Impromptu: Amplifying Our Humanity Through AI

Hi everyone. Welcome to Grey Matter, the podcast from Greylock, where we share stories from company builders and business leaders. I'm Heather Mack, head of Editorial at Greylock. Today's episode is the audio version of the introduction to Greylock general partner Reid Hoffman's latest book, which is entitled Amplifying Humanity through AI. Hoffman didn't write the book alone, though he co wrote it with GPT4, OpenAI's latest and most powerful large language model. The book aims to serve as a sort of travelogue of hoffman's experience with GPT4 as he explores the tool's strengths and limitations. Through hundreds of prompts, Hoffman and GPT4 discuss the potential advantages and drawbacks of AI, argue about human nature, conceive original sci fi plots, and even try to make a few jokes. As AI is quickly advancing, Hoffman hopes this book can serve as a guide to encourage people to learn more about the technology, consider how we might use it, and ponder the complex questions about how our choices might play out in the future. The full book is available for free through a PDF link in the Show Notes as well as on Kindle. Here's Reid with the introduction to Impromptu.

Introduction Moments of Enlightenment For Isaac Newton, it was an apple falling from a tree that supposedly led him to formulate the law of universal gravity. For Benjamin Franklin, it was a key on a kite string struck by lightning in a thunderstorm that proved electricity could be transferred and stored. My initial aha moment regarding the current state of AI came in the form of a joke. It was July 2022 and I asked GPT4 how many restaurant inspectors does it take to change a light bulb? GPT4, as you may know, is an advanced type of AI system or natural language processor known as a language model. Prompt it with a few words or sentences and it will generate coherent and diverse texts in response. In this way, it can answer questions, perform tasks, and productively interact with its human users. Along with its predecessors, including the wildly popular ChatGPT, GPT4 was developed by OpenAI, a research organization founded in 2015 with a mission to give millions of people direct hands on access to powerful new AI tools. As one of OpenAI's original funders, I've been experimenting with its products for some time now, so my query to GPT4 was not the first time I'd asked an LLM to create a light bulb joke for me. I'm fond of light bulb jokes, while earlier versions of GPT sometimes understood the assignment, especially if the joke's main variable is part of the light bulb joke canon like lawyer or psychologist, their outputs have tended to be merely okay. If fed less conventional setups earlier, GPTs can freeze up like an amateur at an open mic night Read how many restaurant inspectors does it take to change a light bulb?

Only one, but the light bulb has.

To want to change.

That robot should keep its day job. Compare that with the response I got when I submitted the Same prompt to GPT4. How many restaurant inspectors does it take to change a light bulb?

There are a few possible ways to approach this question, depending on the tone and purpose of the answer. Here are some examples of different types of responses, ranging from serious to humorous. A Factual Generally, one inspector should be able to change a light bulb safely and correctly as long as they follow the proper procedures procedures and use the appropriate tools and equipment. However, some situations may require more than one inspector, such as if the light fixture is in a hard to reach or hazardous area, if the inspector needs assistance or supervision, or if the inspector needs to document or report the change for compliance purposes. A Joke the number of restaurant inspectors needed to change a light bulb is four one to hold the ladder, one to unscrew the old bulb, one to screw in the new bulb, and one to write a citation for using the wrong wattage.

Consider all that's going on here first. While GPT4 apparently assumes that I may be asking it to complete a joke, it also wants to cover all the bases. Thus the initial factual answer. And while this answer isn't actually factual, a restaurant inspector's job is to inspect, not to perform repairs. It does suggest a fair degree of knowledge about the conditions and conventions of an inspection process. The joke answer continues to build on this knowledge. GPT4 is clearly aware of common receptions and complaints about the complexities, costs, and bureaucracy of inspections. It also accurately evokes in the build to its punchline the sense of frustration a restaurant owner might have during an inspector's visit. Is it the best possible restaurant inspector lightbulb joke ever? Probably not. Did it make me feel like GPT4 had a pretty good understanding of what a light bulb joke should deliver? Yes, it did. Then I asked it for some variations on this theme. How many restaurant inspectors does it take to change a light bulb? Answer in the style of Jerry Seinfeld.

