Better OfflineIt’s been just under a year and a half since ChatGPT - an AI-powered chatbot launched by so-called non-profit OpenAI - ushered in a new era of investor and media hype around how artificial intelligence would change the world. But what if this we're actually at the peak of what generative AI can do? In this episode, Ed Zitron walks you through the four intractable problems that are stopping Large Language Models like ChatGPT in their tracks - and why they're all-but-impossible to overcome.
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Hello and welcome to Better Offline. I'm your host ed z tron. It's been just under a year and a half since chat gpt, an AI powered chatbot launched by so called nonprofit open Ai, ushered in a new investor in media hype cycle around how AI would change the world. Chat GPT's instant success made both open Ai and its CEO, Sam Mortman overnight celebrities. As a result of chat GPT's alleged intelligence, which seemingly allowed you to do everything from generate an entire essay from a simple prompt to writing entire reams of software code. You can theoretically ask it anything and it will spit out an intelligent sounding response thanks to being trained on terabides of text data, like a search engine that's able to think for itself. Ah. Big problem is chat gpt doesn't think at all. It doesn't know anything. Chat gpt is actually probabilistic. It uses a statistical model to generate the next piece of information in a sequence. If you ask it what an elephant is, it'll guess that the most likely answer to that prompt is that an elephant is a large mammal, and then perhaps describe its features, such as a long trunk. Chat GPT doesn't know what an elephant is, or what a trunk is, or what the alefant Haantai family is. It's simply ingested enough information to reliably guess what an elephant is meant to be, or indeed know that that's what you're asking it. This is the technology underpinning the latest artificial intelligence boom. It's called generative artificial intelligence, and it's powered by large language models, and they underpin tools like open ais, chat, GPT, Anthropics, claude x dot COM's horrifying chatbot Grock, and of course Google's Gemini. Essentially, they're AI systems that ingest vast quantities of written text or other data, and then, through mathematics, try and identify patterns of relationships between words or symbols, or basically any meaning from the text or thing they're being fed. And it almost seems like magic because it's able to generate this plausible, seeming almost human content at this remarkable speed. These models are now capable of generating text, images, and even video in response to simple chat prompts or by learning the patterns and structures of their data. Yet underneath the hood, there's always something a bit wrong. Generative AI, at times authoritatively spits out incorrect information, which can range from funny like telling you that you can melt an egg, to outright dangerous, like when the recently launched AI powered New York City chatbot for small business owners started telling them that it was legal to fire somebody for refusing to cut their dreadlocks. This is why you'll see strange glitches in images generated by AI hands with too many fingers, horrifying looking people in the back of realistic looking photos, and so on and so far. Because these models don't actually know what anything is. They don't have meaning, they don't have consciousness or intelligence. They're guessing, and when they guess, they sometimes hallucinate, which I'll get too soon. And while they might be really really good at guessing, there are effectively a very very powerful version of auto complete. I don't know anything. I really mean that these things aren't even intelligent. But because these models seem like they know stuff, and they seem to be able to do stuff, and the things that they create almost seem right. The media and the Vocal Investor class on Twitter have declared that large language models would change everything to them. Llms like chat GPT would upend entire business models, render once unassailable tech giants nunerable, and rewrite our entire economic playbook by turning entire industries into something you tell a chatbot to do. In a sentence, you know, it doesn't really matter that genera ive AA is mostly good at reams of generic slop, and that it's also clogging services like Amazon's Kindle eBookstore. And I guess the rest of the Internet with generative content that doesn't matter at all, because it's kind of good. Obviously, I'm being sarcastic. This is all very, very bad. In the last year, there have been hundreds of muling articles about how AI will replace everything from drive through workers to medical professionals. Theoretically, you could just feed whatever information a potential customer could ask for into a vast database and have an AI chew it up, and then they could just generate exactly the answer you'd need. AI would just naturally slip into areas of disorganization and inefficiency and spit out remarkable new ideas, all with minimum human input. In every one of these stories carries with them a shared belief one might even call it a shared hallucination. And they all believe that generative AI will actually be able to do these things, that it will actually be able to replace people. And what we're seeing today is just the beginning of our glorious automated future. But what if it's not. What if generative AI can't actually do much more than it can today? What if we're actually at peak AI. In the next two episodes, I'm going to tell you why I think that is. I'm going to tell you how I think this whole thing falls apart. AI's media hype train has been fairly relentless since November twenty twenty two, when chat GBT launched, and AI champions like open Ai CEO Sam Mortman have proven all too willing to grease its wheels, making bold promises about what AI could do and how today's problems are so easily overcome. It's also helped that the tech media has largely accepted these promises without asking basic questions like how and when will it do this stuff? And can it do this stuff? Don't believe me, Go and look at any interview with Sam Moltman from the last few years, watch any of them. In fact, just look at any AI figurehead getting interviewed and count the amount of times they've actually received any pushback or been asked to elaborate on any specific issue. It's actually very rare. Let me play you one of the few times that anyone's actually interrogated an AI person. Specifically Joanna Stern of The Wall Street Journal, who you might remember from the Vision Pro episode A Better Offline. She interviewed open AI's Chief Technology Officer, Mirror Marathi about Sora, which is open aiy's video based version of chat GPT where you can theoretically ask it to generate videos. Just to be clear, it's unreleased and unclear whether it'll ever actually get released, and the videos look good at first, then they look really weird. But just listen to this particular question. It's Joanna asking Mirror, the CTO of open ai and eighty billion dollar AI company, Hey, did you train on YouTube? What data was.
