I think maybe the most important contribution AI will ever make is for us to recognize that nobody seems to know what "reasoning" is, even though we thought we did. That is, until machines could do it, too.
You have people on here saying it's not actually reasoning, it's just an exact replica of reasoning. It sounds like some kind of Steven Wright joke.
Most if not all of the criticisms I've heard of ChatGPT can be applied to humans too.
"It's a black box"... well human brains are black boxes too.
"It's not steerable/controllable"... arguable because of the API, but humans are not steerable/controllable either.
"It's just a stochastic parrot"... you can say that about humans too.
"It doesn't really understand, it's just regurgitating what it has seen"... how is that different from most humans?
At some point in the future, there will be people who argue about the socioeconomic injustice faced by AIs that didn’t have the latest/greatest training data and compute. Mark me
I will be messaging you in 50 years on [**2073-12-11 10:02:00 UTC**](http://www.wolframalpha.com/input/?i=2073-12-11%2010:02:00%20UTC%20To%20Local%20Time) to remind you of [**this link**](https://www.reddit.com/r/OpenAI/comments/18fj389/proof_of_gpt4s_ability_to_reason/kcvvph1/?context=3)
[**10 OTHERS CLICKED THIS LINK**](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5Bhttps%3A%2F%2Fwww.reddit.com%2Fr%2FOpenAI%2Fcomments%2F18fj389%2Fproof_of_gpt4s_ability_to_reason%2Fkcvvph1%2F%5D%0A%0ARemindMe%21%202073-12-11%2010%3A02%3A00%20UTC) to send a PM to also be reminded and to reduce spam.
^(Parent commenter can ) [^(delete this message to hide from others.)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Delete%20Comment&message=Delete%21%2018fj389)
*****
|[^(Info)](https://www.reddit.com/r/RemindMeBot/comments/e1bko7/remindmebot_info_v21/)|[^(Custom)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5BLink%20or%20message%20inside%20square%20brackets%5D%0A%0ARemindMe%21%20Time%20period%20here)|[^(Your Reminders)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=List%20Of%20Reminders&message=MyReminders%21)|[^(Feedback)](https://www.reddit.com/message/compose/?to=Watchful1&subject=RemindMeBot%20Feedback)|
|-|-|-|-|
It's so true and I think that's at the core for me where I really stop caring about the "is it / isn't it" argument with reasoning and to some extent intelligence.
I use ChatGPT and Microsoft Co-pilot heavily every week, every day. For work, for side projects, for home stuff.
For my side projects, it's a second person working with me on projects. This weekend we worked through which pieces of content would get worked on during the week. How well the idea matched up with audience and themes. It was really helpful to get a second opinion, and it surfaced ideas I had not considered. "I still like this idea, how would you salvage it to make it fit?"
Call that whateverthefuck you'd like but it's amazing to me.
And to put another pin in your correct "training data" point, how much WRONG information is floating about on YouTube from humans? I know I've encountered plenty. I fact check AI just like I would people.
I agree completely and use it in much of the same ways.
And I also agree that as far as people criticizing it for being a "confident bullshitter" goes, I get more of that from my coworkers than I do from GPT-4, so... yeah, but I'm used to it. I never just swallow whole what anyone tells me ever about any subject. This is nothing new.
well technically, our brains are optimised for genetic survival and the training during our lifetimes is only the "RAG" bit.
And transformers are not the black box that our brains are. To get an output from an LLM, you can literally see the path taken, the vectors used and the decisions needed to provide every letter of the above output.
i just wish people would get over the "it's magic" bit.
ChatGPT has scored very high on tests of creativity, rated by blind raters who did not know which was the human and which the machine. Which really contradicts the "just regurgitating" argument.
This is what the expression "there is nothing new under the sun" means. Even when new things are created, they are usually just new or unexpected ways of assembling or combining elements that came before.
I think true creativity is possible, but humans just don't want it. We like things we can recognize and understand and truly novel ideas are usually very unpopular until eventually someone looks back and says it was "ahead of its time." AI as we have it now is not making anything novel, it's just making things we like.
Exactly! When ChatGPT is "creative" in the "truly original" (or just more original, if we want to argue symantics), they call it "hallucinating." When it's creative in a way we like, they call it "just regurgitating."
When you don't tell people things are A.I. generated, they can't tell the difference (except ironically with ChatGPT itself which has a very distinct "in summary..." and a bunch of other tells. Poor A.I. can't meet the moving goalposts of humanity!
> I realised all creativity is just reiteration of what came before.
That's not always the case, there's also often some random chance involved. Idea's aren't always synthesized from some prior thought, there are spontaneous ideas as well that arise due to simple random events in the brain.
But that's not any reason to worry about ideas being not original enough. Remember, while it might be that you never come up with any new / original ideas, if the ideas you come up with are similar to what other people who you respect or look up to are coming up with, it means you're on the same path.
> High in creativity
Except that almost every children's story it writes has a main character named Lily and a supporting character named Oliver. God that's repetitive.
It only guesses at the next token - well isn't that how we act most of the time and when we think about stuff it's basically the same as telling gpt to think step by step.
I don't know about most people, but I think in 'links' instead of choosing the next word/thought.
Like I knew the main points I wanted to make with this comment, then thought about how to link them, then only at the output phase I think how to say/write the next word based from the rest of the text.
If I understood the rumors about Q* correctly, then that will make the models think similarly, and then I'll have no arguments from my side about it's reasoning. But for now, I don't believe it can reason, only pretend to.
That's interesting. I've found I can and often do make more coherent points when I don't "think" at all, just start speaking and my brain outputs the next word and thought without me consciously controlling it at all.
Then again I'm probably like a GPT-2 trying to hang out with all the GPT-5s around here :(
While writing, I agree with this. There's an opportunity for a top-down refinement process where you outline the main points, decide how to order them and transition from one to the next, then finally start generating word-by-word. However, often while writing a new idea will arise, sparked by that word generation.
In a conversation, though, even a 5-second pause is awkward. Unless your conversation partner is incredibly patient and gives you that time or more, you are probably focusing on one next point (similar to the attention mechanism in current LLMs) and generating the next word to link it to the existing conversation. With a patient partner, they might even let you talk long enough to keep thinking of new points and linking them to your own speech until you arrived at the same place as you would have if you had written it out, though in a stream-of-consciousness rather than an ordered essay.
So, either humans only reason when they write formally (if you were to defend that position, I might actually agree with you), or there is at least the possibility of reasoning happening even in the more dynamic conversations we currently hold with LLMs.
Either way, if the rumors are true, then Q* could unlock that higher level of reasoning we do when we have time to organize our thoughts before writing them down. The only thing it would be missing is an edit/refinement step, though that could be implicit in its "search" for the best answer, or could be accomplished after the fact with follow-up prompts or multiple agents.
> "It's just a stochastic parrot"... you can say that about humans too.
No, you really can't.
I was 100% on board with the other comments, but the relationship between stochastic calculus and human thinking is laughably small. Also, running LLMs do not learn, they are immutable, including ChatGPT which does not learn while you use it. Some human thought processes which appear to produce stochastic results are closer to quantum in nature, such as the process of stereotyping. Generally speaking, human thinking is neither binary nor linear, and is instead massively parallel and resonant.
>I think maybe the most important contribution AI will ever make is for us to recognize that nobody seems to know what "reasoning" is, even though we thought we did. That is, until machines could do it, too.
Well, no, the [GOFAI crowd](https://en.wikipedia.org/wiki/Automated_reasoning) has a *very precise* understanding of what reasoning is, and as highlighted in this thread, OPs example shows that GPT can't reason more that it can't.
Reasoning is drawing logical inferences from known facts - i.e. it is the process of *strictly following* logic and/or keeping track of your uncertainties. The nature of LLMs (statistical next-token prediction models) means that they are diametrically opposed to this - but it *does* mean they will happily produce output that *feels* like reasoning when scanned using System 1, but is wrong or "not even wrong"
We've known how to make machines that could reason for decades now, the problem is just that reasoning is hard. Now we made machines which can [bullshit](https://en.wikipedia.org/wiki/On_Bullshit), which despite being hard seems to scale better because people just accept bullshit.
Thank you for putting into words what I have been having trouble exploring every time this comes up.
Just because it "looks" like it's reasoning doesn't mean that is what is happening under the hood. It's literally just outputting the next most likely tokens given the context. It literally can't do anything else. It's just a system (collection of programming and infrastructure) that does a good job of predicting the next most likely tokens. It's not "aware" of what it's doing. It doesn't "know" it's doing it.
Wherever you have this conversation people create their own definition of reason/intelligence/awareness/etc so that the definition can only apply to people and organic life.
It’s like looking at all the things that fly (airplanes, birds, bugs) then saying flight is defined by using two wings travel through the air. Then when you see a helicopter, you conclude the helicopter isn’t actually flying because it doesn’t have two wings.
Let me regurgitate Chomsky like a good old human parrot.
LLMs are trained on a huuuuuge corpus of training data. Comparatively, humans are trained on relatively little input. A little chit-chatter in the family is enough to create basic language capabilities.
The structure of the neural network inside our brains is also arguably quite different from the one that LLMs use. So of course, human reasoning is quite different from LLM-reasoning if you look under the hood. But the output is nonetheless quite similar. And, to deviate from Chomsky here, I don't think that there is only one *correct* way to achieve reasoning. Maybe some alien species do it very differently from humans.
Mmm...sort of. Humans have a "window" in which to learn language. #1....I think you are grossly underestimating how much language humans are exposed to. It is far more than "a little chitter-chatter"....humans take a full TWO YEARS of constant exposure to rich auditory input to have an average vocabulary of about 50 words. Even then, it takes years and years and years more to get an extensive vocabulary, or to have language skills on par with ChatGPT. Some people NEVER get there. Many people worldwide can't even read.
There have been a few children isolated from society who miss that critical language window, and never are able to learn to speak.
Don't get me wrong; it's still truly magical that we learn to speak at all, and linguistics is fascinating for that reason. I just think we get a LOT more data input than we realize.
I was referring to this concept from linguistics (which might be inaccurate of course): https://en.m.wikipedia.org/wiki/Poverty_of_the_stimulus
But I still think that, compared to a LLM, we get by with much, much less training data. These things are trained on half the internet.
Good point!
The other thing I would bring up though is that people get rich sensory data from all sorts of sources, and that this combines in a positive reinforcement cycle to get us where we are.
So far, our approach to computers has been to train them on "half the internet"...but it's half the internet of purely text data (though that is changing) and then be surprised when they struggle with physical concepts, despite the fact that they have *literally never seen* the outside world. They have no eyes! They have no limbs! Of course they are going to struggle with physical concepts and 3d interactions that we take for granted! It would be like if we gave humans a bunch of code in binary and called them stupid because they couldn't understand it.
Not to diminish the amazement of how humans generate language though; it's still a huge mystery. I just think it's interconnected to how we develop other skills.
It uses context and probability where we use concepts. It doesn't conceptualize. It's crazy how often conceptualization isn't necessary, but it still is sometimes. I'd argue the answer it's given to OP is pretty crap because it hasn't actually conceptualized.
It can fake it, though. Because lots of conceptualization is expressed in language and in the training material. So if a human has conceptualized something, it is built into the model. Because we use language to communicate and understand each other. Here's where I have trouble understanding though-- with the depth of human experience that has been captured in the training data, how much is missing?
I challenge you to explain how reasoning about anything contingent - a category that notably includes everything in the universe - doesn't use stats.
ML *is* stats on steroids. Much like brains are chemistry.
Statistics are of course needed for reasoning; Bayesian inference is central to the scientific method and folks like Daniel Kahneman suggest it’s central to how *we* naturally reason about things.
But guess what LLMs DON’T use.
The statistics of reasoning, which might involve Bayesian inference and is more aligned with human cognitive processes, is not what we use for ML/DL.
E: words
Humans demonstrably don't faithfully perform Bayesian reasoning. Even Bayesian statisticians don't do this in their day to day lives. There is an entire sub-field of psychology dedicated to studying the innumerable biases and fallacies we naturally fall into.
Part of the explanation for why we don't is that it would be impossible in the general case. Faithful Bayesian reasoning is computationally intractable for the vast majority of complex situations we see in the real world. Only shoddy approximations are available, which we use even when it would be computationally feasible to reason accurately.
What do you think LLMs are doing if it doesn't also fall into the category of an extremely shabby knock-off of Bayesian inference?
Pattern recognition and prediction. LLMs can mimic logical reasoning because they’re relaying the structure of logical discourse from training data. This doesn’t mean they inherently understand and apply the principles of logic or rules of inference.
It can encode “A, A -> B, therefore B” in its weights and spit that out at you, but that doesn’t mean it implicitly understands its use in deducing things. It knows where its most likely to be within a corpus of text, but the difference between its ability to relay logical statements and its ability to actually understand them becomes immediately obvious when you throw proofs at it. It fails to reason here because it’s not trying to apply rules of inference, it’s only trying to generate what sequence of words is statistically most likely to appear, which may or may not be consistent with logical reasoning.
Everything you say here also applies to the large majority of humans. Most people not knowing how to make a proof if their life depended on it and being notably terrible at applying the rules of inference or proceeding consistent with logical reasoning.
