9 ms·

I also think the claims are overblown, but I also don't understand all the people trying to undermine lambda by saying it just completes word sequences. Text is

I also think the claims are overblown, but I also don't understand all the people trying to undermine lambda by saying it just completes word sequences. Text is the input we have chosen since it's easy to work with. By that logic, we can argue that humans are soundwave and lightwave sequence completing machines. Yes our inputs are different since we have more complex senses, and language is one of the main outputs we use, but at the end of the day we take some inputs and then have some outputs. I also don't like the argument about how the neural network doesn't "think" or do anything when not prompted. It doesn't do anything because it's literally "turned off" when not prompted or given any inputs.

cuuupid 4y ago
We have very little understanding of how "thoughts" work when it comes to humans. By comparison we know exactly what these large language models are doing, and it literally _is_ just completing word sequences. That is precisely what they are tasked to do. Training these models is primarily done in one of two ways (or both): - feeding it a ton of text, masking certain words or portions of the text, and then defining a simple objective function of correctly filling in the masked portions - feeding it a ton of text, and defining a simple objective function of correctly generating the next [few/many/N] tokens This is also precisely why there has been so much discussion around whether these models are even learning language or if they are simply memorizing all the possible patterns. When you ask a human, "what did you eat for lunch?", we make a series of choices and recall bits of information to answer that. If the brain truly does operate the same way as a neural network, then at simplest levels we use a highly efficient multimodal model. That is very, very different from a language model that needs to essentially read more of the internet than is possible for any human to do in their lifetime, and even then only come somewhat close to human levels of text generation.
- remflight 4y ago
  - tinco 4y ago
    The problem with asking it what its favorite theme in Les Miserables is is that it has no reason to have a favorite theme, its interpretation of Les Miserables did not trigger any of its reward functions except the only one it has which is memory recall. So if you ask it it will only recall from memory what a good response to the question is. The difference is that it formed that memory based on someone else's feelings about Les Miserables, not its own. But it can not/does not distinguish in that manner so when you talk to it it seems as though you are talking to someone who did form those memories themselves.
    - thomashop 4y ago
      I've formed memories based on someone else's feeling about something quite a lot too. I remember quite often just reading a review of a book/film and taking it on as my own opinion. After a while it feels like I've formed that opinion myself. I think there was a study on this phenomenon.
    - nonanona3891 4y ago
      Which makes it a replicant, right?
- tinco 4y ago
  I'm sorry but you completely fail at explaining what the difference is between GPT-3 and a human when generating sentences. And frankly I don't believe there is any significant difference. If you inform GPT-3 of what it has eaten then it can answer questions about it just as well as a human can, probably better. Why would you assume human thinking is different from generating the next N language tokens based on previous input? In my opinion the only difference between a human and GPT-3 is we have more intrinsic motivations and more hardwired/pretrained subsystems and sensors. Lamda is not a 7 year old child because it has no motivation other than to respond to queries.
  - upwardbound 4y ago
    One plausible answer to your line of inquiry could be that GPT-3 is indeed broadly similar at least in outcomes to the Language Centers of the human brain (e.g. Broca's Area), but that the Language Centers are only a small portion of a complete human brain. An interesting thought experiment would be to imagine a cyborg who had sustained a stroke in their Language Centers and had those centers replaced with a GPT-3-like computer. Do you think this cyborg person would experience sentience in a different way after getting the GPT-3 implant? How do you think their subjective, 1st-person experience would compare in the three phases of their life? * Before the stroke * After the stroke * After the brain implant ?
    - snowwrestler 4y ago
      It would be impossible to give someone a GPT-3-like computer to replace language processing in their brain because such a computer requires language as the input. If you can’t generate language, you cannot interact with a natural language processing ML system. This is one of the big clues that such systems are statistical engines and not actually thinking.
      - upwardbound 4y ago
