7 ms·

I've said it before, and I'll say it again: there is absolutely zero evidence indicating that we are any closer to AGI than what James Watt was to realizing nuc

I've said it before, and I'll say it again: there is absolutely zero evidence indicating that we are any closer to AGI than what James Watt was to realizing nuclear fusion. There are extremely well researched worms with only a few hundred neurons which we cannot yet simulate with anything resembling accuracy. How can that statement be true, if LLMs are close to delivering superhuman intelligence?

Jabrov 2y ago
Why do we have to simulate real neurons to achieve intelligence? We don't simulate a bird's bones and muscles to achieve flight. LLMs are a huge step forward. Sure, they might not be the thing to ultimately deliver superhuman intelligence. But it's unfair to say that we're not any closer at all.
- splwjs 2y ago
  We've had markov chain generators for a while, having enough computing power to grant them the power to regurgitate wikipedia reddit and stackoverflow content is not "a huge step towards agi"
  - Jabrov 2y ago
    I disagree. It's true that Markov chain generators have existed for years. But historically their output was usually just this cute thing that gave you a chuckle; they were seldomly as useful in a general sense like LLMs currently are. I think that the increase you mention in compute power and data is itself a huge step forward. But also transformers have been super important. Transformer-based LLMs are orders of magnitude more powerful, smarter, trained on more data, etc. than previous types of models because of how they can scale. The attention mechanism also allows them to pay attention to way more of the input, not just the few preceding tokens.
    - dijit 2y ago
      I think you missed OPs point. If you want something useful, then we're getting closer. AGI is something specific, as a requisite, it must understand what is being asked, and what we have now is a puppet show that makes us humans think that the machine is thinking, similar to Markov chains. There is absolutely some utility in this- but it's about as close to AGI as the horse-cart is to commercial aircraft. Some AI hype people are really uncomfortable with that fact, I'm sorry, but that reality will hit you sooner rather than later. It does not mean what we have is perfect, cannot be improved in the short term, or that it has no practical applications already. EDIT: downvoting me wont change this, go study the field of academic AI properly please
      - drdeca 2y ago
        AGI is something fairly specific, yes, but depending on what you mean by “understand”, I don’t think it necessarily needs to “understand”? To behave (for all practical purposes) as if it “understands” is good enough. For some senses of “understand” this may be the same thing as for it to “understand”, so for those senses of “understand”, yes it needs to “understand”. It seems clear to me that, if we could programmatically sample from a satisfactory conditional probability distribution, that this would be sufficient for it to, for all practical purposes, behave as if it “understands”, and moreover for it to count as AGI. (For it to do so at a fast enough rate would make it both AGI and practically relevant.) So, the question as I see it, is whether the developments with ANNs trained as they have been, is progress towards producing something that can sample from a conditional probability distribution in a way that would be satisfactory for AGI. I don’t see much reason to conclude that they are not? I suppose your claim is that the conditional probability distributions are not getting closer to being such that they are practically as if they exhibit understanding? I guess this might be true… It does seem like some things would be better served by having variables with a fixed identity but a changing value, rather than just producing more variables? I guess that’s kind of like the “pure functional programming vs not-that” distinction, and of course as pure functional programming shows, one can still compute whatever one wants while only using immutable values, but one still usually uses something that is as if a value is changing. And of course, for transformer models, tasks that take more than O(N^2) or whatever (… maybe O(N^3) because on N tokens, each is processed in ways depending on each pair of the results of processing previous ones?) can’t be done in producing a single output token, so that’s a limitation there.. I suppose that the thing that is supposed to make transformers faster to train, by making it so that the predictions for each of the tokens in a sequence can be done in parallel, kinda only makes sense if you have a ground truth sequence of tokens… though there is the RLHF (and similar) where the fine-tuning is done based on estimation of a score on the final output… which I suppose possibly neither is great at getting behavior sufficiently similar to reasoning? (Note: when I say “satisfactory probability distribution” I don’t mean to imply that we have a nice specification of a conditional probability distribution which we merely need to produce a method that can sample from it. But there should exist (in the abstract (non-constructive) mathematical sense) probability distributions which would be satisfactory.)
