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The generality of its intelligence within text is still spurious at best. Context-switching is practically nonexistent, and whenever it appears to do so, it's o

by z3c0 4y ago

The generality of its intelligence within text is still spurious at best. Context-switching is practically nonexistent, and whenever it appears to do so, it's only because your prompt pushed it towards statistically appearing to switch context. And frankly, computers have been exceeding our benchmarks for some time - Turing mistakenly thought that appearing human was a sufficient marker of intelligence, but chatbots have been passing the Turing Test for about a decade now. Saying it would surpass our benchmarks only illustrates how little we understand intelligence, not that LLMs are anything close to intelligence. Edit: that final point is why I propose an in-to-out approach over an out-to-in approach (which is what LLMs are). Let the intelligence make itself, instead of us trying to make intelligence.

dr_dshiv 4y ago
“ not that LLMs are anything close to intelligence.” Well, that’s ridiculous out of hand. What makes you say something like that? If they feel intelligent, pass intelligence benchmarks and are designed to be intelligent— why not ascribe to them intelligence? Is it because they aren’t wet?
- z3c0 4y ago
  I'll turn that right back around and ask if you considered any of the chatbots of the last decade to be "intelligent". They "felt" intelligent enough to pass Turing Tests. Nonetheless, nobody was saying it was ridiculous to conclude that they were only superficial representations of intelligent behavior. I don't call any of the ML models that I create "intelligent" - I call them probabilistic. Feigning intelligence with probabilistic methods is nothing new (eg ELIZA). If you can scientifically demonstrate that feigning intelligence is equivocal to de facto intelligence, with something more than "feelings", I'll be happy to read your write-up.
  - dr_dshiv 4y ago
    I don’t understand. Who is “feigning?” Why is that the basis? I’m saying, let’s judge intelligence based on benchmarks. No problem with probabilistic methods. Human intelligence is also probabilistic (ie, predictive coding). Maybe the biggest question: do you think these will be exponentially more powerful in 5 years, learning from failures, self-improving and generally economically “a very big deal” due to human replacement? Because that’s what AGI is supposed to do, rapidly—and that’s why it is worth acknowledging the possibility. From my perspective, if it barks like a dog, walks like a dog… fulfills all the “dog” benchmarks… even if it came about through unusual means, you’d better consider the possibility. All these experts Pooh-poohing the possibility of AGI because “I’ve worked with LLMs” is really unconvincing. The debate isn’t balanced because most professionals wouldn’t want to claim AGI for professional reasons.
    - z3c0 4y ago
      > most professionals wouldn’t want to claim AGI for professional reasons Most professionals in my field would probably consider this a career-defining moment that would land one in the halls of the greats. I don't really follow this thinking. And I get the "looks like a duck, quacks like a duck" argument, but as I've repeatedly stated, that's not good enough. Frankly, I don't buy the "predictive coding" theory - at least, not to the extent that I believe the brain is only probabilistically modeling the phenomena it experiences. This ignores the glaring fact that our senses in-of-themselves exhibit some astounding capabilities right out-of-the-box, and predictive coding has no ground to stand on without the cross-referential analysis of the senses. Human babies are developing their intelligence long before any heavy stimuli. Hearing is the most active before birth, eyesight - our most rich sense - doesn't kick in until after birth. Nonetheless, babies come out of the womb with intelligent reflexes, such as rooting. That bundle of rudimentary intelligence is the basis upon which predictive coding begins - a functional default. An LLM is a set of statically-coded weights, incapable of context switching or object permanence. A child may take a while to understand the difference between "you" and "me", but they still can intuit "thing A" and "thing B". LLMs cannot - at least not without being paired with another model of some sort. And that model will be what tips the scale, not scaling up the features of what's merely a beautiful implementation of the logit function.
      - dr_dshiv 4y ago
        Ok, so you don’t buy predictive coding for humans — that helps explain your position. You also see that some basic production rules (for basic reflexes) could mean the difference between AGI chatGPT and chatGPT today. Because of how it would help chatGPT orient towards the world, I suppose? I buy that critique because I do view a cybernetic loop as essential to intelligence — there must be some goalstate (however vague) to be achieved successfully. But, have you seen many claim that chatGPT is AGI? My issue is not with your argument (it’s good!) but the lack of acknowledgment for the reasonability of the position that chatGPT can be called AGI. Most people DO buy predictive coding. And many would view the missing “reflexes” in chatGPT just a matter of good UI/UX (for instance). Yet, I haven’t seen anyone stand up and systematically argue that chatGPT is AGI. (But I’m not on Twitter, so…)