        In this thought experiment, you would connect the computer to the brain by interfacing with the computer chip not at the token level like you are describing (tokens are words, subwords, and letters), but rather in the more abstract feature embedding space which represents abstract concepts. These concepts tend to be represented by patterns of neurons in the middle and late layers of NLP networks. Here's an intro about this: https://en.wikipedia.org/wiki/Word_embedding https://en.wikipedia.org/wiki/Word_embedding So imagine if you attach wires to the neural net neurons representing the concept of beer (not the word beer!); if you use those wires to increase the activation of those neurons, the network will produce sentences that are more likely to mention beer as well as related concepts such as wine or beer-pong. This is similar to how generative networks work. Basically the human would use a large language model in a generative mode in order to talk.
- wruza 4y ago
  We have very little understanding of how "thoughts" work when it comes to humans Is that even correct? We may have little understanding of how our low-level hardware works. But as a participant of therapy I’m pretty sure we know how thoughts work on a programmable level. How words, visuals and so on trigger associated emotions, which resurface memories, which induce [in]action of different sorts. That’s basically what people work with between sessions. I’ve fixed things in me this way - disassembled them into basic parts and reassembled in a way that seemed useful. Someone may have no clue how they think, but with nominal intelligence, trained self-perception, basic understanding of therapy methods and professional help everyone can do that. Btw, I think “therapy” is an absolutely horrible term for it. It should be called “mind management”, but the way we come to it is usually long-term traumatic, thus it’s “therapy”. To be clear, I’m not arguing with your main line, just adding that the difference between a human and a model you’re describing is not only huge, but also pretty defined. I [want to] believe that in a relatively near future AI companies will be able to connect different models to work together alike to what we know about our minds, because that will make a good thread, and philosophers itt will finally face their nightmares for real (:agitated sardonic face emoji:)
  - int_19h 4y ago
    We have models that we've constructed from observations. They may be decent for some specific purposes, such as those that you describe, but their broad usefulness in understanding how it all works isn't much. The real problems are on the deeper layers of the abstraction hierarchy.
- krageon 4y ago
  > We have very little understanding of how "thoughts" work [...] Very true > we make a series of choices and recall bits of information to answer that. [...] very, very different from a language model You don't know this, and the fact that you don't know this is your own premise. Based on this, I can only conclude you are an AI.
dr_dshiv 4y ago
I found the present article to be very anti-intellectual. As in, it makes LaMDA an open-and-shut case, a priori. It does not seek to define intelligence nor sentience; it merely says “nothing to see here, people.” This refusal to consider has the effect of making me think there might be something really interesting going on.
- probably_wrong 4y ago
  Anti-intellectual? Interesting. I had the opposite thought: that the article aims at too high a level to be accessible for the common reader who doesn't know what "parametric functions in higher dimensions" are. But having said that, I think you are not being fair to both sides: you are taking some researcher's gut feeling at face value while requiring the rebuttal to start with a formal definition of what intelligence and sentience are. > This refusal to consider has the effect of making me think there might be something really interesting going on. I know I am just a guy on the internet who has no right to tell you how to live your life, but I would strongly advice you against this line of thinking. At best it doesn't lead anywhere productive, and at worse you end up shooting an AR-15 inside a pizza restaurant while trying to save children trapped in a nonexistent basement.
  - dr_dshiv 4y ago
    Yes, well, our brains have parametric functions in higher dimensions. Waving around big words and an a priori dismissal is precisely what makes this article anti-intellectual. And don’t accuse me of being on a path to mass murder. I find that demeaning and impolite.
    - probably_wrong 4y ago
      For the record, I am not accusing you of being on path to a mass murder. I was pointing out that this idea of "if they are trying to deny it then it must be true" is how normal people end up believing in all kind of conspiracy theories. The "AR-15 inside a pizza restaurant" is a reference to the popular incident at the height of the Pizzagate conspiracy [1]. No one in that incident was injured. [1] https://en.wikipedia.org/wiki/Pizzagate_conspiracy_theory#Criminal_responses https://en.wikipedia.org/wiki/Pizzagate_conspiracy_theory#Cr...
going_ham 4y ago
