Hello. I'm Ryan Cook, and this is Civic Tech Chat, a show that looks at the way technology, politics, and policy impacts the world around us. The tools we use, the way services are delivered, and how we talk about and set policy all shape our society. We'll gather around and have a chat about these things together and more. Before we get started, I do wanna let you all know that we've started a Discord for the podcast. There will be a link with an invite down in the episode description. Do feel free to go check that out. It's a small community right now, but hoping to grow it. It's a great way to reach out to me and let me know things that you might want us to cover or to just hang out and talk about civic tech. Anyway, let's go ahead and start the show. Hello, folks. A quick disclaimer as we hop in this episode. For me, your host, Ryan, this entire thing that you're about to listen to, aside from this opener, was generated using large language artificial intelligence models and a little bit of light editing from me. I fed it some samples of past monologue style episodes and asked it to try to write this script in my style. I thought it would be a fun way to dive into this topic using the very technologies we'll be describing. So I hope you enjoy. Let's go ahead and hop into the show. Welcome back to another episode of Civic Tech Chat. Today, we're going to delve into a topic that's at the forefront of technological innovation and revolution, generative AI and large language models. Now if these terms sound like they're straight out of a sci fi novel, don't fret. We're gonna break it down into easily digestible bits, just like we do with all complex subjects here. So what are we talking about when we say generative AI? In simple terms, it's a subset artificial intelligence that creates something new. It could be a piece of music, a painting, or in our case today, a piece of writing. It's like giving a robot an artist's brush and asking it to paint. The result is an entirely new piece of art generated by artificial intelligence. Now let's narrow it down to large language models. These are a type of generative AI, but their focus is specifically on understanding and generating human language. Picture a colossal digital brain trained on vast amounts of text from the Internet. The job of this brain is to generate human like text based on the input that it was given. If you ask it a question, it tries to provide a suitable answer. If you give it a sentence and ask it to continue the story, it does its best to spin an interesting tale. A prime example of a large language model is GPT four. It was developed by OpenAI, which has learned to generate text by predicting the next word in a sentence, based on all the previous words that it has seen during the training. It's like the predictive text in your smartphone, but a bit more advanced. So that's a high level explanation of what generative AI and large language models are. Remember they're tools designed to create something new, whether it's a melody, an image, or a piece of text. And large language models are specifically about understanding and generating human like text. In the course of this episode, we're gonna dive deeper into why these tools are useful, some potential risks that come from their use, and the ethical considerations they bring up. Let's not shift our focus to understanding why they hold such a magnetic attraction for the tech world and beyond. First and foremost, these technologies are incredibly powerful tools for automation. They have the potential to take over laborious tasks, save hours of human effort. Think of a customer service spot that can handle common inquiries, freeing up human representatives to deal with more complex issues. Or a large language model generating initial drafts and news reports, allowing the journalists to spend more time on investigative work and interviews. Secondly, the versatility of generative AI is nothing short of astounding. Since it can generate new original content, its applications are nearly limitless. From creating unique pieces of artwork, to drafting emails, writing code, or even developing game dialogues. This breadth of application means that almost every industry has some way to use these technologies. Whether it's media and entertainment, education, health care, or manufacturing. But the importance of these technologies extends beyond utility. Their development is a testament of human ingenuity and our endless pursuit of knowledge. It showcases how far we've come in understanding and replicating the complexity of human language, one of our most unique traits as a species. Moreover, it opens up new avenues for research and exploration. For instance, how can these models be fine tuned to generate even more coherent, contextually accurate text? How can they be made to understand and respect cultural nuances? How can they be adapted for different languages or specific industries? There are many questions, and the pursuit of these answers is part of what drives innovation forward. In essence, the excitement around generative AI and large language models comes from their immense potential for efficiency, their extraordinary versatility, and the thrilling promise of innovation and discovery that they represent. But as we dive deeper into this world, it becomes increasingly clear that the journey isn't without bumps and turns. It's crucial we take a hard look at the potential risks and pitfalls that lie ahead. These new technologies, while undoubtedly exciting, come with their own sets of challenges that we must confront and acknowledge. First and foremost, let's consider the potential for misuse and the propaganda of misinformation. With these tools at their disposal, malicious actors could easily generate false narratives, fake news, or misleading information at an unprecedented scale. Imagine an artificial intelligence churning out countless versions of an unfounded rumor, each version slightly different from the others, and all of them designed to spread and sow discord. The efficiency with which these models can produce convincing, albeit false content presents a serious threat to our collective ability to discern fact from fiction. Next, we come across a rather paradoxical risk, that of AI models being confidently incorrect. AI, like humans, can make mistakes. However, unlike humans, AI lacks the capacity for self awareness or the ability to question its own certainty. This means that a model could make a mistake, but without any inherent checks and balances, it could continue propagating that mistake with unwavering confidence. In critical applications, such as health care or safety systems, the implications for this could be quite grave. Moreover, the issue of privacy cannot be overlooked. Generative AI and large language models are trained on vast amounts of data, some