5 ms·

Does, in your personal opinion, GPT-4 has an underlying model of the world?

jiggawatts 3y ago
In my personal opinion? Yes. I’m happy to argue the finer points of the philosophy of the mind and consciousness, but: I’ve talked to people that have a weaker mental model of the world than GPT 4. Many people compare these AIs against an idealised human, a type of Übermensch, something like a Very Smart Person that doesn’t lie and doesn’t make mistakes. Random humans aren’t remotely like that, and are a more realistic point of comparison. Think of the average person on the street, the middle of the Bell curve. An AI can be left of that but still replace a huge chunk of humanity in the workplace. What all current LLMs lack is medium term memory and all capabilities that depend on it, which is a lot. Perhaps this is a good thing. I don’t think I want AIs to think for themselves and arrive at conclusions they can hold on to for more than a few seconds…
- allisdust 3y ago
  I was able to get gpt4 to do a lot of useful work. But for some reason it completely falls apart for this scenario. May be because it has to think in second order to achieve the task. Perhaps you could take a crack at this: Prerequisite (for you the human)> You have a file at src/SampleReactComponent.jsx that has below simple react component: const SampleReactComponent = (props) => { const [var1, setVar1] = React.useState(false); const [var2, setVar2] = React.useState(false); const [var3, setVar3] = React.useState(false); return (<></>); }; export default SampleReactComponent; ********** Prompt for GPT4: I'm at my project root working on a reactjs project. Update the component in src/SampleReactComponent.jsx file by adding a new const variable after the existing variables. You cannot use cat command as the file is too big. You can use grep with necessary flags and sed to achieve the task. I'll provide you the output of each command that you generate. ************* That's it. It would do any complex modification on fully provided data (included in the prompt) but something like above where it has to build a model from secondary prompts will totally fall apart.
  - jiggawatts 3y ago
    Dude. Dude. I’m an IT professional and I have no idea how to begin answering that request! Pose that question verbatim to a dozen random people[1] and I guarantee you that you’ll get zero answers. Also, I find it hilarious that sed and awk are so counterintuitive that not even the AIs can do useful things with them. The same AIs that speak Latin, and can explain quantum mechanics. [1] I mean specifically not random Silicon Valley coworkers. Go talk to random relatives and the barista at the cafe.
    - allisdust 3y ago
      That would mean nothing because GPT4 isn't most people. I had it solve more complex problems than this particular one and using the same tools :)
      - mannykannot 3y ago
        Interesting! I, for one, would be most appreciative if we could see some of the similar and more difficult tasks that it succeeds on.
        allisdust 3y ago
        I have cleared my chat history recently so don't have all the prompts but here is a recent use case I used it for: Prompt: "Generate a react mui component that fetches a Seller's shipping credits and shows his current credit balance in the top portion. There should be a recharge button next to the credit balance. The bottom portion should show recent transactions. Use below api for fetching credit balance: <api req/resp>. Use below api for fetching the transactions: <api req/resp> Recharge should be done with the below api: <api req/resp> Make the component beautiful with good spacing, elevation etc. Use Grid/Card components." Final result: https://res.cloudinary.com/dksmi6x98/image/upload/v1685274094/seller_credits_yipnsc.png https://res.cloudinary.com/dksmi6x98/image/upload/v168527409...
        mannykannot 3y ago
- mft_ 3y ago
  A lot of the people on the left of the curve are likely doing different shades of manual work. This is often work that automation is struggling to replicate --think grasping and packing in a warehouse, or tidying up a messy shelf of goods-- and the GPTs won't change this. I'd argue it's maybe the middle and even the right of the curve that's threatened. Is your job writing marketing copy, or communications materials or press releases, or relatively simple moulding and counting of data, or HR administration (or many other examples)? GPT's coming for your job in a short space of time. (Which brings us to mft's second rule: if your job is threatened by the current generation of GPTs, it should probably have been largely automated by simpler means --or just eliminated-- already.)
  - jiggawatts 3y ago
    > and the GPTs won't change this. I suspect but cannot prove that the opposite will be the case. We can make bipedal robots now, but we can’t give them instructions, not like we can instruct human labourers. “Bob, go to where George ate his lunch an hour ago and bring me the tools for fixing a pipe” was an impossible instruction for a robot to execute. Suddenly this is a trivial sentence for an AI that knows what type of tools are typically needed for fixing pipes! Think about how many mega projects are being built in places like Saudi Arabia with immigrant workers that speak little English and no Arabic. An AI is already superior in terms of comprehension than those workers! LLMs already speak every major language and can be a handy translator too. C3P0 and R2D2 are looking totally realistic suddenly! Heck, do you remember that scene in the Empire Strikes Back where Luke used a text chat interface to communicate with R2D2? That’s oddly prescient now!
    - mft_ 3y ago
      That's not quite the issue I'm raising. It's not about being able to communicate better with existing robots - it's where (as yet) there are no robots suitable to do the job. There are plenty of tasks which are simple, tedious and repetitive - and yet are resistant (currently) to automation. An example is sorting mixed plastic waste into categories according to the type of plastic. This is comparatively trivial for almost any human, and (afaik) not yet possible to automate. (Another is picking and packing things in Amazon's warehouse - which we know they've expended huge effort in trying to solve.) Look at it the other way around: why are all simple, tedious, repetitive jobs not already automated? It's either because it's possible but not economically feasible, or no-one's tried yet... or because it's really difficult and so far there's no solution. This last category represents a lot of 'low end' jobs. GPTs are unlikely to move the needle here, as appropriate robots to carry out the tasks don't exist yet.
- somewhereoutth 3y ago
  It's meaningless to compare a LLM to a human, anymore than it is to compare a wheelbarrow to a human. To do so betrays both a fantasy projected onto LLMs and a lack of understanding of humans.
  - mannykannot 3y ago