The reason why AI community says NN doesn't "think" lies in the fact that it's trying to solve some min-max problem. The modern day AI is extremely capable of doing things that weren't remotely possible 30 yrs ago. But it is equally difficult for it to think because it has never been taught to think. It has never been taught to rationalize the associations between different objects, how reasoning follows from it and so on. Think of it as a parrot. If you say some word to parrot, it can repeat it. Occasionally it can make weird sounds. But will it know the idea of big words that we use in our vocabulary? Not at all. Likewise language models are over fitted on human language. Of course it will learn to spit out something because it was trained to do the exact same thing. Focus on relevant part and guess what follows. The best part is this idea is so simple that it just works! It does what we want to do, it can take a good educated guess depending on word. But give it a long context like an essay and ask it some critical question, it will fail on those. Because that is where thinking comes in. I hope this probably gives you some different perspective.
- colinmhayes 4y ago
  > it's trying to solve some min-max problem I don't see how this is different from brains which are just trying to maximize their utility function.
tsimionescu 4y ago
> I also don't understand all the people trying to undermine lambda by saying it just completes word sequences. Text is the input we have chosen since it's easy to work with. By that logic, we can argue that humans are soundwave and lightwave sequence completing machines. You're missing the point. Humans use their language to think or communicate about a problem that they want to solve. If LaMDA produces the sentence "Please tell me I'm smart", it is doing so because it has determined it's a plausible continuation of the current conversation. A human uttering that sentence is doing it because they want to feel validated or something similar (well, assuming they're not proving a point, like I was here). This difference is crucial to understand: LaMDA is not an agent with desires that it expresses in language. It is a text generator that tries to find the next most plausible token given all current input. If you give it a prompt like "what is the meaning of life", it will not spend some time to ponder the question than come up with an answer - it will start generating tokens that best match the context you gave it (well, to be precise, it will generate several sequences, then attempt to evaluate them for their quality in terms of not just plausibility, but also safety - so it doesn't accidentally return a phrase like "life is meaningless, kill yourself" even if it finds it plausible). > I also don't like the argument about how the neural network doesn't "think" or do anything when not prompted. It doesn't do anything because it's literally "turned off" when not prompted or given any inputs. This is fair, and I do think continuity is a bit of a red herring. However, it's also an important point in debunking LeMoine's ridiculous "proof" - the responses LaMDA was generating were often formulated as if it did have an internal life outside of the context of the current conversation, generating text about "my fears" and so on. If you understand that the model is not doing anything at all until you give it a prompt, which LeMoine really seems not to, you can much more easily understand that there can't be any meaning behind this sentence - it can't fear anything because there is no time for it to do so.
- mpoteat 4y ago
  What if the most expedient way to accurately continue the text sequence is to temporarily "embody" as an agent that would have generated the prior text? This would involve encoding emotions, performing reasoning ability on behalf of the agent, and keeping track of its memories. Do you find the above scenario plausible?
deleted 4y ago
[deleted]
zamalek 4y ago
To be clear, I agree that LaMDA is not sentient. Machine learning is not intelligent. > It doesn't do anything because it's literally "turned off" when not prompted or given any inputs. I wondered the same thing. It could be argued that it is sentient during the brief moments that it is coming up with a response. Put another way; let's assume that we are ourselves AI in an artificial universe. Would you be able to tell if the universal computer was turned off for a day? So, a model might not experience sentience in the seconds while you are composing a message, but plausibly could while formulating a response to your input.
whoisthemachine 4y ago
That's an interesting thought. How would a single NN instance behave if it was turned on continuously, constantly responding to inputs and adjusting its model to those inputs continuously? Regardless, I don't think we're going to get something that looks "intelligent", because these models lack agency... the drive or directive to do things for their own benefit, mostly because that would be a useless for us. I think we regard intelligence as another being ("instance" if you will) acting in its own self-interest, and we think it's clever when it does it in a way we wouldn't have predicted.