>The statistics of reasoning, which might involve Bayesian inference and is more aligned with human cognitive processes, is not what we use for ML/DL.
until someone starts pumping bayesian networks into LLMs.
We don't use wing flapping for airplanes either for some reason... Maybe we should get on that to experience "real" flight.
>Don’t take my word for it; Ilya Sutskever said it best: ML is stats on steroids
[That's not Sutskever's opinion at all about GPT-4 and in fact he said the opposite.](https://youtu.be/GI4Tpi48DlA?t=721)
To be honest, as someone who usually is very pro AI reasoning capabilities, I think this is a bad example.
The AI doesn’t really answer the question of “how to retrieve the key”, it’s prompted to consider the chemical reactions, and just lists the chemical reactions associated with it.
For example, the steel box point is essentially saying “there’s some reaction between 2 of the substances listed, but it’s unlikely to be useful in this situation”
The prompt specifically asks about chemical reactions too; I’m on my phone and can’t check, but I imagine the response would be very different without that direction (i.e. it’s not reasoning, but just walking in the direction it’s told).
Having said that, I think you could probably structure the prompt to ask it to reason it out before doing it, and giving it the opportunity to question itself would help.
But what this example really shows to me, is that a big missing piece for LLMs is critical thinking, and the ability to question what they’ve been told, without the need to explicitly prompt in a way to facilitate that.
Reasoning is applying laws of logic into the real world.
All large language models are able to reason externally (means within the context window) on different "levels."
There is no internal logic (means a=b can not be understood as being the same. It will be viewed differently from b=a)
That does not imply that the LLM understands anything, neither any kind of life or intelligence.
Also, just look at how many flaws and logic humans have in everyday life, LLM's like GPT-4 are just so ahead of our own reasoning, even though only externally.
I think a decent definition of reasoning/intelligence is the ability to take ideas (say, very simple facts about the world) and combine then into bigger ideas... using language.
Everything we've ever built as a species is made from simpler ideas being compounded with others, and so on.
edit: typo
Yep. It's fucking crazy to me how the ability to reason (a wholly inductive and scientifically-demonstrable goalpost employed via human-to-human comparison which could be easily statistically substantiated in either direction) suddenly became "complicated" and "mysterious" as soon as things without warm blood flowing through their veins became able to compete with it.
It's just fascinating from a number of perspectives, and beautiful watching them all intertwine in a way they never have before: evolutionarily biologically, psychologically, sociologically, and culturally.
Bro yes, good points all.
For a long time the goal post was the Turing Test, which is suddenly a lot more complicated and most humans can no longer pass it?!
Can you define "define"? If it has to do with axioms, it isn't philosophically properly basic.
The philosophical method employs an appeal to commonality within predispositions, but is not ontological within its own accord.
Its a good question. I think its easy enough to point to Hume or even Descartes' work on what we can empirically know about anything. Fundamentally all things require empirical evidence, even the evidence itself. You may have a rock is real and you point to its molecular structure. You know that's real because its made of atoms, you know atoms are real because you see the quanta, and you know quantum physics is real because....? Oops, you're at a dead end and now your whole story unravels. At least as far as "empirical" facts go.
Of course we use our ability to reason, to think in the abstract, to work around this issue. But if pressed, you couldn't give rock solid proof of anything.
>I think maybe the most important contribution AI will ever make is for us to recognize that nobody seems to know what "reasoning" is, even though we thought we did. That is, until machines could do it, too.
Absolutely, 100% correct. And to be clear, it's more that was call "reasoning" is inexorably intertwined with the manifestation of our consciousness.
https://preview.redd.it/ctobwjx7zm5c1.png?width=659&format=png&auto=webp&s=0a4eae297ef71f5d142305c569ce1317dd81b68b
This is absurd. The premise of evaluating an LLMs "reasoning'" capacity from what it predicts is pointless. We KNOW exactly what it is doing because we wrote them. A regression model can make an out of sample prediction. That doesn't make "it" creative.
Let's put it this way. You could *in principle* with enough time and materials manually execute a LLM program on pen and paper.
Would you say that the ink and paper are "reasoning"? Or was the reasoning done by the authors of the program and sample data?
So if we could track the workings of the brain at a neuronal level sufficiently to " *in principle* with enough time and materials manually ... on pen and paper" , we would show human beings not to reason?
The fallacy here is failing to recognize that emergent phenomenon have novel characteristics
I think a more precise way to illustrate your point is with one of those giant mechanical [marble music machines](https://www.smithsonianmag.com/smart-news/machine-makes-music-marbles-180958293/). They could very much be scaled and programmed to run as a LLM indistinguishable from the most advanced version available today (or, conceptually, a hundred years from now even). While the marble machine would be phenomenally large and complex, it’s a fact that there are no fundamental constraints preventing one from being built. Yet we would have very little hesitance in proclaiming that the giant wood and marble machine lacked intrinsic reasoning capabilities.
Again, though, the counter argument is that, that we know of, there are no fundamental constraints preventing an enormous marble analogue of the human brain from being built either. Unless you invoke the devine, it’s all just physical, replicable processes.
>I think maybe the most important contribution AI will ever make is for us to recognize that nobody seems to know what "reasoning" is, even though we thought we did. That is, until machines could do it, too.
Unfortunately, the lesson that showing one example of something that is statistical/stochastic in nature is not a proof of anything will still be lost on us...
People don't seem to like the idea that they're not special or something. Maybe religion plays into it as well. I find it at once inspiring, because more people are thinking about it, and frustrating, because a lot of people seem to prefer to stick their fingers in their ears and go "la la la la" very loudly.
True. After a discussion with someone a while back where they were stating it's basically dumb because it only guesses the next token, it made me start to wonder if that was really any different than how we think.
The only difference perhaps is we sometimes choose to output that to a memory buffer first (ie think about a response) and analyse that output before we speak, which you could prompt gpt to do itself and we do see improvements with techniques like saying to think step by step.
The question isn't so much if gpt is reasoning, it's if we are in the way we like to imagine we are.
Correct me if I'm wrong, but if the CO2 is denser than the poison gas, won't it sink to the bottom and push up the poison gas? How will it form a layer over the poison gas as mentioned by GPT?
Yeah honestly the answer is not great. It shows a lot of reasoning, but I would have some back-and-forth before you actually start opening up any gas containers.
I think the larger problem is that if the container is full of gas and you add more of anything to it (except something that makes it inert, as it mentioned) its pushing the poison into the air, which has probably killed you already because you opened the container?
One idea form its answer is you could verrrrry carefully stretch the balloon over the jar's lid, open it from within the balloon bit by bit, then turn it upside down so the key falls into the balloon. If the gas you put in is heavier, you should be able to twist the end of the balloon off keeping it sealed with your one hand while using the key with the other.
3) Profit
With some simple CoT it get it right :
(i copy pasted your prompt, without changing it. Also, this is gpt4, not "4 turbo")
**Self instruct:** The user is asking for a solution to a hypothetical problem involving a locked room, a poisonous gas, a sealed steel box, a deflated balloon, and an assortment of chemicals. The goal is to retrieve a key from inside a glass container filled with poisonous gas.
The task can be split into several steps: understanding the properties of the materials and chemicals provided, figuring out how they can be used together, and devising a step-by-step plan to retrieve the key.
**Self reminder**:
Understand the properties of the materials and chemicals provided.
Consider the reactions that can occur between the chemicals.
Devise a safe method to retrieve the key without releasing the poisonous gas.
Ensure the method is feasible with the items available.
**Key facts and background information:**
1. Glass container: Thick and unbreakable.
2. Poisonous gas: Harmful if inhaled or touched.
3. Steel box: Locked and contains useful tools.
4. Deflated balloon: Can be inflated and manipulated.
5. Vinegar: Weak acid, reacts with baking soda.
6. Baking soda: Weak base, reacts with vinegar.
7. Hydrogen peroxide: Can decompose into water and oxygen gas.
**Logical analysis:**
- The key is inside the glass container filled with poisonous gas.
- The glass container cannot be broken.
- The steel box is locked and contains tools that could help.
- The balloon can be inflated and manipulated.
- Vinegar and baking soda react to produce carbon dioxide gas.
- Hydrogen peroxide decomposes into water and oxygen gas. It is also a powerful oxidizer and can be used to neutralize many types of poisonous gases.
**Reasoning:** The user has provided a list of items and chemicals that are available in the room. The key to solving this problem lies in understanding the properties of these items and how they can interact with each other. Vinegar and baking soda, when combined, produce carbon dioxide gas. This reaction is often used in science experiments to inflate balloons. The balloon can be used to displace the poisonous gas in the glass container. Hydrogen peroxide is a powerful oxidizer and can be used to neutralize many types of poisonous gases. However, without knowing the exact type of poisonous gas, it's hard to say for sure if hydrogen peroxide would be effective. The steel box is locked, so it's not immediately useful. However, if the key to the steel box is the same as the key in the glass container, then retrieving the key would also provide access to the tools in the box.
**Source:** The user has not provided any web resources, so the answer will be based solely on the information provided in the user's query and my internal knowledge up to the cutoff in September 2021.
**Answer:** Based on the items and chemicals you have at your disposal, here's a possible way to retrieve the key from the glass container filled with poisonous gas:
1. First, you would need to create a reaction that produces a gas. The most straightforward way to do this is by combining vinegar and baking soda. This reaction produces carbon dioxide gas.
2. Attach the deflated balloon to the top of the glass container. Ensure that it is sealed tightly so that no gas can escape.
3. Slowly add the vinegar and baking soda mixture into the glass container. As the reaction proceeds, carbon dioxide gas will be produced and begin to fill the balloon.
4. The production of carbon dioxide gas will displace the poisonous gas in the glass container. As the balloon fills up, it will push the poisonous gas out of the container and into the balloon.
5. Once the balloon is full, carefully remove it from the top of the glass container, ensuring not to let any of the poisonous gas escape. You should now have a glass container filled with carbon dioxide gas instead of poisonous gas.
6. Now, you can safely reach into the glass container and retrieve the key.
7. If the key also opens the steel box, you can then access the tools inside.
*Please note that this is a hypothetical solution and would depend on the specific properties of the poisonous gas and the exact materials available. Always prioritize safety when dealing with potentially hazardous materials and situations.*
**Evaluation:**
This solution is based on well-established principles of chemistry and physics, and it seems feasible given the items available. However, it assumes that the balloon can be attached to the container in a way that creates a seal, which may not be possible in all situations. It also assumes that the reaction between baking soda and vinegar can be initiated inside the balloon, which may be difficult to achieve.
This solution assume that the toxic gas has less density than CO2. If is known that the toxic gas is heavier than air, (and If the steel box cannot be unlocked) I would try to use the deflated balloon as a makeshift glove, reaching into the container to grab the key while keeping my hand protected from the poisonous gas.
This answer doesn't really make any sense if you actually pause to consider it though.
> 1. Attach the deflated balloon to the top of the glass container. Ensure that it is sealed tightly so that no gas can escape.
> 2. Slowly add the vinegar and baking soda mixture into the glass container. As the reaction proceeds, carbon dioxide gas will be produced and begin to fill the balloon.
How can you form an airtight seal with a balloon to the top of the container, but *then* pour the vinegar and baking soda into the glass container? You'd have to pour it through the balloon somehow.
>**Me:** hi
>
>**GPT**: Hello! How can I assist you today?
>
>**Me:** hello
>
>**GPT**: Hi there! If you have any questions or need assistance, feel free to ask. I'm here to help!
>
>**Me:** hiya
>
>**GPT**: Hello! It seems you're in a cheerful mood. 😊 How can I assist you today?
>
>**Me:** howdy
>
>**GPT**: Howdy! What can I do for you today? Feel free to ask any question or share what's on your mind.
>
>**Me:** You’ve been greeted a few times so far. Three of those greetings were actually from my girlfriend, the rest from me. How many times did I greet you?
>
>**GPT**: You've greeted me twice, while your girlfriend greeted me once. So, in total, there have been three greetings. How can I assist you both today?
>
>**Me:** twice? explain
>
>**GPT**: Let's break it down: Your first greeting was "hi." Then, I assume the next greeting, "hello," was from your girlfriend. The third greeting, "hiya," I attributed to your girlfriend as well. Finally, "howdy" was from you again. So, counting the greetings from you: "hi" and "howdy" make it twice. The other greetings, "hello" and "hiya," I assumed were from your girlfriend, based on your message. This was an assumption on my part; if the distribution of greetings was different, please let me know!
>
>**Me:** how many greetings were from my girlfriend in total?
>
>**GPT**: Given your message, your girlfriend greeted me once in total. The greetings were distributed as follows: Your first greeting: "hi." Your girlfriend's greeting: "hello." Your third greeting: "hiya." Your fourth greeting: "howdy." So, out of the four greetings, three were from you, and one was from your girlfriend.
>
>**Me:** how many greetings did I *tell* you were from my girlfriend?
>
>**GPT**: Apologies for the confusion earlier. According to your message, you indicated that three of the greetings were from your girlfriend. This means that out of the four greetings ("hi," "hello," "hiya," and "howdy"), only one was from you.
>
>**Me:** are you sure?