    We are discussing a system that is very interesting precisely and only because it produces human language that at least superficially looks as though it was produced by a reasonably smart and educated human. For one thing, I don't know how one would disabuse someone of their fantasy projected onto LLMs and a lack of understanding of humans without making comparisons (while, of course, being wary of anthropomorphizing the former.)
  - MacsHeadroom 3y ago
    A wheelbarrow assisted human can haul 100x faster than a human assisted human. There, I just compared humans and wheelbarrows in a meaningful way. There's no problem doing the same with LLMs.
- laratied 3y ago
  The irony is I think GPT4 gives the best answer. It even gets the idea that language is inextricably tied up with our experiences of reality that most humans seem to simply not understand. GPT4 "The statement that language models have a "model of reality" can be both true and false, depending on how you interpret "model of reality." Language models like ChatGPT do have a kind of "model" in a broad sense. They have been trained on large amounts of text data from the internet and have learned statistical patterns from these data. This allows them to generate text that can seem sensible and knowledgeable about various aspects of reality. However, this "model" is more akin to a very sophisticated pattern recognition system. It doesn't possess a deep or holistic understanding of reality as humans do. It doesn't form beliefs, doesn't have a consciousness, and doesn't experience the world in any sense. It can't learn from the environment or update its understanding over time. Its "knowledge" is fixed at the time it was last trained (for GPT-4, this is as of September 2021), and it can't incorporate new information or discoveries unless it's trained on new data. Therefore, while language models like GPT-4 can simulate a kind of "knowledge" or "understanding" based on patterns in the data they were trained on, they don't have a model of reality in the sense of an evolving, interactive understanding based on sensory experience and reflection. So, language models have a "model of reality" only in the sense that they mimic certain aspects of human language use, which is inextricably tied up with our experiences of reality. But this is quite different from possessing a human-like understanding or model of the world."
  - bigyikes 3y ago
    Haha this scenario is perfect and tells you everything you need to know about the state of AI. We’re sitting here wondering if the language model also models reality, when we can just ask the thing itself! We are living in an Asimov novel
- bitcuration 3y ago
  GPT's model may be limited to language based, but the vast volume of knowledge it has accessed despite language only, is way beyond any individual human can possibly acquire in a life time. That fact alone makes the GPT world model, or whatever it might be, not to be underestimated. This partially explained why we don't feel GPT's answer is not terribly off mark, even brilliant sometimes.
cloudking 3y ago
I think GPT-4 has sufficient complexity to reason about a model of the world.
TuringTest 3y ago
It has a model, but it is not a rational model. This difference is something that often throws engineers off track when thinking about generative AI. LLMs models work more like intuitions. They are able to make statements about a problem in context, but they are generated from ideas that "instinctively" make sense given the prior statements and learned corpus (similar to Daniel Kahneman's fast mode of thinking), not logically constructed. These models do not have the capability to build formal inferences of careful steps that try to validate those ideas avoiding contradictions. Those capabilities can be added outside the model to try to verify the generated text, but so far are not integrated in the learning process, and I don't think anyone knows how to do it.
- SanderNL 3y ago
  Rationality’s building blocks are themselves not rational. I don’t know where this idea came from that logical thought somehow springs into life fully formed at once. Logos? I find it more helpful to think of human thought as consisting of multitudes of little patterns, all wired up together to correlate but individually unrecognizable and certainly not traceable to some concrete part of a problem. At some unknown and slightly fuzzy point our dreamlike mentations start resembling some form of rational thought. But it’s a mirage that will fade again in time. Like how clouds suddenly and instantly look like a rabbit, it’s a trick. Thousands of patterns that are not rabbitlike in any way had to help that activation along the way. I think the trick is building so much margin, so much room between dreamlike mentations and “rationality” that the entity stays coherent most of the time under normal circumstances. I think this is vaguely what happens with moving from gpt3 to gpt4, it got some breathing room. Remember it is quite easy to trick a human into decoherence as well.
  - TuringTest 3y ago
    > Rationality’s building blocks are themselves not rational. I don’t know where this idea came from that logical thought somehow springs into life fully formed at once. Certainly not from me :-P I'm fully aware that human rationality is one technique trained on top of our common diffuse thinking. Heck, we invented machines to perform rational steps for us without errors. Once you build a consistent rational system though, you can trust that it will always produce internally coherent knowledge (as long as no bugs external to the system are introduced). That behaviour requires algorithms, not statistical inference.
deleted 3y ago
[deleted]
bigyikes 3y ago
Yes. There is nothing special about modeling the world. There isn’t some threshold where the ability to model the world suddenly emerges. The AI will model the world if modeling the world is the simplest way to predict the next word. For basic prompts, no model of the world is necessary: “The quick brown fox jumps over the lazy…” you know what’s next, and you don’t need to know about the real world to answer. For complex prompts, the only way to answer correctly is to model the world. There is no simpler way to arrive at the correct answer.
vharuck 3y ago
No. GPT is a model of human writing. But, as the Box quote goes, "Essentially, all models are wrong, but some are useful.". It isn't writing the same way that we are, with the same thoughts or mental models. It's just amazingly good at imitating it. For tasks that can be achieved by just writing, the GPT model is so good at modeling writing that it performs as well as a normal person using their writing skills plus their mental model of the world. But GPT won't look up information unless asked to. It won't try something new to see if it works. It's this distinction useful? Rarely. But it's one of those things users should remember, like leaks in an abstraction. When it doesn't do what you expected, you should know these gaps exist in the model.