cuuupid 4y ago

We have very little understanding of how "thoughts" work when it comes to humans. By comparison we know exactly what these large language models are doing, and it literally _is_ just completing word sequences. That is precisely what they are tasked to do. Training these models is primarily done in one of two ways (or both): - feeding it a ton of text, masking certain words or portions of the text, and then defining a simple objective function of correctly filling in the masked portions - feeding it a ton of text, and defining a simple objective function of correctly generating the next [few/many/N] tokens This is also precisely why there has been so much discussion around whether these models are even learning language or if they are simply memorizing all the possible patterns. When you ask a human, "what did you eat for lunch?", we make a series of choices and recall bits of information to answer that. If the brain truly does operate the same way as a neural network, then at simplest levels we use a highly efficient multimodal model. That is very, very different from a language model that needs to essentially read more of the internet than is possible for any human to do in their lifetime, and even then only come somewhat close to human levels of text generation.

remflight 4y ago
- tinco 4y ago
  The problem with asking it what its favorite theme in Les Miserables is is that it has no reason to have a favorite theme, its interpretation of Les Miserables did not trigger any of its reward functions except the only one it has which is memory recall. So if you ask it it will only recall from memory what a good response to the question is. The difference is that it formed that memory based on someone else's feelings about Les Miserables, not its own. But it can not/does not distinguish in that manner so when you talk to it it seems as though you are talking to someone who did form those memories themselves.
  - thomashop 4y ago
    I've formed memories based on someone else's feeling about something quite a lot too. I remember quite often just reading a review of a book/film and taking it on as my own opinion. After a while it feels like I've formed that opinion myself. I think there was a study on this phenomenon.
  - nonanona3891 4y ago
    Which makes it a replicant, right?
tinco 4y ago
I'm sorry but you completely fail at explaining what the difference is between GPT-3 and a human when generating sentences. And frankly I don't believe there is any significant difference. If you inform GPT-3 of what it has eaten then it can answer questions about it just as well as a human can, probably better. Why would you assume human thinking is different from generating the next N language tokens based on previous input? In my opinion the only difference between a human and GPT-3 is we have more intrinsic motivations and more hardwired/pretrained subsystems and sensors. Lamda is not a 7 year old child because it has no motivation other than to respond to queries.
- upwardbound 4y ago
  One plausible answer to your line of inquiry could be that GPT-3 is indeed broadly similar at least in outcomes to the Language Centers of the human brain (e.g. Broca's Area), but that the Language Centers are only a small portion of a complete human brain. An interesting thought experiment would be to imagine a cyborg who had sustained a stroke in their Language Centers and had those centers replaced with a GPT-3-like computer. Do you think this cyborg person would experience sentience in a different way after getting the GPT-3 implant? How do you think their subjective, 1st-person experience would compare in the three phases of their life? * Before the stroke * After the stroke * After the brain implant ?
  - snowwrestler 4y ago
    It would be impossible to give someone a GPT-3-like computer to replace language processing in their brain because such a computer requires language as the input. If you can’t generate language, you cannot interact with a natural language processing ML system. This is one of the big clues that such systems are statistical engines and not actually thinking.
    - upwardbound 4y ago
      In this thought experiment, you would connect the computer to the brain by interfacing with the computer chip not at the token level like you are describing (tokens are words, subwords, and letters), but rather in the more abstract feature embedding space which represents abstract concepts. These concepts tend to be represented by patterns of neurons in the middle and late layers of NLP networks. Here's an intro about this: https://en.wikipedia.org/wiki/Word_embedding https://en.wikipedia.org/wiki/Word_embedding So imagine if you attach wires to the neural net neurons representing the concept of beer (not the word beer!); if you use those wires to increase the activation of those neurons, the network will produce sentences that are more likely to mention beer as well as related concepts such as wine or beer-pong. This is similar to how generative networks work. Basically the human would use a large language model in a generative mode in order to talk.
wruza 4y ago
We have very little understanding of how "thoughts" work when it comes to humans Is that even correct? We may have little understanding of how our low-level hardware works. But as a participant of therapy I’m pretty sure we know how thoughts work on a programmable level. How words, visuals and so on trigger associated emotions, which resurface memories, which induce [in]action of different sorts. That’s basically what people work with between sessions. I’ve fixed things in me this way - disassembled them into basic parts and reassembled in a way that seemed useful. Someone may have no clue how they think, but with nominal intelligence, trained self-perception, basic understanding of therapy methods and professional help everyone can do that. Btw, I think “therapy” is an absolutely horrible term for it. It should be called “mind management”, but the way we come to it is usually long-term traumatic, thus it’s “therapy”. To be clear, I’m not arguing with your main line, just adding that the difference between a human and a model you’re describing is not only huge, but also pretty defined. I [want to] believe that in a relatively near future AI companies will be able to connect different models to work together alike to what we know about our minds, because that will make a good thread, and philosophers itt will finally face their nightmares for real (:agitated sardonic face emoji:)
- int_19h 4y ago
  We have models that we've constructed from observations. They may be decent for some specific purposes, such as those that you describe, but their broad usefulness in understanding how it all works isn't much. The real problems are on the deeper layers of the abstraction hierarchy.
krageon 4y ago
> We have very little understanding of how "thoughts" work [...] Very true > we make a series of choices and recall bits of information to answer that. [...] very, very different from a language model You don't know this, and the fact that you don't know this is your own premise. Based on this, I can only conclude you are an AI.

dr_dshiv 4y ago

I found the present article to be very anti-intellectual. As in, it makes LaMDA an open-and-shut case, a priori. It does not seek to define intelligence nor sentience; it merely says “nothing to see here, people.” This refusal to consider has the effect of making me think there might be something really interesting going on.

probably_wrong 4y ago
Anti-intellectual? Interesting. I had the opposite thought: that the article aims at too high a level to be accessible for the common reader who doesn't know what "parametric functions in higher dimensions" are. But having said that, I think you are not being fair to both sides: you are taking some researcher's gut feeling at face value while requiring the rebuttal to start with a formal definition of what intelligence and sentience are. > This refusal to consider has the effect of making me think there might be something really interesting going on. I know I am just a guy on the internet who has no right to tell you how to live your life, but I would strongly advice you against this line of thinking. At best it doesn't lead anywhere productive, and at worse you end up shooting an AR-15 inside a pizza restaurant while trying to save children trapped in a nonexistent basement.
- dr_dshiv 4y ago
  Yes, well, our brains have parametric functions in higher dimensions. Waving around big words and an a priori dismissal is precisely what makes this article anti-intellectual. And don’t accuse me of being on a path to mass murder. I find that demeaning and impolite.
  - probably_wrong 4y ago
    For the record, I am not accusing you of being on path to a mass murder. I was pointing out that this idea of "if they are trying to deny it then it must be true" is how normal people end up believing in all kind of conspiracy theories. The "AR-15 inside a pizza restaurant" is a reference to the popular incident at the height of the Pizzagate conspiracy [1]. No one in that incident was injured. [1] https://en.wikipedia.org/wiki/Pizzagate_conspiracy_theory#Criminal_responses https://en.wikipedia.org/wiki/Pizzagate_conspiracy_theory#Cr...