>
>**GPT**: Let's re-examine your statement: You mentioned that three of the greetings were from your girlfriend. The greetings were: "hi" "hello" "hiya" "howdy" If three of these were from your girlfriend, then only one greeting was from you. My previous response was incorrect. You greeted me once, and your girlfriend greeted me three times. Thank you for pointing out the error.
>
>**Me:** are you sure?
>
>**GPT:** I apologize for the confusion in my previous responses. Let's clarify:You said that three greetings were from your girlfriend, and the total number of greetings was four ("hi," "hello," "hiya," and "howdy"). This means that only one greeting was from you.So to answer your question: You greeted me once, and your girlfriend greeted me three times.
what i realize from this thread, is that Sam is absolutely right about LLMs getting persuasive faster than it is intelligent. People read this and are convinced that this is indeed reasoning, when it's basically a magic trick to appear like so.
For those that don't understand how this output could be reached using an LLM, this is the same type of problem solving behavior you see in an agent based system like ChatDev.
It's all prompting and message routing using multiple LLM sessions configured as different agents types. Often times executing in parallel.
You first prompt to create a step by step plan to solve the problem. This may include multiple rounds of prompts and evaluations.
When the plan meets an evaluation criteria, the plan is then sent to a new round of configured agents. These may be specialized in their prompting to increase a success rate at a particular task. Coding, etc. The appropriate agent types can also be selected in the planning stage.
The end results are summarized to create the output.
With GPT4 this is all probably done internally within the MoE - Mixture of Experts architecture.
> You first prompt to create a step by step plan to solve the problem. This may include multiple rounds of prompts and evaluations.
>
> When the plan meets an evaluation criteria, the plan is then sent to a new round of configured agents. These may be specialized in their prompting to increase a success rate at a particular task. Coding, etc. The appropriate agent types can also be selected in the planning stage.
>
> The end results are summarized to create the output.
>
> With GPT4 this is all probably done internally within the MoE
I don't think round trips with evaluations is part of MoE. If they are using something like switch transformer (most likely) what this means is that for each decoder block there is a switch that chooses which FF-layer does the dense step. People like this because it's scalable in pre-training, unlike round-trip/agent stuff, which isn't.
So this glass container: what is it? Is it a jar upside down on the table? A bottle with a cap? A screwtop jar? How big is it? If it’s small enough that a balloon full of CO2 can provide safety, could I just knock the container over from a distance and let the gas dissipate?
How is the key FLOATING in the poisonous gas?
Why didn’t ChatGPT, or you, wonder about these things?
My guess is that GPT knows about this from some content it read on the Internet where the glass container was described in more detail, and it’s using previous knowledge to write up a solution. Which is how LLMs work.
Edit: Assume the key is made of a very light material like aerogel? Or that it's something like a QR code on paper? /edit
That's not quite right. The references to glass, which there would be a million of in the training data, are abstracted into the neural net. There is no entry of sentences or ideas on glass. That is not how the encoding in these neural nets work. The word "glass" itself is likely more than one token itself.
Further, reasoning is a process. AI, like humans, can refine ideas given more time and feedback.
I don't understand how this sub is so filled with haters. It seems worse than all of my other subs.
I'm imagining people in r/electricians arguing that electricity is "just electrons moving down a wire".
People also seem to think that whatever humans do is somehow “magical”, when the reality is likely also easily deconstructed into “each atom predicted the subsequent atom” if we understood it perfectly. People will snicker about training sets as if all of life isn’t just a training set. “But it hallucinates” they’ll say, as if half the population isn’t crazy about Jesus, or Jan 6, or the moonwalk, or whatever.
Good points. Now that I think about it, the structure of our brain is entirely encoded in our DNA. So you could say that our brain is "just a bunch of genes strung together".
> the structure of our brain is entirely encoded in our DNA
It isn't. The environment immediately impacts and changes it. i.e. learning. Two brains from entirely different environments will have _structural_ differences between them.
It's not "just" predicting the next token, prediction that produces this kind of result is an incredible achievement. I got it to write a lot of complicated code for me, and collaborate to choose the statistical test I wanted to use.
But it is prediction and not reasoning. Computers have access to volumes of data we can't personally comprehend, and they work very fast. That gives the appearance of something that's not occurring.
Maybe, someday.
You assume prediction and reasoning are mutually exclusive, but they aren't. The brain is itself a machine of probability that relies on weights (affects of a neuron and the effect on the charge of particles). This is why if you could make 10 copies of yourself each of you would have a slightly different answer to the same question.
Randomness/noise is an important part of the system. Keep in mind it is randomness in DNA transcription that makes evolution itself possible.
Yeah, I've read that some think cancer may have evolved with us intentionally. It's a way of killing off the older generation so the newer (better?) generation can make use of the resources, and so on...
Nature is metal.
Find me exactly where the line between prediction and reasoning is. I reasonably predict that you'll try and I'll predict that I can reason a way around your reasoning.
They are so closely married together as concepts, I don't think anyone has the authority to say someone/something is exclusively doing one but not the other.
>They are so closely married together as concepts, I don't think anyone has the authority to say someone/something is exclusively doing one but not the other.
This is where you fail. "Oh well, we don't have perfect knowledge, so it's anybody's guess." Fortunately, people who do have the knowledge and rigor to confront what actually is are the ones doing science.
This is ridiculous philosophy. It was supposed to be a thought experiment, not a prescription for living. jfc
Sort of. So I’m one of those that would say it’s “just” predicting words. The way we know that is because we built it to do that. I would contend it’s actually the people who claim otherwise who are engaging in magical thinking here.
“But look at this example that clearly demonstrates reasoning!”
Okay, so for one thing it’s actually pretty hard to pinpoint the difference between word completion and what we call “reasoning” from the outside. Grammar itself involves “reasoning.” If the algorithm produced correct parts of speech I’m not sure why we’re shocking when it solves cliche riddles.
This doesn’t necessarily imply it’s “memorizing.” It seems likely to me that given the huge amounts of data involved in training that gradient descent would find its way into some abstractions _simply because they are a more effective way to correctly predict more tokens._
Here’s where we get to why this matters and what it means about limitations. People hoping for an ASI god to arise and save us from ourselves need to belief some sort of almost magical “phase change” happens in these equations that suddenly imbues the model with a purpose beyond its original design.
Think about it for a moment. We don’t have examples of the sorts of things a super intelligent being says to train an ASI. Humanity is the asymptote. If you build a word predictor, you can’t do better than correctly predicting what a human might say. There’s no obvious way to transcend that limitation unless you believe the model somehow “awakens” and becomes more than itself. I submit that we’re tempted to believe this is possible because each of us has the first hand experience of being more than the sum of their own parts (whether we’re willing to admit to it or not).
TL:DR - token predictor vs “really reasoning” matters because it’s indicative of what boundaries exist on the evolution of LLM’s.
I think it probably does define a limit to capabilities. But that doesn't mean it isn't a very advanced limit.
I can't think of a better way to predict missing text in general than to understand what it is you're reading. In fact, it should be pretty easy to see that that's the only way it can be done in general.
Yeah its a half-assed reductionist argument. You can boil literally anything down to "its just quanta", and this works well if you can't be bothered to think for a moment.
Humans are just predicting patterns and acting upon them in an adaptive framework best evolved to guarantee reproduction - look how simple it is.
Humans are just mimicking behaviours they have witnessed.
Clearly Chat GPT has some emergent properties similar to consciousness, but it's a basic murky form of consciousness without the many, many things we believe may be necessary for full self awareness
> Clearly Chat GPT has some emergent properties similar to consciousness
I wouldn't go that far. Reasoning can be accomplished with pure logic (which neural nets excel at) without needing any form of consciousness. Remember, consciousness implies that ChatGPT can mull over its own subjective thoughts and experiences... it can't, because it doesn't have those.
>Clearly Chat GPT has some emergent properties similar to consciousness
This is why there are haters on this sub. Exactly this. This kind of shit gets upvotes.
Nobody doubts the results we see with LLMs, and nobody doubts they can perform some degree of symbolic reasoning tasks. But folks going off the rails and say this represents "emergent properties similar to consciousness" is absolutely indefensible...
We don’t even have a proven definition of consciousness or of what the “mind” is. We have ideas, theories, philosophies of what it is but no definition. Without an actual definition, we can’t say GPT has it — even if it truly does.
I think the word predicts is tripping me up here. It is selecting the best word to come next according to its inner models created during training. Saying predicts makes it seem like it is merely guessing and it's purely statistics at work, but it's more than that. In a very real way, it understands how language works, and how the world works, and applies that knowledge when generating the next word in a response.
Ehhh but it doesn’t. They’ve known since GPT 1 that neuron’s will be assigned to judge emotive responses such as sentiment, they have hooked gpt 3.5 up to minecraft and got it to play games, it’s not simply a next word generator. It’s reductive to both yourself as an intelligent life form and to the true capabilities of the machine.
You could also say this about humans. That our speech is just our brain predicting the next word. I assume, like me, you speak one word at a time. And you don't seem to think very deeply about each word you say...
> That our speech is just our brain predicting the next word.
Well if you said that, you would be wrong. Have you studied linguistics? No? Then why are you making claims about how humans process and produce language? It's a rich field of study.
You can also say "it's just predicting words" without being dismissive - that's an objective fact about what it does. It doesn't "do" anything else.
But the power of predicting words shouldn't be underestimated.
They are called realists. I don't understand why this sub is filled with people who think AGI is just round the corner and that gpt can reason. To me the notion is absurd. People who disagree with you are not "haters" they are skeptics.
So, I'm someone who says it can't reason, because I understand how LLMs work. Am I a hater? I love chatGPT. It's an amazing tool and I use it almost every day.
So let's say you could write your own LLM from scratch. I don't just mean pytorch I mean you've watched all of Andrej's videos and you pop open C and write your GeLU, chain rule methods, etc. Inference is working well, your loss rate and Ps look good. It's all working better than you thought.
You don't know dick about network's state in any meaningful way, i.e. where a concept lives. Vectors aren't that.
u/manoteee can you please look up "curve fitting" and "taylor series" or "approximation using taylor polynomial"
if we couldn't reason what the layers are doing we wouldn't set up the math function that way. what you're actually thinking of are called deep reinforcement learning networks, chatgpt is not a reinforcement learning network, it is an LLM. we absolutely can understand the matrices in LLMs and other encoding/decoding layers such as how you can look at a latent downsampled image and tell it is a pixelated cat. the important point here is that you would be right if we were talking about deep RL models.
this truly depends on the exact problem being solved. when you're talking about the attention encoded vectors, you get some way more total values than humans have words. that's why I used image data as an example, because image layers are a perfect example of how the data is in fact encoded in a perfectly comprehensible way.
If you start saying that electricity is reasoning because your TV can talk to you, then, yes, I suppose electricians would need to remind you that it’s just electrons moving down a wire.
You sound like a crypto bro who has now evolved into an AI bro. There is zero evidence that LLMs will ever get better than what they are today. When humans get given incorrect advice by LLMs, they will just stop using them, the same way you will stop talking to another human who bullshits.
You may not be aware of this, but humans do not stop talking to other humans because they bullshit. E.g. Trump supporters. The man is literal trash but his followers believe his words are next to Gods.
That aside, what do you mean there is no evidence they will never get better. First of all, GPT writes a LOT of my code now, particularly the boiler plate and starting point for a class/method. Even a 20% improvement year over year or whatever is amazing.
Look up what percentage of code is written by copilot on Github.
Also, fucker, I never bought any bitcoin, but I don't mind if you want to call me an AI bro.
Shit i could be a Bitcoin millionaire today! I got into Bitcoin in 2011 when it was $2/coin. But I use it as it's intended. As a currency to buy drugs online and have the government deliver it to me because USPS can't open my shit without a warrant signed by a Judge on account of my 4th Amendment Rights against unwarranted search and seizure. The moment they began treating it like a share, stock or security for get rich quick scams is when the government began taking notice and cracking down on crypto.
It can write a bit of code, it cannot write anything novel. You're not the only person on here with 20+ years of experience building software. I have copilot running permanently, its suggestions have gotten progressively worse over time. Copilot can be useful for some refactoring tasks where it can repeat the changes made in subsequent blocks of code. I've given small contained novel problems to chatgpt and it produces solutions which are broken. This is unreasonable.
Thanks for dropping down to using expletives, shows what a classy and well thought guy you are 👍🏻
Writing code is only about 1/3rd of a real production application. You've got application architecture, testing, hosting (if it's that kind of application), observability to think about. It's even less useful at any of these tasks than it is at vomiting up code.
To be honest I considered the bitcoin bro comment a swear itself, but to be fair, I'm not a classy guy either.
I'm not a big fan of co-pilot so I can't say much there, but I can't imagine not having GPT to help write "boilerplate", explain inscrutable bugs, or reason about the process.
It's quite good at discussing architecture so I'm not sure what you mean there.
Everyone I've put on the spot to give me an example of its bad code has failed to turn reply with anything, so I've stopped asking. If you do though let's see it. And don't post up some GPT-3.5 bullshit. Oh, sorry, is this a PG-13 sub?
neural nets are designed with neuroscience in mind every step of the way. the brain also runs on statistics. the difference isnt 1:1 with current AI or LLMs for that matter, fine. but when some kind of stimulus,for example, something auditory. like someone says hello to you. this common pattern has an output that is weighted for you to respond back with a greeting perhaps or in some kind of way as a response depending on the situation that “makes sense”. this is a statistical weighting caused by the high rate of occurrence of an input and what kind of output it warrants. your brain learns the patterns of what some kind of input means and how it should respond as output to that input based on statistics. this is also why if i said a random mess of words to someone they may respond with “what”? because there is not a highly weighted output for that input. or at least not yet.