In my personal opinion? Yes. I’m happy to argue the finer points of the philosophy of the mind and consciousness, but: I’ve talked to people that have a weaker mental model of the world than GPT 4. Many people compare these AIs against an idealised human, a type of Übermensch, something like a Very Smart Person that doesn’t lie and doesn’t make mistakes. Random humans aren’t remotely like that, and are a more realistic point of comparison. Think of the average person on the street, the middle of the Bell curve. An AI can be left of that but still replace a huge chunk of humanity in the workplace. What all current LLMs lack is medium term memory and all capabilities that depend on it, which is a lot. Perhaps this is a good thing. I don’t think I want AIs to think for themselves and arrive at conclusions they can hold on to for more than a few seconds…

allisdust 3y ago
I was able to get gpt4 to do a lot of useful work. But for some reason it completely falls apart for this scenario. May be because it has to think in second order to achieve the task. Perhaps you could take a crack at this: Prerequisite (for you the human)> You have a file at src/SampleReactComponent.jsx that has below simple react component: const SampleReactComponent = (props) => { const [var1, setVar1] = React.useState(false); const [var2, setVar2] = React.useState(false); const [var3, setVar3] = React.useState(false); return (<></>); }; export default SampleReactComponent; ********** Prompt for GPT4: I'm at my project root working on a reactjs project. Update the component in src/SampleReactComponent.jsx file by adding a new const variable after the existing variables. You cannot use cat command as the file is too big. You can use grep with necessary flags and sed to achieve the task. I'll provide you the output of each command that you generate. ************* That's it. It would do any complex modification on fully provided data (included in the prompt) but something like above where it has to build a model from secondary prompts will totally fall apart.
- jiggawatts 3y ago
  Dude. Dude. I’m an IT professional and I have no idea how to begin answering that request! Pose that question verbatim to a dozen random people[1] and I guarantee you that you’ll get zero answers. Also, I find it hilarious that sed and awk are so counterintuitive that not even the AIs can do useful things with them. The same AIs that speak Latin, and can explain quantum mechanics. [1] I mean specifically not random Silicon Valley coworkers. Go talk to random relatives and the barista at the cafe.
  - allisdust 3y ago
    That would mean nothing because GPT4 isn't most people. I had it solve more complex problems than this particular one and using the same tools :)
    - mannykannot 3y ago
      Interesting! I, for one, would be most appreciative if we could see some of the similar and more difficult tasks that it succeeds on.
      - allisdust 3y ago
        I have cleared my chat history recently so don't have all the prompts but here is a recent use case I used it for: Prompt: "Generate a react mui component that fetches a Seller's shipping credits and shows his current credit balance in the top portion. There should be a recharge button next to the credit balance. The bottom portion should show recent transactions. Use below api for fetching credit balance: <api req/resp>. Use below api for fetching the transactions: <api req/resp> Recharge should be done with the below api: <api req/resp> Make the component beautiful with good spacing, elevation etc. Use Grid/Card components." Final result: https://res.cloudinary.com/dksmi6x98/image/upload/v1685274094/seller_credits_yipnsc.png https://res.cloudinary.com/dksmi6x98/image/upload/v168527409...
        mannykannot 3y ago
mft_ 3y ago
A lot of the people on the left of the curve are likely doing different shades of manual work. This is often work that automation is struggling to replicate --think grasping and packing in a warehouse, or tidying up a messy shelf of goods-- and the GPTs won't change this. I'd argue it's maybe the middle and even the right of the curve that's threatened. Is your job writing marketing copy, or communications materials or press releases, or relatively simple moulding and counting of data, or HR administration (or many other examples)? GPT's coming for your job in a short space of time. (Which brings us to mft's second rule: if your job is threatened by the current generation of GPTs, it should probably have been largely automated by simpler means --or just eliminated-- already.)
- jiggawatts 3y ago
