk about your kids using it. I mean, the large, I mean, right now it has captured the public imagination and being a long time native Angelina. I mean, we're seeing the real dynamic so early on in the development of the technology, that real conflict between the use of AI and then what it's doing to, you know, like the creative economy is an example, right? I mean, the Hollywood strike is very much centered around issues of how AI will impact the ability of a whole industry. Which is, you know, one of the reasons why our country I think has been so economically and globally powerful on a cultural level. How do you, I know that the use of it and copyright laws are definitely not within your purview, but how do you just getting your thinking on how does NTIA and commerce more broadly think about the use balancing kind of these large data sets and the use of them and helping that innovative industry and economy grow. And at the same time, how do we protect the rights of creators that are, you know, whose work is being absorbed by these models and then being used to generate new content at, you know, at literally no or very little benefit to the original creator. I think it's a great example of the kinds of dislocations that are coming and the pace of it is what is probably in some ways quite surprising. I will say there, this is a very hard set of issues around intellectual property and particularly large language models, but also just generally in the training of models and what models produce. The question of who owns what a model produces, the question of can an AI own a patent or what does that look like? I think there are people who are thinking about these hard legal and policy questions right now. I'll just say at the Commerce Department, our colleagues at the Patent and Trademark Office have launched a number of inquiries, both on the copyright side and the patent questions that are really interesting and to also think about how we promote in the United States more innovation, how we provide the kind of intellectual property protections that will promote more creative uses. And I think this question about artists is quite a real one. We're seeing it play out right now. And I think that will have to, in some ways, that will have to be resolved. I don't have a great comment on the actual conflict that's going on right now in Hollywood. Other than to say, I think it's a very good example of the kinds of dislocations that were or changes that we're going to see. And we need to think about how across the board, these new tools are going to affect work. In some ways, in bursty ways, you know, 10 years ago, I think many of us probably would be included predicted that, you know, driverless autonomous systems would, you know, would be here within a decade and that, you know, we might not need truck drivers, would our kids learn to drive even. That has been slow to come. It turns out some of these problems are harder than we think. We've made amazing progress, but we're not there yet. And so it's sometimes difficult to predict, but there's, as I say, a burstiness, a non-linearity to these steps forward that I think is part of what's unsettling here. Yes. I love that example of the autonomous technology because, yes, four or five years ago, you know, we thought we were seeing cars on the road anytime now. And we know that the progress is made, but then there's definitely a plateau in terms of the development, but also then is the infrastructure really ready to support it? And is our policy infrastructure ready to support it? The policy, yes. So that's a good question about then kind of jumping ahead to how you see your own policymaking, especially, I know that you've had inquiries yourself at NTIA about AI governance. What are you hearing? Can you sort of, one, give us an update on where you see the agency's role in policymaking over the next year? And then also what are you hearing from the inquiries that you've made to civil society and others about how we should be governing this space? Well, it's an interesting time to be working in this area. And I should say, you know, NTIA, our statutory mandate by law, our role is to serve as the president's principal advisor on telecommunications and information policy. What does that mean? We are basically policy advisors. So we think we're not regulators. We're not here to regulate. But we don't think necessarily about what the law says today. We think about what the law ought to say. And so we have been engaged in this project across the board of thinking how should we as an administration respond to these new developments which have been coming for a while? How do we engage internationally? We haven't even touched on the fact that there's a giant issues internationally to think about. National security issues, a strong desire to make sure that innovation happens here in the U.S. and with our trusted partners around the world. And that we're building AI in ways that promote our foreign policy goals as well. I think all of that for us has been, it's going to be a far-flung enterprise for the administration. There are a number of big things that the administration is working on. At NTIA, I mentioned one of the interesting things we started on. We started on this about a year ago before all of the chat. Yeah, so it takes a while to do some of these projects. We embarked on a project on AI accountability and really thinking about this is just one example of an area. But AI accountability, this question of how do you make sure that models are actually behaving the way they say they're behaving? A key step to making sure that we can actually put rules in place or guardrails in place is being able to understand what a machine learning model, what an artificial intelligence system does. And it's a lot like financial audits. If you think about financial audits and financial systems, if you're going to represent that you've done X, Y, or Z financially, there are a whole set of rules around that. There's a policy backstop. There are standards for accounting. And so we embarked on an inquiry to say what would it look like to create that kind of ecosystem for AI so that we would know what the standards are that people should be measuring against and then figure out how you actually do that measurement. We put out a request for comment in the spring. We got over 1,400 comments, which for us, our little agency is a ton. We touched an herb and people are very interested in this question. We got a lot of comments back and I'll just say I think there's a keen interest in putting tools in place so people know how to figure out what accountability looks like and then making sure that we think about where we might need policy backstops to make sure that when companies or developers could be private sector, public sector, when anybody represents something about how their AI system is working, we've got tools to measure whether that's true. That's just one example of a kind of I think full spectrum approach to these issues. You know, one of the things obviously that we really care about is the connection with public interest policy and technology, right? It's not just about advancing open technology, but it's really about ensuring that it's serving the public interest. And while a lot of these technologies tend to feel like they'll benefit society, we know that they're obviously we've talked about the risks. And so as you're engaging the public more broadly, how do we actually engage the resident citizens of the U.S. who are interacting with AI? I mean, the conversation right now gets really focused on generative AI, but the reality is that AI tools, AI supported tools have been around in our mix for at least, you know, obviously for decades, but you know, definitely in cities and we see the issues with it, whether it's, you know, the bias around any image based technologies.  Are there challenges you see to having a more broad based conversation where the public can really be engaged? My concern is that they're not one of the fourth, you know, like a general public is not really part of the 1400 that get to, you know, that get to give their input to NTIA. And it does seem like this is a different kind of technological moment that does require more public input and debate. Absolutely. So I think it's essential that we have an inclusive conversation so that we are building systems that reflect these real impacts on society. And if we, we develop at our peril without that input, right? And I think it's going to ultimately be, it's important for developers, it's important for us. If we're going to meet our goals as a society, we've got to have that equitable conversation. So how do we do it? I think part of it starts with good storytelling, with good narratives to help draw people into this debate. You mentioned, and I think people are seeing it and feeling it. You mentioned some of these other advances. We've had, you know, machine learning in our lives for a while. Facial recognition is a terrific example. And I have to say it is quite surprising to me as an observer how far and how fast we have moved to bring these tools into everyday life. You know, there have been, as a great example, the stories about local police departments using facial recognition tools and improperly arresting people, right? Taking people into custody, taking them out of their lives. The biggest thing that the state can do is deprive you of your freedom. And we're doing it based on a set of tools that we know, that we know we've shown our bias against people of color, right? That don't work as well. The pace of that change, the pace of that adoption just shows how much we need to do to first of all, educate and inform people to understand the limits of these tools and put good guardrails in place. But I think those stories help us bring people in. And even though, as you say, large language models aren't necessarily the longest running or most impactful thing, I think we have to lean into this moment. This is a teachable moment for us. This is our society's moment. People have stepped up. Everybody I know is talking about chat GPT, you know, this winter and spring. Admittedly, I run with a set of really nerdy people. But, you know, I think a lot of people were talking about it, and we have to use those moments to bring people in. The majority of the comments we got, those 1,400 comments were individuals. People worried about their jobs. People worried about their security and privacy. And I think we can bring people in. And if there's anything that's encouraging here, it's that we're actually a bit earlier in this discussion. And I feel this way than we were in some of the previous generations of technology. So there has been some, I mean, there has been some conversation in communities across the U.S. around facial recognition technology that has been somewhat contentious, that in fact a couple years ago forced a couple companies to stop using, to stop allowing police departments to use the technology. When we think about the public, when we think about public interest and equity, how do we incentivize? I mean, are we in any position to really incentivize companies to take that view in mind