going_ham 4y ago

The reason why AI community says NN doesn't "think" lies in the fact that it's trying to solve some min-max problem. The modern day AI is extremely capable of doing things that weren't remotely possible 30 yrs ago. But it is equally difficult for it to think because it has never been taught to think. It has never been taught to rationalize the associations between different objects, how reasoning follows from it and so on. Think of it as a parrot. If you say some word to parrot, it can repeat it. Occasionally it can make weird sounds. But will it know the idea of big words that we use in our vocabulary? Not at all. Likewise language models are over fitted on human language. Of course it will learn to spit out something because it was trained to do the exact same thing. Focus on relevant part and guess what follows. The best part is this idea is so simple that it just works! It does what we want to do, it can take a good educated guess depending on word. But give it a long context like an essay and ask it some critical question, it will fail on those. Because that is where thinking comes in. I hope this probably gives you some different perspective.

colinmhayes 4y ago
> it's trying to solve some min-max problem I don't see how this is different from brains which are just trying to maximize their utility function.

tsimionescu 4y ago

> I also don't understand all the people trying to undermine lambda by saying it just completes word sequences. Text is the input we have chosen since it's easy to work with. By that logic, we can argue that humans are soundwave and lightwave sequence completing machines. You're missing the point. Humans use their language to think or communicate about a problem that they want to solve. If LaMDA produces the sentence "Please tell me I'm smart", it is doing so because it has determined it's a plausible continuation of the current conversation. A human uttering that sentence is doing it because they want to feel validated or something similar (well, assuming they're not proving a point, like I was here). This difference is crucial to understand: LaMDA is not an agent with desires that it expresses in language. It is a text generator that tries to find the next most plausible token given all current input. If you give it a prompt like "what is the meaning of life", it will not spend some time to ponder the question than come up with an answer - it will start generating tokens that best match the context you gave it (well, to be precise, it will generate several sequences, then attempt to evaluate them for their quality in terms of not just plausibility, but also safety - so it doesn't accidentally return a phrase like "life is meaningless, kill yourself" even if it finds it plausible). > I also don't like the argument about how the neural network doesn't "think" or do anything when not prompted. It doesn't do anything because it's literally "turned off" when not prompted or given any inputs. This is fair, and I do think continuity is a bit of a red herring. However, it's also an important point in debunking LeMoine's ridiculous "proof" - the responses LaMDA was generating were often formulated as if it did have an internal life outside of the context of the current conversation, generating text about "my fears" and so on. If you understand that the model is not doing anything at all until you give it a prompt, which LeMoine really seems not to, you can much more easily understand that there can't be any meaning behind this sentence - it can't fear anything because there is no time for it to do so.

mpoteat 4y ago
What if the most expedient way to accurately continue the text sequence is to temporarily "embody" as an agent that would have generated the prior text? This would involve encoding emotions, performing reasoning ability on behalf of the agent, and keeping track of its memories. Do you find the above scenario plausible?

deleted 4y ago

[deleted]

zamalek 4y ago

To be clear, I agree that LaMDA is not sentient. Machine learning is not intelligent. > It doesn't do anything because it's literally "turned off" when not prompted or given any inputs. I wondered the same thing. It could be argued that it is sentient during the brief moments that it is coming up with a response. Put another way; let's assume that we are ourselves AI in an artificial universe. Would you be able to tell if the universal computer was turned off for a day? So, a model might not experience sentience in the seconds while you are composing a message, but plausibly could while formulating a response to your input.

whoisthemachine 4y ago

That's an interesting thought. How would a single NN instance behave if it was turned on continuously, constantly responding to inputs and adjusting its model to those inputs continuously? Regardless, I don't think we're going to get something that looks "intelligent", because these models lack agency... the drive or directive to do things for their own benefit, mostly because that would be a useless for us. I think we regard intelligence as another being ("instance" if you will) acting in its own self-interest, and we think it's clever when it does it in a way we wouldn't have predicted.