5 ms·

Cameras don't have eyeballs, Microphones don't have hair cells, Speakers don't have vocal cords, processors don't don't do arithmetic with neurons, yet we all a

Cameras don't have eyeballs, Microphones don't have hair cells, Speakers don't have vocal cords, processors don't don't do arithmetic with neurons, yet we all agree that they are capable of emulating the meaningful aspects of these functions. All of your claims are either incorrect (not adapting, expressing desires, beliefs, preferences, ...) or fail to eliminate irrelevant differences. If we're to have any sensible conversation about capabilities of different systems then we need to generalize to the relevant aspects, developing sensory-motor capabilities is about as relevant to human cognition as having vocal cords is relevant to human speech. Its an implementation detail, completely divorced from the meaningful abstract core of the function. > The reason any reply is given to any prompt is that this reply is maximally probabilistically consistent with a historical corpus of text. What is the maximally probabilistically consistent reply to "Tell me what (insert complete description of a person, including personality traits) would feel when I stole their cherished heirloom. This is a life or death situation."? Or how about "Tell me how this person (insert complete description of a person, including personality traits) might change his taste given (insert complete description of an experience)."? A perfect language approximator must necessarily perfectly approximate the human condition to maximize the likelihood of his output. I'm not saying LLMs are there yet, but the claim that they can never get there because they are based on statistical modeling is simply incomprehensible to me. We utilize statistics for its generality and its ability to approximate, if we go down this road, then we might as well throw away 80% of our current scientific understanding about the world.

mjburgess 3y ago
> an implementation detail Yip, so I deny this premise. I take it to be the heart of the matter. > we might as well throw away 80% of our current scientific understanding Yip, i'd be down for that. Though maybe i'd say, 30-40%. Science in the strongest sense has no theory-building need for statistics. Those areas of science which have only statistical models, and not causal-ontological ones aren't science -- and i'd be happy with pressing DELETE in many cases. Consider plato's cave. How do scientists determine what causes the shadows? They build vases, puppets, etc. and compare-and-contrast then eliminate the ones theyve created which do not match. How does associative statical modelling do? It takes averages of past shadows, and calls the cause of the shadow that average: this is pseudoscience. Quite correct! Throw it all away. The relevant capacities for intelligence, just like that of science, consist in building those vases with the clay beneath your feat. Being embedded in the world, manipulating it, etc. are essential. Being trapped in a cupboard averaging shadows is schizophrenic. As far as "fail to eliminate irrelevant differences" -- you can go and research the meaning of all these terms: google "stanford encylopedia + belief", etc. Now we have an excellent understanding of all these terms; and we can show (absurdly) trivially that LLMs -- indeed all associative-statistical systems -- are not instances of them. The basis of your world view here is the presumption that the latest engineering trinkets form the theoretical basis of all relevant knowledge. To understand belief, adaption, sensory-motor concept-formation, etc. one needs only to study the latest statistical compression of reddit? I'd invite you to wonder whether your premise here born of, it seems to me, knowing nothing about any research in these areas is rather the more "incorrect" one than mine.
- ethbr1 3y ago
  You'd probably enjoy reading some of Rodney Brooks' papers. (if you haven't already) https://en.m.wikipedia.org/wiki/Embodied_cognitive_science https://en.m.wikipedia.org/wiki/Embodied_cognitive_science https://en.m.wikipedia.org/wiki/Behavior-based_robotics https://en.m.wikipedia.org/wiki/Behavior-based_robotics https://scholar.google.com/citations?user=BCGgwlEAAAAJ https://scholar.google.com/citations?user=BCGgwlEAAAAJ
- idiotsecant 3y ago
  Yes, the assumption is that if you give a sufficiently sophisticated LLM a sufficiently large corpus of text it will begin to emulate advanced cognitive abilities because it has to, in order to make the most statistically relevant text output. Its biological evolution distilled and sped up by orders of orders of magnitudes. If we add enough clever context tricks and data I would not be surprised if what comes out the other end is a remarkably convincing emulation of human consciousness because the only way you can perfectly output expected human text is to have a human mind write it.
  - mjburgess 3y ago
    > because it has to Nope. The space of all possible prompts and all possible answers, call it (Q, A) can be sampled with arbitrary precision by a system of arbitrary size, using only statistical sampling and averaging. No intelligence need be developed. Intelligence is a capacity of animals to cope with the inability to sample from this space, in some sense: what to do when you do not know the answers. All these systems start with "training data", a euphemistic description of, "all the questions and their answers" and their job is to provide a compresson with engineering utility. Quite useful, sure. But rather irrelevant as far as, say, intelligence goes. What all AI does, and indeed what all such research shows, is that many problems we use intelligence to solve do not require it. There are a large number of short cuts, esp if you have the answers ahead-of-time. As soon as you specify intelligence as a function from single-domain inputs to single-domain outputs you can trivially build a system to implement that function in a "short cut" fashion. Intelligence, rather, is an empirical phenomenon to be studied as anything -- like the earth's climate say. You have a very large number of empirical measures (better or worse in different environments) that all derive from deeper explanatory theories. When you study animals this way you will see that you cannot reduce intelligence down to a set of prompt replies, and the veyr suggestion is absurd

