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Ill give you a more formal definition. A model can be said as reasoning, when it can use existing information to figure out new data that has not been in the tr

Ill give you a more formal definition. A model can be said as reasoning, when it can use existing information to figure out new data that has not been in the training set. Here is a better example - lets say your input is 6 pictures of some object from each of the cardinal viewpoints, and you tell model these are the views and ask it how much it weighs. The model should basically figure out how to create a 3d shape and compute a camera view, and iterate until the camera view matches the pictures, then figure out that the shape can be hollow or solid, and to compute the weight you need the density, and that it should prompt the user for it if it cannot determine the true value for those from the information and its trained dataset. And it should do it without any specific training that this is the right way to do this, because it should be able to figure out this way through breaking the problem down into abstract representations of sub problems, and then figuring out how to solve those through basic logic, a.k.a reasoning. What that looks like, I don't know. If I did I would certainly have my own AI company. But i can tell you for certain we are not even close to figuring it out yet, because everyone is still stuck on transformers, like multiplying matricies together is some groundbreaking thing. In the cypher example, all its doing is basically using a separate model to break a particular model into chain of thought, and prompting that. And there is plenty in the training set of GPT about decrypting cyphers. >Forward only What I mean is that when its generate a response, the computation happens on a snapshot from input to output, trying to map a set of tokens, into a set of tokens. Model doesn't operate on a context larger than the window. Humans don't do this. We operate on a large context, with lots of previous information compressed, and furthermore, we don't just compute words, we compute complex abstract ideas that we then can translate into words. >even using the internet to look up parts it can either do out of the box or could do if given a plug-in to allow that. So apparently the way to AI is to manually code all the capability into LLMS? Give me a break. Just like with Chat GPT4, when people were screaming about how its the birth of true AI, give this model a year, it will find some niche use cases (depending on cost), and then nobody is going give a fuck about it, just like nobody is really doing anything groundbreaking with GPT4.

BriggyDwiggs42 2y ago
Your conclusion is absurd. If you agree this model is overall an improvement on the prior one, ie performs better on the same tasks and can do tasks the previous one couldn’t, it’s basically a given that it will get more use than GPT4
- ActorNightly 2y ago
  Better in niche areas doesn't mean its going to get more use. Everyone was super hyped about all the "cool" stuff that GPT4 could solve, but in the end, you still can't do things like give it a bunch of requirements for a website, let it run, and get a full codebase back, even though that is well within its capabilities. You have to spend time with prompting in to get it to give you what you want, and in a lot of cases, you are better off just typing the code yourself (because you can visualize the entire project in your head and make the right decisions about how to structure stuff), and using it for small code generations. This model is not going to radically change that. It will be able to give you some answers that you had to specifically manually prompt before automatically, but there is no advanced reasoning going on.
  - BriggyDwiggs42 2y ago
    What is “advanced reasoning” and why isn’t this doing it? If you made a chinese room to output coherent chains of reasoning, it would functionally be equally useful to an actual reasoner, with or without the capacity for sentience or whatever.
    - ActorNightly 2y ago
      Basically, if you had a model that could reason, it should be able to figure out new infromation. I.e lets say you map some bytes of the output to an api for creating a tcp socket and communicating over it. The model should be able figure out how to go out on the internet and search for information, all by itself, without any explicit training on how to do that. So without prior information, it should be able to esentially start out with random sequences in those bytes, and seeing what the output is, then eventually identify and remember patterns that come out. Which means there has to be some internal reward function for someting that differentiates good results from bad results, and some memory that the model has to remember what good results are, and eventually a map of how to get information that it needs (the model would probably stumble across Google or ChatGPT at some point and time after figuring out http protocol, and remember it as a very good way to get info) Philosophically, I don't even know if this is solvable. It could be that we just through enough compute at all iterations of architectures in some form of genetic algorithm, and one of the results ends up being good.
gdhkgdhkvff 2y ago
For your “better example”, it can literally already do this. I just tested this with 4o and it worked great (and I’ll say more accurately than a human would estimate most likely). I used 4o because it appears that the chain of thought models don’t accept image input yet. I don’t want to post identifiable information so I will avoid linking to the convo or posting screenshots but you can try it yourself. I took 5 pictures of a child’s magnetic tile sitting on the floor and here is the output: Me: (5 pictures attached) Me: Estimate how much this weighs. ChatGPT 4o: From the images, it appears that this is a small, plastic, transparent, square object, possibly a piece from a magnetic tile building set (often used in educational toys). Based on the size and material, I estimate this piece to weigh approximately 10 to 20 grams (0.35 to 0.7 ounces). If it's part of a toy set like Magna-Tiles, the weight would be on the lower end of that range. But for some reason I have a feeling this isn’t going to be good enough for you and the goalposts are about to be pushed back even farther. “In the cypher example, all it’s doing is basically using a separate model to break a particular model into chain of thought, and prompting that. And there is plenty in the training set of GPT about decrypting cyphers.” I’m sorry, but are you suggesting that applying a previously learned thought process to new variables isn’t reasoning? Does your definition of reasoning now mean that it’s only reasoning if you are designing a new-to-you chain of thought? As in, for deciphering coded messages, you’re saying that it’s only “reasoning” if it’s creating net new decoding methodologies? That’s such an absurd goalpost. You wouldn’t have the same goalposts for humans. All of your examples I bet the average human would fail at btw. Though that may just be because the average human is bad at reasoning haha.
- ActorNightly 2y ago
  I didn't ask for an estimation, I asked for the exact weight. A human can do this given the process I described. If the chain of thought was accurate, then it would be able to give you an internemdiate output of the shape in some 3d format spec. But nowhere in the model does that data exist, because its not doing any reasoning, it just still all statistically best answers. I mean sure, you could train a model on how to create 3d shapes out of pictures, but again, thats not reasoning. I don't get why people are so attached to these things being intelligent. We all agree that they are usefull. Like it shouldn't matter if its not intelligent to you or anyone else.
aws_ls 2y ago
>What I mean is that when its generate a response, the computation happens on a snapshot from input to output, trying to map a set of tokens, into a set of tokens. Model doesn't operate on a context larger than the window The weights in the model have the larger context, the context length size of data is just the input, which then gets multiplied by those weights, to get the output.

