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I still don't see your point behind your first 4 paragraphs. How you decide to treat your fellow humans is up to you. Just as you can decide to view your fellow

by Rury 4y ago

I still don't see your point behind your first 4 paragraphs. How you decide to treat your fellow humans is up to you. Just as you can decide to view your fellow humans however you want. It's entirely possible to treat them like "humans" while still viewing them as nothing but jumbles of molecules and atoms. So again why does perspective matter here (particularly with ChatGPT being a statistical word generator)? Your last paragraph lacks proof, and is a reflection of how you feel and want to view it - as something more than a statistical word generator. That's fine, but people with graduate level math/statistics education know that math/statistics is capable of doing everything ChatGPT does (and even more). To me, it sounds like you're the fearful one.

grugagag 4y ago
> To me, it sounds like you're the fearful one. What do you think that fear may be?
- Rury 4y ago
  IDK you tell me? All I can say is your OP, said "I think part of it is a subconscious fear ... I understand what I'm saying is dramatic". Why do you think it is a fear (you explained your thoughts so no need to re-explain), and why do you think what you say is dramatic? It appears to me you are projecting your thoughts and fears. I do though, find your last post dramatic, as you have capital words "ARE MORE" and "OBVIOUS" in your last paragraph, emphasizing emotion. So you must have strong emotions over this.
  - dnaddie 4y ago
    You asking me or him? I can't speak for the person you responded to but my own answer is here: https://news.ycombinator.com/item?id=34800063 https://news.ycombinator.com/item?id=34800063 To be honest I'm sort of in denial too. My actions contradict my beliefs. I'm not searching for occupations and paths that are separate from AI, I'm still programming as if I could do this forever. Also I capitalize words for emphasis. It doesn't represent emotion. Though I do have emotions and I do have bias, but not on the topics I am describing here.
  - grugagag 4y ago
    Im not the OP, i just asked the question. And imagined the answer would involve something about existential threat to human intelligence or something like that.. I simply asked out of curiosity
    - Rury 4y ago
      My mistake, hopefully I addressed your question though.
  - imtringued 4y ago
    The AI is pretty benign. The people surrounding it are the danger. I can already imagine a culture where people who type code manually are treated as code monkeys and your boss wants all your code to be typed out by their pre approved specifically to the company tailored LLM and the employees have to be frustrated with the crappy technology the same way they get frustrated by Java EE or ORMs/compilers that refuse to generate the queries they want. I am extremely worried about people telling me it can do things it can't. I asked it a simple question that you can easily get an answer for on stack overflow. It repeatedly generated garbage answers with compiler errors. I gave up, and gave it a stack overflow snippet to get it on the right track. Nope. Then I just literally pasted in the explanation from the official Java documentation. It got it wrong again but not completely but corrected itself immediately. Then it generated ok code that you would expect from stackoverflow. Finally I wanted to see if it actually understood what it just wrote. I am not convinced. It regurgitated the java docs which is correct but then it proceeded to tell me that the code it tried to show me first is also valid... This thing doesn't learn and when it is wrong it will stay wrong. It is like having a child but it instantly loses its memory after the conversation is over and even during conversations it loves repeating answers. Also, in general it feels like it is trying to overwhelm you with walls of text which is ok but when you keep trying to fix a tiny detail it gets on your nerves to see the same verbose sentence structures over and over again. I am not worried that adding more parameters is going to solve these problems. There is a problem with the architecture itself. I do not mind having an AI tool that is very good at NLP but just because some tasks can be solved with just NLP doesn't mean it will reach general intelligence. It just means that a major advancement in processing unstructured data has been made but people want to spin this into something it isn't. It is just a large language model.
    - yourapostasy 4y ago
      > I do not mind having an AI tool that is very good at NLP but just because some tasks can be solved with just NLP doesn't mean it will reach general intelligence. I've entertained the possibility that we might discover that "feelings" and language communication are emergent properties of statistical possibly partly stochastic nets similar to LLM's, and that the next tough scientific and engineering nut to crack is integrating multiple different models together into a larger whole, like logical deduction, logical inference and LLM's. LLM's are undoubtedly an NLP breakthrough, but I have difficulty imagining how its architecture can from using first principles as the training corpus derive troubleshooting steps to diagnose and repair an internal combustion engine, for example.
dnaddie 4y ago
(hit some ratelimiter on the other account, so using this account to reply) Ok let me make this more clear. I choose how to view things, yes this is true. But if I choose to treat human beings as jumbles of molecules, most people would consider that viewpoint flawed, inaccurate and slightly insane. Other humans would think that I'm in denial about some really obvious macro effects of configuring molecules in a way such that it forms a human. I can certainly choose to view things this way, but do you see how such a singular viewpoint is sort of stubborn and unreasonable? This is why solely viewing LLMs as simple statistical word generators is unreasonable. Yes it's technically correct, but it is missing a lot. There's another aspect to this too. What I'm seeing, to stay inline with the analogy, is people saying that the "human" viewpoint is entirely invalid. They are saying that the jumble of molecules only forms something that looks like a human, a "chinese room" if you will. They are saying the ONLY correct viewpoint is to view the jumble of molecules as a jumble of molecules. Nothing more. So to bring the analogy back around to chatGPT. MANY people are saying that chatGPT is nothing more then a word generator. It does not have intelligence, it does not understand anything. I am disagreeing with this perspective because OP just linked a scientific paper CLEARLY showing that LLMs are building a realistic model of the information you are feeding it.