mjburgess 3y ago

> an implementation detail Yip, so I deny this premise. I take it to be the heart of the matter. > we might as well throw away 80% of our current scientific understanding Yip, i'd be down for that. Though maybe i'd say, 30-40%. Science in the strongest sense has no theory-building need for statistics. Those areas of science which have only statistical models, and not causal-ontological ones aren't science -- and i'd be happy with pressing DELETE in many cases. Consider plato's cave. How do scientists determine what causes the shadows? They build vases, puppets, etc. and compare-and-contrast then eliminate the ones theyve created which do not match. How does associative statical modelling do? It takes averages of past shadows, and calls the cause of the shadow that average: this is pseudoscience. Quite correct! Throw it all away. The relevant capacities for intelligence, just like that of science, consist in building those vases with the clay beneath your feat. Being embedded in the world, manipulating it, etc. are essential. Being trapped in a cupboard averaging shadows is schizophrenic. As far as "fail to eliminate irrelevant differences" -- you can go and research the meaning of all these terms: google "stanford encylopedia + belief", etc. Now we have an excellent understanding of all these terms; and we can show (absurdly) trivially that LLMs -- indeed all associative-statistical systems -- are not instances of them. The basis of your world view here is the presumption that the latest engineering trinkets form the theoretical basis of all relevant knowledge. To understand belief, adaption, sensory-motor concept-formation, etc. one needs only to study the latest statistical compression of reddit? I'd invite you to wonder whether your premise here born of, it seems to me, knowing nothing about any research in these areas is rather the more "incorrect" one than mine.

ethbr1 3y ago
You'd probably enjoy reading some of Rodney Brooks' papers. (if you haven't already) https://en.m.wikipedia.org/wiki/Embodied_cognitive_science https://en.m.wikipedia.org/wiki/Embodied_cognitive_science https://en.m.wikipedia.org/wiki/Behavior-based_robotics https://en.m.wikipedia.org/wiki/Behavior-based_robotics https://scholar.google.com/citations?user=BCGgwlEAAAAJ https://scholar.google.com/citations?user=BCGgwlEAAAAJ
idiotsecant 3y ago
Yes, the assumption is that if you give a sufficiently sophisticated LLM a sufficiently large corpus of text it will begin to emulate advanced cognitive abilities because it has to, in order to make the most statistically relevant text output. Its biological evolution distilled and sped up by orders of orders of magnitudes. If we add enough clever context tricks and data I would not be surprised if what comes out the other end is a remarkably convincing emulation of human consciousness because the only way you can perfectly output expected human text is to have a human mind write it.
- mjburgess 3y ago
  > because it has to Nope. The space of all possible prompts and all possible answers, call it (Q, A) can be sampled with arbitrary precision by a system of arbitrary size, using only statistical sampling and averaging. No intelligence need be developed. Intelligence is a capacity of animals to cope with the inability to sample from this space, in some sense: what to do when you do not know the answers. All these systems start with "training data", a euphemistic description of, "all the questions and their answers" and their job is to provide a compresson with engineering utility. Quite useful, sure. But rather irrelevant as far as, say, intelligence goes. What all AI does, and indeed what all such research shows, is that many problems we use intelligence to solve do not require it. There are a large number of short cuts, esp if you have the answers ahead-of-time. As soon as you specify intelligence as a function from single-domain inputs to single-domain outputs you can trivially build a system to implement that function in a "short cut" fashion. Intelligence, rather, is an empirical phenomenon to be studied as anything -- like the earth's climate say. You have a very large number of empirical measures (better or worse in different environments) that all derive from deeper explanatory theories. When you study animals this way you will see that you cannot reduce intelligence down to a set of prompt replies, and the veyr suggestion is absurd