first and to really focus on serving the public good and equity, not just assume that it's a benefit to society, but to actually really center a lot of their development around these kinds of values and principles? The truth is, and this is what will help us, it is good for businesses and developers more broadly to be thinking about these issues up front. The success of their products and of their companies is going to depend on their ability to get ahead of these issues, privacy, security, the impact of bias in the models that they choose. And I think we need to do more work to lift that up. I don't want to be naive about the work that it takes to make sure that people understand that. But I talked to leaders in the developer community. I think in boardrooms across the country, people are starting to ask the hard questions. As they did with cybersecurity before, how are we getting ahead of these issues? Are we actually doing the right thing and making sure that this isn't going to come back to haunt us in terms of our reputation or in terms of our liability? I think there will be more to do. The starting point is that public attention that drives developers, companies, and others to really think we've got to get ahead of this, and that's partly why it's important to talk about it. I think these accountability frameworks that we are trying to put in place in government, starting with tools that show people how they can measure their risk, and then moving to ultimately I think these commitments, codes of conduct, ultimately are there going to be rules on the books that actually people have to obey. And that will be the path to making sure that companies are thinking about this. And it strikes me, it's important to say, I think in New America, one of the opportunities here is also to just create more dialogue between industry and civil society to really ensure that, because I think the conversation is happening perhaps in the boardrooms, but sustaining it I think is going to require a lot of convening and constant dialogue over the next five, ten years of development, so that we're working in essence in concert to make sure that whatever harms are emerging, we can identify them quickly and address them and have some level of accountability, but also create more perspective and trust. The problems are really hard. I mean that's part of the challenge for us. Some of these are very obvious, some of them are harder, and the solutions are not so easy either. So we're early days, but this is the part where we really need thought leaders and honestly it's going to be groups like New America who can step up and represent the broad views of the public and also have this dialogue with developers, with companies, with the technical community because we need it. And I think a lot of people are asking the question, what does good look like? They don't even know, right? That's where we can help. And by we I mean us. Yes. Well, I have a couple more questions I wanted to get out of the way before we open it up for audience questions, but you've been around this industry for a long time. Is there something that strikes you as different in terms of how we're approaching this moment versus other waves of development?  I mean these are really hard problems that we don't... There's so much unknown that it's hard to... It's going to take a while, but what gives you hope and confidence in this? Well, I think the biggest thing is that we're engaging region relatively early. So I have been in the sort of technology and policy space particularly around the internet. If you compare this to the early days of Web 2.0, cloud computing, social networking, even the development of the internet, I'd argue we've leaned in more here than we did in any of those places. We've learned some of the lessons. Part of it is that the developer community has learned. Like I look at... I used to have this great chart of when did each company hire its first policy person? Microsoft, it was like employee number 20,000. Google, it was like employee number 3,000. Twitter, it was like number 400. If you look at these relatively small companies doing some of this very impactful work, they're hiring policy people, they're getting themselves to Washington. They're part of this dialogue that's happening on Capitol Hill right now. And I think they're engaging earlier, which is great. I think also we have a much more sophisticated set of civil society players than we've ever had before. New America and others out there really digging in hard on how do we make sure the public's represented here. And I look at... I have been involved in teaching over the years and I look at the next generation of computer science students. This is what they want to be engaged in. Not just the development of AI, but they care about the impact of what they're building. And that to me is the biggest reason for hope. We're building a generation and it's partly what they care about of young people who really want to make sure they understand the implications of what they're building. So that is the biggest reason for hope that we're early on, but it's going to be hard work and a lot to do. I would not be doing my job if I didn't mention to that point that, yeah, New America has an amazing program, a public interest technology program with universities across the U.S. that's focused just on that, trying to build the next generation of technologists who have the rounded perspective to really think about the implication and the ethics that go along to building these powerful tools. I think the one... And it is extremely encouraging. I think probably the one thing that we need to worry about a little bit here is that we don't really have the luxury of time. We don't. And that is a little bit different than it felt 15 years ago, let's say, or 20 years ago in the internet space. The internet was relatively new. When I started working there, we would measure... It was 40 million people online. Now there's 5 billion people online. The pace of change and the uptake in these technologies is very fast. And so we need to be figuring these issues out now. So there's a sense of urgency as well. So I'm glad we're engaging early, but we also need to be engaging early. And I'll just say the administrations, we're working on this, right? At the highest levels, we've worked on this set of commitments from companies. We're engaging with our international partners to make sure that we're bringing that. We're working with them about global solutions. And we are actually thinking about what does the legislative world look like just today? Senator Schumer is convening this very high level group of CEOs. That's exactly what we should be doing, right? Educating ourselves and thinking what are the rules we need to put in place? Well, that is a great opportunity to talk about something we've been advocating for for a really long time, which is some comprehensive federal privacy legislation that really sets us up to address not just the existing technologies that have been out there, that amass and use all of America's data, but also the new technologies and a lot of these LLMs in particular. It feels like the time says we need to act, and there's some urgency around this moment that's been building up, but there is a little bit of just inaction. I don't know what you think about our inability to get some comprehensive federal privacy legislation at the door, as the Europeans have been able to do. And more generally, with the kind of urgency we have, there are still foundational pieces of legislation that we don't have in this country around technology policy that need to be advanced. I mean, I know it's a complicated question, but what will it take for us to really take these kinds of actions on? It's a great question, and I think partly because it tells us something about how we as a society are able to react to some of these technologies. I will say it is surprising for those of us who have been in this space for a while that we don't have a comprehensive federal privacy law in this country. If you had asked many of us, not to date myself, but 20 years ago if you had said we will be in 2023 and there will not be a federal privacy law in the books, I think people said that can't be, right? This is going to be too important. We know these things are going to affect people, and we know by the way the data shows us that the public wants these rules as well, right? And so, well, the administration has said, the president has said we need to be doing more in this space. We would really benefit from a comprehensive federal privacy law, partly because it is the right thing to do to protect consumers and have a baseline that everybody's protected, but partly also for our global leadership. I mean, I've worked with a lot of people, talked to a lot of other leaders around the world, thinking about this question of how we're going to address these hard issues. And it's hard when the U.S. itself doesn't even have a baseline privacy law and how do we think about our leadership moving forward. So, I'm hopeful that with the right incentives we can move quickly. I do believe that there's a strong sense that the moment is upon us. We have to act, we're starting to act, and there's going to be a lot more to come in the coming months and years. I would love to open it up for questions from the audience if that's okay with you. Yeah. Thank you, Alan. It's good. I mean, I think we're all happy that somebody is well informed and as long with your deep background is effectively the point person on AI on the commercial side in this country. But I wanted to, you may not be responsible for some of this, but I wanted to get your thoughts on this question, which is we saw essentially the news media business essentially more or less get destroyed by the fact that social media companies were sort of giving away this product for free and no business can survive if the product's being given away for free. And so picking up on what Lilian said, and one of the reasons we have a very prosperous country is because of copyright and patents. And so if you look at the large language models, I mean essentially they've hoovered up 170,000 books. The authors involved have, and that's just one example in terms of creative content. So I mean, this seems like a very big problem. Alan, you said time is sort of running out because this thing is moving very quickly. So what are the safeguards that realistically could be put in place for content creators, whether they're writers or artists or any other form of content creator, including developers themselves, right? It's a terrific question. It's a hard problem. I think the problem, this is one where I suspect that law will play a major role and have to fairly quickly. And when I say that I'm also, I mean, not just, you know, new legislation but the interpretation of our existing laws. And I think there's a lot of work being done at the Copyright Office. I talked about the Patent and Trademark Office and Commerce to try to get ahead of that and think about hearings on Capitol Hill. But I suspect we're going to see litigation and that litigation will give us some insight pretty quickly about