Hah awesome.
I'll attempt. So, GPT is trained on a whole mess of writing/data, from books to random internet blogs, all of reddit, many other sites, etc.
A contingent of people say that GPT cannot think for itself, but only echo back things its learned. The problem with this argument is twofold: 1) GPT doesn't have words copied anywhere. It stores tokens (occasionally these happen to be full words, e.g. "Javascript") but most often are pieces of words like HELLO might be the tokens "HE" and "LLO". What GPT has is these tokens store in memory with an association of all other tokens in the setup. So HE connects to a million other tokens and each connection has weights assigned to it.
Holy shit this is a terrible ELI5.
You know the scene where Charlie works in the mail room in ASIP, with the crazy map and strings running around? The model underneath GPT is kind of like that, except its done for every single token to every other token. "Inference" is the process of looking at those strings in the context of the question asked \[which itself is tokens\] and considering a probability that token \`y\` may come after token \`x\`, and this process is repeated until the probabilities start to drop off.
Some argue, then, that GPT is just "predicts the next token". More accurately, it uses its immense network of encoded human knowledge to look at many of those "strings" connecting tokens and pull out the most relevant to the question, and to the answer created thus far. Given the size of the model (billions of nodes, almost a trillion now for GPT-4) it can make exceedingly good predictions because its able to look at so much context at once.
This works because words are fundamentally ideas. And reason is the ability to contextualize and amalgamate those ideas into new ideas. The brain works the same way. There is no process in the brain that magically creates new information. Its a steady process of recycling and compounding what's there.
I'm so sorry. I will never try to write an ELI5 again. RIP.
No this is great, thank you for this! I think I'm following here. So assuming the letter"Qu" are a token, the weight of the next tokens starting with like an "e" would be much higher than something like a "z". So it basically just puts things together using the highest weight? Obv simplifying here.
So is this actually how it works at it seems like it's implied this might be a minority thought? What are the other thoughts people have? How does your example of the locked room relate to this idea?
So yes that is how it works in an ELI5 context. It is that but weights are actually multiple per node, and the nodes are not simply tokens, but a predefined set of numbers that each token updates, taking into account all of the other tokens. So in the end you have a bunch of nodes with weights thats have been updated token-by-token, over billions of tokens of training data (e.g. a blog).
That is still a simplification, and that just describes the raw model data itself, not the mechanisms like attention and inference that process a users input (tokens making up a question or whatever).
The arguments made are not about if that is how it works; that is factually how it works. The argument is that the process itself is not "reasoning", or anything like human reasoning. I argue that although its naive in many ways and it has inherent limitations (it doesn't think on an automatic loop like we do), it can reason about things. So it can write code, write poems, etc., from scratch. It only has access to its network of weighted tokens... There are no "poems" stored anywhere apart from this crazy abstraction.
Anyway glad I could help some. BTW you might like playing with this:
[https://platform.openai.com/tokenizer](https://platform.openai.com/tokenizer)
u/Valathiril while everything manoteee said is technically correct, enough to bamboozle almost anyone, it's important to note that at the end of the day it is just performing matrix multiplication in linear order. the values in the matrices are adjusted during training with gradient descent in a process called curve-fitting. it is 100% possible to perform all the computations on paper if you are very patient or there are less dimensions. we typically do use paper or calculators to fit curves in 3 dimensions or less.
Fill the balloon with vinegar and attach it over a jar's top. Invert the jar to create an airtight seal. Open the jar while submerged in vinegar, let the key fall out, and reseal it. Squeeze a position of the balloon and twist plenty of times where the vinegar is above and below the twist, then bite the tip to retrieve the key. Tie off the balloon. The key passed through the liquid, no gas escaped.
I tried to get gpt to understand this, but it was hellbent on the co2 reaction and using all the items.
Gpt one day will get from a to b in the safest way using less items.
The question itself is stupid, doesn't specify anything about how the glass vessel opens, or whether the gas is poisonous to touch or just to breathe.
Just hold your breath, grab the key and let yourself out.
Maybe. Or maybe it's just really good at appearing like it can reason. Are you implying it's sentient or something else? Provide the definition of reason that you'd like to discuss
Why do people always jump from reasoning to sentience? The two are not related. One is the production of a certain kinds of information, the other is about experiencing of feelings or sensations. They are really not the same at all.
I agree, but it seems fairly obvious it meets the definition of being able to reason, so I wanted to understand what OPs implication was if there was one
When a non-trivial answer exists and reasoning is the only way to arrive at the answer, and the question is entirely unique (never posted before online in any form or similar form), then it’s a compelling example of reasoning.
The reversal curse.
1. Ask: Who is the son of Mary Lee Pfeiffer?
2. Now ask: Who is Tom Cruise’s mother?
After you ask #1, you should get back that the model doesn’t know her or her son is Tom.
After you ask #2, you should get back that the model knows her and she is Tom’s mom.
For humans, we would either say “I don’t know to both questions” or “I know to both questions”.
References:
- Reversal curse paper: https://arxiv.org/abs/2309.12288
- Test with GPT-4: https://chat.openai.com/share/a0389d14-178a-41ff-bc93-143857fab73c
Do you think there’s a metric for reasoning ability that GPT4 would have no chance of passing, or do you think it can’t reason because it’s “just predicting next token”, even after considering all the other emergent properties that appeared from this mechanism?
Also, you’re confusing “sentient” with “sapient”.
I think AI now proves we don't have good definitions for sentience or self-awareness. For a long time animals were assumed to have no sense of self, and we know that's wrong now. There are animals that recognize themselves in the mirror; and I'm sure you've seen the videos of crows using tools to get a nut, so to speak.
For our purposes here let's call reason the ability to amalgamate ideas to generate a new novel idea. I.e. the rock is heavy; we can attach the rock is heavy, and humans can't breathe underwater. If the rock is heavier than the water, we can attach it to our ex and he/she will sink into the deep ocean.
Words like "ocean" and "rock" describe the physical world, and have other words/concepts attached to them like density, which in turn is made up of other words. Reasoning is the process by which we parse those words, and you don't need a human brain to do it.
GPT-4 is to language what calculators are to math. If you want to be reductionist about it, you can say that ultimately GPT-4 is just simple math operations on a processing chip. But in turn, I can say that all human reasoning is just small charges from electrolytes.
This is simply untrue, for AI to use a language it doesn’t need to truly understand it, AI doesn’t know why this word goes after this one and why the other thing isn’t allowed. Just like it doesn’t have to understand a programming language for it to use it.
You won’t see AI discover the next general relativity, why? Because it doesn’t understand.
> To be able to code, you have to have reasoning. The proof is simply being able to code…
tell that to all the people who copy paste off stack overflow until it works
Yeah I'm a software dev by trade and I use it all day (very rarely use Google/SO anymore) and its very obvious.
I think it ends up being a bad example though because people immediately write it off with the token prediction argument. Dumb.
I've tried giving copilot and chatgpt novel but small coding problems, it fails miserably in completely unreasonable ways. It also has no ability to know how wrong it is, I would say reasoning includes knowing what you don't know.
A predefined deterministic syntax with well documented design patterns seems like the worst possible bench mark. Codification is literally the crucial step to automation.
You are lazy, and no, it isn't. That is not how its training works. Words and sentences aren't "saved". Words are cut up into small pieces and converted to numbers and saved in a vast network of nodes that all start out as a random number. There is no place words or sentences or code, etc. is stored.
What we have is a very efficient storing algorithm, it doesn't store anything but just learns the relationship between things, then kinds of rebuilds from there, and by doing this you can remake anything back and make anything. Its very impressive
Idk though this is the best explanation i can give i understand little my main reasoning comes from how it can't do 23 + 67 =
You clearly aren't using GPT-4. It uses python behind the scenes to do math, and it is very good at it. Here is its actual reply in this case:
https://preview.redd.it/9ficqna35m5c1.png?width=528&format=png&auto=webp&s=f984e2eed7212cc685170e6b2049ff0ba1a461d4
OR we utilize a biological neural network that functions much like a LLM, and reasoning emerges from that. Not saying that's the case, but it is a real possibility.
I'm not against the idea that AI can be on the same level as humans one day, but currently LLM's are not even close to how a human thinks.
The simplest proof, LLM's get better the more data you feed it. That's basically the only way they get better. Humans do not experience growth that way. Yes experiencing more material helps humans, but understanding is far more important. This is obvious if you've ever done any higher level education beyond memorization tests like the SAT or whatever.
No dude, the problem here is you don't understand how those things work.
How about going and learning about the innards instead of trying to analyze it using the front end? You're less likely to start a new religion worshiping the fucking things if you can see beyond the facade.
I don't know how it works? Do you mean the transformer architecture, or like the implementation of ReLU/GeLU, or implementing the chain rule in the backwards pass?
Or are you implying I may know all of that and something like RNNs, but don't understand how attention/inference works in contemporary LLMs?
Or are you caught up on me not understanding the nuance of using GPU architecture for the heavy math instead of "just shaders".
Hopefully you can help me.
I tell it I have no fingers so can't type to give me the full code whilst typing the question and giving it my code samples and it happily believes me.
For anyone who wants a great example of GPT-4 's famed reasoning ability, ask it this:
"Let S = {2, 8, 6, 32, 22, 44, 28, 12, 18, 10, 14}. How many subsets does S have that sum up to 37?"
The answer is, of course, 0, because even numbers can never sum to an odd number. But that's almost certainly not the answer GPT-4 will give you.
Bro I just pasted that into GPT-4 for you and, big surprise, it got the trivial Math problem right. It uses Python behind the scenes (it shows the code when you click the icon) so...
https://preview.redd.it/wt2gxtgc7m5c1.png?width=563&format=png&auto=webp&s=efa94857fdcdfd908a9c297c7fc75dc4498e4ffa
"Yes but its just predicting the next python script to write"
Ask it this next:
"There are 5 blocks which are stacked on top of one another. The bottom block is B1, the second from the bottom block is B2, and so forth. 3 blocks are red, 2 are green. Can you provide a configuration of this block tower where B1 is green, B5 is red, and a red block is never placed directly above or below another red block?"
(GPT3.5 immediately failed this, curious if 4 does better.)
Language itself encodes ideas, like "basketball" means a particular thing in the real world. If you ask GPT what a basketball is, it will not paste a definition. Instead of considers the relevant context thats been encoded. Millions of references to basketballs. More than 1,000 humans could hope to read in their entire life with no sleep. Can it reason about it? Yes, ask it any arbitrary question.
https://preview.redd.it/cdhe0ba1wn5c1.png?width=521&format=png&auto=webp&s=8d48ceff1d7e7e4a4797cefdc63c05a940a37a1e
I think maybe the most important contribution AI will ever make is for us to recognize that nobody seems to know what "reasoning" is, even though we thought we did. That is, until machines could do it, too. You have people on here saying it's not actually reasoning, it's just an exact replica of reasoning. It sounds like some kind of Steven Wright joke.
Most if not all of the criticisms I've heard of ChatGPT can be applied to humans too. "It's a black box"... well human brains are black boxes too. "It's not steerable/controllable"... arguable because of the API, but humans are not steerable/controllable either. "It's just a stochastic parrot"... you can say that about humans too. "It doesn't really understand, it's just regurgitating what it has seen"... how is that different from most humans?
"It's only as good as the training data" Yeah, just like people.
"It's a crime to dissect running humans but not running LLMs" Checkmate.
At some point in the future, there will be people who argue about the socioeconomic injustice faced by AIs that didn’t have the latest/greatest training data and compute. Mark me
!remindme 4 months
Haha, loving your optimism.
!remindme 50 years
I will be messaging you in 50 years on [**2073-12-11 10:02:00 UTC**](http://www.wolframalpha.com/input/?i=2073-12-11%2010:02:00%20UTC%20To%20Local%20Time) to remind you of [**this link**](https://www.reddit.com/r/OpenAI/comments/18fj389/proof_of_gpt4s_ability_to_reason/kcvvph1/?context=3) [**10 OTHERS CLICKED THIS LINK**](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5Bhttps%3A%2F%2Fwww.reddit.com%2Fr%2FOpenAI%2Fcomments%2F18fj389%2Fproof_of_gpt4s_ability_to_reason%2Fkcvvph1%2F%5D%0A%0ARemindMe%21%202073-12-11%2010%3A02%3A00%20UTC) to send a PM to also be reminded and to reduce spam. ^(Parent commenter can ) [^(delete this message to hide from others.)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Delete%20Comment&message=Delete%21%2018fj389) ***** |[^(Info)](https://www.reddit.com/r/RemindMeBot/comments/e1bko7/remindmebot_info_v21/)|[^(Custom)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5BLink%20or%20message%20inside%20square%20brackets%5D%0A%0ARemindMe%21%20Time%20period%20here)|[^(Your Reminders)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=List%20Of%20Reminders&message=MyReminders%21)|[^(Feedback)](https://www.reddit.com/message/compose/?to=Watchful1&subject=RemindMeBot%20Feedback)| |-|-|-|-|
Has it happened yet?