of which may be personal or sensitive. While efforts are made to anonymize and cleanse the data, the potential for unintentional privacy breaches still Let's also Let's also turn our attention to an ethical dilemma that's of an increasing concern, displacement of content creators and artists. Many of these models are trained on data that includes works of art, literature, music, and more. Once trained, these models can generate similar content, potentially replacing the very creators whose work was used for the training. This raises questions about the right to originality, creativity, and fair compensation for artists and creators in a world where AI can replicate their style or even their ideas. Each of these risks present significant challenges, and they serve as a reminder that the path of progress is rarely straightforward. It's essential that we continue to develop and use these technologies. We also dedicate time and resources to understanding and mitigating potential risks. We need to create frameworks for responsible use, establish safeguards, and continuously evaluate the impact of these technologies on our society. Which brings us to our next point, the ethical ramifications of using generative AI and large language models. As we navigate the intricate terrain of these models, we can't sidestep these profound ethical questions. It is not just about what the technologies can do, but also about what they should do and how we ought to use them. Let's begin with the question of authorship and ownership. Is it ethical to accept achievements for work done using these models? If an AI model generates a hit song, or a best selling novel, who deserves the credit? The person who ran the model, the developers of the model, or the countless creators whose work trained the model in the first place? The answer may not be clear cut. It's a day between skill and tool inspiration and automation, and it's a conversation that we need to have in earnest. Let's also consider attribution for those whose work has trained the models. As we mentioned earlier, these models learn from vast amounts of data, often encompassing creative works like books, articles, music, and more. However, the current approach to training does include a way to track and attribute the original creators. So how do we ensure fair attribution and compensation for those creators? Should there be licensing fees for their work to betraying these models? Again, the answers aren't always straightforward, but it's a conversation that we must have. Lastly, the emergence of this technology focus forces us to reevaluate our economic structures. As AI models become more capable and versatile, they may displace jobs and roles. In fact, they could create an economy where human effort is significantly less required generally. How do we prepare for such a shift? What does it mean for our labor rights, income distribution, or even our sense of self worth that's been tied up in our work? It's It's a profound shift that requires careful consideration, preemptive policy making, and perhaps even a reshaping of our societal norms. These questions don't have simple answers, nor can they be resolved overnight. However, posing these questions is the first step in confronting the ethical ramifications of these technologies. It's a testament to the importance of marrying technological investment advancement with ethical foresight. It underscores our commitment to building a future that is not just technologically advanced, but also ethically sound and socially just. As we get to a close with our discussion today on generative AI and large language models, we find ourselves standing at an exciting yet challenging crossroads of technological innovation and ethical decision making. We started by peeling back the layers of what these technologies are, explaining how they have evolved into sophisticated generators of text, music, and more. These models learn from vast amount of data to produce outputs that are becoming increasingly difficult to distinguish from human produced content. Their emergence signals a profound shift in our interactions with technology, and their applications are limited by our imagination. Then we discussed the allure of these models. Their promise lying not only in their ability to enhance creative processes and boost productivity, but in their potential to revolutionize industries, professions, from journalism and content creation to software development and scientific research. The upshot is that these models could be transformative for society, catalyzing an entirely new era of human machine collaboration. Yet, as we venture deeper into our exploration, we also confronted the risks and potential harms these technologies can bring about. From generation of misinformation to privacy concerns, from displacement of creators, to confident incorrectness. These models present challenges that need to be addressed proactively. But perhaps most importantly, we delved into the ethical considerations that these technologies inevitably provoke. Questions about authorship, ownership, attribution, and economic reshaping abound, reminding us of the intertwined relationship between technology and ethics. To put it simply, the growth and development of generative AI and large language models can't be separated from the social, economic, and ethical contexts in which they operate. We are not simply bystanders in this process, but active participants with the responsibility to shape a future that aligns with our shared values and principles. We need to participate in informed dialogues, ask tough questions, engage with policymakers, technologists, and communities alike. Because it's through these collective efforts that will ensure the evolution of these technologies occurs in a matter that is ethical ethical, equitable, and truly beneficial for all. In the final analysis, the potential for generative AI and large language models is astounding, and the possibilities are thrilling. But we have a lot of work to do. And perhaps that's not a conclusion, but more a call to action. So let's roll up our sleeves and get to work. Thanks for joining us on this exploration. Stay curious, stay informed, and most importantly, stay engaged. Thank you all for making it here to the end. This is Ryan, your host on this podcast, Civic Tech Chat. Just again reminding you that the script for this one focused on AI generated content was itself generated by an AI large language model with a bit of light editing for me. And the recording was my actual voice. I didn't go so far as to generate my voice using one of those tools, though that is something that is theoretically possible. Thank you all again for listening, and I hope to catch you again next time. You can follow us on Twitter using the handle at civic tech chat. Visit us on the web at civictech.chat,