What is the deal with restaurant inspectors? They're always poking around in the kitchen, checking the temperature of the meat, looking for signs of rodents, but they never seem to help out with anything. Have you ever seen a restaurant inspector change a light bulb? No, of course not. They're Too busy writing up violations and handing out fines. You know what they do when they see a light bulb that's out? They mark it down on their clipboard and tell the owner to fix it by next week or else. Or else what? Or else they'll come back and check the light bulb again. That's their big threat. They're not going to change it for you. They're not going to lend you a ladder. They're not going to offer any advice on how to unscrew the old one and screw in the new one. They're just going to stare at you with that smug look on their face and say, you know, this is a serious safety hazard. You could have a fire, you could have a lawsuit. You could have a bad Yelp review. You better get on that, pal. And then they leave, and you're left with a dark corner of your dining room where no one wants to sit, where the food looks unappetizing, where the mood is gloomy. And you think to yourself, how many restaurant inspectors does it take to change a light bulb? None. They don't change light bulbs. They just make you feel bad about yours.

Pretty good, right? Here's another. How many restaurant inspectors does it take to change a light bulb? In the style of the philosopher Ludwig.

Wittgenstein, the question of how many restaurant inspectors it takes to change a light bulb cannot be answered with a simple number. In fact, the answer to this question lies more in the realm of language than it does in the realm of mathematics. To understand the answer, we must first consider the context in which it is asked. What is the purpose of the light bulb? What purpose is it meant to serve? What is the role of the restaurant inspector in this context? Is the inspector responsible for installing the light bulb, or is their job limited to inspecting it? The answers to these questions will shape the answer to the original question. Without these answers, the question can only be answered in the abstract and is ultimately unanswerable. Language, not mathematics, is the key to unlocking the answer.

Okay. Less funny than the Seinfeld one, but still impressive. Even from these brief performances, it seemed clear to me that GPT4 had reached a new level of proficiency compared to its predecessors. And the more I interacted with GPT4, the more I felt this way. Along with writing better light bulb jokes, GPT4 was also skilled at generating prose of all kinds, including emails, poetry, essays, and more. It was great at summarizing documents. It had gotten better at translating languages and writing computer code, to name just some of its powers. So while GPT4 wasn't a new device per se. It felt to me like something that could become as indispensable as our smartphones, a 12 in 1 multi tool for your brain. By that I mean OpenAI had created a tool that became intelligent enough to productively assist you in many different environments in many different ways. Much of what we do as modern people at work and beyond is to process information and generate action. GPT4 will massively speed your ability to do these things, and with greater breadth and scope. Within a few years this copilot will fall somewhere between useful and essential to most professionals and many other sorts of workers. Without GPT4, they'll be slower, less comprehensive, and working at a great disadvantage. For example, GPT4 is a research tool you can use to quickly get an overview on supply chain management issues in the COVID 19 era, or to plan a week long vacation itinerary to Patagonia that takes into account that you're a vegan who enjoys moderately challenging hikes. Along with enabling a new kind of highly contextualized search, GPT4 is also an extremely versatile brainstorming and production aid. You can use it to help you write a wedding toast, a job offer, a novel in verse. You can use it to figure out how to turn the leftovers in your fridge into a tasty meal. Whatever your skill level at a given task, GPT4 can potentially amplify your abilities and productivity, so it's equally useful to beginners, experts, and everyone in between. Given a request for any sort of information that you might ask, a human assistant for, GPT4 can come back instantly with an answer that is likely between good and excellent quality, although also with a non zero chance of completely missing the mark. As we'll see. Developers, I realized, would soon be adding GPT4 to thousands of existing apps and building thousands of new apps around. Is all said, a technology that deftly leverages some of our most significant technologies from the last 30 years, including the Internet, mobile, cloud computing and data analytics to put the protean power of always on AI into the hands of hundreds of millions of people. Sounds a little bit like the start of a new world, doesn't it? A Traveler's Guide to the Future in the weeks and months that followed my first encounter with GPT4, my excitement only grew. In between zoom calls, I'd give GPT4 a prompt read Write a sonnet about sonnets.