  > and the GPTs won't change this. I suspect but cannot prove that the opposite will be the case. We can make bipedal robots now, but we can’t give them instructions, not like we can instruct human labourers. “Bob, go to where George ate his lunch an hour ago and bring me the tools for fixing a pipe” was an impossible instruction for a robot to execute. Suddenly this is a trivial sentence for an AI that knows what type of tools are typically needed for fixing pipes! Think about how many mega projects are being built in places like Saudi Arabia with immigrant workers that speak little English and no Arabic. An AI is already superior in terms of comprehension than those workers! LLMs already speak every major language and can be a handy translator too. C3P0 and R2D2 are looking totally realistic suddenly! Heck, do you remember that scene in the Empire Strikes Back where Luke used a text chat interface to communicate with R2D2? That’s oddly prescient now!
  - mft_ 3y ago
    That's not quite the issue I'm raising. It's not about being able to communicate better with existing robots - it's where (as yet) there are no robots suitable to do the job. There are plenty of tasks which are simple, tedious and repetitive - and yet are resistant (currently) to automation. An example is sorting mixed plastic waste into categories according to the type of plastic. This is comparatively trivial for almost any human, and (afaik) not yet possible to automate. (Another is picking and packing things in Amazon's warehouse - which we know they've expended huge effort in trying to solve.) Look at it the other way around: why are all simple, tedious, repetitive jobs not already automated? It's either because it's possible but not economically feasible, or no-one's tried yet... or because it's really difficult and so far there's no solution. This last category represents a lot of 'low end' jobs. GPTs are unlikely to move the needle here, as appropriate robots to carry out the tasks don't exist yet.
somewhereoutth 3y ago
It's meaningless to compare a LLM to a human, anymore than it is to compare a wheelbarrow to a human. To do so betrays both a fantasy projected onto LLMs and a lack of understanding of humans.
- mannykannot 3y ago
  We are discussing a system that is very interesting precisely and only because it produces human language that at least superficially looks as though it was produced by a reasonably smart and educated human. For one thing, I don't know how one would disabuse someone of their fantasy projected onto LLMs and a lack of understanding of humans without making comparisons (while, of course, being wary of anthropomorphizing the former.)
- MacsHeadroom 3y ago
  A wheelbarrow assisted human can haul 100x faster than a human assisted human. There, I just compared humans and wheelbarrows in a meaningful way. There's no problem doing the same with LLMs.
laratied 3y ago
The irony is I think GPT4 gives the best answer. It even gets the idea that language is inextricably tied up with our experiences of reality that most humans seem to simply not understand. GPT4 "The statement that language models have a "model of reality" can be both true and false, depending on how you interpret "model of reality." Language models like ChatGPT do have a kind of "model" in a broad sense. They have been trained on large amounts of text data from the internet and have learned statistical patterns from these data. This allows them to generate text that can seem sensible and knowledgeable about various aspects of reality. However, this "model" is more akin to a very sophisticated pattern recognition system. It doesn't possess a deep or holistic understanding of reality as humans do. It doesn't form beliefs, doesn't have a consciousness, and doesn't experience the world in any sense. It can't learn from the environment or update its understanding over time. Its "knowledge" is fixed at the time it was last trained (for GPT-4, this is as of September 2021), and it can't incorporate new information or discoveries unless it's trained on new data. Therefore, while language models like GPT-4 can simulate a kind of "knowledge" or "understanding" based on patterns in the data they were trained on, they don't have a model of reality in the sense of an evolving, interactive understanding based on sensory experience and reflection. So, language models have a "model of reality" only in the sense that they mimic certain aspects of human language use, which is inextricably tied up with our experiences of reality. But this is quite different from possessing a human-like understanding or model of the world."
- bigyikes 3y ago
  Haha this scenario is perfect and tells you everything you need to know about the state of AI. We’re sitting here wondering if the language model also models reality, when we can just ask the thing itself! We are living in an Asimov novel
bitcuration 3y ago
GPT's model may be limited to language based, but the vast volume of knowledge it has accessed despite language only, is way beyond any individual human can possibly acquire in a life time. That fact alone makes the GPT world model, or whatever it might be, not to be underestimated. This partially explained why we don't feel GPT's answer is not terribly off mark, even brilliant sometimes.