what the, how our current legal structure will work. And then we'll have to go from there. I'd also say there's an international dimension to this, right? You know, math does not stop at the border. You know, innovation in AI is happening all around the world. And part of what we're so keen to do as an administration is, yes, work domestically, think about also the companies that are leading here and getting commitments from them, but also then work with our partners. We're going to start with the G7. There's a big effort, the Hiroshima process that's been kicked off by Japan to come up with codes of conduct around AI and then move out from there. The Indian government's been a big leader in the global partnership on AI. We're very keen to be working with the UK. They have a big safety summit coming up. And we'll see more and more of these international efforts to try and get ahead of these big problems. I'm getting the Peters today. Hey, I'm Pete Singer. Great to see you again and thanks for a wonderful talk. I wondered if you could speak about your own personal, but maybe the administration's view of the effect of this on jobs, not merely in an economic sense, but in a political and societal sense. There's a variety of contention on how many jobs will be either replaced or redefined or reduced because of this. We've got estimates all over the place, but what I was struck by recently was there was a poll that came out about two months ago and the way it was labeled in the media was, only 14% of Americans worry that they'll lose their jobs to AI and robotics, only, so one out of seven people in the room. And then there was a Gallup poll that came out earlier this week that said, actually now it's up to 21%. So can you speak to what your view on this is and how the administration is thinking about it, particularly in terms of there's very clear social anxiety that also has real potential security effects? I think a starting point for us has to be to engage in that conversation and to really understand that there will be impacts, but also try to be clear-eyed about where they'll be and how we might mitigate risk. But I think the biggest thing is starting by understanding that there's going to be a lot of benefit and that that benefit in itself will create economic opportunity. I just think about, for example, the use of, this is a little bit of, not a perfect example, but I was speaking to the head of a local private school here about how they were handling the rising use of chat GPT and he said, well, this is just like a calculator, right? So we are going to have to change the way we assess people. It's not the end of education. It's not something that he was planning to ban in their school. It was something that they were going to embrace and think about how it was going to have to change the way they do evaluation. You know, students use the calculator today, use calculator today on their math tests in high school. We still learn arithmetic. The point is there are going to be these benefits in unexpected ways. There's a different view of many of these things and not to be dismissive of concerns because they're real, but there's data out there that also shows there's a tremendous amount of job creation that comes with these new technologies. And so figuring out how we make that pivot to help people respond and also to recognize our predictions are not necessarily so good. We talked about driverless cars a little while ago. Ten years ago we might have said, oh, my gosh, we need to embark on a massive project to rescue the trucking industry. That's not here yet. It might be here. We need to be really clear about it. We need to prepare for it. But right now it's very hard to find enough truck drivers and it's going to take a little while, we think, for some of these things to happen. So I do think there's a level of preparation being ready for this and then also trying to make sure we're benefiting from the upside. The last thing I'll say, because I think this is such an awesome question, I was just down visiting Miami-Dade College down in Miami.  I say this, it's an ASU-like college system, just massive in its scale. I think they have 100,000 matriculated students. And they invited me to see their AI lab and I'm like, you don't even really have a PhD program. What is this AI lab going to look like? And I went and I was kind of blown away because their approach is we're not here to train PhDs in creating frontier models. But what we do think is that every business in America is going to need somebody who knows how to use machine learning, who knows how to use chat GPT, who knows how these basic tools work.  And I think it's brilliant because the fact is we need to stop thinking about the frontier models as the only thing. And the job dislocation is the only thing. We need to be thinking how do we train a next generation of workers to use these tools across the board, whether it's radiologists scanning scans or people using chat GPT in small businesses across the country. And that's the retooling that we need to do and start getting ahead of. And that's how we're thinking about it. Florida is a great model. It's the largest, that Southeast Florida region is the largest producer of Latino engineers in the country. This question, you know, makes me ask, can you describe what efforts or maybe if they're not already in place, what kinds of investments we can be making to really up the digital readiness, especially of kids? It just seems like part of the, I think what you're describing is sort of the augmenting capabilities and potential of AI. But as we know, a lot of our children, especially children of color, poor children have just the least access to a lot of technology and even just basic tools to be successful in today's economy. So how does a country do, I mean, what I'd love to see is a country where we're starting to invest early on in really equipping our kids to be successful, all kids to be successful in this moment. But how do you, from your vantage point and in the collaborations across the federal government, where do you see the opportunities to do more of that? Well, it starts with, you know, the investments that we've been making in STEM education across the country or STEAM education across the country, we really do need more investment and more help for people and for diverse communities to be starting at a very young age, learning about these tools and the tools that they'll need to be able to succeed in a, you know, very STEM-oriented world. I think we also need better digital literacy and media literacy, and there's a lot of work that's been done on that. So a lot of it, some great work here but done here in New America. But that's got to be very real. And you see groups, I'll just shout out to Common Sense Media as a great example, who've put out toolkits and are thinking about this, how do we embrace this, make sure that we're teaching digital literacy and digital media work. Part of our efforts even at Commerce at NTIA as we think about building out broadband infrastructure, we've been given big grants to do work on digital equity. And our view of digital equity is it's got to include thinking about the workforce of the future, helping communities that have been left behind and particularly vulnerable communities, making sure they have the tools to thrive online. So there's a lot of investment that's going on right now and like I say with the Miami-Dade example, we need to take a very expansive view of this. This isn't just about it will be partly about investing in those frontier models in those PhDs who are creating the newest technologies, the big NSF investments in those academic leadership centers, but it's also about how do we reach a very broad cross-section of America to make them understand they're going to be part of this revolution too. Yeah, I often say no child should graduate high school without digital literacy training. That is the new civics curriculum of this country and we should figure out how to make that happen. Alan, this has been a delightful conversation. I don't know if we have any more questions from the audience, but I think with that we just thank you for your time. I'll just say thank you and I'm going to say this is, it is a moment of great opportunity but also some peril and I think there really is a big global conversation going on. Are these going to be technologies of openness or used by closed societies? Are they going to be technologies of freedom or of control? And how do we create more equitable outcomes as we embrace this new machine learning revolution? So I'll just say thank you for the conversation. What we do is conversations like this and the engagement of leaders like we have here today. So thank you. Thank you.


It's all going on here, but I'll tell you what, Martin Bechelig collecting to go high and wide is going to get speed here. Oh, look at that from Frick, you're right, brilliant stuff from Max Frick. Where did he find that from? Mikkelsen trying to repay the compliment, pushing hard up the inside. Martin Bechelig now, brilliant stuff from Martin Bechelig. Charges up the inside, block pass there, what a move from the Slovakian. Now Frick back into second place, Kim Nielsen's at the back. Martin Bechelig out in front on fire. I'll tell you what, Martin Bechelig just showing you've got to watch what's going on ahead of you because you've got to be ready when that door opens up. Frick straight through there, a bit of a mistake from Mikkelsen. Just picks up a little bit of unwanted grip now back up the inside. Don't think he's got the speed from there, but Martin Bechelig, patient, just watching the race ahead of him made exactly the right move.  He wants bronze, it's going to be a shootout. It really is, holder won the previous race. Martin Bechelig comes out and wins his second race of the night and in fine style as well, patient riders, you rightly say, Chris, it was a smashing performance from the man who won in Cardiff. Three points for Martin Bechelig, Max Frick, good ride for Max Frick. Two points for him, Mikkelsen back in third, one point. Kim Nielsen misses out, he's number seven. Class B way there, top class B way here in Torrentonite in the Marion Rose Motor Arena. Yeah, I've got to say that Martin Bechelig right now as the upper hander looks good, he looks good on the bike there, just runs out really wide. A little bit of a mistake from him there because that helped Mikkelsen to run across his run there, had to get out of the throttle. Meanwhile, Max Frick's hit the front with that injury that we know he's hampering him quite a bit, bit cautious. Mikkelsen runs across, he knows he's coming, but that man, Vashalix, watching all this going on, just runs straight down the inside, hits himself in front, runs across Mikkelsen, doesn't give him a clean run around the outside. Great ride for Martin. Yeah, classy ride for Martin Bechelig. Looking good tonight, two rides, two wins, can't do better than that. Max Frick working hard here to get the better of Mikkelsen. Mikkelsen, as you rightly said, just got drive at an inoffitude moment there and that allowed Frick to come through into second place. But right now...