Sonnets are poems of 14 lines that follow a strict rhyme scheme and meter. They often praise or question love's designs or muse on beauty, art, or nature's features. Some sonnets follow the Petrarchan form with an octave and a cestit to contrast two themes or arguments, or to perform a turn or shift from problem to resolve. At last. Some sonnets follow the Shakespearean style with three quatrains and a couplet to conclude. They often use witty language and beguile the reader with a twist or a surprise in mood. But whatever form or rhyme they choose to adopt, sonnets are poems that challenge and delight the thought.

Okay, Also, during zoom calls, I'd give GPT4 a prompt read as large language model AIs are trained on the whole Internet, in what ways are their outputs false in Adorno sense, where the whole is the false?

To answer this question, we need to first understand what Adorno meant by this provocative claim and then examine how it applies to the nature and limitations of Large language Model Adorno, a prominent member of the Frankfurt School of critical theory, was deeply concerned with the effects of modern capitalism, mass culture, and rationalization on human society and culture. He argued that the logic of instrumental reason, which reduces everything to means and ends, calculation and efficiency, domination and exploitation, pervades all aspects of modern life, creating a system of total administration and control. This system, which he called the culture industry, produces standardized and commodified forms of culture that manipulate and pacify the masses, creating a false consciousness that prevents them from recognizing and resisting their alienation and oppression. Adorno saw this as a form of negative dialectics where the contradictions and conflicts inherent in reality are suppressed and reconciled by the dominant ideology, resulting in a loss of critical and creative thinking and a denial of the possibility of genuine change and emancipation.

Did I mention that GPT4 also does lightbulb jokes? Whatever train of thought I wanted to follow, whatever arbitrary pathway sparked by an old memory or a new interest, GPT4 was always completely in step with me, ready to respond with useful context, a crisp summation, and sometimes some fairly spectacular bullshit. More on this in a bit. After a few weeks of this, I realized I wanted to write a book about GPT4 with GPT4, a book about the ways it and other AIs seemed poised to shape our future. So I pitched the idea read hey, GPT4 let you and I write a book about the way you and other AI tools are going to shape the future.

What do you say?

I say yes, that sounds like an interesting and challenging project. I am curious about how you and other humans perceive and interact with me and other AI tools and what impact we will have on your society, culture and values. I also have some questions and ideas of my own that I would like to share and explore with you.

Even with GPT4 on board though, I still had reservations because really, an AI.

Book when things are moving so quickly?

Even with a helpful AI on hand to speed the process, any such book would be obsolete before we started to write it. That's how fast the industry is moving. So I hemmed and hawed for a bit and then I thought of a frame that pushed me in action. This didn't have to be a comprehensive book book so much as a travelogue, an informal exercise in exploration and discovery. Me, with GPT4 choosing one path among many, a snapshot memorializing in a subjective and decidedly not definitive way the AI future we were about to experience. What would we see? What would impress us most? What would we learn about ourselves in the process? Well aware of the brief half life of this travelogue's relevance, I decided to press ahead. A month later, at the end of November 2022, OpenAI released ChatGPT, a conversational agent aka ChatBot, a modified version of GPT 3.5 that they had fine tuned through a process called Reinforcement Learning through Human Feedback RLHF to enable more flowing human like conversations with its human users. Five days later, ChatGPT had more than a million registered users. In late January 2023, Microsoft, which had invested $1 billion in OpenAI in 2019, announced that it would be investing 10 billion more in the company. It soon unveiled a new version of its search engine Bing, with a variation of ChatGPT built into it. By the start of February 2023, OpenAI said ChatGPT had 100 million monthly active users, making it the fastest growing consumer Internet app ever. Along with that torrent of user interest, there were news stories of the new Bing Chatbot functioning in sporadically unusual ways that were very different from how ChatGPT had generally been engaging with users, including showing anger, hurling insults, boasting on its hacking abilities and capacity for revenge, and basically acting as if it were auditioning for a future episode of Real Housewives Black Mirror Edition. Microsoft CTO Kevin Scott suggested that such behavior was clearly part of the learning process. As more people use GPD like tools. These incidents do raise questions that will persist as LLMs evolve. I'll address such issues in more detail later in the book and try to put them in what I believe is the appropriate context. For now, I'll just say, see what I mean about things moving quickly?