kilgnad 4y ago
Ok let me make this more clear. I choose how to view things, yes this is true. But if I choose to treat human beings as jumbles of molecules, most people would consider that viewpoint flawed, inaccurate and slightly insane. Other humans would think that I'm in denial about some really obvious macro effects of configuring molecules in a way such that it forms a human. I can certainly choose to view things this way, but do you see how such a singular viewpoint is sort of stubborn and unreasonable? This is why solely viewing LLMs as simple statistical word generators is unreasonable. Yes it's technically correct, but it is missing a lot. There's another aspect to this too. What I'm seeing, to stay inline with the analogy, is people saying that the "human" viewpoint is entirely invalid. They are saying that the jumble of molecules only forms something that looks like a human, a "chinese room" if you will. They are saying the ONLY correct viewpoint is to view the jumble of molecules as a jumble of molecules. Nothing more. So to bring the analogy back around to chatGPT. MANY people are saying that chatGPT is nothing more then a word generator. It does not have intelligence, it does not understand anything. I am disagreeing with this perspective because OP just linked a scientific paper CLEARLY showing that LLMs are building a realistic model of the information you are feeding it.
- qprofyeh 4y ago
  That realistic model it builds of the information you are feeding it, if that’s how we measure its intelligence, then why is the answer it gives sometimes so “smart” yet in other instances where the question is simpler it seems “dumb” (provides an incorrect answer confidently)?
  - kilgnad 4y ago
    Don't know. But the fact that it often produces a "smart" answer is a phenomenon that needs investigation. You cannot simply dismiss this thing that passes a Google L3 interview and bar exam just because it got some addition problem wrong. That would be bias.
    - mrguyorama 4y ago
      No, it's not bias to understand a stopped clock is right twice a day. The reason LLMs sometimes generate accurate things and sometimes don't is because what they have been trained to do is chose a word to add next. Truth and lies are not a property of the structure of language, but rather information that is conveyed by language. Therefore, both truth and lie are perfectly valid continuations to make "valid" sentences with. LLMs develop no understanding of truth or lie. They just have a statistical model of what words go with what other words, and are plucking the next word based on that and whatever magical metaparams are being tweaked by the data scientists in charge. This is also why it is so good at programming. Programming languages are intentionally designed, often to be very regular, often to be easy to learn, and with usually very strict and simple structures. The syntax can often be diagrammed on one normal sheet of paper. It makes perfect sense that "add a token to this set of tokens based on the statistical likelihood of what would be a common next token" produces often syntactically correct code, but more thorough observers note that the code is often syntactically convincing but not even a little correct. It's trained on a bunch of programming textbooks, a bunch of "Lets do the common 10 beginner arduino projects" books, a bunch of stackoverflow stuff, probably a bunch of open source code etc. OF COURSE it can pass a code interview sometimes, because programming interviews are TERRIBLE at actually filtering who can be good software developers and instead are great at finding people who can act confident and write first-glance correct code.
vanviegen 4y ago
> That's fine, but people with graduate level math/statistics education know that math/statistics is capable of doing everything ChatGPT does (and even more). Well, obviously statistics are capable of doing that, as demonstrated by ChatGPT. But do these authorities you bring to the table actually understand how the emergent behavior occurs? Any better than they understand what's happening in the brain of an insect?
_blu 4y ago
Because you are reducing it and devaluing it to something you think you understand. if you understand humans as "jumbles of molecules and atoms..." then you don't understand humans. it's not a point of view. You even end your comment with another similar statement intended to display your "expertise"
spion 4y ago
The fundamental bit I was aiming at here is that running gradient descent or similar training algorithms on a thing doesn't mean you will understand the end result fully - and we don't understand it fully. Its entirely possible that to predict the next word correctly, ChatGPT had to build / encode some kind of model of the universe (as written down by humans) within its weights. We know its not possible to encode the full probabilities of what words will follow what other words, as the article itself states [1]. So how do you best "compress" these probabilies? By trying to find the most correct generalizations that are more widely appplicable? Perhaps even developing meta-facilities in recognizing good generalizations from bad? [1]: https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/#:~:text=It%E2%80%99s%20getting%20slightly,deduce%20those%20probabilities https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-....

by Rury 4y ago

grugagag 4y ago

> To me, it sounds like you're the fearful one. What do you think that fear may be?

