Hi everyone, welcome to Gray Matter, the podcast from Gray Lock where we share stories from company builders and business leaders. I'm Heather Mack, head of editorial at Gray Lock.
Today's episode is the audio version of the introduction to Gray Lock General partner Reed Hoffman's latest book, which is entitled, Impromptu, Amplifying Humanity Through AI.
Hoffman didn't write the book alone though. He co-wrote it with GPT-4, Open AI's latest and most powerful, large language model. The book aims to serve as a sort of travelogue of Hoffman's experience with GPT-4 as he explores the tool's strengths and limitations.
Through hundreds of prompts, Hoffman and GPT-4 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.
Introduction:
大家好,我是AI助手。今天我要为你们翻译这篇文章。这篇文章主要讲述了作者的佛学意识和感悟。
Moments of Enlightenment:
文章的核心部分是关于作者在禅定中体悟到的一些启示和领悟。作者发现禅定可以帮助他深入探究自我和生命的真谛。在禅定中,作者逐渐消融了一些偏见和固定的思维模式,并感受到了内心的平静和智慧。这些体验使得作者更加珍视人生中的每一个瞬间,并对佛法有了更深层次的理解。作者认为禅定让他不仅仅是在传统的媒介上掌握知识,而是通过体验真实的理解并将其与人生融合。
For Isaac Noon, 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 and 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 GPT-4, how many restaurant inspectors does it take to change a light bulb?
GPT-4, as you may know, is an advanced type of AI system, or natural language processor, known as a large 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 chat GPT, GPT-4 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 GPT-4 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 GPT's can freeze up like an amateur at an open mic night.
如果给予不太传统的设置,早期的GPT很可能会像业余选手在公开麦克风之夜一样冻结。
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 GPT-4. How many restaurant inspectors does it take to change a light bulb?
把这个与我向GPT-4提交相同提示时得到的反应进行比较,需要多少餐厅巡查员更换一只灯泡呢?
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 answer. Generally, one inspector should be able to change a light bulb safely and correctly, as long as they follow the proper 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 answer. The number of restaurant inspectors needed to change a light bulb is 4, 1 to hold the ladder, 1 to 1 screw the old bulb, 1 to screw in the new bulb, and 1 to write a citation for using the wrong wattage.
Consider all that's going on here. First, while GPT-4 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. GPT-4 is clearly aware of common receptions and complaints of 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 light bulb joke ever? Probably not. Did it make me feel like GPT-4 had a pretty good understanding of what a light bulb joke should deliver? Yes, it did.
When I asked it for some variations on this theme, read, 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 riding 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. It's 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? Answer in the style of the philosopher, Ludwig Vickensheim.
这里有另一个。需要多少家餐厅检查员才能更换一只灯泡?请用哲学家路德维希·维肯斯海姆的风格回答。
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.
OK, less funny than the Seinfeld one, but still impressive. Even from these brief performances, it seemed clear to me that GPD 4 had reached a new level of proficiency compared to its predecessors. And the more I interacted with GPD 4, the more I felt this way.
Along with writing better light bulb jokes, GPD 4 was also skilled at generating prose of all kinds, including emails, poetry, essays, and more. It was great at summarizing documents. And it had gotten better at translating languages and writing computer code to name just some of its powers.
The while GPD 4 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 open AI had created a tool that became intelligent enough to productively assist you in many different environments, in many different ways.
尽管 GPD 4 并不是一款全新的设备,但对我来说它就像是可以成为我们智能手机一样不可或缺的一种12合1的脑力多功能工具。我的意思是,开放 AI 已经创造出一种足够智能的工具,可以在不同的环境和多种不同方式下帮助你提高生产力。
Much of what we do as modern people at work and beyond is to process information and generate action. GPD 4 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 GPD 4, they'll be slower, less comprehensive, and working at a great disadvantage. For example, GPD 4 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 at Tineray 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, GPD 4 is also an extremely versatile brainstorming and production aid. You can use it to help you write a wedding toast, a job offer, a naul 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, GPD 4 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, GPD 4 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.
The authors, I realized, would soon be adding GPD 4 to thousands of existing apps and building thousands of new apps around it. It is, all said, a technology that definitely leverages some of our most significant technologies from the last 30 years, including the internet, mobile, cloud computing, and data analytics, to put the protein 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.