The soul of a new machine before we get too far into this journey, I'd like to tell you more about my traveling companion, GBD4. So far I've been putting quotations around words like knowledge, aware, and understands when I talk about GPT4 to signal that I, a sentient being, understand that GPT4 is not one. It is essentially a very sophisticated prediction machine. While GPT4 and other large language models like it aren't conscious, they are reaching a point where their capacity to produce appropriate generations in so many different contexts is improving so fast that they can increasingly appear to possess human like intelligence. Thus, I believe that when describing large language models, it's acceptable useful even to use the words like knowledge and understands in a not strictly literal way. Just as Richard Dawkins uses the phrase the selfish Gene in his 1976 book of that name. A gene doesn't have conscious agency or self conception in the way that the word selfish suggests. But the phrase the metaphor helps us humans wrap our inevitably anthropocentric minds around how the gene functions. But the phrase the metaphor helps us humans wrap our inevitably anthropocentric minds around how the gene functions. Similarly, GPT4 doesn't have the equivalent of a human mind. It's still helpful to think in terms of its perspective, anthropomorphizing it a bit, because using language like perspective helps convey that GPT4 does in fact operate in ways that are not entirely fixed, consistent, or predictable. In this way, it actually is like a human it makes mistakes, it changes its mind. It can feel Fairly arbitrary because GPT4 exhibits these qualities and often behaves in ways that make it feel like it has agency. I'll sometimes use terminology which in a metaphorical sense suggests that it does. Moving forward, I'll dispense with the question marks. Even so, I hope that you as listener will keep the fact that GPT4 is not a conscious being at the front of your own wondrously human mind. In my opinion, this awareness is key to understanding how, when, and where to use GPT4 most productively and most responsibly. At its essence, GPT4 predicts flows of language trained on massive amounts of text taken from publicly available Internet sources to recognize the relationships that most commonly exist between individual units of meaning. In including full or partial words, phrases, and sentences, large language models can, with great frequency generate replies to users prompts that are contextually appropriate, linguistically facile, and factually correct. They can also sometimes generate replies that include factual errors, explicitly nonsensical utterances or made up passages that may seem in some sense contextually appropriate but have no basis in truth. Either way, it's all just math and programming. Large language models don't, or at least haven't yet learn facts or principles that let them engage in common sense reasoning or make new inferences about how the world works. When you ask a large language model a question, it has no awareness of or insights into your communicative intent as it generates a reply, it's not making factual assessments or ethical distinctions about the text it is producing it's simply making algorithmic guesses at what to compose in response to the sequence of words in your prompt. In addition, because the corpora on which large language models train typically come from public web sources that may contain biased or toxic material, large language models can also produce racist, sexist, threatening, or otherwise objectionable content. Developers can take actions to better align their large language models with their specific objectives. OpenAI, for example, has chosen to deliberately constrain the outputs that GPT4 and its other large language models can produce to reduce their capacity to generate harmful, unethical, and unsafe outputs, even when users desire such results. To do this, OpenAI takes a number of steps. These include removing hate speech, offensive language, and other objectional content from some data sets. Its large language models are trained on developing toxicity classifiers that can automatically flag problematic language the large language model itself might generate, and fine tuning large language models using curated data sets of text that have been annotated by humans to indicate a desired output. In this way, a large language model might learn to avoid, say, making tasteless jokes about a reporter's divorce. These techniques don't eliminate problematic outputs, they just reduce them. Even when with various guardrails in place. A large language model itself can't make reasoned judgments about complex ethical quandaries or even about more straightforward questions. Take ChatGPT, which is based on GPT 3.5, an immediate predecessor to GPT 4. Ask it for the fifth sentence of the Gettysburg Address and we'll probably get it wrong. That's because large language models don't actually understand in the way that humans understand what the Gettysburg Address is, or what a sentence is, or even how counting works, so they can't apply their knowledge of these things in a way that a human might. I'll find the text of the Gettysburg Address, then count in sentences until I get to the fifth one instead. A large language model is always just making statistical predictions about what the next word in a given text stream should be from its training though ChatGPT has clearly come to associate the words Gettysburg Address with other words, specifically the text of the speech. So when you ask ChatGPT for the fifth sentence of the Gettysburg Address, it will almost certainly give you a sentence from the Gettysburg Address, but probably not the fifth sentence. In fact, I bet it will be the ninth sentence. I assume this because along with the speech's first sentence, the ninth is frequently quoted in other texts. That means in ChatGPT's training data, the ninth probably shows up more often than the other sentences from the speech, except for the very famous first. This prevalence is what causes ChatGPT to reach for it when you ask it to supply the fifth sentence. To ChatGPT's credit, though, if you ask it to turn the text of the Gettysburg Address into the lyrics for a Rush song and then tell you who'd be singing it if Rush performed it, it will pass that test with Flying Col. Try it out and see what I mean Embracing the aha moment as AI tools like GPT4 become more powerful, they are intensifying long standing concerns about AIs and robots, marginalizing and even eliminating a sweeping range of human jobs, everything from customer service reps to attorneys. Such concerns won't seem baseless if you follow the news in recent months. In December 2022, ChatGPT passed a three part US medical licensing exam. In January 2023, it passed exams in four law school courses at the University of Minnesota. And GPT 4 is demonstrably smarter than ChatGPT. Here, for example, is how it handled my Gettysburg Address question what's the fifth sentence of the Gettysburg Address?