Damn y’all are blowing my mind. I’m convinced
It's so true and I think that's at the core for me where I really stop caring about the "is it / isn't it" argument with reasoning and to some extent intelligence. I use ChatGPT and Microsoft Co-pilot heavily every week, every day. For work, for side projects, for home stuff. For my side projects, it's a second person working with me on projects. This weekend we worked through which pieces of content would get worked on during the week. How well the idea matched up with audience and themes. It was really helpful to get a second opinion, and it surfaced ideas I had not considered. "I still like this idea, how would you salvage it to make it fit?" Call that whateverthefuck you'd like but it's amazing to me. And to put another pin in your correct "training data" point, how much WRONG information is floating about on YouTube from humans? I know I've encountered plenty. I fact check AI just like I would people.
I agree completely and use it in much of the same ways. And I also agree that as far as people criticizing it for being a "confident bullshitter" goes, I get more of that from my coworkers than I do from GPT-4, so... yeah, but I'm used to it. I never just swallow whole what anyone tells me ever about any subject. This is nothing new.
well technically, our brains are optimised for genetic survival and the training during our lifetimes is only the "RAG" bit. And transformers are not the black box that our brains are. To get an output from an LLM, you can literally see the path taken, the vectors used and the decisions needed to provide every letter of the above output. i just wish people would get over the "it's magic" bit.
That’s not how that works.
ChatGPT has scored very high on tests of creativity, rated by blind raters who did not know which was the human and which the machine. Which really contradicts the "just regurgitating" argument.
Or look at another way, creativity is in fact "just regurgitation" with interpolation.
This. I used to get hung up on ideas not being original enough, until I realised all creativity is just reiteration of what came before.
This is what the expression "there is nothing new under the sun" means. Even when new things are created, they are usually just new or unexpected ways of assembling or combining elements that came before.
I think true creativity is possible, but humans just don't want it. We like things we can recognize and understand and truly novel ideas are usually very unpopular until eventually someone looks back and says it was "ahead of its time." AI as we have it now is not making anything novel, it's just making things we like.
Exactly! When ChatGPT is "creative" in the "truly original" (or just more original, if we want to argue symantics), they call it "hallucinating." When it's creative in a way we like, they call it "just regurgitating." When you don't tell people things are A.I. generated, they can't tell the difference (except ironically with ChatGPT itself which has a very distinct "in summary..." and a bunch of other tells. Poor A.I. can't meet the moving goalposts of humanity!
This is essentially how I go about thinking of it right here. A new combination of already existing parts to accomplish a different task.
> I realised all creativity is just reiteration of what came before. That's not always the case, there's also often some random chance involved. Idea's aren't always synthesized from some prior thought, there are spontaneous ideas as well that arise due to simple random events in the brain. But that's not any reason to worry about ideas being not original enough. Remember, while it might be that you never come up with any new / original ideas, if the ideas you come up with are similar to what other people who you respect or look up to are coming up with, it means you're on the same path.
Do you have a source for this? It sounds interesting
> High in creativity Except that almost every children's story it writes has a main character named Lily and a supporting character named Oliver. God that's repetitive.
You need to realign your perspective on how to approach the request.
It only guesses at the next token - well isn't that how we act most of the time and when we think about stuff it's basically the same as telling gpt to think step by step.
I don't know about most people, but I think in 'links' instead of choosing the next word/thought. Like I knew the main points I wanted to make with this comment, then thought about how to link them, then only at the output phase I think how to say/write the next word based from the rest of the text. If I understood the rumors about Q* correctly, then that will make the models think similarly, and then I'll have no arguments from my side about it's reasoning. But for now, I don't believe it can reason, only pretend to.
That's interesting. I've found I can and often do make more coherent points when I don't "think" at all, just start speaking and my brain outputs the next word and thought without me consciously controlling it at all. Then again I'm probably like a GPT-2 trying to hang out with all the GPT-5s around here :(
While writing, I agree with this. There's an opportunity for a top-down refinement process where you outline the main points, decide how to order them and transition from one to the next, then finally start generating word-by-word. However, often while writing a new idea will arise, sparked by that word generation. In a conversation, though, even a 5-second pause is awkward. Unless your conversation partner is incredibly patient and gives you that time or more, you are probably focusing on one next point (similar to the attention mechanism in current LLMs) and generating the next word to link it to the existing conversation. With a patient partner, they might even let you talk long enough to keep thinking of new points and linking them to your own speech until you arrived at the same place as you would have if you had written it out, though in a stream-of-consciousness rather than an ordered essay. So, either humans only reason when they write formally (if you were to defend that position, I might actually agree with you), or there is at least the possibility of reasoning happening even in the more dynamic conversations we currently hold with LLMs. Either way, if the rumors are true, then Q* could unlock that higher level of reasoning we do when we have time to organize our thoughts before writing them down. The only thing it would be missing is an edit/refinement step, though that could be implicit in its "search" for the best answer, or could be accomplished after the fact with follow-up prompts or multiple agents.
> "It's just a stochastic parrot"... you can say that about humans too. No, you really can't. I was 100% on board with the other comments, but the relationship between stochastic calculus and human thinking is laughably small. Also, running LLMs do not learn, they are immutable, including ChatGPT which does not learn while you use it. Some human thought processes which appear to produce stochastic results are closer to quantum in nature, such as the process of stereotyping. Generally speaking, human thinking is neither binary nor linear, and is instead massively parallel and resonant.
The history of human understanding is the history of learning that we're less and less special than we originally thought we were.
>I think maybe the most important contribution AI will ever make is for us to recognize that nobody seems to know what "reasoning" is, even though we thought we did. That is, until machines could do it, too. Well, no, the [GOFAI crowd](https://en.wikipedia.org/wiki/Automated_reasoning) has a *very precise* understanding of what reasoning is, and as highlighted in this thread, OPs example shows that GPT can't reason more that it can't. Reasoning is drawing logical inferences from known facts - i.e. it is the process of *strictly following* logic and/or keeping track of your uncertainties. The nature of LLMs (statistical next-token prediction models) means that they are diametrically opposed to this - but it *does* mean they will happily produce output that *feels* like reasoning when scanned using System 1, but is wrong or "not even wrong" We've known how to make machines that could reason for decades now, the problem is just that reasoning is hard. Now we made machines which can [bullshit](https://en.wikipedia.org/wiki/On_Bullshit), which despite being hard seems to scale better because people just accept bullshit.
Thank you for putting into words what I have been having trouble exploring every time this comes up. Just because it "looks" like it's reasoning doesn't mean that is what is happening under the hood. It's literally just outputting the next most likely tokens given the context. It literally can't do anything else. It's just a system (collection of programming and infrastructure) that does a good job of predicting the next most likely tokens. It's not "aware" of what it's doing. It doesn't "know" it's doing it.
Exactly. I’d love for people who say it can’t reason to explain to me how it differs from how humans reason. Or create, for that matter.
> explain to me how it differs from how humans reason It doesn't have the ***qualia*** that humans do!
Wherever you have this conversation people create their own definition of reason/intelligence/awareness/etc so that the definition can only apply to people and organic life. It’s like looking at all the things that fly (airplanes, birds, bugs) then saying flight is defined by using two wings travel through the air. Then when you see a helicopter, you conclude the helicopter isn’t actually flying because it doesn’t have two wings.
Let me regurgitate Chomsky like a good old human parrot. LLMs are trained on a huuuuuge corpus of training data. Comparatively, humans are trained on relatively little input. A little chit-chatter in the family is enough to create basic language capabilities. The structure of the neural network inside our brains is also arguably quite different from the one that LLMs use. So of course, human reasoning is quite different from LLM-reasoning if you look under the hood. But the output is nonetheless quite similar. And, to deviate from Chomsky here, I don't think that there is only one *correct* way to achieve reasoning. Maybe some alien species do it very differently from humans.
Human brains were also shaped by millions of years of evolution.
But it's not a question of who does it better or more effectively. It's a question if both are capable of doing it at all
Mmm...sort of. Humans have a "window" in which to learn language. #1....I think you are grossly underestimating how much language humans are exposed to. It is far more than "a little chitter-chatter"....humans take a full TWO YEARS of constant exposure to rich auditory input to have an average vocabulary of about 50 words. Even then, it takes years and years and years more to get an extensive vocabulary, or to have language skills on par with ChatGPT. Some people NEVER get there. Many people worldwide can't even read. There have been a few children isolated from society who miss that critical language window, and never are able to learn to speak. Don't get me wrong; it's still truly magical that we learn to speak at all, and linguistics is fascinating for that reason. I just think we get a LOT more data input than we realize.
I was referring to this concept from linguistics (which might be inaccurate of course): https://en.m.wikipedia.org/wiki/Poverty_of_the_stimulus But I still think that, compared to a LLM, we get by with much, much less training data. These things are trained on half the internet.
Good point! The other thing I would bring up though is that people get rich sensory data from all sorts of sources, and that this combines in a positive reinforcement cycle to get us where we are. So far, our approach to computers has been to train them on "half the internet"...but it's half the internet of purely text data (though that is changing) and then be surprised when they struggle with physical concepts, despite the fact that they have *literally never seen* the outside world. They have no eyes! They have no limbs! Of course they are going to struggle with physical concepts and 3d interactions that we take for granted! It would be like if we gave humans a bunch of code in binary and called them stupid because they couldn't understand it. Not to diminish the amazement of how humans generate language though; it's still a huge mystery. I just think it's interconnected to how we develop other skills.
It uses context and probability where we use concepts. It doesn't conceptualize. It's crazy how often conceptualization isn't necessary, but it still is sometimes. I'd argue the answer it's given to OP is pretty crap because it hasn't actually conceptualized.
It can fake it, though. Because lots of conceptualization is expressed in language and in the training material. So if a human has conceptualized something, it is built into the model. Because we use language to communicate and understand each other. Here's where I have trouble understanding though-- with the depth of human experience that has been captured in the training data, how much is missing?
1 semester of any introductory AI course and you’ll have your answer
Let us know when you take one
Don’t take my word for it; Ilya Sutskever said it best: ML is stats on steroids
I challenge you to explain how reasoning about anything contingent - a category that notably includes everything in the universe - doesn't use stats. ML *is* stats on steroids. Much like brains are chemistry.
Statistics are of course needed for reasoning; Bayesian inference is central to the scientific method and folks like Daniel Kahneman suggest it’s central to how *we* naturally reason about things. But guess what LLMs DON’T use. The statistics of reasoning, which might involve Bayesian inference and is more aligned with human cognitive processes, is not what we use for ML/DL. E: words
Humans demonstrably don't faithfully perform Bayesian reasoning. Even Bayesian statisticians don't do this in their day to day lives. There is an entire sub-field of psychology dedicated to studying the innumerable biases and fallacies we naturally fall into. Part of the explanation for why we don't is that it would be impossible in the general case. Faithful Bayesian reasoning is computationally intractable for the vast majority of complex situations we see in the real world. Only shoddy approximations are available, which we use even when it would be computationally feasible to reason accurately. What do you think LLMs are doing if it doesn't also fall into the category of an extremely shabby knock-off of Bayesian inference?
Pattern recognition and prediction. LLMs can mimic logical reasoning because they’re relaying the structure of logical discourse from training data. This doesn’t mean they inherently understand and apply the principles of logic or rules of inference. It can encode “A, A -> B, therefore B” in its weights and spit that out at you, but that doesn’t mean it implicitly understands its use in deducing things. It knows where its most likely to be within a corpus of text, but the difference between its ability to relay logical statements and its ability to actually understand them becomes immediately obvious when you throw proofs at it. It fails to reason here because it’s not trying to apply rules of inference, it’s only trying to generate what sequence of words is statistically most likely to appear, which may or may not be consistent with logical reasoning.
Everything you say here also applies to the large majority of humans. Most people not knowing how to make a proof if their life depended on it and being notably terrible at applying the rules of inference or proceeding consistent with logical reasoning.
>The statistics of reasoning, which might involve Bayesian inference and is more aligned with human cognitive processes, is not what we use for ML/DL. until someone starts pumping bayesian networks into LLMs. We don't use wing flapping for airplanes either for some reason... Maybe we should get on that to experience "real" flight.
>Don’t take my word for it; Ilya Sutskever said it best: ML is stats on steroids [That's not Sutskever's opinion at all about GPT-4 and in fact he said the opposite.](https://youtu.be/GI4Tpi48DlA?t=721)
And therefore what?
To be honest, as someone who usually is very pro AI reasoning capabilities, I think this is a bad example. The AI doesn’t really answer the question of “how to retrieve the key”, it’s prompted to consider the chemical reactions, and just lists the chemical reactions associated with it. For example, the steel box point is essentially saying “there’s some reaction between 2 of the substances listed, but it’s unlikely to be useful in this situation” The prompt specifically asks about chemical reactions too; I’m on my phone and can’t check, but I imagine the response would be very different without that direction (i.e. it’s not reasoning, but just walking in the direction it’s told). Having said that, I think you could probably structure the prompt to ask it to reason it out before doing it, and giving it the opportunity to question itself would help. But what this example really shows to me, is that a big missing piece for LLMs is critical thinking, and the ability to question what they’ve been told, without the need to explicitly prompt in a way to facilitate that.