我意识到,作者们不久将把 GPD 4 添加到成千上万的现有应用程序中,并围绕它建立成千上万的新应用程序。总之,这是一个明显利用了过去30年中我们一些最重要的技术,包括互联网、移动、云计算和数据分析,把随时开启的 AI 的蛋白质能力放在数亿人的手中的技术。听起来有点像一个新世界的开始,不是吗?未来的旅行者指南。
In the weeks and months that followed my first encounter with GPD 4, my excitement only grew. In between Zoom calls, I'd give GPD 4 a prompt. Read, write a sonnet about sonnets. Sonnets are poems of fourteen lines that follow a strict rhyme scheme, and meter they often praise or question loves designs, or muse on beauty, art, or nature's features some sonnets follow the petrocan form with an octave and assess it 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 quadrains 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.
OK, also during Zoom calls, I'd give GPD 4 a prompt. Read, as large language model AIs are trained on the whole internet. In what ways are their outputs false in a Dorno sense, where the whole is the false? To answer this question, we need to first understand what a Dorno meant by this provocative claim, and then examine how it applies to the nature and limitations of large language model AIs.
A Dorno, 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. A Dorno 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 denial of the possibility of genuine change and emancipation.
A Dorno 是法兰克福学派批判理论的杰出成员,他深切关心现代资本主义、大众文化和理性化对人类社会和文化的影响。他认为工具性理性的逻辑支配了现代生活的方方面面,把一切都变成了手段和目的、计算和效率、统治和剥削,创造了一种全面管理和控制的系统。这个系统,他称之为文化工业,生产出标准化和商品化的文化形式,操纵和安抚大众,创造出虚假的意识,阻碍他们认识和抵抗自己的异化和压迫。A Dorno 认为这是一种消极的辩证法,现实中固有的矛盾和冲突被主导意识形态压制和调和,导致了对批判和创造性思维的丧失,否认了真正变革和解放的可能性。
Did I mention that GPD4 also does lightbulb jokes? Whatever train of thought I wanted to follow, whatever arbitrary pathway sparked by an old memory or a new interest, GPD4 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 GPD4, with GPD4. A book about the ways it and other AIs seem to poise to shape our future, so I pitched the idea. Read, Hey GPD4, let's 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 GPD4 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. It's how fast the industry is moving. So I hemmed in hard 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 and informal exercise in exploration and discovery. Me with GPD4 choosing one path among many. A snapshot, memorializing, and 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?
即便有了 GPD4,我仍然有所保留。因为,一本关于人工智能的书?当事物发展得如此之快,即使有一位有帮助的 AI 加快了进程,任何这样的书都将在我们开始写它之前过时。这就是这个行业的发展速度。所以,我有些犹豫,然后我想到了一个框架,推动我行动起来。这不必是一本全面的书,而是旅行日记和探索和发现的非正式练习。我和 GPD4 选择其中之一。这是一个快照,纪念,并以主观和非决定性的方式描述我们即将经历的人工智能未来。我们会看到什么?什么会给我们留下深刻印象?在这个过程中,我们会学到什么有关自己的东西?
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 ChatGBT, a conversational agent, aka Chatbot, a modified version of GPD3.5 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, ChatGBT 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 ChatGBT built into it. By the start of February 2023, OpenAI said ChatGBT 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 ChatGBT 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's 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. After 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, GPD-4. So far, I've been putting quotations around words like knowledge, aware, and understands when I talk about GPD-4 to signal that I, a sentient being, understand that GPD-4 is not one, it is essentially a very sophisticated prediction machine.
While GPD-4 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. Justice 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.
Similarly, GPD-4 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 GPD-4 doesn't affect operate in ways that are not entirely fixed, consistent, or predictable.
But if you think about it, you can't think about it. It's not like to 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 GPD-4 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 GPD-4 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 GPD-4 most productively and most responsibly. At its essence, GPD-4 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, 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 fast-ile, 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, contextual 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 copora 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 GPT-4 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 objectionable 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 texts 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 with various guardrails in place, a large language model itself can't make reason judgments about complex ethical quandaries or even about more straightforward questions. Take Chat GPT, which is based on GBD 3.5 and 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 than count sentences until I get to the fifth one.