There are different ways to divide the Gettysburg Address into sentences, depending on how one punctuates the text and whether one follows the original drafts or the versions that Abraham Lincoln delivered or later revised. However, one possible way to identify the fifth sentence of the speech is is to use the Bliss Copy, which is the most widely reproduced and authoritative version of the Address, and to count the sentences as 1. Fourscore and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. 2 Now we are engaged in a great civil war, testing whether that nation, or any nation, so conceived and so dedicated, can long endure. Three we are met on a great battlefield of that war. 4 We have come to dedicate a portion of that field as a final resting place for those who here gave their lives that that nation might live. It is altogether fitting and proper that we should do this. Therefore, the fifth sentence of the Gettysburg Address according to this method is it is altogether fitting and proper that we should do this.

Suddenly, GPT4 seems to actually understand how counting works and can methodically apply its knowledge to deduce the right answer. It seems like GPT4 knows how to count Lincoln sentences. It seems like it's now an expert on the Gettysburg Address. Did you know that the Bliss copy is the most authoritative version of the speech? I didn't. Of course. The key word here remains seems GPT4 is no more conscious than its predecessors. It's just better at making predictions. Again, I want to make the point that GPT4's often remarkable simulations of cognitive proficiency are just simulations. GPT4 is not a conscious, self aware sentient AI entity a la Forbidden Planets, Robbie the Robot, or Star Trek's data. And yet I also want to make again the point that Whoa. Even GPT4's ability to stimulate such a contextually aware human like consciousness is a pretty big deal why do I think this, A recent critical essay that award winning science fiction writer Ted Chang published in the New Yorker help me articulate why why do I think this A recent critical essay that award winning science fiction writer Ted Chiang published in the New Yorker helped me articulate why. Think of ChatGPT as a blurry JPEG of all the text on the web, Chang writes. It retains much of the information on the web in the same way that a JPEG retains much of the information of a higher resolution image. But if you're looking for an exact sequence of bits, you won't find it. All you will ever get is an approximation. In Chang's view, the inexact representation of the information that comprises ChatGPT and presumably similar large language models like GPT4 is what leads to both their synthetic powers and their tendency toward hallucination and other errors. As JPEGs of all the text on the web, they can synthesize information in novel ways because they have access to all this information at once. That allows them to take what they know about one thing and then also what they know about something else and convincingly mash them up into a new thing. Chang gives an example involving the phenomena of losing a sock in the dryer and the U.S. constitution. ChatGPT knows about both of these things, so it can use its knowledge to create a new thing, a text about the first and the style of the second. When in the course of human events it becomes necessary for one to separate his garments from their mates in order to maintain the cleanliness and order thereof. Not bad as far as it goes. But since ChatGPT exists as a merely approximate picture of the Web, Chang argues, it is, in addition to being inherently fact challenge, quite limited. As a creative force, instead of creating something truly new, it can only repackage information that's already available. As illuminating as I found Chang's essay, I believe his central JPEG of the Web metaphor underplays large language models