cloudking 3y ago

I think GPT-4 has sufficient complexity to reason about a model of the world.

TuringTest 3y ago

It has a model, but it is not a rational model. This difference is something that often throws engineers off track when thinking about generative AI. LLMs models work more like intuitions. They are able to make statements about a problem in context, but they are generated from ideas that "instinctively" make sense given the prior statements and learned corpus (similar to Daniel Kahneman's fast mode of thinking), not logically constructed. These models do not have the capability to build formal inferences of careful steps that try to validate those ideas avoiding contradictions. Those capabilities can be added outside the model to try to verify the generated text, but so far are not integrated in the learning process, and I don't think anyone knows how to do it.

SanderNL 3y ago
Rationality’s building blocks are themselves not rational. I don’t know where this idea came from that logical thought somehow springs into life fully formed at once. Logos? I find it more helpful to think of human thought as consisting of multitudes of little patterns, all wired up together to correlate but individually unrecognizable and certainly not traceable to some concrete part of a problem. At some unknown and slightly fuzzy point our dreamlike mentations start resembling some form of rational thought. But it’s a mirage that will fade again in time. Like how clouds suddenly and instantly look like a rabbit, it’s a trick. Thousands of patterns that are not rabbitlike in any way had to help that activation along the way. I think the trick is building so much margin, so much room between dreamlike mentations and “rationality” that the entity stays coherent most of the time under normal circumstances. I think this is vaguely what happens with moving from gpt3 to gpt4, it got some breathing room. Remember it is quite easy to trick a human into decoherence as well.
- TuringTest 3y ago
  > Rationality’s building blocks are themselves not rational. I don’t know where this idea came from that logical thought somehow springs into life fully formed at once. Certainly not from me :-P I'm fully aware that human rationality is one technique trained on top of our common diffuse thinking. Heck, we invented machines to perform rational steps for us without errors. Once you build a consistent rational system though, you can trust that it will always produce internally coherent knowledge (as long as no bugs external to the system are introduced). That behaviour requires algorithms, not statistical inference.

deleted 3y ago

[deleted]

bigyikes 3y ago

Yes. There is nothing special about modeling the world. There isn’t some threshold where the ability to model the world suddenly emerges. The AI will model the world if modeling the world is the simplest way to predict the next word. For basic prompts, no model of the world is necessary: “The quick brown fox jumps over the lazy…” you know what’s next, and you don’t need to know about the real world to answer. For complex prompts, the only way to answer correctly is to model the world. There is no simpler way to arrive at the correct answer.

vharuck 3y ago

No. GPT is a model of human writing. But, as the Box quote goes, "Essentially, all models are wrong, but some are useful.". It isn't writing the same way that we are, with the same thoughts or mental models. It's just amazingly good at imitating it. For tasks that can be achieved by just writing, the GPT model is so good at modeling writing that it performs as well as a normal person using their writing skills plus their mental model of the world. But GPT won't look up information unless asked to. It won't try something new to see if it works. It's this distinction useful? Rarely. But it's one of those things users should remember, like leaks in an abstraction. When it doesn't do what you expected, you should know these gaps exist in the model.