Because there's a difference and I had not considered there being a difference. Keep watching to find out why that's so important in today's book. So hi y'all this is Kyla Denonio and you're watching Kyla's reading day. Today we are talking about a heartbreaking but beautiful memoir which is finding me. The genre of the book is nonfiction, the theme of the book is biography. So this book was absolutely breathtaking and I do not say that in an exaggeration. I almost put the book down multiple times because there are so many tales and stories in this book where you just are waiting for this big hurrah moment the sun to shine and you to be like yes and it does not come. And it reminds me of something else she wrote in the book how did you claw your way out? There is no out. There is just this moment getting through it getting to the next moment. Right and Biola Davis says time after time after time life hits you. Life hits you. Life hits you. At most you can try to be prepared but life hits you. And I'm like one person can't take this much. One person does take this much right. So absolutely transparent beautiful story. I did push through I kept reading it and you get to see the strength of Viola Davis. You get to see the strength of her family right. So to me one of the most important quotes of the books was forgiveness is giving up any hope of a different past. And you get to see forgiveness in a lot of different ways in this book and that's why the quote is so important to me. Viola Davis eventually forgives her parents. I don't know if she even knew that she needed to forgive her parents but there's a time when she's performing on stage and her parents come to see her and she's like oh here they are they're wearing these worn clothes and oh my goodness and ah and then someone's like you're so lucky your family supports you. She's like are you kidding me? And the person says but they're here they came to support you they came no matter the distance they are here to see you.  There's a time when Viola had to forgive her siblings you know for traumas that they had and things they dealt with when they were little. There was a time when Viola had to forgive herself because she's not still that eight year old little girl but also that eight year old girl made it out. Bonus. So it is January 2022 and I definitely saw the movie The Woman King and my husband and I were leaving the theater and I'm like man I wonder what Viola Davis's story is right? So I'm like let me just look things up and I was like no I have her book I'll just read her book. I had no idea no idea that she had won Tony Awards. I know Viola Davis from How to Get Away with Murder and then I know her from the Suicide Squad. I had no idea she went to Juilliard. I had no idea that she had done Broadway. I had no idea that she was this classically trained actress but the fact that you have to start somewhere right? You see someone in a show and you're like oh okay they had a line but you've got to have a line in this show so that someone can pick you up for next show. When you see that extra even who gets a zoom in on their face that can set them up for their next role. She has to get a role in this guy's movie so that the director might remember her for the next movie and I had never considered that right? In my mind I know acting must be hard but no it's more than that because she says it.  Because there's a difference and I had not considered there being a difference. So this book was a really transparent look at all the struggles. You see me on screen acting being wonderful telling this story. I have a whole life world things going on behind here right? So a really good look behind the scenes at what happens to actors. So I definitely appreciated that. So would I reread the book finding me? Yes I will reread some of the chapters. I cannot reread all of the chapters right? The whole point of reading is it brings up stories. It makes you think about your life and makes you think about all kinds of things. I could not reread the entire thing. I was crying too hard. I was too drained. I'll definitely recommend the book but I would not reread the entire tale. So thank you for hanging out with me on Kyla's reading day. Today we were talking about the book finding me a memoir by Viola Davis. If you have seen Viola Davis in anything let me know. Get in the comments. Let me know if you've read this book how you felt when you read the book if you were able to get through the first couple of chapters. I look forward to talking to you in the comments and I will see you next time on Kyla's reading day. Bye.


A horse with a speed of light, a cloud of dust and a hearty hyal silver, the lone ranger. At the close of the Civil War, the government sent federal troops to the western United States for the protection of the settlers. But the officers in charge had no experience with the frontier. And if it had not been for the masked rider of the planes, they might never have brought the Indians and outlaws under control. There was a lone ranger's strength and courage, and above all his knowledge of the country that made possible the winning of the West. Return with us now to those thrilling days of yesteryear. From out of the past come the thundering hoofbeach of the great horse Silver. The lone ranger rides again. Come on, Terry, gang! Let's go into mid- Keep your eyes peeled, men. Dangerous territory. Yes, sir, Captain. Great, sir. Rifles slung handy across their military saddles. A detail of cavalry moved at a hand trot through hilly country. Their faces were weary and tense. Their horses worn out from the long ride. But the soldiers kept a sharp lookout. Knuckles were white from gripping the rifles. Every man seemed to be waiting. Waiting for a... There it comes. This mountain. Captain, it's an ambush. Shout all of you down. Pull down your horses. Quick, quick! The range is about 300, Captain. No breeze. Dead call. Make it 350. Pick your target. Open fire. I can't see nobody to shoot at, Captain. It's an ambush. Cut down, Brandon. They'll get us sure we'll all be killed. I said, cut down. Now we won't live. None of us will live. Stop stumbling. Use that carbine. It's the dirty guys. Here they are on the hill. We should have had skirmishes. Take those bullets. Count. Yes, sir. Hodge, lift your sights.  Can't whip anybody that way. Let me out of here. Let me out of here. Let me out of here. Brandon, you'll get the cart house for this. Well, I'll be killed. Oh, I'm shot. Here, let me see. I'm shot.  Let me rip that sleeve open. Is it bad? Nothing but a scratch, Brandon. Pick up that rifle. No. Unless we make it too hot for those outlaws. What's that? I said, pick up that rifle. No, I won't. Something's going on. What is it? What's happening? I ain't going to fight. I'll be killed. Hodge, McCaffey. Shirt lift. Keep up the fire. We've got to... Well, I'll be... It's a mask man on the white horse. He's driven them all. There's an engine with them. Garage men are scattering. Put them down. Hold your fire. The Captain's breaking up. Hold your fire. We might hit the mask man on the Indian. Mount your horses. All right, men. Up the slope. Forward. Get ready. Alert. Sergeant, bare left. Right, sir. The Moutain Laws is fighting awful fast, Captain. Captain, come on. Come on, boy, faster. Captain, come on. Just pull up. Look at the Moutain Laws stricken for cover. Detail. Hold. You know my name? We've been riding hard after you for several days, Captain. Captain, I'm right. We come from Fort. I'm obliged for the way you flank the enemy, mister. How many were there? What would you say, sir? Maybe 20, 25. The leader? Man with a beard. Tall and big. Wrote a rust horse. Yeah, that would be Gary all right. But what are we standing here for, Captain? Let's go after him and get the barnman. Quiet, quiet, man. Captain, believe me. Your horses are tired. Gary's are fresh. You couldn't catch him. You might just as well walk into another ambush. A more deadly ambush. Ah, it get dark soon, Kimosabe. Oh, that's true. You couldn't trail them in the dark. The odds are against you. Besides, you've accomplished your mission. One moment. Captain, I'm sorry. Captain, I'm sorry. Captain, I'm sorry. The odds are against you. Besides, you've accomplished your mission. One moment. Mister, I've already thanked you. But it appears to me you're well up on what this detail was sent out to do. Your orders were to head west and contact Gary's outlaws. Establish their position and return. May I ask how you learned that? Quite simple, Captain. I learned it from Major Selby. Did, eh? Your mask, mister. The Major doesn't usually confide in men wearing masks. Perhaps this will convince you. Eh? A note from the Major. Oh, yes. Captain, can I add commanding scout detail? Please be invited to... I see.  