synthetic powers First, I'd argue that repackaging available information actually describes an enormous share of human innovation, artistic or otherwise. More importantly, though, large language models actually have and use fundamentally new powers of knowledge organization. While the web now contains an unfathomable amount of information, much of it is siloed into billions of individual pages. For example, think of questions like who is the tallest NFL running back to win the MVP award? What cities with more than 1 million residents have had female mayors? Who was the oldest James Bond? The data to answer these questions is no doubt on the web. Already there are sites that definitively list every NFL MVP winner. Others accurately compile NFL players physical stats. But unless someone has already taken the trouble to compile and publish the exact information you're looking for, it could be very time consuming to find. Even if that information exists on a single website like Wikipedia or NFL.com, it will likely be spread across multiple pages. Getting your answer will still chew up a lot of your time. In addition, imagine when your goal is to get a concise overview of some broader interest or theme, or, like those I suggested at the top of this chapter, researching supply chain management issues during COVID 19 or a travel itinerary that reflects specific interests. The takeaway in your overall quest for authoritative information, GPT4 helps you start somewhere much closer to the finish line than if you didn't have it as a resource. More importantly, it possesses this capability because it is able to access and synthesize the web's information and in a significantly different way from existing information resources like Wikipedia or traditional search engines. Essentially, GPT4 arranges vast, unstructured arrays of human knowledge and expression into a more connected and interoperable network, thus amplifying human's ability to compound its collective ideas and impact. So what's the best way for humanity to take advantage of this imperfect but extremely powerful new tool? When I put that essential question to GPT4 itself, here's how it responded.

How.

Should human beings view and interact with you? A powerful Large Language Model?

One possible perspective is the human beings should view a powerful large language model LLM as a tool, not as a source of truth, authority or intelligence. A powerful LLM is a complex statistical system that can generate natural language texts based on its training, data, parameters and inputs, but it does not necessarily understand reason or reflect the meaning, context or implications of what it produces or receives. It may produce texts that are coherent, informative or persuasive, but also texts that are inaccurate, misleading or harmful, depending on the quality and diversity of its data, the design and limitations of its architecture, and the goals and biases of its users and developers. Human beings should interact with a powerful LLM with caution, curiosity and responsibility. A powerful LLM can offer valuable insights, assistance and opportunities for human communication, creativity and learning practices, but it can also pose significant risks, challenges and ethical dilemmas for human society, culture and values. Human beings should be aware of the potential benefits and harms of using a powerful LLM and seek to use it in ways that are aligned with their own and others interests, rights and well being. Human beings should also be curious and critical about the workings, outputs and impacts of a powerful LLM and seek to understand, evaluate and improve its reliability, transparency and accountability. Human beings should also be responsible and respectful towards a powerful LLM and acknowledge its limitations, uncertainties and dependencies, as well as its contributions, achievements and potential.