Used to train?
We used publicly available data and licensed data, so videos on YouTube?
Now, I encourage you to go and look up this clip because at this point Maurti makes the strangest face I've ever seen in a tech interview.
I'm actually not.
Sure about that okay, videos from Facebook, Instagram.
You know, if they were publicly available available, publicly available to use, there might be the data, but I'm not sure. I'm not confident about it.
What about Shutterstock, I know you guys have a deal with them.
I'm just not going to go into the details of the data that was that was used, but it was publicly available or licensed data.
The remarkable part about this interview is that it's a relatively simple question. You, as the CTO of an eighty billion dollar AI company, what training data did you use to train your model? Did you use YouTube? So yes or no? Question? Mirror mirror, answer the bloody question, mirror all right, right? The answer is, of course Open Ai likely trained it's video generating model, Sora on YouTube videos, which might be why they're yet to launch it. And the videos generated by Soora also feature some remarkably similar images to say, SpongeBob SquarePants, and I wouldn't be surprised if they carry with them multiple weird biases about race and gender that we'll see in the future. But also when you watch these videos, much like most generative AI content, there's something a bit off about them. In The Wall Street Journal's interview. You get to see some of the prompts that were used and some of the videos that came out, and you see crazy things happening, like a robot completely changing shape as it turns, cars disappearing and appearing behind the robot. It's not very good. It seems cool at first. If you squint really hard, it looks real, but there's always something off. And that's because, as I've said before, these models don't know anything and don't know what at robot looks. They can make a really good guess though. Anyway, Sterne's interview with Marati of Open AI is a great example of how the entire AI artifst falls apart at the slightest touch, because it's fundamentally flawed and not actually able to deliver the society defining promises that Sam Morltman and the venture capital sect would have you believe. In a year and a half, despite billions of dollars of investment, despite every major media outlet claiming otherwise, generative artificial intelligence has proven itself incapable of replacing or even meaningfully enhancing human work. And the thing is, all of these problems I'm talking about with generative AI all of these hallucinations, all of these weird artifacts that are popping up throughout these videos, the weird mistakes that the texts that are popped out by chat GPT have, all of these. The problems are problems that aren't necessarily just technological. Their physics, they're mathematics. These aren't things you can just outrun. And I believe that there are four intractable problems that will stop generative AI from progressing much further than it is today. The first is, of course, its energy demands, the massive amounts of power requires. The second are its computational demands, the amount of compute power it requires to even crunch the simplest things out of chat GPT, It's hallucinations, the authoritative failures it makes when it spits out nonsense or creates a human hand with eighteen fingers, and of course the fact that these large language models have an insatiable hunger for more training data. Now, let me break that down. Large language models are extremely technologically and environmentally demanding. The New York Are reported in March twenty twenty four the CHATGBT uses more than half a million kis what hours of electricity to respond to the two hundred million requests it receives in a day, or seventeen thousand times the amount that the average American household uses in a day, and others have suggested it might be as high as thirty three thousand households worth. Generative AI models demand specialist chips called graphics processing units, typically a souped up version of the technology used to drive the graphics in a gaming console, albeit at a much higher cost, with each one costing tens of thousands of dollars each. They do this because large language models like chat GPT are highly computationally intensive. I'm going to break that down. Don't worry. When you ask chat GPT a question, it tokenizes it, breaking it down into smaller parts for the model to understand. It then feeds these tokens into various mechanisms that help it understand the meaning of the thing you asked it to do based on the parameters that it learned in training. Chat GPT generates a response by predicting the most likely sequence of things that you might want it to do. An answer to a question, an image, so on, and so forth. Each one of these steps is extremely demanding, processing hundreds of billions of these parameters learned patterns from ingesting training data such as how the English language works or what a dog looks like to produce even the simplest thing. Training these models is equally intensive, requiring chat GPT to process massive amounts of data. Another problem I'll get to in a bit adjusting those hundreds of billions of parameters and developing new ones based on what the data says as it quote unquote learns more. Though as we're