- blastro 2y ago
  Not trying to be pedantic, but: > We don't simulate a bird's bones and muscles Isn't really true, is it? We simulate the bones with an airframe, and we simulate the muscles with a prop/jet. The wings are similarly simulated.
  - exe34 2y ago
    yes, the wings flap. the airframes are made of bone.
  - suriya-ganesh 2y ago
    The type of propulsion is not, though. Nothing in nature has 360° rotatory motion. Airplane is not an exact simulation of a bird. Only an approximation
    - dboreham 2y ago
      https://phys.org/news/2018-04-bacterial-propeller.html https://phys.org/news/2018-04-bacterial-propeller.html
  - r2_pilot 2y ago
    A more suitable word would be that we "emulate" the bones, etc., rather than simulate, which to me implies that you are testing against a replica of a thing. But I don't really think emulate works here because they're not especially analogous; these are fundamentally different approaches to flight so any form following the function would be incidental at best.
    - blastro 2y ago
      That's fair.
  - Jabrov 2y ago
    I was thinking OP meant more simulate as in https://en.wikipedia.org/wiki/OpenWorm https://en.wikipedia.org/wiki/OpenWorm ie. a complete bottom-up simulation at the cellular level By your definition of simulate, I think artificial neural networks are absolutely on their way to simulate intelligence
- a_wild_dandan 2y ago
  We don't. That person has no idea what they're talking about. Virtually no AI researcher would require "neuron simulations" for intelligence, nor agree with their AGI claims. (See: recent machine learning expert surveys, for example.) Use HN comments for comic relief. When you see one worth taking seriously, you'll know it. Otherwise we're just giving oxygen to contrarians.
  - semi-extrinsic 2y ago
    Happy to be providing some comic relief! Just to clarify, I'm definitely not saying "neuron simulation" is required in any way. I'm just asking, how can we be very close to "solving" a significant part of the most complex brains, yet miles away from solving the simplest brains? You should be able to answer that question (or a steelmanned version of it), not just ridicule strawmen.
    - kwhitefoot 2y ago
      Ai in practice doesn't have anything to do with simulation, nor with 'solving a significant part of the most complex brains'. The immediate threat is that humans will use the leverage that LLMs give to replace and to influence other humans, in other words to gain power over us. Whether this is AGI or not is beside the point.
      - semi-extrinsic 2y ago
        Ah, but the replacing humans issue is a completely different "threat", and one we've overcome many many times since the Luddites and even before. Every time we make something more efficient we face this exact problem, and we eventually solve it. As for the influencing part, what specific actions to gain power over us can be achieved now with LLMs, that could not be achieved before using a few tens of thousands of paid humans?
        kwhitefoot 2y ago
        > what specific actions to gain power over us can be achieved now with LLMs, that could not be achieved before using a few tens of thousands of paid humans? It's being able to do it without having to employ the tens of thousands of humans that makes it different. With the LLM you are able to react much faster and pay fewer people more money.
- gambiting 2y ago
  >>Why do we have to simulate real neurons to achieve intelligence? We don't simulate a bird's bones and muscles to achieve flight. Is this.....a reference to Greg Egan's Permutation City? Because that was the exact argument some characters used against real AGI(which let's assume they had, it's a Sci-Fi book). Basically it went along the lines of "even though we can simulate digestion at molecular level, nothing is actually digested. Why should simulating neuron activity create actual thoughts and intelligence?"
andyjohnson0 2y ago
I disagree. Machine AGI does not have to be based on the same foundations as biological intelligence.
- semi-extrinsic 2y ago
  Sure, that's OK. But taking a step back, how can we be close to solving one system, yet far away from solving another system that is a milion times simpler in every metric?
  - gizmo 2y ago
    We cannot perfectly replicate how real life worms behave because we don't have the necessary training data. We can create AI worms based on fake behavioral data, of course, but that will not result in prefect AI replicas of real world worms. We do have untold terabytes of the written word and that's sufficient to bootstrap intelligence with. It turns out that going to space, splitting the atom, and creating general superintelligence is easier than understanding insect behavior or replicating exactly how a bumblebee flies. Our intuitions about the relative difficulty of unsolved scientific questions are pretty much useless. Some seemingly easy things are in fact impossibly difficult and the inverse is true as well.