by z3c0 4y ago

dr_dshiv 4y ago

“ not that LLMs are anything close to intelligence.” Well, that’s ridiculous out of hand. What makes you say something like that? If they feel intelligent, pass intelligence benchmarks and are designed to be intelligent— why not ascribe to them intelligence? Is it because they aren’t wet?

z3c0 4y ago
I'll turn that right back around and ask if you considered any of the chatbots of the last decade to be "intelligent". They "felt" intelligent enough to pass Turing Tests. Nonetheless, nobody was saying it was ridiculous to conclude that they were only superficial representations of intelligent behavior. I don't call any of the ML models that I create "intelligent" - I call them probabilistic. Feigning intelligence with probabilistic methods is nothing new (eg ELIZA). If you can scientifically demonstrate that feigning intelligence is equivocal to de facto intelligence, with something more than "feelings", I'll be happy to read your write-up.
- dr_dshiv 4y ago
  I don’t understand. Who is “feigning?” Why is that the basis? I’m saying, let’s judge intelligence based on benchmarks. No problem with probabilistic methods. Human intelligence is also probabilistic (ie, predictive coding). Maybe the biggest question: do you think these will be exponentially more powerful in 5 years, learning from failures, self-improving and generally economically “a very big deal” due to human replacement? Because that’s what AGI is supposed to do, rapidly—and that’s why it is worth acknowledging the possibility. From my perspective, if it barks like a dog, walks like a dog… fulfills all the “dog” benchmarks… even if it came about through unusual means, you’d better consider the possibility. All these experts Pooh-poohing the possibility of AGI because “I’ve worked with LLMs” is really unconvincing. The debate isn’t balanced because most professionals wouldn’t want to claim AGI for professional reasons.
  - z3c0 4y ago
    > most professionals wouldn’t want to claim AGI for professional reasons Most professionals in my field would probably consider this a career-defining moment that would land one in the halls of the greats. I don't really follow this thinking. And I get the "looks like a duck, quacks like a duck" argument, but as I've repeatedly stated, that's not good enough. Frankly, I don't buy the "predictive coding" theory - at least, not to the extent that I believe the brain is only probabilistically modeling the phenomena it experiences. This ignores the glaring fact that our senses in-of-themselves exhibit some astounding capabilities right out-of-the-box, and predictive coding has no ground to stand on without the cross-referential analysis of the senses. Human babies are developing their intelligence long before any heavy stimuli. Hearing is the most active before birth, eyesight - our most rich sense - doesn't kick in until after birth. Nonetheless, babies come out of the womb with intelligent reflexes, such as rooting. That bundle of rudimentary intelligence is the basis upon which predictive coding begins - a functional default. An LLM is a set of statically-coded weights, incapable of context switching or object permanence. A child may take a while to understand the difference between "you" and "me", but they still can intuit "thing A" and "thing B". LLMs cannot - at least not without being paired with another model of some sort. And that model will be what tips the scale, not scaling up the features of what's merely a beautiful implementation of the logit function.
    - dr_dshiv 4y ago
      Ok, so you don’t buy predictive coding for humans — that helps explain your position. You also see that some basic production rules (for basic reflexes) could mean the difference between AGI chatGPT and chatGPT today. Because of how it would help chatGPT orient towards the world, I suppose? I buy that critique because I do view a cybernetic loop as essential to intelligence — there must be some goalstate (however vague) to be achieved successfully. But, have you seen many claim that chatGPT is AGI? My issue is not with your argument (it’s good!) but the lack of acknowledgment for the reasonability of the position that chatGPT can be called AGI. Most people DO buy predictive coding. And many would view the missing “reflexes” in chatGPT just a matter of good UI/UX (for instance). Yet, I haven’t seen anyone stand up and systematically argue that chatGPT is AGI. (But I’m not on Twitter, so…)