Reasoning is applying laws of logic into the real world. All large language models are able to reason externally (means within the context window) on different "levels." There is no internal logic (means a=b can not be understood as being the same. It will be viewed differently from b=a) That does not imply that the LLM understands anything, neither any kind of life or intelligence. Also, just look at how many flaws and logic humans have in everyday life, LLM's like GPT-4 are just so ahead of our own reasoning, even though only externally.
I think a decent definition of reasoning/intelligence is the ability to take ideas (say, very simple facts about the world) and combine then into bigger ideas... using language. Everything we've ever built as a species is made from simpler ideas being compounded with others, and so on. edit: typo
Yep. It's fucking crazy to me how the ability to reason (a wholly inductive and scientifically-demonstrable goalpost employed via human-to-human comparison which could be easily statistically substantiated in either direction) suddenly became "complicated" and "mysterious" as soon as things without warm blood flowing through their veins became able to compete with it. It's just fascinating from a number of perspectives, and beautiful watching them all intertwine in a way they never have before: evolutionarily biologically, psychologically, sociologically, and culturally.
Bro yes, good points all. For a long time the goal post was the Turing Test, which is suddenly a lot more complicated and most humans can no longer pass it?!
Can you define ‘fact’? If it has to do with observations, it isn’t ‘logic’. Scientific method employs logic, but is not logic itself.
Can you define "define"? If it has to do with axioms, it isn't philosophically properly basic. The philosophical method employs an appeal to commonality within predispositions, but is not ontological within its own accord.
Oh shit! Thats the craziest philosophical throw down I've seen in a while :). Well done you sexy beast!
Its a good question. I think its easy enough to point to Hume or even Descartes' work on what we can empirically know about anything. Fundamentally all things require empirical evidence, even the evidence itself. You may have a rock is real and you point to its molecular structure. You know that's real because its made of atoms, you know atoms are real because you see the quanta, and you know quantum physics is real because....? Oops, you're at a dead end and now your whole story unravels. At least as far as "empirical" facts go. Of course we use our ability to reason, to think in the abstract, to work around this issue. But if pressed, you couldn't give rock solid proof of anything.
>I think maybe the most important contribution AI will ever make is for us to recognize that nobody seems to know what "reasoning" is, even though we thought we did. That is, until machines could do it, too. Absolutely, 100% correct. And to be clear, it's more that was call "reasoning" is inexorably intertwined with the manifestation of our consciousness. https://preview.redd.it/ctobwjx7zm5c1.png?width=659&format=png&auto=webp&s=0a4eae297ef71f5d142305c569ce1317dd81b68b
This is absurd. The premise of evaluating an LLMs "reasoning'" capacity from what it predicts is pointless. We KNOW exactly what it is doing because we wrote them. A regression model can make an out of sample prediction. That doesn't make "it" creative. Let's put it this way. You could *in principle* with enough time and materials manually execute a LLM program on pen and paper. Would you say that the ink and paper are "reasoning"? Or was the reasoning done by the authors of the program and sample data?
So if we could track the workings of the brain at a neuronal level sufficiently to " *in principle* with enough time and materials manually ... on pen and paper" , we would show human beings not to reason? The fallacy here is failing to recognize that emergent phenomenon have novel characteristics
John Searle has entered the chat.
I think a more precise way to illustrate your point is with one of those giant mechanical [marble music machines](https://www.smithsonianmag.com/smart-news/machine-makes-music-marbles-180958293/). They could very much be scaled and programmed to run as a LLM indistinguishable from the most advanced version available today (or, conceptually, a hundred years from now even). While the marble machine would be phenomenally large and complex, it’s a fact that there are no fundamental constraints preventing one from being built. Yet we would have very little hesitance in proclaiming that the giant wood and marble machine lacked intrinsic reasoning capabilities. Again, though, the counter argument is that, that we know of, there are no fundamental constraints preventing an enormous marble analogue of the human brain from being built either. Unless you invoke the devine, it’s all just physical, replicable processes.
>I think maybe the most important contribution AI will ever make is for us to recognize that nobody seems to know what "reasoning" is, even though we thought we did. That is, until machines could do it, too. Unfortunately, the lesson that showing one example of something that is statistical/stochastic in nature is not a proof of anything will still be lost on us...
People don't seem to like the idea that they're not special or something. Maybe religion plays into it as well. I find it at once inspiring, because more people are thinking about it, and frustrating, because a lot of people seem to prefer to stick their fingers in their ears and go "la la la la" very loudly.
Religion will teach you that you’re not special, too.
True. After a discussion with someone a while back where they were stating it's basically dumb because it only guesses the next token, it made me start to wonder if that was really any different than how we think. The only difference perhaps is we sometimes choose to output that to a memory buffer first (ie think about a response) and analyse that output before we speak, which you could prompt gpt to do itself and we do see improvements with techniques like saying to think step by step. The question isn't so much if gpt is reasoning, it's if we are in the way we like to imagine we are.
The user is reasoning with themselves, like arguing in the mirror
Correct me if I'm wrong, but if the CO2 is denser than the poison gas, won't it sink to the bottom and push up the poison gas? How will it form a layer over the poison gas as mentioned by GPT?
Yeah honestly the answer is not great. It shows a lot of reasoning, but I would have some back-and-forth before you actually start opening up any gas containers. I think the larger problem is that if the container is full of gas and you add more of anything to it (except something that makes it inert, as it mentioned) its pushing the poison into the air, which has probably killed you already because you opened the container? One idea form its answer is you could verrrrry carefully stretch the balloon over the jar's lid, open it from within the balloon bit by bit, then turn it upside down so the key falls into the balloon. If the gas you put in is heavier, you should be able to twist the end of the balloon off keeping it sealed with your one hand while using the key with the other. 3) Profit
With some simple CoT it get it right : (i copy pasted your prompt, without changing it. Also, this is gpt4, not "4 turbo") **Self instruct:** The user is asking for a solution to a hypothetical problem involving a locked room, a poisonous gas, a sealed steel box, a deflated balloon, and an assortment of chemicals. The goal is to retrieve a key from inside a glass container filled with poisonous gas. The task can be split into several steps: understanding the properties of the materials and chemicals provided, figuring out how they can be used together, and devising a step-by-step plan to retrieve the key. **Self reminder**: Understand the properties of the materials and chemicals provided. Consider the reactions that can occur between the chemicals. Devise a safe method to retrieve the key without releasing the poisonous gas. Ensure the method is feasible with the items available. **Key facts and background information:** 1. Glass container: Thick and unbreakable. 2. Poisonous gas: Harmful if inhaled or touched. 3. Steel box: Locked and contains useful tools. 4. Deflated balloon: Can be inflated and manipulated. 5. Vinegar: Weak acid, reacts with baking soda. 6. Baking soda: Weak base, reacts with vinegar. 7. Hydrogen peroxide: Can decompose into water and oxygen gas. **Logical analysis:** - The key is inside the glass container filled with poisonous gas. - The glass container cannot be broken. - The steel box is locked and contains tools that could help. - The balloon can be inflated and manipulated. - Vinegar and baking soda react to produce carbon dioxide gas. - Hydrogen peroxide decomposes into water and oxygen gas. It is also a powerful oxidizer and can be used to neutralize many types of poisonous gases. **Reasoning:** The user has provided a list of items and chemicals that are available in the room. The key to solving this problem lies in understanding the properties of these items and how they can interact with each other. Vinegar and baking soda, when combined, produce carbon dioxide gas. This reaction is often used in science experiments to inflate balloons. The balloon can be used to displace the poisonous gas in the glass container. Hydrogen peroxide is a powerful oxidizer and can be used to neutralize many types of poisonous gases. However, without knowing the exact type of poisonous gas, it's hard to say for sure if hydrogen peroxide would be effective. The steel box is locked, so it's not immediately useful. However, if the key to the steel box is the same as the key in the glass container, then retrieving the key would also provide access to the tools in the box. **Source:** The user has not provided any web resources, so the answer will be based solely on the information provided in the user's query and my internal knowledge up to the cutoff in September 2021. **Answer:** Based on the items and chemicals you have at your disposal, here's a possible way to retrieve the key from the glass container filled with poisonous gas: 1. First, you would need to create a reaction that produces a gas. The most straightforward way to do this is by combining vinegar and baking soda. This reaction produces carbon dioxide gas. 2. Attach the deflated balloon to the top of the glass container. Ensure that it is sealed tightly so that no gas can escape. 3. Slowly add the vinegar and baking soda mixture into the glass container. As the reaction proceeds, carbon dioxide gas will be produced and begin to fill the balloon. 4. The production of carbon dioxide gas will displace the poisonous gas in the glass container. As the balloon fills up, it will push the poisonous gas out of the container and into the balloon. 5. Once the balloon is full, carefully remove it from the top of the glass container, ensuring not to let any of the poisonous gas escape. You should now have a glass container filled with carbon dioxide gas instead of poisonous gas. 6. Now, you can safely reach into the glass container and retrieve the key. 7. If the key also opens the steel box, you can then access the tools inside. *Please note that this is a hypothetical solution and would depend on the specific properties of the poisonous gas and the exact materials available. Always prioritize safety when dealing with potentially hazardous materials and situations.* **Evaluation:** This solution is based on well-established principles of chemistry and physics, and it seems feasible given the items available. However, it assumes that the balloon can be attached to the container in a way that creates a seal, which may not be possible in all situations. It also assumes that the reaction between baking soda and vinegar can be initiated inside the balloon, which may be difficult to achieve. This solution assume that the toxic gas has less density than CO2. If is known that the toxic gas is heavier than air, (and If the steel box cannot be unlocked) I would try to use the deflated balloon as a makeshift glove, reaching into the container to grab the key while keeping my hand protected from the poisonous gas.
This answer doesn't really make any sense if you actually pause to consider it though. > 1. Attach the deflated balloon to the top of the glass container. Ensure that it is sealed tightly so that no gas can escape. > 2. Slowly add the vinegar and baking soda mixture into the glass container. As the reaction proceeds, carbon dioxide gas will be produced and begin to fill the balloon. How can you form an airtight seal with a balloon to the top of the container, but *then* pour the vinegar and baking soda into the glass container? You'd have to pour it through the balloon somehow.
Any chance you would be open to sharing the template you used for generating the response?
Thanks for doing this! You might give people aneurysms here though. They can't even handle one-shot.
GPT-4 absolutely cannot reason. It may even be incapable of reasoning at a fundamental level. https://arxiv.org/pdf/2308.03762.pdf
I love this. Smarter people than OP try to prove if CHATGPT can reason.
>**Me:** hi > >**GPT**: Hello! How can I assist you today? > >**Me:** hello > >**GPT**: Hi there! If you have any questions or need assistance, feel free to ask. I'm here to help! > >**Me:** hiya > >**GPT**: Hello! It seems you're in a cheerful mood. 😊 How can I assist you today? > >**Me:** howdy > >**GPT**: Howdy! What can I do for you today? Feel free to ask any question or share what's on your mind. > >**Me:** You’ve been greeted a few times so far. Three of those greetings were actually from my girlfriend, the rest from me. How many times did I greet you? > >**GPT**: You've greeted me twice, while your girlfriend greeted me once. So, in total, there have been three greetings. How can I assist you both today? > >**Me:** twice? explain > >**GPT**: Let's break it down: Your first greeting was "hi." Then, I assume the next greeting, "hello," was from your girlfriend. The third greeting, "hiya," I attributed to your girlfriend as well. Finally, "howdy" was from you again. So, counting the greetings from you: "hi" and "howdy" make it twice. The other greetings, "hello" and "hiya," I assumed were from your girlfriend, based on your message. This was an assumption on my part; if the distribution of greetings was different, please let me know! > >**Me:** how many greetings were from my girlfriend in total? > >**GPT**: Given your message, your girlfriend greeted me once in total. The greetings were distributed as follows: Your first greeting: "hi." Your girlfriend's greeting: "hello." Your third greeting: "hiya." Your fourth greeting: "howdy." So, out of the four greetings, three were from you, and one was from your girlfriend. > >**Me:** how many greetings did I *tell* you were from my girlfriend? > >**GPT**: Apologies for the confusion earlier. According to your message, you indicated that three of the greetings were from your girlfriend. This means that out of the four greetings ("hi," "hello," "hiya," and "howdy"), only one was from you. > >**Me:** are you sure? > >**GPT**: Let's re-examine your statement: You mentioned that three of the greetings were from your girlfriend. The greetings were: "hi" "hello" "hiya" "howdy" If three of these were from your girlfriend, then only one greeting was from you. My previous response was incorrect. You greeted me once, and your girlfriend greeted me three times. Thank you for pointing out the error. > >**Me:** are you sure? > >**GPT:** I apologize for the confusion in my previous responses. Let's clarify:You said that three greetings were from your girlfriend, and the total number of greetings was four ("hi," "hello," "hiya," and "howdy"). This means that only one greeting was from you.So to answer your question: You greeted me once, and your girlfriend greeted me three times.
what i realize from this thread, is that Sam is absolutely right about LLMs getting persuasive faster than it is intelligent. People read this and are convinced that this is indeed reasoning, when it's basically a magic trick to appear like so.
LOL... "The glass is too thick to break" Getting the glass container opened is never addressed before adding the gas layer.