Rury 4y ago
IDK you tell me? All I can say is your OP, said "I think part of it is a subconscious fear ... I understand what I'm saying is dramatic". Why do you think it is a fear (you explained your thoughts so no need to re-explain), and why do you think what you say is dramatic? It appears to me you are projecting your thoughts and fears. I do though, find your last post dramatic, as you have capital words "ARE MORE" and "OBVIOUS" in your last paragraph, emphasizing emotion. So you must have strong emotions over this.
- dnaddie 4y ago
  You asking me or him? I can't speak for the person you responded to but my own answer is here: https://news.ycombinator.com/item?id=34800063 https://news.ycombinator.com/item?id=34800063 To be honest I'm sort of in denial too. My actions contradict my beliefs. I'm not searching for occupations and paths that are separate from AI, I'm still programming as if I could do this forever. Also I capitalize words for emphasis. It doesn't represent emotion. Though I do have emotions and I do have bias, but not on the topics I am describing here.
- grugagag 4y ago
  Im not the OP, i just asked the question. And imagined the answer would involve something about existential threat to human intelligence or something like that.. I simply asked out of curiosity
  - Rury 4y ago
    My mistake, hopefully I addressed your question though.
- imtringued 4y ago
  The AI is pretty benign. The people surrounding it are the danger. I can already imagine a culture where people who type code manually are treated as code monkeys and your boss wants all your code to be typed out by their pre approved specifically to the company tailored LLM and the employees have to be frustrated with the crappy technology the same way they get frustrated by Java EE or ORMs/compilers that refuse to generate the queries they want. I am extremely worried about people telling me it can do things it can't. I asked it a simple question that you can easily get an answer for on stack overflow. It repeatedly generated garbage answers with compiler errors. I gave up, and gave it a stack overflow snippet to get it on the right track. Nope. Then I just literally pasted in the explanation from the official Java documentation. It got it wrong again but not completely but corrected itself immediately. Then it generated ok code that you would expect from stackoverflow. Finally I wanted to see if it actually understood what it just wrote. I am not convinced. It regurgitated the java docs which is correct but then it proceeded to tell me that the code it tried to show me first is also valid... This thing doesn't learn and when it is wrong it will stay wrong. It is like having a child but it instantly loses its memory after the conversation is over and even during conversations it loves repeating answers. Also, in general it feels like it is trying to overwhelm you with walls of text which is ok but when you keep trying to fix a tiny detail it gets on your nerves to see the same verbose sentence structures over and over again. I am not worried that adding more parameters is going to solve these problems. There is a problem with the architecture itself. I do not mind having an AI tool that is very good at NLP but just because some tasks can be solved with just NLP doesn't mean it will reach general intelligence. It just means that a major advancement in processing unstructured data has been made but people want to spin this into something it isn't. It is just a large language model.
  - yourapostasy 4y ago
    > I do not mind having an AI tool that is very good at NLP but just because some tasks can be solved with just NLP doesn't mean it will reach general intelligence. I've entertained the possibility that we might discover that "feelings" and language communication are emergent properties of statistical possibly partly stochastic nets similar to LLM's, and that the next tough scientific and engineering nut to crack is integrating multiple different models together into a larger whole, like logical deduction, logical inference and LLM's. LLM's are undoubtedly an NLP breakthrough, but I have difficulty imagining how its architecture can from using first principles as the training corpus derive troubleshooting steps to diagnose and repair an internal combustion engine, for example.

dnaddie 4y ago

(hit some ratelimiter on the other account, so using this account to reply) Ok let me make this more clear. I choose how to view things, yes this is true. But if I choose to treat human beings as jumbles of molecules, most people would consider that viewpoint flawed, inaccurate and slightly insane. Other humans would think that I'm in denial about some really obvious macro effects of configuring molecules in a way such that it forms a human. I can certainly choose to view things this way, but do you see how such a singular viewpoint is sort of stubborn and unreasonable? This is why solely viewing LLMs as simple statistical word generators is unreasonable. Yes it's technically correct, but it is missing a lot. There's another aspect to this too. What I'm seeing, to stay inline with the analogy, is people saying that the "human" viewpoint is entirely invalid. They are saying that the jumble of molecules only forms something that looks like a human, a "chinese room" if you will. They are saying the ONLY correct viewpoint is to view the jumble of molecules as a jumble of molecules. Nothing more. So to bring the analogy back around to chatGPT. MANY people are saying that chatGPT is nothing more then a word generator. It does not have intelligence, it does not understand anything. I am disagreeing with this perspective because OP just linked a scientific paper CLEARLY showing that LLMs are building a realistic model of the information you are feeding it.