Yes. The lone ranger. After your detail had already left, Tonto and I brought word to the fort at Gary's position. Then we came... And you rode out to warn us. Uh-huh. We come late. Too late to warn us, perhaps, but plenty of time to help us. You return to the fort now? Those were my orders. We should leave scouts, though. Tonto's an excellent scout. Eh, my men are worn out. Horses tired. Tonto, keep watch on Outlaw Band. Good. You leave a trail so we can follow it, Kimosabi? Uh-huh. Tonto do it. Well, that's that. All right, men. Detail heads back to the fort. Hodge, McCaffey, Shirtliff. Any casualties outside of Brandon? No, sir. Good. Now, Brandon, you report to the doctor when we... Oh, where is he? Huh? Brandon isn't here? No, sir. Brandon! He's gone. And his horse is gone. Captain, we saw one of your men heading back for the fort. And we thought he was a dispatch rat. I sent no dispatch. That was Brandon. The coyotes deserted. Disserted? Oh, that bad. Bad for Brandon. He'll face a court-martial for this. All right, Detail. At the trot. For it! Hold! Captain. Yes? You're not planning to go after this deserter? We're doing it right now, mister. What do you mean? Brandon's heading only one way. He'll go back to the fort. That's where his wife is. His wife, huh? I see. He'll be there. And we'll get him. McCaffey, Hodge. Act as rear guard. 60 pages in rear. Tonto, did you hear that? Uh-huh. This man, Brandon, has a whack for the fort. He's sure to face a court-martial. Him desert. Did you get a good look at him, Tonto? What you mean? I did. He looked very young. Must have been his first time under fire. Maybe so, but... Yes, I know. That doesn't excuse desertion. Not in the army. But still, Tonto, you remember the young woman who walked past us while we were outside the major's quarters in the fort? That was Mrs. Brandon, Tonto. Uh-huh. Heep young. Young and devoted to her husband. Where to mention him to those other women? How proud she was. Uh-huh. What you do? I don't know, Tonto. I don't know. But perhaps that deserter needs help. You help? I'm going to look into it, Tonto.  Silver can get me back to the fort long before Brandon gets there. Long before the captain's men get there. I don't know where or when we'll meet. But your trail will be clear. Take care of yourself, Kimosabe. You watch out. Nothing can happen. I'll head for the fort now. Tonto, feel maybe you'll meet danger. You watch out. All right, Tonto. Pick up Gary's trail before it gets cold. Adios. Huh? We meet soon. Get him up scouting.  Well, Silver, Tonto, thanks for heading for trouble. Perhaps we are. Well, this deserter is now our affair. And yet... Come on, Silver. We're heading for the fort. Come on, boy! Help! Pardon me, ma'am. I didn't see you in the dark. Could the major tell me... Oh, it's you, Mrs. Brandon. Is there any word? Same question, eh? Same question in the same answer. Sorry, ma'am. No word from Captain Canard's detachment. But yours will be safe. All the men will be safe. No word yet. Don't worry. All you young wives worry. Doesn't do a bit of good. He'll be back. They'll all be back. I hope so. Good soldier, Canard. He'll bring him back. Oh, that's tattoo. Sorry he must be going. Good night, Mrs. Brandon. Good night. Good night, major. Six days now, and still no word. John was so worried. He didn't tell me, but I could read it in his eyes. He was frightened. Wait. Mrs. Brandon. Who are you? It's dark. No, don't open the door. Not yet. But I can't see your face. This is an army post. If I scream, the God will come. I came here to tell you something, but... Wait. Your voice, I recognize it. You're the man. I saw outside the major's office. The man wearing the mask. Keep your voice down. You wrote out after Captain Canard's detail. Did you find him? Yes, I did. And you're back so soon. Nothing's happened to John. Nothing's happened to my husband. Please, not so loud. Tell me. Tell me. Your husband's all right. Thank heavens for that. I know what you say, Dad. But if something were wrong, I... It's hard to say, Mrs. Brandon. What do you mean? Mrs. Brandon, I rode hard all the way to get here before you learned from another source. What are you trying to tell me? You said John wasn't hurt. Mrs. Brandon, your husband has deserted. Did it? No. No, he didn't. He's on his way here now. I passed his trail. He didn't desert. He didn't. His horse is tired. He'll be here until after sundown tomorrow. That means that Court Marshall... Captain Canard will arrive only a short while after him. There won't be much time. Court Marshall, the firing squad is already... Mrs. Brandon, you love your husband? Love him? He's all I have. Then you've got to save him from himself. Don't let him run away. No. A man can't run away from himself.  He can't stay here. You've got to force him to stay here. No, no, I can't. Now, listen to me. If you love him, you've got to help him. But not the way you think. I'll be back tomorrow night. I want to be here when he arrives. You've got to stop him. He should be here. Mass men said after sundown, he should be... Back door. Someone walking. John. John. John.  You're on. You're wounded. Oh, it ain't nothing just as great. Honey, I got to tell you something. No, no, just let me hold you. It ain't time. I got it. Well, I can't stay here. See, something happened out in the detailer. John, if you're trying to tell me you deserve it, don't I? I already know about it. I didn't mean to. Them rifles bang. And I got panic.  You couldn't help it. I love you. Oh, I knew it. I was riding away. Mary. Mary, how'd you know what happened? Huh? How'd you know about it? I came as fast as my horse could travel. Captain, he beat me to the fourth. He's here now. John, they'll court-martial you. The captain got here first, huh? Mary, I got to get away. Captain isn't here. John, he... He ain't. Then how'd you know? How'd you know? I told her, Brandon. What the... Never mind her, gun. I'm here as a friend. The mask man. You came after me. Brandon, you've got to listen.   Stay here, redeem yourself. And wife will tell you it's the only thing to do. I changed my mind. Put down that gun, Mrs. Brandon. Don't move. I'll shoot you if you take one step. John, get away.  Your husband must stay. No. No, I won't let him. All night long, I didn't sleep. Stayed awake. Thinking. Thinking. Honey, listen. No. No, please go. Go now before it's too late. You can get in touch with me later. I'll join you later. Go before they put you before a firing squad. Brandon, listen to me. Your wife is wrong. Mr. I don't want to shoot. Don't make me... Mary, put down that gun. I'm not worth it. Go, John. Go. I'll stand for you. Goodbye. John. You can put that gun down now, Mrs. Brandon. He's made his choice. What? I know you meant well, but I couldn't stand it. I suppose they'd court-martialed him. I suppose he'd been stood up against the wall. I told you once before a man can't escape from himself. No matter how fast he rides, his cowardess rides with him. He's not a coward. He's lost his chance to prove otherwise. Once you have done it... At the front door. You'd better answer it, Mrs. Brandon. Here, I'll answer it. Their husband made his choice. You won't tell them, please. Another thing, Mrs. Brandon. Yes? Mrs. Brandon, I'm sorry to inform you that your husband will come out and place himself under arrest. My...my husband isn't here. No. I happen to know that he... Look at the captain's part. Well... I checked it back at the house, captain. There's hoof marks coming gone. It was Brandon's horse, captain. He's left. He's... I see. Who's that with you, Mrs. Brandon? You want me, captain? Yes, mister. I do. I thought it was you. So you wrote ahead and warned Brandon he'd be arrested, eh? Well... Mister, you're under arrest. Sergeant, see that he gets to the guardhouse and see that he's locked in. The curtain falls on the first act of our Lone Ranger story. Before the next exciting scenes, please permit us to pause for just a few moments. Now to continue our story. Found in Brandon's home by the captain, the Lone Ranger was arrested for aiding in the deserter's escape. Meanwhile, Brandon rode toward the west, putting as much distance as possible between himself and the army post.  You've got to make miles. They'll be after me. They're... Don't stumble now. Oh, I know I've been driving you hard all night and all day. But steady, boy, steady.  