Instead, a large language model is always just making statistical predictions about what the next word and a given text dream should be. From its training, though, Chat GPT has clearly come to associate the words Gettysburg address, with other words, specifically the text of the speech.
So when you ask Chat GPT 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 speeches first sentence, the ninth is frequently quoted in other texts. That means, in Chat GPT'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 Chat GPT to reach for it when you ask it to supply the fifth sentence.
To Chat GPT'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 colors. Try it out and see what I mean.
Embracing the aha moment. As AI tools, like GPT-4, become more powerful, they are intensifying longstanding concerns about AI's 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, Chat GPT passed a three-part U.S. 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 Chat GPT.
Here, for example, is how it handled my Gettysburg address question. Read, 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 to use the bliss copy, which is the most widely reproduced and authoritative version of the address and to count the sentences as follows.
1. For four 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.
5. It is altogether fitting and proper that we should do this.
5. 这样做是完全合适和正确的。
Therefore, the fifth sentence of the Gettysburg address, according to this method, is it is altogether fitting and proper that we should do this.
因此,通过这种方法,葛底斯堡演说的第五句话是这样的,我们这样做是完全适当和恰当的。
3. Suddenly, GBD4 seems to actually understand how counting works and can methodically apply its knowledge to deduce the right answer. It seems like GBD4 knows how to count Lincoln's 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.
4. GBD4 is no more conscious than its predecessors. It's just better at making predictions. Again, I want to make the point that GBD4's often remarkable simulations of cognitive proficiency are just that.
4. GBD4 is not a conscious, self-aware, sentient AI entity, Allah, forbidden planets, roby the robot, or star tracks, data. And yet, I also want to make again the point that, whoa, even GBD4's ability to stimulate such a contextually where a human-like consciousness is a pretty big deal.
Think of Chat GBT 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 Chat GBT and presumably similar large language models like GBD4 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 US Constitution. Chat GBT 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 of their own. Not bad as far as it goes. But since Chat GBT 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 re-package information that's already available. As illuminating as I found Chang's essay, I believe is central JPEG of the web metaphor under plays, large language models, synthetic powers.
First, I'd argue that re-packaging available information actually describes a normal 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 un-thousandable amount of information, much of it is siloed in a billions of individual pages. For example, think of questions like these. Who is the tallest NFL running back to win the MVP award? What cities with more than one 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 show up a lot of your time.
In addition, imagine when your goal is to get a concise overview of some broader interest or theme, 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 a thwart of information, GBD4 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 in a significantly different way from existing information resources like Wikipedia or traditional search engines. Essentially, GBD4 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 GBD4 itself, here's how it responded.
Read, how should human beings view and interact with you a powerful, large language model? One possible perspective is the following. 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, 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 GBD4 might not get funded with that pitch. The vision of AI presents is both nuanced and strikingly different from how AI has generally been 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 site set on a much more ambitious form of AI, machines that can operate completely autonomously, machines that are capable of human-like common-sense reasoning and self-awareness.
GBD4 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 GBD4 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 GBD4 as a co-pilot or a collaborative partner, it becomes far more powerful. You compound GBD4'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 GBD4 makes possible, we get what I believe to be the sound dyspormila, producing the best potential overall outcomes.
In this approach, GBD4 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 GBD4 this way, I call it an aha moment, to underscore the amplifying human abilities, perspective at the heart of that choice.
当人们选择以这种方式看待GBD4时,我称之为“啊哈时刻”,以强调选择中心的放大人类能力和视角。
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.
我写这篇游记,既是为了鼓励人们接受这种选择,也是邀请大家探索这种选择可能产生的不同方式。
What are the ways we can use GBD4 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 GBD4 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 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. Dasing such uncertainty, it's only natural to have concerns about our jobs and careers, about the speed and scale of potential changes, but what it even means to be human in a new era of increasingly intelligent machines.
Our path forward won't always be smooth and predictable. Sydney's now infamous outbursts won't be the only grimacing, new 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 travel log. 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.
That concludes this episode of Gray Matter. If you're interested in reading the full text of Improm2 by Reid Hoffman in GPT-4, 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 Chat GPT, check out Reid's Fireside Chatbot series on Gray Matter, where you discuss as many aspects of AI with AI.