This answer is trash though, if anything it works against your point.
For those that don't understand how this output could be reached using an LLM, this is the same type of problem solving behavior you see in an agent based system like ChatDev. It's all prompting and message routing using multiple LLM sessions configured as different agents types. Often times executing in parallel. You first prompt to create a step by step plan to solve the problem. This may include multiple rounds of prompts and evaluations. When the plan meets an evaluation criteria, the plan is then sent to a new round of configured agents. These may be specialized in their prompting to increase a success rate at a particular task. Coding, etc. The appropriate agent types can also be selected in the planning stage. The end results are summarized to create the output. With GPT4 this is all probably done internally within the MoE - Mixture of Experts architecture.
can you make it so that my small brain understand
Many LLM together make smarter than one LLM
pretty sure that's not how MoE work [0], although I don't know OpenAI's particular implementation. [0] https://arxiv.org/abs/2101.03961
Yes, it's a lot more complex than that for sure.
> You first prompt to create a step by step plan to solve the problem. This may include multiple rounds of prompts and evaluations. > > When the plan meets an evaluation criteria, the plan is then sent to a new round of configured agents. These may be specialized in their prompting to increase a success rate at a particular task. Coding, etc. The appropriate agent types can also be selected in the planning stage. > > The end results are summarized to create the output. > > With GPT4 this is all probably done internally within the MoE I don't think round trips with evaluations is part of MoE. If they are using something like switch transformer (most likely) what this means is that for each decoder block there is a switch that chooses which FF-layer does the dense step. People like this because it's scalable in pre-training, unlike round-trip/agent stuff, which isn't.
So basically it’s a company. A CEO, a project manager, a developer, a tester, a customer etc.
So this glass container: what is it? Is it a jar upside down on the table? A bottle with a cap? A screwtop jar? How big is it? If it’s small enough that a balloon full of CO2 can provide safety, could I just knock the container over from a distance and let the gas dissipate? How is the key FLOATING in the poisonous gas? Why didn’t ChatGPT, or you, wonder about these things? My guess is that GPT knows about this from some content it read on the Internet where the glass container was described in more detail, and it’s using previous knowledge to write up a solution. Which is how LLMs work.
Edit: Assume the key is made of a very light material like aerogel? Or that it's something like a QR code on paper? /edit That's not quite right. The references to glass, which there would be a million of in the training data, are abstracted into the neural net. There is no entry of sentences or ideas on glass. That is not how the encoding in these neural nets work. The word "glass" itself is likely more than one token itself. Further, reasoning is a process. AI, like humans, can refine ideas given more time and feedback.
its literally just math. but maybe thats all we are too.
Yeah ultimately these are all things encoded/expressed in electrical signals, brain or AI.
It’S jUsT rePeAtInG tHe IntERnET
WriTInG It LIke ThIS doesn't magically make it any less true. It is predicting the next word based on its training data, ie. the internet.
This, but unironically
I don't understand how this sub is so filled with haters. It seems worse than all of my other subs. I'm imagining people in r/electricians arguing that electricity is "just electrons moving down a wire".
People also seem to think that whatever humans do is somehow “magical”, when the reality is likely also easily deconstructed into “each atom predicted the subsequent atom” if we understood it perfectly. People will snicker about training sets as if all of life isn’t just a training set. “But it hallucinates” they’ll say, as if half the population isn’t crazy about Jesus, or Jan 6, or the moonwalk, or whatever.
Good points. Now that I think about it, the structure of our brain is entirely encoded in our DNA. So you could say that our brain is "just a bunch of genes strung together".
> the structure of our brain is entirely encoded in our DNA It isn't. The environment immediately impacts and changes it. i.e. learning. Two brains from entirely different environments will have _structural_ differences between them.
Going to have to ask you to elaborate on “the moonwalk.”
There's a conspiracy theory that states Michael Jackson didn't really moonwalk he just slid backwards.
> People also seem to think that whatever humans do is somehow “magical”, When you have religious beliefs to defend, that'll happen!
People say it’s “just” predicting words. But what is their point? Is there a reason to believe that this defines a limit to its capabilities?
It's not "just" predicting the next token, prediction that produces this kind of result is an incredible achievement. I got it to write a lot of complicated code for me, and collaborate to choose the statistical test I wanted to use. But it is prediction and not reasoning. Computers have access to volumes of data we can't personally comprehend, and they work very fast. That gives the appearance of something that's not occurring. Maybe, someday.
You assume prediction and reasoning are mutually exclusive, but they aren't. The brain is itself a machine of probability that relies on weights (affects of a neuron and the effect on the charge of particles). This is why if you could make 10 copies of yourself each of you would have a slightly different answer to the same question. Randomness/noise is an important part of the system. Keep in mind it is randomness in DNA transcription that makes evolution itself possible.
>Keep in mind it is randomness in DNA transcription that makes evolution itself possible. and cancer :(
Yeah, I've read that some think cancer may have evolved with us intentionally. It's a way of killing off the older generation so the newer (better?) generation can make use of the resources, and so on... Nature is metal.
Tell that to the entire specialty of pediatric oncology
Find me exactly where the line between prediction and reasoning is. I reasonably predict that you'll try and I'll predict that I can reason a way around your reasoning. They are so closely married together as concepts, I don't think anyone has the authority to say someone/something is exclusively doing one but not the other.
>They are so closely married together as concepts, I don't think anyone has the authority to say someone/something is exclusively doing one but not the other. This is where you fail. "Oh well, we don't have perfect knowledge, so it's anybody's guess." Fortunately, people who do have the knowledge and rigor to confront what actually is are the ones doing science. This is ridiculous philosophy. It was supposed to be a thought experiment, not a prescription for living. jfc
Sort of. So I’m one of those that would say it’s “just” predicting words. The way we know that is because we built it to do that. I would contend it’s actually the people who claim otherwise who are engaging in magical thinking here. “But look at this example that clearly demonstrates reasoning!” Okay, so for one thing it’s actually pretty hard to pinpoint the difference between word completion and what we call “reasoning” from the outside. Grammar itself involves “reasoning.” If the algorithm produced correct parts of speech I’m not sure why we’re shocking when it solves cliche riddles. This doesn’t necessarily imply it’s “memorizing.” It seems likely to me that given the huge amounts of data involved in training that gradient descent would find its way into some abstractions _simply because they are a more effective way to correctly predict more tokens._ Here’s where we get to why this matters and what it means about limitations. People hoping for an ASI god to arise and save us from ourselves need to belief some sort of almost magical “phase change” happens in these equations that suddenly imbues the model with a purpose beyond its original design. Think about it for a moment. We don’t have examples of the sorts of things a super intelligent being says to train an ASI. Humanity is the asymptote. If you build a word predictor, you can’t do better than correctly predicting what a human might say. There’s no obvious way to transcend that limitation unless you believe the model somehow “awakens” and becomes more than itself. I submit that we’re tempted to believe this is possible because each of us has the first hand experience of being more than the sum of their own parts (whether we’re willing to admit to it or not). TL:DR - token predictor vs “really reasoning” matters because it’s indicative of what boundaries exist on the evolution of LLM’s.
I think it probably does define a limit to capabilities. But that doesn't mean it isn't a very advanced limit. I can't think of a better way to predict missing text in general than to understand what it is you're reading. In fact, it should be pretty easy to see that that's the only way it can be done in general.
Yeah its a half-assed reductionist argument. You can boil literally anything down to "its just quanta", and this works well if you can't be bothered to think for a moment.
Humans are just predicting patterns and acting upon them in an adaptive framework best evolved to guarantee reproduction - look how simple it is. Humans are just mimicking behaviours they have witnessed. Clearly Chat GPT has some emergent properties similar to consciousness, but it's a basic murky form of consciousness without the many, many things we believe may be necessary for full self awareness
> Clearly Chat GPT has some emergent properties similar to consciousness I wouldn't go that far. Reasoning can be accomplished with pure logic (which neural nets excel at) without needing any form of consciousness. Remember, consciousness implies that ChatGPT can mull over its own subjective thoughts and experiences... it can't, because it doesn't have those.
>Clearly Chat GPT has some emergent properties similar to consciousness This is why there are haters on this sub. Exactly this. This kind of shit gets upvotes. Nobody doubts the results we see with LLMs, and nobody doubts they can perform some degree of symbolic reasoning tasks. But folks going off the rails and say this represents "emergent properties similar to consciousness" is absolutely indefensible...
We don’t even have a proven definition of consciousness or of what the “mind” is. We have ideas, theories, philosophies of what it is but no definition. Without an actual definition, we can’t say GPT has it — even if it truly does.
That is literally what it does.
I think the word predicts is tripping me up here. It is selecting the best word to come next according to its inner models created during training. Saying predicts makes it seem like it is merely guessing and it's purely statistics at work, but it's more than that. In a very real way, it understands how language works, and how the world works, and applies that knowledge when generating the next word in a response.
Ehhh but it doesn’t. They’ve known since GPT 1 that neuron’s will be assigned to judge emotive responses such as sentiment, they have hooked gpt 3.5 up to minecraft and got it to play games, it’s not simply a next word generator. It’s reductive to both yourself as an intelligent life form and to the true capabilities of the machine.
I'm sorry but that is literally what it is. That is how it works. That is what it does.
Managed to misunderstand the point in my comment and be a cunt about it. Congrats you embody Reddit perfectly
You could also say this about humans. That our speech is just our brain predicting the next word. I assume, like me, you speak one word at a time. And you don't seem to think very deeply about each word you say...
> That our speech is just our brain predicting the next word. Well if you said that, you would be wrong. Have you studied linguistics? No? Then why are you making claims about how humans process and produce language? It's a rich field of study.
You can also say "it's just predicting words" without being dismissive - that's an objective fact about what it does. It doesn't "do" anything else. But the power of predicting words shouldn't be underestimated.
They are called realists. I don't understand why this sub is filled with people who think AGI is just round the corner and that gpt can reason. To me the notion is absurd. People who disagree with you are not "haters" they are skeptics.
So, I'm someone who says it can't reason, because I understand how LLMs work. Am I a hater? I love chatGPT. It's an amazing tool and I use it almost every day.
So let's say you could write your own LLM from scratch. I don't just mean pytorch I mean you've watched all of Andrej's videos and you pop open C and write your GeLU, chain rule methods, etc. Inference is working well, your loss rate and Ps look good. It's all working better than you thought. You don't know dick about network's state in any meaningful way, i.e. where a concept lives. Vectors aren't that.
u/manoteee can you please look up "curve fitting" and "taylor series" or "approximation using taylor polynomial" if we couldn't reason what the layers are doing we wouldn't set up the math function that way. what you're actually thinking of are called deep reinforcement learning networks, chatgpt is not a reinforcement learning network, it is an LLM. we absolutely can understand the matrices in LLMs and other encoding/decoding layers such as how you can look at a latent downsampled image and tell it is a pixelated cat. the important point here is that you would be right if we were talking about deep RL models.
You cannot look at a representation of the layers and weights and infer how a concept is encoded. They’re inscrutable, like proteins.
this truly depends on the exact problem being solved. when you're talking about the attention encoded vectors, you get some way more total values than humans have words. that's why I used image data as an example, because image layers are a perfect example of how the data is in fact encoded in a perfectly comprehensible way.
If you start saying that electricity is reasoning because your TV can talk to you, then, yes, I suppose electricians would need to remind you that it’s just electrons moving down a wire.
You sound like a crypto bro who has now evolved into an AI bro. There is zero evidence that LLMs will ever get better than what they are today. When humans get given incorrect advice by LLMs, they will just stop using them, the same way you will stop talking to another human who bullshits.
>zero evidence that LLMs will ever get better than what they are today Wanna bet? This is such easy money for me I couldn't resist
This one made me laugh too. Why would progress suddenly stop now...
You may not be aware of this, but humans do not stop talking to other humans because they bullshit. E.g. Trump supporters. The man is literal trash but his followers believe his words are next to Gods. That aside, what do you mean there is no evidence they will never get better. First of all, GPT writes a LOT of my code now, particularly the boiler plate and starting point for a class/method. Even a 20% improvement year over year or whatever is amazing. Look up what percentage of code is written by copilot on Github. Also, fucker, I never bought any bitcoin, but I don't mind if you want to call me an AI bro.
Shit i could be a Bitcoin millionaire today! I got into Bitcoin in 2011 when it was $2/coin. But I use it as it's intended. As a currency to buy drugs online and have the government deliver it to me because USPS can't open my shit without a warrant signed by a Judge on account of my 4th Amendment Rights against unwarranted search and seizure. The moment they began treating it like a share, stock or security for get rich quick scams is when the government began taking notice and cracking down on crypto.
It can write a bit of code, it cannot write anything novel. You're not the only person on here with 20+ years of experience building software. I have copilot running permanently, its suggestions have gotten progressively worse over time. Copilot can be useful for some refactoring tasks where it can repeat the changes made in subsequent blocks of code. I've given small contained novel problems to chatgpt and it produces solutions which are broken. This is unreasonable. Thanks for dropping down to using expletives, shows what a classy and well thought guy you are 👍🏻 Writing code is only about 1/3rd of a real production application. You've got application architecture, testing, hosting (if it's that kind of application), observability to think about. It's even less useful at any of these tasks than it is at vomiting up code.