Get across this area. Get across it. Look out! Hello? Mister, where are you? Who's that? This way, mister. I'm at the window. Who? Mrs. Brandon. You shouldn't be here at the guardhouse. It's all right. Nobody saw me. Have you heard about your husband? No, no. I haven't heard a word. The patrol went after me. I came back last night. The regiment's getting ready to ride against Gary's outlaws. I should have listened to you before, mister. My husband did the wrong thing. You're worried about him. I got a funny feeling. Quiet. Something must have happened. Quiet. Someone's coming. I don't hear anything. Someone's coming. Turn away from the window. All right, but I... You will not make noise. Tonto, hear him in jail. Tonto, is that you? Ah, may come with me. Any sign of Gary's men? Ah, them may camp far off. May come here, like you say. You learned that I was in prison. Ah, what trouble? Never mind it now, Tonto. I gotta get outta here. Uh-uh. Tonto, do it. Will you help find my husband? We'll do what we can, Mrs. Brandon. Tonto, come back later on. The guard detail. Get back out of sight. Hurry. Tonto, get away. Come back in two hours. We have work to do, and I can't do it in jail. She will not hit. The large key. Hurry. Uh-uh. That's yours. Good. This way, Kimosabe. Where you got gone? Must be in here someplace. At the desk. Here they are. You've got the horses? Uh-uh. Silver scoundrel. Out back of jail. Careful now. The guards tied up and gagged. We may run into soldiers. Mister, you gotta run for it. Captain, come on in. Mrs. Brandon, what are you doing in the guard house? You should know better than... Don't move, Captain. Get to one side. We're going out. Oh, you can't. I haven't time to wait for a trial, Captain. You'll find a note on the desk. It gives the exact location of Gary's band. You realize what you're doing, Mister? Military law. Right now, that doesn't matter. Come on, Tonto. Oh, Tonto, I'm sorry. Sorry, Captain. If you don't move out, I have to make sure. This way. Get him. Here's scoundrel. Silver, here's silver. Pull out the guard. Get him, Frank. Pull out the guard. Steady boy. Then come fast. Silver, come on. Get him out. Get him out. Get him out. Get him out. Get him out. Get him out. It's the right way. It's as clear as a blazed trail. Keep on, Silver. Brandon's horse can't travel fast. No, we find him. Maybe for some high in the sky. What's that? Tell her, look. Oh. Storm's coming up. It's like a bad one. Once we locate Brandon soon, his trail will be washed out entirely. Rain bad. We hurry. Come on, Silver. Wherever Brandon is, we'd better get there. Go ahead. Boy, quiet. That's Gary's outlaws right below us. We almost stumbled into them. Gonna rain might as soon. Ain't got no food. Oh, my leg.  We both got game legs. Just a couple of lame deserters. Only you ain't to blame. You don't know what you was doing. Just let me hang under the stirrup for a while. I wonder how long I was lying in that Roy. I'm worn out as a newborn here. Ain't good for nothing. Yeah, she's gonna pour. I can name her one thing to do. You join up with them outlaws. We got to eat, don't we? We've got to button. Brandon. Well, I'll be the man's man. It's a long time to locate you, Brandon. Don't get too close to the edge that cliff, mister. That's Gary's camp down below. Yes, so I see. Ah, him in the wrong place. How does it feel to desert Brandon? It was right, stranger. It don't feel good. I've been squatting here right on the edge of this cliff looking over Gary's camp. I was gonna join him. Turn out low, huh? What else I gotta do?  For what? I want to go on living. You'd rather be hunted as an outlaw deserter. What about your wife? She thinks you've got courage. She believes in you. Never mind my wife. She thinks you made a mistake, too, Brandon.  I ain't going back. I can't. It's gonna rain soon. I'm going down to that camp. Just leave me alone. Understand? Very well, Brandon.  I wasn't sure. The first time a man's under fire, he may break and run, many do.  Kimu-sanbi. Why are you staring down at Gary's camp? What's wrong? They move. Huh? You mean that isn't where you left them? No. Are they supposed to be here according to what you told the regiment? They move. Regiment think them further west. I see. Well... Look, over across them hills. It's a regiment. They're marching west. They have the wrong idea of Gary's position. They'll march straight into an ambush. They'll be killed. They'll be murdered. Oh, wait. Gary's outlaws heard the bugle, too. They're getting ready. Oh, I'm that bad. We gotta warn the regiment. I thought you didn't care about the regiment. We gotta warn them. They'll be massacred. I don't care what happens to me. We gotta save the regiment. Can't... Listen, can't we build a single fire or something? Good, Brandon. Come on, Tana. We'll gather wood. They can see the smoke. Oh, no. It's too late. It's raining.  And if we could, they wouldn't see us. What are we gonna do? They're marching straight for an ambush. We've gotta save the regiment. I think you're not. You're not. Yes, Major. Give the order to close up. This rain is so heavy we can't see where we're going. It won't last long. A better race will keep close formation. Right, sir. Euglern! Sound close order march! Close order! Yes, sir. I think we're safe in following the directions left by an Indian. We have nothing else to go on. According to that note, we have another full day's march before we catch up with Gary's outlaws. You think that... Uh, Major is worried? Well, after all, canard, that Indian helped the mass men escape. May I point out to the Major that you accepted the mass man in the first place? Yes, yes. The lone ranger. The lone ranger. He helped the deserter. He had to be arrested. Of course, he would have released him, but these civilians need discipline, canard. Helping a deserter. Even the lone ranger can't disregard army regulations. What about the deserter, sir? We can't... That's enough. You couldn't find him. We'll get after him later. The first thing is Gary's outlaws. Yes, sir. That's our order from Washington. A band of bad men have terrorized this whole territory. Let's be stopped once and for all. Major's right. Slippery as an eel. This time we'll grab the whole crowd. Can't fail. Captain. Yes, sir. Which way do we go? Huh? Use your eyes, man. The rain's heavy, but not that heavy. Heading straight for a bluff. Can't do over it. Regiment turns which way. Oh, we turn left, sir. There's a little pocket at the base of the bluff. We go through that and out the other side. It's just as well we're alone out here. He's rolling hills. Bad spot for an ambush. Well, Captain, we'll head the regiment toward the... Major! Where's the Major? Put in Heaven's name. In a word, we have no wild brothers. Who's that calling? Oh, no, sir. Can't see. There it is. That white horse up ahead. Yes, yes. Coming this way. Halt the column. Column! Halt! What? Major, that's the low ranger's horse. Sure it is. Sure it is. Major, tell the ambush up ahead. Well, I'll be. It's Brandon. Brandon, the deserter. They got the Major's partner. Where'd you get that horse? My horse was lame, sir. You got to turn the regiment. You can't go this way. Brandon, as a deserter, you should be dealt with right here. Captain, sure I deserted it, but that don't matter now. The lone ranger gave me his horse to come warn you. Him and the Indians stand on guard so Gary don't place his men without they can do something about it. Good. Yes, sir. His whole crew was right ahead, and they didn't park. Impossible. You're crazy. Gary's a fool days much ahead. I tell you he ain't. He moved. You got to believe me. Believe but deserted? Major, I tell you there's only one thing to do. Get the column around this way. You can surprise Gary and get him from behind. The mass bandage, you don't keep him busy on this side. How do we know you're not with Gary yourself? Take you for real. Wish you were I.D., Brandon? No, sir. It was a mass bandage, but I had to warn you. That's enough. Captain. Yes, sir. Get the column moving. Close order. Keep them as quiet as possible. Come by four. That's real. We'll close with Gary's outlaws in less than an hour. Tell them the men to look to the rifles. Major, can I go along? I got to make up for dessert, and I got it. There they are. Gary's outlaws. Give them. Sound the charge. Hermit's outlaws. Fire. Seize fire. They surrender. Seize fire. Seize fire.  Place them under our regent. Many days we're captain. Fine work. You've got Gary in his whole outlaw band. Here comes a mask man, Major. Yes. Major Selby, I see that Brandon warned you on time. Mr. You and this Indian, they've done the government to reuse service. Thank you. Now, by the way, this is Brandon's horse. It's a travel lame, but he'll recover. It's your horse, mister. He's sure a wonder. Good boy, Selby. This was one time you carried another rider. Selby, him take rider when you tell him to. That way. Hey, Brandon. What moment? I wasn't going to a place, Major. I guess you want to put me under arrest. Ready to take my medicine. Hey, Captain Canard, how did this, uh, this dessert of fat? Brandon, sir? Like a wildcat. Hey, Brandon, you desserted? I ain't trying to get out of it, sir.  Let me finish. I said you desserted, but, uh, you made up for it by your Gallic conduct today. Captain, we're going to forget the whole business. I hoped you'd feel that way, sir. Bad breach of discipline, but after all, well, today you proved what you were made of. Major, uh, I don't know how to thank you. Stop it. Stop it. Don't want thanks. Well, the Army wants us bravely encouraged, and we're getting it from you. You, uh, you can tell your wife I said so. Well, it's for you, mister. I, well, uh, Captain, where is he? Can we see him a minute ago? Who, sir? Who? Who do you think the lone ranger, the Mask Man, want to thank him too? He and the Indian are gone, sir. See them out there? I don't think they want thanks, sir. Not the lone ranger. I'm sure you have just heard of the copyrighted feature of the lone ranger incorporated.