clear, chap GPT doesn't learn anything. It just makes new parameters to read things. A model like chat GBT grows, making it more complex, which in turn requires more data to train on and more compute power to both ingest the data, create more parameters and turn it into something resembling an answer, And because it doesn't know anything, it's suggesting the most likely to be correct answer, which leads it to hallucinating and correct things that, based on proper ability, kind of seem like the right thing to say. These hallucinations are the dirty little secret of generative AI, and are impossible to avoid thanks to the fact that every single thing these models say is a mathematical equation rather than any kind of intellectual exercise. If you ask CHATGPT how many days there are in a week. It doesn't know that there are seven days, but it's been trained on patterns of language and generates a result based on those patterns, which at times can be correct and can also be wrong. There's no way of fixing this problem. You can mitigate it, you can make it less likely it will mess up, but hallucinations will happen because there is no consciousness. It is not learning anything. This thing has no knowledge. More computing power would allow it more parameters to give it more rules, so that a generative AI will be more likely to give a correct answer, but there's no eliminating them, and doing so may require more computing power than actually exists or is possible without an AI of consciousness, an impossible dream known as average generalized intelligence that Sam Mortman would have you believe is imminent. There's really no solving hallucinations. When you answer questions using probability, you're always going to have mistakes because you're not actually answering them using knowledge, intellect, or experience. You're using dice rolls. It's a bloody game of dungeons and dragons. We turned in Carter into dungeons and dragons anyway. A newly published paper by Tepo Felon and Matthias Holweg of the University of Oxford agrees funding that large language models like chat GPT are incapable of generating new knowledge. It's a remarkably in depth rundown of the fundamental differences between a large language model and a human brain, and it combines both psychological and mathematical research going back to child psychology as well the basic building blocks of how we consume and learn things and how we make decisions. As a result, the paper title theory is or You Need AI Human Cognition and Decision Making argues that AI's data and prediction based orientation is an incomplete view of human cognition and the forward thinking theorizing of the human mind. In Layman's terms, the mess of the information we've learned over our lives, our experiences, and our ability to look forward and consider the future is just fundamentally different to a model that predicts things only based off of past data. Think of it like this, If you've read a book and you might think about writing a new book based on those ideas, you're not remembering every part of the book. You don't have a perfect memory, and you're also constantly thinking about things as your day goes on. The human brain is a goddamn mess. Generative AI is in some level stuck in amber. Though the billions of parameters might change, the data never does the way it consumes the data. May be that the data doesn't change. In essence, generative AI is held back by the fact that it can't consider the future and is actually permanently mired in the data of the past. Their largest problem might be a fast, simpler one, a farcillio, or a kind of an ironic one. There might not be enough data for these bloody things to actually train on. While the Internet may at times feel limitless, a researcher recently told The Wall Street Journal that only a tenth of the most commonly used web data set, the common Crawl, are freely available. Two hundred and fifty billion page dump of the web's information is actually of high enough quality data for large language models like CHATGBT to actually train on. Putting aside the fact that I can't find a single definition of what high quality actually means, the researcher pat Blow Vilobos suggested that the next version of chat GBT would require more than five times the amount of data it took to train in its previous version, GPT four. The new one is called GPT five. By the way, and other researchers have suggested that AI companies are going to run out of training data in the next two years. Now. That sounds dire, but don't worry. They've come up with a very funny and extremely stupid idea to fix it. One specifically posed by The Wall Street Journal is that the AR companies are going to create their own synthetic data to train their models, a computer science version of inbreeding. The researcher Jason Stadowski calls habsburg AI. This is, of course, an absolutely terrible idea. A research paper from last year found that feeding model generated data into models to train them creates something called model collapse, a degenerative learning process where models start forgetting improbable events over time as the model becomes poison with its own projection of reality. The paper, called the Curse of recursion. Training on generated data makes models forgam highlights an example where feeding a generative AI its own data eventually destroys its