BriggyDwiggs42 2y ago

Your conclusion is absurd. If you agree this model is overall an improvement on the prior one, ie performs better on the same tasks and can do tasks the previous one couldn’t, it’s basically a given that it will get more use than GPT4

ActorNightly 2y ago
Better in niche areas doesn't mean its going to get more use. Everyone was super hyped about all the "cool" stuff that GPT4 could solve, but in the end, you still can't do things like give it a bunch of requirements for a website, let it run, and get a full codebase back, even though that is well within its capabilities. You have to spend time with prompting in to get it to give you what you want, and in a lot of cases, you are better off just typing the code yourself (because you can visualize the entire project in your head and make the right decisions about how to structure stuff), and using it for small code generations. This model is not going to radically change that. It will be able to give you some answers that you had to specifically manually prompt before automatically, but there is no advanced reasoning going on.
- BriggyDwiggs42 2y ago
  What is “advanced reasoning” and why isn’t this doing it? If you made a chinese room to output coherent chains of reasoning, it would functionally be equally useful to an actual reasoner, with or without the capacity for sentience or whatever.
  - ActorNightly 2y ago
    Basically, if you had a model that could reason, it should be able to figure out new infromation. I.e lets say you map some bytes of the output to an api for creating a tcp socket and communicating over it. The model should be able figure out how to go out on the internet and search for information, all by itself, without any explicit training on how to do that. So without prior information, it should be able to esentially start out with random sequences in those bytes, and seeing what the output is, then eventually identify and remember patterns that come out. Which means there has to be some internal reward function for someting that differentiates good results from bad results, and some memory that the model has to remember what good results are, and eventually a map of how to get information that it needs (the model would probably stumble across Google or ChatGPT at some point and time after figuring out http protocol, and remember it as a very good way to get info) Philosophically, I don't even know if this is solvable. It could be that we just through enough compute at all iterations of architectures in some form of genetic algorithm, and one of the results ends up being good.

gdhkgdhkvff 2y ago

For your “better example”, it can literally already do this. I just tested this with 4o and it worked great (and I’ll say more accurately than a human would estimate most likely). I used 4o because it appears that the chain of thought models don’t accept image input yet. I don’t want to post identifiable information so I will avoid linking to the convo or posting screenshots but you can try it yourself. I took 5 pictures of a child’s magnetic tile sitting on the floor and here is the output: Me: (5 pictures attached) Me: Estimate how much this weighs. ChatGPT 4o: From the images, it appears that this is a small, plastic, transparent, square object, possibly a piece from a magnetic tile building set (often used in educational toys). Based on the size and material, I estimate this piece to weigh approximately 10 to 20 grams (0.35 to 0.7 ounces). If it's part of a toy set like Magna-Tiles, the weight would be on the lower end of that range. But for some reason I have a feeling this isn’t going to be good enough for you and the goalposts are about to be pushed back even farther. “In the cypher example, all it’s doing is basically using a separate model to break a particular model into chain of thought, and prompting that. And there is plenty in the training set of GPT about decrypting cyphers.” I’m sorry, but are you suggesting that applying a previously learned thought process to new variables isn’t reasoning? Does your definition of reasoning now mean that it’s only reasoning if you are designing a new-to-you chain of thought? As in, for deciphering coded messages, you’re saying that it’s only “reasoning” if it’s creating net new decoding methodologies? That’s such an absurd goalpost. You wouldn’t have the same goalposts for humans. All of your examples I bet the average human would fail at btw. Though that may just be because the average human is bad at reasoning haha.

ActorNightly 2y ago
I didn't ask for an estimation, I asked for the exact weight. A human can do this given the process I described. If the chain of thought was accurate, then it would be able to give you an internemdiate output of the shape in some 3d format spec. But nowhere in the model does that data exist, because its not doing any reasoning, it just still all statistically best answers. I mean sure, you could train a model on how to create 3d shapes out of pictures, but again, thats not reasoning. I don't get why people are so attached to these things being intelligent. We all agree that they are usefull. Like it shouldn't matter if its not intelligent to you or anyone else.

aws_ls 2y ago

>What I mean is that when its generate a response, the computation happens on a snapshot from input to output, trying to map a set of tokens, into a set of tokens. Model doesn't operate on a context larger than the window The weights in the model have the larger context, the context length size of data is just the input, which then gets multiplied by those weights, to get the output.