kilgnad 4y ago

Ok let me make this more clear. I choose how to view things, yes this is true. But if I choose to treat human beings as jumbles of molecules, most people would consider that viewpoint flawed, inaccurate and slightly insane. Other humans would think that I'm in denial about some really obvious macro effects of configuring molecules in a way such that it forms a human. I can certainly choose to view things this way, but do you see how such a singular viewpoint is sort of stubborn and unreasonable? This is why solely viewing LLMs as simple statistical word generators is unreasonable. Yes it's technically correct, but it is missing a lot. There's another aspect to this too. What I'm seeing, to stay inline with the analogy, is people saying that the "human" viewpoint is entirely invalid. They are saying that the jumble of molecules only forms something that looks like a human, a "chinese room" if you will. They are saying the ONLY correct viewpoint is to view the jumble of molecules as a jumble of molecules. Nothing more. So to bring the analogy back around to chatGPT. MANY people are saying that chatGPT is nothing more then a word generator. It does not have intelligence, it does not understand anything. I am disagreeing with this perspective because OP just linked a scientific paper CLEARLY showing that LLMs are building a realistic model of the information you are feeding it.

qprofyeh 4y ago
That realistic model it builds of the information you are feeding it, if that’s how we measure its intelligence, then why is the answer it gives sometimes so “smart” yet in other instances where the question is simpler it seems “dumb” (provides an incorrect answer confidently)?
- kilgnad 4y ago
  Don't know. But the fact that it often produces a "smart" answer is a phenomenon that needs investigation. You cannot simply dismiss this thing that passes a Google L3 interview and bar exam just because it got some addition problem wrong. That would be bias.
  - mrguyorama 4y ago
    No, it's not bias to understand a stopped clock is right twice a day. The reason LLMs sometimes generate accurate things and sometimes don't is because what they have been trained to do is chose a word to add next. Truth and lies are not a property of the structure of language, but rather information that is conveyed by language. Therefore, both truth and lie are perfectly valid continuations to make "valid" sentences with. LLMs develop no understanding of truth or lie. They just have a statistical model of what words go with what other words, and are plucking the next word based on that and whatever magical metaparams are being tweaked by the data scientists in charge. This is also why it is so good at programming. Programming languages are intentionally designed, often to be very regular, often to be easy to learn, and with usually very strict and simple structures. The syntax can often be diagrammed on one normal sheet of paper. It makes perfect sense that "add a token to this set of tokens based on the statistical likelihood of what would be a common next token" produces often syntactically correct code, but more thorough observers note that the code is often syntactically convincing but not even a little correct. It's trained on a bunch of programming textbooks, a bunch of "Lets do the common 10 beginner arduino projects" books, a bunch of stackoverflow stuff, probably a bunch of open source code etc. OF COURSE it can pass a code interview sometimes, because programming interviews are TERRIBLE at actually filtering who can be good software developers and instead are great at finding people who can act confident and write first-glance correct code.

vanviegen 4y ago

> That's fine, but people with graduate level math/statistics education know that math/statistics is capable of doing everything ChatGPT does (and even more). Well, obviously statistics are capable of doing that, as demonstrated by ChatGPT. But do these authorities you bring to the table actually understand how the emergent behavior occurs? Any better than they understand what's happening in the brain of an insect?

_blu 4y ago

Because you are reducing it and devaluing it to something you think you understand. if you understand humans as "jumbles of molecules and atoms..." then you don't understand humans. it's not a point of view. You even end your comment with another similar statement intended to display your "expertise"

spion 4y ago

The fundamental bit I was aiming at here is that running gradient descent or similar training algorithms on a thing doesn't mean you will understand the end result fully - and we don't understand it fully. Its entirely possible that to predict the next word correctly, ChatGPT had to build / encode some kind of model of the universe (as written down by humans) within its weights. We know its not possible to encode the full probabilities of what words will follow what other words, as the article itself states [1]. So how do you best "compress" these probabilies? By trying to find the most correct generalizations that are more widely appplicable? Perhaps even developing meta-facilities in recognizing good generalizations from bad? [1]: https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/#:~:text=It%E2%80%99s%20getting%20slightly,deduce%20those%20probabilities https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-....