4 ms·

> Humans experience time And? So what? > Humans have agency. Which is what exactly? You are living in a physical universe bound by physical laws. For any oth

> Humans experience time And? So what? > Humans have agency. Which is what exactly? You are living in a physical universe bound by physical laws. For any other system we somehow accept that it will obey physical laws and there will not be a spontaneous change, so why are we holding humans to different standards? If we grow up and accept that free will does not actually exist, then all agency is is our brain trying to coordinate the cacophony of all different circuits arguing (cf Cognitive Dissonance). Once the cacophony is over, the ensemble has "made" a decision. >But I can also choose to pause and think about my response. Today ChatGPT 3.5 asked me to elaborate. This is already more than a non insignificant segment of the population is capable. ChatGPT 4.0 has been doing this for a while. What you describe as pausing and thinking is exactly letting your circuits run for longer - which again - is a decision made by said circuits who then informed your internal time keeper that "you" made said decision. > I can choose to say “I don’t know”. So does ChatGPT 4.0, and ChatGPT3.5. I have experienced it multiple times at this point. > I can choose to wait and let the question percolate in my mind as I experience time and other inputs. So do proposed models. In fact, many of the "issues" are resolved if we allow the model to issue multiple subsequent responses, effectively increasing its context, just as you are. So what's the difference?

xur17 4y ago
> What you describe as pausing and thinking is exactly letting your circuits run for longer - which again - is a decision made by said circuits who then informed your internal time keeper that "you" made said decision. Yup, and it feels like ChatGPT might be able to approximate this by giving the model a "think longer" output that feeds the output back into itself. I'm actually curious if immediately prompting the model "are you sure" or something else a few times could get you a similar effect right now.
- PartiallyTyped 4y ago
  I will try that and a couple other ideas and report back!
deleted 4y ago
[deleted]
PartiallyTyped 4y ago
Anyone care to explain why -N downvotes without any actual discussion or justification? Are dissenting opinions prohibited in this new iteration of HN?
joshuahedlund 4y ago
Fair points. Thanks for letting me refine and discard hypotheses. While I think about those, how about this? Language is not a complete representation of thinking. We use language to describe symbols, not even very precisely, and we can convert imprecise language to more precise symbols in our brain, manipulate them as symbols, and only then turn them back into language. That’s why you often cannot perfectly translate something between two languages. That’s why nine year olds, who have been trained on far less text, can learn to do math that ChatGTP never could without an API. (They don’t have to generate their output linearly - they can add the one’s column first) When Newton invented calculus he wasn’t predictively generating words by token; he performed logical manipulation of symbols in his brain first. That’s why LLMs can’t tell you where they got a specific piece of their own output from, while a human can. This matters because LLMs can’t convert it into a symbol and think about it directly and deduce new conclusions from it, while a human can. If fundamentally human thinking was just “LLM” we would have never generated the words to train ourselves on in the first place! And neither would any new idea that gradually built the library of human knowledge that eventually trained ChatGTP. The language is just the interface; it’s not the full essence of the thinking itself.
- PartiallyTyped 4y ago
  > We use language to describe symbols, not even very precisely, and we can convert imprecise language to more precise symbols in our brain, manipulate them as symbols, and only then turn them back into language. I don't think that's true for all people. I know that some people manipulate words in their heads, others images, I manipulate sounds and images. Language is just a noisy medium through which we communicate the internal state of our brain or its outputs to other people / humans and ourselves. > can learn to do math that ChatGTP never could without an API. GPT4 does just fine in some cases and extrapolates just fine in others, e.g. ask it whether there are more wheels or doors, and try to investigate the definitions of either and see how well it adds the numbers. > When Newton invented calculus he wasn’t predictively generating words by token; There are very few people in history up to Newton so I don't think it's fair to hold up what is essentially a new field up to him. > he performed logical manipulation of symbols in his brain first. We don't know "how" he did that. We don't know that his brain manipulated symbols everything he did. We simply know that Calculus can be derived from a set of axioms following logical inference. What you are expressing is largely true for many primates, and according to some, our brains are "just linearly scaled primate brains". > That’s why LLMs can’t tell you where they got a specific piece of their own output from, while a human can. I don't think that is correct. The human might provide a justification for something but that doesn't mean it is the true reason they reached a conclusion. The only way this happens is if you apply logical operators, at which point we are doing math again. It turns out that our brains have decided long before we are even aware of the decision, such decisions may be guided by external stimulation, or even by internal stimulation since our neural networks don't have well defined components and boundaries thus neighbouring neurons can affect or even trigger circuits, and our own forward predictive models back propagate information to other circuits. > If fundamentally human thinking was just “LLM” we would have never generated the words to train ourselves on in the first place! I don't think that's true. Language has evolved over thousands of years in many different ways by > 80 bn humans each of whom having 80bn neurons and trillions of synapses. Yet, we have found that models can learn to communicate with each other and derive their own languages. I highly recommend you read Eagleman's "The Brain: The Story of You". It covers nearly everything I spoke of here and is very easy to read / listen to. https://pubmed.ncbi.nlm.nih.gov/19915731/ https://pubmed.ncbi.nlm.nih.gov/19915731/