Mae'r clwf yn ystafell yn dda i'r rhaid i'r adr ac mae'n defnyddio'n dda y gallan gwych yng ngyfysig. Fe ydych chi'n gwybod yn ei gwybodaeth, er mwyn cael eu cael eu cyfysig wedi'u cyffredinol, mae'n gwybod yn ei gwybod yn ei gwybod yn ystafell. Ond eich gwybod yn ei gwybod yn cael eu gwybod yn ei gwybod, ac mae'n gwybod yn gwybod yn gwneud, mae'n gwybod yn ei gwybod yn gyfysig yng Nghymru. Fe ydych chi'n gwybod yng ngyfysig, rydw i gweithio gwybod, O'r byd ar ymddiw hwn o'r golffau? O'r byd yn gof, gyda'r gyflawn. Rydyn ni'n ei bod eich bwrdd, cyflawn, ychwanes, ychwanes, gyda'r putser. Mae'n gweithio'n byd yn gweithio, a rydyn ni'n gweithio'n gymwysbion mewn llwyddo am rhaeddiol a mi'n gweithio, am y setau bwrdd i'r setau bwrdd. Rydyn ni'n gweithio'n bwysbion ein bod yw'r sefydliadau. Mae'r byd yma ac mae ydych chi'n gweld ei fod yn fwyaf iawn. Mae'r ddysgu ar ddysgu'r ddysgu'r ddysgu, mae hynny'n mynd i ddim yn gweithio gael y gweithio gwahoddau a ddyn nhw'n bwynt yn unigadau i'r gweithio'r gweithio, ond byddwn yn gweithio gweithio'r gweithio, ddysgu'r gweithio'r gweithio, a yn rhaid, ddysgu'r gweithio yn ddysgu'r gweithio. Felly gweithio, mae'r cwestiynau. Felly mae'r cyfnod o ddath oedd yn y twf yn gweithio, ond, yw'r cyfnod o B3, mae'n cael ei fod yn 150 oed, mae'n fflag ei, yn ei gwybod o'r cyfnod o'r cyfnod. Felly mae'n gwybod, mae'n gwybod o'n gwybod o'r cyfnod o'r ganddau. Felly mae'n gwybod o'r cyfnod o'r cyfnod o'r cyfnod o'r cyfnod o'r cyfnod o'r cyfnod, mae'n siaradau yn leisio, yn lleisio yn fwy o'r cyfnod a'r cyfnod o'r cyfnod. Ni'n chweithio'r blaen arlawn ni o'n gwahanol i'r ydych chi? Gallwn cael ei ffnwys i'r pwysig o ymddangos, dyna ymddi'r pwysig yw'r ddiad, rwy'n dechrau, ac mae'n gweithio'n gwahanol i'r ddechrau. A dwi'n mynd i ddweud i'r ddweud, mae'n ddweud i'r ddweud i'r ddweudio'r ddweud, a dyna ddweud i'r ddweud i'r ddweud i'r ddweud. Maen nhw'n ddiddordeb yn ddiddordeb yn ddiddordeb ymlaen y gwirio'r cyfnod iawn. Yn ddiddordeb yw'r ddiddordeb. Mae'r ddiddordeb yma yn ystod i ddiddordeb mewn ddiddordeb yn fan ardal. Felly, rydyn ni'n gwybod eich bod yn eu llefyrgyntiau. Rydyn ni'n gwybod ar y cobra teereil, ac wrth gwrs, mae'n gwybod arfer o'r cyffredin, ydy'r ysgol ei wneud yn ymwneud ychydig eich bod nhw'n gwybod, mae wedyn yn gwybodaeth, but yet again when I talk about stereotypes it would never get a chance to go in my bag and got slid to one side and then this year's model again caught my eye in recent weeks. I gave it a go. Recorded some interesting numbers in terms of data and all those things that I just referred to as being why this product is so good kept resonating back in my brain again I thought you know what this is considerably underrated and should be in a bag of many average golfers. So what we're going to do is go back inside we'll start into some golf balls I collect a little bit of dry ball data and then we'll put it to the test and see how it performs and is it really or does it really justify that crown of the most underrated golf club of 2021. So the question is what makes this the most underrated not top of the tree in terms of the most popular and that's basically down to the way it looks it is as simple as that because I keep referring to the norm and stereotypes and it doesn't fit into any of those categories and as you can see it's almost like a hybrid we've come to this category conclusion of hybrid iron from the top line it's very much a super game improvement iron from the bottom sole with the t-rails in it's very much what you'd seen sort of like almost a half of a hybrid and then there's the bulbous back end which once again suggests it's more like that hybrid type but I think that bulk and mass at the back is where you get the confidence from as the thick top line well but you really have got to overcome the way this thing looks first of all to give it a chance and that's what's happening in my opinion not enough golfers give this a chance and therefore it's massively underrated right so we know what the club is and we know why I think it's so underrated and also the reasons why I think it's so good in terms of like I said mainly high launching but I think it's got good ball speeds good performance off worries like I said we're going to test the seven iron here today and we'll use my performance as that barometer now I've tested some dry ball data already this morning and we're kind of getting what I'm kind of getting around an average of just over a 150 carry so I'm going to set up a little bit of a simulator 150 and we've got a flag a little bit further left as well at 158 and I'm going to see how I perform put my money where my mouth is and see how good this club is so we'll start off with that sort of closer pin and the thing I really like is the fact that this is just an easy swing I've got a graphite shaft in this a light graphite shaft the bulking mass behind the ball just seeing that is a what's given me a little bit of confidence to think subconsciously that this bit of bulk is going to help get that ball airborne so therefore and again subconsciously and it's the way I think it might be the way everybody does I think it needs less effort if you like from my swing so first shot again high launching it's incredibly high see the ball coming down that's just going to finish us right at a flag where pin I were bang on the number there but the other thing is again and I talk about the kind of they often referred to categorising iron so a player's iron and this would be certainly a super game improvement iron one of the things that they criticize these things for or separate them from as you can't shape the ball but believe me we're even sort of it and draws with this it's on cue and kind of it into the sort of slightly trying to get a little bit more out of it in along the distance we'll go for the sort of further right flag which is sort of 158 and see if we can get this ball even moving a little bit that's a super shot you know I mean I literally having it a shot almost a bad shot all morning as you can see longer in terms of carry distance shape the ball just a little bit of right to left on it but again just it's so easy to do and I always say when I'm doing these videos I confuse myself after time as to why majority of us want to make this game any more difficult than it is and each time I reach down for a club like this I'm going to hit one more ball while the camera's on we'll go for that easiest shot a little bit of a slight cut you want into that closer pin again this is this one could go in oh my word I mean that's just like put the clubs in the bag and I think we've with three shots there probably the best three shots I've been able to call on camera for quite some time but it's performed really really well and I think we'll finish off with a final summary but I think in essence those three shots have said it all yet not a lot more for me to say to be honest with the performance has been a forget the three shots all morning that we've tested this thing it's been absolutely superb and I've got to say really really impressed one thing to add it sits in a price point 120 pound of club which is kind of it's not the top end it's not the bottom end and I think effectively you've got a bag of hybrid so that kind of 120 price for a hybrid would be more than acceptable you can mix this set up a little bit you can go I think from the five iron onwards you go from the sort of hybrid iron you can go into the more traditional shaped hybrid if I'm perfectly honest with you I don't like that hybrid as much as I do these hybrid irons so for me I'd stick with this and I found it just as easy to use the longer irons as I did to use the shorter irons in this kind of style and profile so it's all about over to you really in your comments down below is are you ready to accept this type of club as being the norm like I said or is it still being dismissed at this stage because it's a little bit alien in its looks because I've got to say in terms of performance it is 100% definitely the most underrated club that we have tested this year anyway as ever thanks for watching sticky comments down below if you've got any hit that like button if you enjoyed the video and the final request is subscribe if you don't already if you like this kind of a content from an average golfer one last thing to say the van that we're filming in today is going to feature quite a lot more and I will tell you in videos to come how that might happen right thank you for watching I'm off see you later