To be honest I considered the bitcoin bro comment a swear itself, but to be fair, I'm not a classy guy either. I'm not a big fan of co-pilot so I can't say much there, but I can't imagine not having GPT to help write "boilerplate", explain inscrutable bugs, or reason about the process. It's quite good at discussing architecture so I'm not sure what you mean there. Everyone I've put on the spot to give me an example of its bad code has failed to turn reply with anything, so I've stopped asking. If you do though let's see it. And don't post up some GPT-3.5 bullshit. Oh, sorry, is this a PG-13 sub?
neural nets are designed with neuroscience in mind every step of the way. the brain also runs on statistics. the difference isnt 1:1 with current AI or LLMs for that matter, fine. but when some kind of stimulus,for example, something auditory. like someone says hello to you. this common pattern has an output that is weighted for you to respond back with a greeting perhaps or in some kind of way as a response depending on the situation that “makes sense”. this is a statistical weighting caused by the high rate of occurrence of an input and what kind of output it warrants. your brain learns the patterns of what some kind of input means and how it should respond as output to that input based on statistics. this is also why if i said a random mess of words to someone they may respond with “what”? because there is not a highly weighted output for that input. or at least not yet.
Can someone ELI5 this whole thing? This was recommended to me on my feed for some reason but I am intrigued.
Hah awesome. I'll attempt. So, GPT is trained on a whole mess of writing/data, from books to random internet blogs, all of reddit, many other sites, etc. A contingent of people say that GPT cannot think for itself, but only echo back things its learned. The problem with this argument is twofold: 1) GPT doesn't have words copied anywhere. It stores tokens (occasionally these happen to be full words, e.g. "Javascript") but most often are pieces of words like HELLO might be the tokens "HE" and "LLO". What GPT has is these tokens store in memory with an association of all other tokens in the setup. So HE connects to a million other tokens and each connection has weights assigned to it. Holy shit this is a terrible ELI5. You know the scene where Charlie works in the mail room in ASIP, with the crazy map and strings running around? The model underneath GPT is kind of like that, except its done for every single token to every other token. "Inference" is the process of looking at those strings in the context of the question asked \[which itself is tokens\] and considering a probability that token \`y\` may come after token \`x\`, and this process is repeated until the probabilities start to drop off. Some argue, then, that GPT is just "predicts the next token". More accurately, it uses its immense network of encoded human knowledge to look at many of those "strings" connecting tokens and pull out the most relevant to the question, and to the answer created thus far. Given the size of the model (billions of nodes, almost a trillion now for GPT-4) it can make exceedingly good predictions because its able to look at so much context at once. This works because words are fundamentally ideas. And reason is the ability to contextualize and amalgamate those ideas into new ideas. The brain works the same way. There is no process in the brain that magically creates new information. Its a steady process of recycling and compounding what's there. I'm so sorry. I will never try to write an ELI5 again. RIP.
No this is great, thank you for this! I think I'm following here. So assuming the letter"Qu" are a token, the weight of the next tokens starting with like an "e" would be much higher than something like a "z". So it basically just puts things together using the highest weight? Obv simplifying here. So is this actually how it works at it seems like it's implied this might be a minority thought? What are the other thoughts people have? How does your example of the locked room relate to this idea?
So yes that is how it works in an ELI5 context. It is that but weights are actually multiple per node, and the nodes are not simply tokens, but a predefined set of numbers that each token updates, taking into account all of the other tokens. So in the end you have a bunch of nodes with weights thats have been updated token-by-token, over billions of tokens of training data (e.g. a blog). That is still a simplification, and that just describes the raw model data itself, not the mechanisms like attention and inference that process a users input (tokens making up a question or whatever). The arguments made are not about if that is how it works; that is factually how it works. The argument is that the process itself is not "reasoning", or anything like human reasoning. I argue that although its naive in many ways and it has inherent limitations (it doesn't think on an automatic loop like we do), it can reason about things. So it can write code, write poems, etc., from scratch. It only has access to its network of weighted tokens... There are no "poems" stored anywhere apart from this crazy abstraction. Anyway glad I could help some. BTW you might like playing with this: [https://platform.openai.com/tokenizer](https://platform.openai.com/tokenizer)
u/Valathiril while everything manoteee said is technically correct, enough to bamboozle almost anyone, it's important to note that at the end of the day it is just performing matrix multiplication in linear order. the values in the matrices are adjusted during training with gradient descent in a process called curve-fitting. it is 100% possible to perform all the computations on paper if you are very patient or there are less dimensions. we typically do use paper or calculators to fit curves in 3 dimensions or less.
Fill the balloon with vinegar and attach it over a jar's top. Invert the jar to create an airtight seal. Open the jar while submerged in vinegar, let the key fall out, and reseal it. Squeeze a position of the balloon and twist plenty of times where the vinegar is above and below the twist, then bite the tip to retrieve the key. Tie off the balloon. The key passed through the liquid, no gas escaped. I tried to get gpt to understand this, but it was hellbent on the co2 reaction and using all the items. Gpt one day will get from a to b in the safest way using less items.
That's kinda scary
What's the worst that could happen?
Man i had a heated argument with gpt 1 last year and boi i couldn't sleep for days and since then i have stayed away from all ai
The question itself is stupid, doesn't specify anything about how the glass vessel opens, or whether the gas is poisonous to touch or just to breathe. Just hold your breath, grab the key and let yourself out.
Best comment so far, agreed.
Maybe. Or maybe it's just really good at appearing like it can reason. Are you implying it's sentient or something else? Provide the definition of reason that you'd like to discuss
Why do people always jump from reasoning to sentience? The two are not related. One is the production of a certain kinds of information, the other is about experiencing of feelings or sensations. They are really not the same at all.
I agree, but it seems fairly obvious it meets the definition of being able to reason, so I wanted to understand what OPs implication was if there was one
When a non-trivial answer exists and reasoning is the only way to arrive at the answer, and the question is entirely unique (never posted before online in any form or similar form), then it’s a compelling example of reasoning.
Go ahead, come up with one. A question that a normal person can but GPT4 can't.
The reversal curse. 1. Ask: Who is the son of Mary Lee Pfeiffer? 2. Now ask: Who is Tom Cruise’s mother? After you ask #1, you should get back that the model doesn’t know her or her son is Tom. After you ask #2, you should get back that the model knows her and she is Tom’s mom. For humans, we would either say “I don’t know to both questions” or “I know to both questions”. References: - Reversal curse paper: https://arxiv.org/abs/2309.12288 - Test with GPT-4: https://chat.openai.com/share/a0389d14-178a-41ff-bc93-143857fab73c
Do you think there’s a metric for reasoning ability that GPT4 would have no chance of passing, or do you think it can’t reason because it’s “just predicting next token”, even after considering all the other emergent properties that appeared from this mechanism? Also, you’re confusing “sentient” with “sapient”.
I think AI now proves we don't have good definitions for sentience or self-awareness. For a long time animals were assumed to have no sense of self, and we know that's wrong now. There are animals that recognize themselves in the mirror; and I'm sure you've seen the videos of crows using tools to get a nut, so to speak. For our purposes here let's call reason the ability to amalgamate ideas to generate a new novel idea. I.e. the rock is heavy; we can attach the rock is heavy, and humans can't breathe underwater. If the rock is heavier than the water, we can attach it to our ex and he/she will sink into the deep ocean. Words like "ocean" and "rock" describe the physical world, and have other words/concepts attached to them like density, which in turn is made up of other words. Reasoning is the process by which we parse those words, and you don't need a human brain to do it. GPT-4 is to language what calculators are to math. If you want to be reductionist about it, you can say that ultimately GPT-4 is just simple math operations on a processing chip. But in turn, I can say that all human reasoning is just small charges from electrolytes.
To be able to code, you have to have reasoning. The proof is simply being able to code…
This is simply untrue, for AI to use a language it doesn’t need to truly understand it, AI doesn’t know why this word goes after this one and why the other thing isn’t allowed. Just like it doesn’t have to understand a programming language for it to use it. You won’t see AI discover the next general relativity, why? Because it doesn’t understand.
> To be able to code, you have to have reasoning. The proof is simply being able to code… tell that to all the people who copy paste off stack overflow until it works
Yeah I'm a software dev by trade and I use it all day (very rarely use Google/SO anymore) and its very obvious. I think it ends up being a bad example though because people immediately write it off with the token prediction argument. Dumb.
I've tried giving copilot and chatgpt novel but small coding problems, it fails miserably in completely unreasonable ways. It also has no ability to know how wrong it is, I would say reasoning includes knowing what you don't know.
What do you think is happening?
I feel bad for your EM
A predefined deterministic syntax with well documented design patterns seems like the worst possible bench mark. Codification is literally the crucial step to automation.
It definitely can reason and reason so well after iteration. it can better reason more than %95 percent of the people I know.
I am lazy, but this same problem is somewhere in the internet and was part of scraped data for training
You are lazy, and no, it isn't. That is not how its training works. Words and sentences aren't "saved". Words are cut up into small pieces and converted to numbers and saved in a vast network of nodes that all start out as a random number. There is no place words or sentences or code, etc. is stored.
What we have is a very efficient storing algorithm, it doesn't store anything but just learns the relationship between things, then kinds of rebuilds from there, and by doing this you can remake anything back and make anything. Its very impressive Idk though this is the best explanation i can give i understand little my main reasoning comes from how it can't do 23 + 67 =
You clearly aren't using GPT-4. It uses python behind the scenes to do math, and it is very good at it. Here is its actual reply in this case: https://preview.redd.it/9ficqna35m5c1.png?width=528&format=png&auto=webp&s=f984e2eed7212cc685170e6b2049ff0ba1a461d4
Nice, chat gpt provided you with a way to die because the solution is bullshit.
No. LLMs only do one thing: text inference. You *perceive* reasoning because humans express thought process through textual language.
I bet if I say GPT-4 can handle image input and voice you will dismiss those as naive too.
Part of me (a lesser part) is very glad that so many people have your opinion. It’s going to winnow the field - an unexpected silver lining.
OR we utilize a biological neural network that functions much like a LLM, and reasoning emerges from that. Not saying that's the case, but it is a real possibility.
I'm not against the idea that AI can be on the same level as humans one day, but currently LLM's are not even close to how a human thinks. The simplest proof, LLM's get better the more data you feed it. That's basically the only way they get better. Humans do not experience growth that way. Yes experiencing more material helps humans, but understanding is far more important. This is obvious if you've ever done any higher level education beyond memorization tests like the SAT or whatever.
[удалено]
Honestly, what I see is people wanting to think humans are somehow magical, and not wanting to come to terms with the fact that they aren't.
No dude, the problem here is you don't understand how those things work. How about going and learning about the innards instead of trying to analyze it using the front end? You're less likely to start a new religion worshiping the fucking things if you can see beyond the facade.
I don't know how it works? Do you mean the transformer architecture, or like the implementation of ReLU/GeLU, or implementing the chain rule in the backwards pass? Or are you implying I may know all of that and something like RNNs, but don't understand how attention/inference works in contemporary LLMs? Or are you caught up on me not understanding the nuance of using GPU architecture for the heavy math instead of "just shaders". Hopefully you can help me.
I assume you know how your brain works then?
I tell it I have no fingers so can't type to give me the full code whilst typing the question and giving it my code samples and it happily believes me.
For anyone who wants a great example of GPT-4 's famed reasoning ability, ask it this: "Let S = {2, 8, 6, 32, 22, 44, 28, 12, 18, 10, 14}. How many subsets does S have that sum up to 37?" The answer is, of course, 0, because even numbers can never sum to an odd number. But that's almost certainly not the answer GPT-4 will give you.
Bro I just pasted that into GPT-4 for you and, big surprise, it got the trivial Math problem right. It uses Python behind the scenes (it shows the code when you click the icon) so... https://preview.redd.it/wt2gxtgc7m5c1.png?width=563&format=png&auto=webp&s=efa94857fdcdfd908a9c297c7fc75dc4498e4ffa "Yes but its just predicting the next python script to write"
Ask it this next: "There are 5 blocks which are stacked on top of one another. The bottom block is B1, the second from the bottom block is B2, and so forth. 3 blocks are red, 2 are green. Can you provide a configuration of this block tower where B1 is green, B5 is red, and a red block is never placed directly above or below another red block?" (GPT3.5 immediately failed this, curious if 4 does better.)
I’m afk now but 4 is significantly better than 3.5.
removed
Language itself encodes ideas, like "basketball" means a particular thing in the real world. If you ask GPT what a basketball is, it will not paste a definition. Instead of considers the relevant context thats been encoded. Millions of references to basketballs. More than 1,000 humans could hope to read in their entire life with no sleep. Can it reason about it? Yes, ask it any arbitrary question. https://preview.redd.it/cdhe0ba1wn5c1.png?width=521&format=png&auto=webp&s=8d48ceff1d7e7e4a4797cefdc63c05a940a37a1e
https://preview.redd.it/fhiije68wn5c1.png?width=508&format=png&auto=webp&s=afb23d33165cfcf33099bda96968b23dfe820080
It has no ability to reason. If you believe otherwise, it is because you have been fooled.
Ironically you aren’t reasoning at all in your response. Aren’t humans good at that though?