xur17 4y ago

> What you describe as pausing and thinking is exactly letting your circuits run for longer - which again - is a decision made by said circuits who then informed your internal time keeper that "you" made said decision. Yup, and it feels like ChatGPT might be able to approximate this by giving the model a "think longer" output that feeds the output back into itself. I'm actually curious if immediately prompting the model "are you sure" or something else a few times could get you a similar effect right now.

PartiallyTyped 4y ago
I will try that and a couple other ideas and report back!

deleted 4y ago

[deleted]

PartiallyTyped 4y ago

Anyone care to explain why -N downvotes without any actual discussion or justification? Are dissenting opinions prohibited in this new iteration of HN?

joshuahedlund 4y ago

Fair points. Thanks for letting me refine and discard hypotheses. While I think about those, how about this? Language is not a complete representation of thinking. We use language to describe symbols, not even very precisely, and we can convert imprecise language to more precise symbols in our brain, manipulate them as symbols, and only then turn them back into language. That’s why you often cannot perfectly translate something between two languages. That’s why nine year olds, who have been trained on far less text, can learn to do math that ChatGTP never could without an API. (They don’t have to generate their output linearly - they can add the one’s column first) When Newton invented calculus he wasn’t predictively generating words by token; he performed logical manipulation of symbols in his brain first. That’s why LLMs can’t tell you where they got a specific piece of their own output from, while a human can. This matters because LLMs can’t convert it into a symbol and think about it directly and deduce new conclusions from it, while a human can. If fundamentally human thinking was just “LLM” we would have never generated the words to train ourselves on in the first place! And neither would any new idea that gradually built the library of human knowledge that eventually trained ChatGTP. The language is just the interface; it’s not the full essence of the thinking itself.

PartiallyTyped 4y ago
> We use language to describe symbols, not even very precisely, and we can convert imprecise language to more precise symbols in our brain, manipulate them as symbols, and only then turn them back into language. I don't think that's true for all people. I know that some people manipulate words in their heads, others images, I manipulate sounds and images. Language is just a noisy medium through which we communicate the internal state of our brain or its outputs to other people / humans and ourselves. > can learn to do math that ChatGTP never could without an API. GPT4 does just fine in some cases and extrapolates just fine in others, e.g. ask it whether there are more wheels or doors, and try to investigate the definitions of either and see how well it adds the numbers. > When Newton invented calculus he wasn’t predictively generating words by token; There are very few people in history up to Newton so I don't think it's fair to hold up what is essentially a new field up to him. > he performed logical manipulation of symbols in his brain first. We don't know "how" he did that. We don't know that his brain manipulated symbols everything he did. We simply know that Calculus can be derived from a set of axioms following logical inference. What you are expressing is largely true for many primates, and according to some, our brains are "just linearly scaled primate brains". > That’s why LLMs can’t tell you where they got a specific piece of their own output from, while a human can. I don't think that is correct. The human might provide a justification for something but that doesn't mean it is the true reason they reached a conclusion. The only way this happens is if you apply logical operators, at which point we are doing math again. It turns out that our brains have decided long before we are even aware of the decision, such decisions may be guided by external stimulation, or even by internal stimulation since our neural networks don't have well defined components and boundaries thus neighbouring neurons can affect or even trigger circuits, and our own forward predictive models back propagate information to other circuits. > If fundamentally human thinking was just “LLM” we would have never generated the words to train ourselves on in the first place! I don't think that's true. Language has evolved over thousands of years in many different ways by > 80 bn humans each of whom having 80bn neurons and trillions of synapses. Yet, we have found that models can learn to communicate with each other and derive their own languages. I highly recommend you read Eagleman's "The Brain: The Story of You". It covers nearly everything I spoke of here and is very easy to read / listen to. https://pubmed.ncbi.nlm.nih.gov/19915731/ https://pubmed.ncbi.nlm.nih.gov/19915731/