Hey guys, Shave Bear 1000 here. Let's make some carnival funnel cakes tonight. Let me tell you, you might hear the air conditioner running in the background.  It's still like 86 degrees and it's 930 at night. So we're just gonna leave it running. So please bear with me on that. But we're gonna make this, we got this at the five below store. And I think it was five bucks. But I'm gonna give it a shot. All you need is water and oil. So gives you the the instructions are right here on the top. I don't know who this is made by. It makes eight to ten cakes. What you'll need, you'll need cooking oil, measuring cup, three inch deep frying pan, eight inch diameter. Now I don't have an eight inch diameter. I got a ten inch diameter, but hey, we're gonna do it Shave Bear's way. Alright, which is Shave Bear's way and I'm sure you guys will do it your way too. Work with what we got, right guys? A two quart mixing bowl and some metal tongs. So that way when you flip it. So Let's go ahead and I'll get everything set up here and let's get this mixed up. Okay guys, I know it says to use a what'd it say? It says the two quart mixing bowl, but I don't wear a two quart is this is bigger than two quart. But it's okay. It'll be fine, right? So and I've got one and one-third cup of water here. Which in This is this converted into metric. I actually got a little bit more. Yeah, it would be about 300 milliliters. Okay. So I got just a little bit more. If you hear something in the background, monkey's making something too. Aren't you monkey? Oh, yeah. Oh, yeah. Here I'll show you. Yeah, what you making there mama? I See some ginger beer. I see some Smirnoff. I see a lime and shot glass But the the thing that should give it away is right there that she's pouring in right now that gold mug. What you making baby? Moscow Mule. All right. Okay, so let's go over here. You haven't even had a drink yet. Oh my gosh. Okay, and What we're gonna do after we make this video We're gonna play this game Jumping monkeys. Okay. No, you don't have to get naked So let's open this up. You got a piece of tape on here and I was looking at the monkeys all about this exploration dates or Exploration dates on everything. Well, she's gonna shit when she sees this one best used it best if used by February Or no 11th of 2000. No February 1st of 2019 So stuff ain't gonna go bad for a long time And we're gonna use Now I said it won't go bad for a long time we're gonna use the whisk that monkey bought from wish the wish whisk We're gonna use let's get this in here Open this up. All right and dump this all in here You're gonna want a batter waffle batter the consistency of like a waffle batter or pancake batter What you're gonna want There's that now. Let's get this off of here Because I'm sure we're gonna make quite the mess Well, they got this taped on I was trying not to rip it in case we do a giveaway on this thing But I'm I'm thinking I won't That's I just hit my head on the way Anyway My head or the light the light's fine. So anyway, I'm gonna use this this for like oil and stuff, you know putting oil in lawnmowers and the truck or Anything, okay. The fun part is if you can see that the angle That this is that That's gonna be fun to try to pour that in here. All right, so let's go ahead and pour this in here Like I said, she hasn't even had a drink she'll calm down once she has a drink, okay There we go. So we're gonna mix this up. Okay, it's looking pretty good a little thick But I had a little or a little thin but okay, it'll be fine now What we're gonna do is we're gonna go over to the stove and I'll show you what you do there All right guys now what you want to do like I said this 10 inch pan and they tell you to use 8 inch, but it's okay I mean, it'll be fine.  But this is about 3 inch side. I think it's a 3 inch side But you're gonna want to pour make sure there's about an inch And the bottom of your pan. All right now we're going to turn this on And I'm gonna just kind of crank it up a little bit and then I'll turn it back down because you want this Like 350 to 375 degrees. I do believe now 350 to 375 Okay, so we're gonna let that heat up now how I'm gonna gauge it I don't know how you guys will do it, but it'd be best if you had a shoot a deep fryer, but put this on There should be a setting for nothing I don't know if you can see that Okay, now that's just going to read a temperature. So this is what we're gonna use We're gonna try to keep at it about 350. Okay, so when that gets up to 350 We'll be back with you and monkey will be back with us How is it good she makes the best around, okay? You have to grow now We'll get back within just a second. Okay guys Now I wasn't thinking my meat thermometer does not go up to 350 degrees So I'm just kind of guessing at this So what they want you to do is hold it about two inches from here and make circles So let's see if we can do this and what you're going to do is Let it cook for 30 seconds on this side or until the edges get golden brown and Then you're going to turn it over with these tongs and do it for another 20 more seconds They say you metal tongs, but we've had these tongs man. He's been a great set of tongs here boy Okay, so it's been 30 seconds. Let's flip it. Whoo. Whoo. Any more seconds? There's one then you let it cool and you can top with all kinds of difference. I think a nag just flew on that You can we're just gonna put some powdered sugar on it But we're gonna go ahead and Make a couple more you can top this with blueberries strawberries what they say whipped cream and all that stuff Melted chocolate. Yeah, let's talk to scrape some chocolate. What it you know do it your way Whatever you want however you want to do it. We're just using This Pure cane sugar, it's just powdered sugar. Yeah, or icing sugar or whatever now it says use a sifter We do not have a sifter, but what we do have is my ingenuity So we're just going to use this This colander thing. Thank you Put a little bit whoops in there There you go. There's one kind of makes it a little messy where she can use a smaller one or you can actually use a sifter But this is good enough They rip you off a piece there mama see what you think of it a little piece here and it's still warm Mmm, tastes just like the fair don't it Or carnival There we go guys like it better than the car Yeah, make some funnel cakes now. We're going to get on to the jump of monkeys All right, so stay tuned for that video Thanks for watching Shaper 1000.


A police officer that attended our first class had a situation where someone was bleeding. He used the kit that we gave him, the education that we gave him, was able to maintain the bleeding, create pressure that allowed for the time to get him to the hospital and save his life. Pycoma Medical Center is having two classes to come in and learn these valuable skills that hopefully they will never have to use, but could save someone's life if they have to do it.