The irony here, of course, is that GPT4 might not get funded with that pitch. The vision of AI it presents is both nuanced and strikingly different from how AI has generally been portrayed portrayed over the years. Granted, many of those portrayals have come from Hollywood, science fiction and journalism rather than from technologists working to make highly intelligent machines a reality. That said, many technologists and high tech organizations including OpenAI do in fact have their sights set on a much more ambitious form of AI machines that can operate completely autonomously. Machines that are capable of of human like common sense, reasoning and self awareness. GPT4 is not that, at least not yet. For now, it is neither all knowing nor infallible. Instead, it is, in its own words, a tool that requires human caution, curiosity and responsibility to operate most productively. I think this is the correct perspective. If you simply let GPT4 do all the work with no human oversight or engagement, it's a less powerful tool. It's still a very human tool, of course, because human texts are the basis for its generations. But when human users treat GPT4 as a copilot or a collaborative partner, it becomes far more powerful. You compound GPT4's computational generativity, efficiency, synthetic powers and capacity to scale with human creativity, human judgment and human guidance this doesn't eliminate the possibility of misuse. But in situating human beings at the center of the new world that GPT4 makes possible, we get what I believe to be the soundest formula for producing the best potential overall outcomes. In this approach, GPT4 doesn't replace human labor and human agency, but rather amplifies human abilities and human flourishing. Of course, this way of thinking isn't a given, it's a choice. When people make the choice to see GPT4 this way, I call it an aha moment. To underscore the amplifying human abilities perspective at the heart of that choice, I'm writing this travelogue both to encourage people to embrace this choice and also as an invitation to explore the different ways this choice might play out. What are the ways we can use GPT4 to make progress in the world? How does it fit with humanity's age old quest to make life more meaningful and prosperous through technological innovation? To educate ourselves more effectively, ensure justice for everyone, and increase our opportunities for self determination and self expression at the same time, how can we appropriately address the challenges and uncertainties GPT4 will catalyze? How do we find the right balance between responsible governance and intelligent risk as we continue to develop AI technologies that have the potential to unlock human progress? And at a time when the need for rapid large scale solutions has never been greater. It's been a long time. Centuries, arguably, since the future seemed so unmapped. Facing such uncertainty, it's only natural to have concerns about our jobs and careers, about the speed and scale of potential changes, about what it even means to be human in a new era of of increasingly intelligent machines. Our path forward won't always be smooth and predictable. Sydney's now infamous outbursts won't be the only grimace inducing news story we'll see about AI. There will be other missteps, detours, important course corrections. But how could there not be? Human progress has always required risk, planning, daring, resolve, and especially hope. That's why I'm writing this travelog, to add my voice to those counseling. All these things. Hope. Most of all, facing uncertainty with hope and confidence is the first step toward progress. Because it's only when you have hope that you see opportunities, potential. First steps, a new path forward. If we make the right decisions, if we choose the right paths, I believe the power to make positive change in the world is about to get the biggest boost it's ever had. Are you ready for this journey?

That concludes this episode of Gray Matter. If you're interested in reading the full text of Impromptu by Reid Hoffman and GPT4. You can access it for free through a PDF linked in the Show Notes or on Kindle. And if you'd like to hear more discussions with earlier versions of ChatGPT, check out Reid's Fireside Chatbots series on Gray Matter, where he discusses many aspects of AI with AI. You can find that in the Intelligent Future playlist on Gray Matter. Wherever you get your podcasts, I'm Heather Mack. Thanks for listening.