ability to answer questions, and within nine generations. One answered a simple prompt about architecture with an insane screed about jack rabbits full of at symbols and weird characters. So not to worry again. The tech overlords have come up with a great idea to fix this problem. Their common retort to the problem of synthetic data is that you could use another generative AI to monitor the synthetic data being fed into all model to make sure it's right. At this point, I'd like to get slightly angry. Are you kidding me? Are you fucking kidding me? You're saying that the way to make sure the data generated by an unreliable generative AI is to use another generative AI, one with the same goddamn problems, which also hallucinates information that knows nothing. You're going to use that AI to monitor whether the data that is created by an AI is any good? Are you completely insane? You insane? You're going to feed the crap from the crap machine into another crap machine to make it not make crap? Why am I reading journalists credulously printing this ridiculous solution in the New York Goddamn Times every time? Every time, these bubbles are inflated because tech executives are able to get their half ass, half baked solutions parroted by reporters who should know better. You don't have to give them the better affair of the goddamn fucking doubt. This is how we got the bloody meta us. Pardon me, I've calmed down now. Anyway. Anyway, if you're worried about model collapse, you're already too late, as these models are likely already being fed their own data. You see, these models are trained on the web, as I previously told you, and they're desperate. They need data. They need more stuff. They need more stuff to ingest so they can spit out more stuff. The problem is that these machines are purpose built to make a lot of content, and so the Web's already being filled with generative AI. Generative AI is already spamming the Internet. A report from four oh four Media from last week said that Google Books has already started to index several different works that were potentially written by AI, featuring the hallmark generic writing tropes of these models. Four or four Media also reports that the same thing is happening over at Google Scholar their index of scientific papers with one hundred and fifteen different articles featuring the phrase as of my last knowledge update a specific phrase spat out by generative models. This is really bad, by the way, and this is only going to get worse. When you have an Internet economy that is built so that the people that can put the most out there will probably get the most traffic. They're going to use these tools. These tools are great for that. If you don't give a rap fuck about the quality, this is the best thing in the world for you. And that's the thing. This is a problem both created and caused by these models. You see. The other dirty little secret of generative II is that these models unashamedly plagiarize the entire web, leading outlets like The New York Times and authors like John Grisham to sue open ai for plagiarism. While open ai won't reveal exactly what their training data is, the New York Times is able to successfully make chat gpt reproduce content from the newspaper, and the company has repeatedly said that it trains on publicly available data from the Internet, which will naturally include things like Google scholar and Google Books. The Times also reports that open ai has become so desperate for data that they've used their whisper tool to transcribe YouTube videos into texts to feed into chat GPT's training data. Pretty sure, that's plagiarism, but who am I to tell you? And as the web gets increasingly pumped full of this generative content, these models are going to just start eating their own swill, slowly corrupting themselves in a kind of ironic death. According to Zakhar Schumelov, one of the authors of the model collapsed paper at the University of Cambridge, the unique problem that synthetic data creates is that it lacks human errors. Human made training data, by the nature of it being written by a human, includes errors and imperfections, and models need to be robust to such errors. So what do we do if models are trained off of content created without them? Do we introduce the errors ourselves? How many errors are there? How do we can introduce them more? And indeed, what are the errors? What do they look like? Do we even know? Are we conscious of the errors in the human language that make us human? The models aren't? Well, maybe they are. It's kind of unclear. It's kind of tough to express how deeply dangerous the synthetic data idea is for AI models like chat, GPT and Claude are deeply dependent on training data to improve their outputs, and their very existence is actively impeding the creation of the very thing they need to survive. While publishers like Axel Springer have cut deals to license their companies data to chat GPT for training purposes, this money is flowing to the writers that create the content that open Ai and Anthropic need to grow their models much further. In fact, I don't think you're going to see more journalists get hired as a result of these deals, which kind of makes them a little bit stupid. This puts AI companies in a kind of Kafka esque bind, but they can't really improve a tool for automating the creation of content without human beings creating more content than they've ever created before, just as said tool actively crowds out human made data. It's a little silly. The solution to these problems, if you ask open AI's Sam Altman, is always more money and power, which is why the information reports he is trying to convince open ai investor Microsoft to build him and I'm not kidding, and one hundred billion dollars supercomputer called Stargate. This massive series of interconnected machines will require entirely new ways to mountain cool processing units and is entirely contingent on pen AI's ability to meaningfully improve chet GPT, something Sam Mortman claims isn't possible without more computing power. To be clear, open Air already failed to build a more efficient model, dubbed Arakis, which ended up getting mothballed because it wasn't more efficient. It's also important to note that every major cloud company now has inextricably tied themselves to the generative AI movement. Google and Amazon have invested billions into chat GPT competitor Anthropic, and both claim to be Anthropic's primary cloud provider, though isn't really obvious which one is. In doing so, they've guaranteed, according to a source of mind, about seven hundred and fifty million dollars a year of revenue for Google's cloud and eight hundred million dollars a year of revenue for Amazon Web Services the cloud service from Amazon by mandating that Anthropic uses their services to power their clawed model. This similar to the thirteen billion dollar investment that Microsoft gave open AI last year, most of which was made up of credits for Microsoft's cy or cloud, And I somehow doubt that Microsoft is going to be the noble party that goes on the earnings and says, well, we don't want to count the credits that we gave open out want to be fair. No, they're going to mash that shit right back into their revenue. Kind of a con kind of makes me angry when I think about it too. Anyway, let me just put that aside and not going to get pissed off again. Look, I'm surprised more people aren't really upset about this very incestuous relationship between big tech and this supposedly independent generative AI movement. Microsoft, Google, and Amazon have effectively handed cash to one or two companies that will eventually hand the cash back to them in exchange for cloud services that are necessary to make their companies work, and all three big tech firms are spending billions to expand their data center operations to capture this theoretical demand from generative AI. Every penny the open AI or anthropic makes will now flow back to one of three big tech firms, even more so in the case of open AI, because Microsoft's investment entitles Microsoft to a share of any future profits from open ai and chat GBT. Yeah, it doesn't even really matter if they make one, because Big tech wins either way. Anthropic has to use Google Cloud and Amazon Web services, open Ai has to use Microsoft's z your Cloud, and Microsoft is actively selling open AI's models to their zero cloud customers. And every time somebody uses open AI's models, that model is being run on a zero cloud, generating revenue for Microsoft. This is the rot economy in action, by the way, Big tech has funded its biggest customers for their next growth revenue stream, justifying this massive expansion of their data center operations because ai is the future and they're telegraphing growth to these brainless drones in the market who will buy any thing, who never think too hard about what they're actually investing in. Hey, i's this big, sexy, exciting and theoretically powerful way to centralized labor. And it's innovative sounding enough that it allows people to dream big about how it might change their lives. Now, it might help them not pay real people to do shit. Yeah, here's the biggest worry I have. Here's the real pickle, here's the thing that keeps me up at night. None of these companies seem to have appeared to consider something. What if generative AI can't actually do any of the things they're excited about. What if genera avii's content, as you probably seen from anything chet GBT spits out, isn't really good enough. Hey, is anyone checked if anyone's actually using these tools, if they're helpful to anyone? Is this actually replacing anyone's work? Huh, that's a bit worrying, mate, I didn't think about that before. Just kidding, I've been thinking about it for months. Look, here's the thing. I think that the big problem here is that Sam Altman and his cronies have allowed the media, the markets, and big tech to fill in the gaps of their specious messaging. They've allowed everybody to think that open AI can do whatever anyone dreams. Yeah, I don't think that generative AI can do much more than it is today. And also, from what I've seen, none of these generative AI companies actually make a profit, and with each new model they become less profitable, and I don't see that changing in the future. And so I've dug in a little more looking under the hood. All the demand that's spurring Microsoft, Google, and Amazon's data center operations might not actually be there. My friends, I think we're in the next tech bubble, and the next episode, I'm gonna walk you through how I think it might pop. Thank you for listening to Better Offline.
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