- Jabrov 2y ago
  Very relevant quote from the article: > In my view comparing Ai’s cognitive, creative or intellectual powers to those of the human brain is not especially helpful. Think of the car. Humans can’t run as fast as horses. But we can build machines that far outpace them. We do not achieve this by imitation. We don’t engineer mechanical legs and hooves of the kind that took evolution 34 million years of tinkering and modification from eohippus to the present day. We go a completely different way and we come up with something that doesn’t at all exist in nature: the wheel. And instead of a mechanical heart and mechanical muscles Karl Benz offers us the internal combustion engine and crankshaft. Ditto with flying, and travelling across or under the waves. The commonly held idea that the best engineering mimics nature is largely misguided. Yes, we look sometimes look to the natural world for inspiration but in the big things, structurally, we go our own way. And as a result we can fly higher and faster than birds, move over land quicker than a cheetah, swim over and under the water faster and further than a salmon or a whale and so on.
dboreham 2y ago
Very long time AI skeptic (since 1980) but I believe you're wrong. It turns out we don't need to figure out exactly how a biological brain works. It's just nature's solution for implementing matrix operations (greatly simplified take, obviously). Having hit on approaches that produce similar results to biological systems, we now don't need to focus so much on emulating them. I do suspect however that there's something to the biological experience of "being the life support system" for the brain, that significantly affects the training process. It might be challenging to simulate that.
jodrellblank 2y ago
The human brain needs 80 billion neurons to hold 5-7 numbers in memory and add and multiply them. How can a MOS integrated circuit from 1965 with only 250 transistors possibly be turned into a faster and more capable arithmetic calculator when it is so woefully underpowered by comparison? > "there is absolutely zero evidence indicating that we are any closer to AGI than what James Watt was to realizing nuclear fusion" James Watt lived before Rutherford split the atom, he didn't know they could be split or fused, he was not trying for nuclear fusion. We do know that information exists and can be processed. Still, James Watt was closer to large scale controlled release of energy than humans before the control of fire. We know that human level intelligence is possible, in a way that Watt didn't know fusion was possible. We have looked for other mechanisms hiding in the brain - Penrose and Hameroff's ideas of quantum tubules for one - and rejected them. We've pretty closely bounded the amount of energy a brain uses, what it can sense, how it can input and output, what it's made of, and through what mechanisms of electric pulses and neurotransmitters it seems likely to operate. We've identified brain regions which cause predictable effects when damaged (on language, memory, executive decision making). We've dissected it, photographed it, x-rayed it, imaged it in layers, imaged it while active. We've represented facts as data, written code which uses genetic algorithms to solve problems, written code which writes code to solve problems, written code which crunches data in large quantities to pull out higher dimensional patterns, code which appears to identify concepts from that, code which does specialised tasks such as face recognition, language recognition, language generation, person recognition. It's pretty indefensible to say "absolutely zero evidence indicating we are any closer than people of 10k BC, people of 0 AD, people of 1800, or people of 1900". You're basically doing the God of the gaps argument, as we map the brain physically in more and more detail, and encroach from the data an information processing side, the gap between them where intelligence could be hiding appears to be shrinking. But with no evidence or support, you suggest something else is hiding there which makes the gap a Jaunt so large that encroachment from either side is immeasurably tiny. And you hide it behind the "G" for general. Twenty years ago you could talk to a nonsense chatbot. Ten-ish years ago you could talk to a somewhat coherent GPT. Five years ago you could talk to a surprisingly coherent ChatGPT. Today people collaborate with Claude, ChatGPT, Gemini, ask them to explain concepts, provide examples and get them to follow on, offload work to them, learn from them, have them proof read documents, have them rewrite in different styles. I think Hofstadter said he wasn't interested in computers because they don't have concepts. Here's an article in the ACM last year saying similar: ""Neural networks can look at a picture and tell whether it contains a dog, a cat, or a car, but they do not have a rich understanding of any of those categories. Take the concept of a bridge. Humans can extend the notion of a bridge to abstract levels. We can talk about a bridge between people or bridging the gender gap. We can instantly understand what these expressions mean because we have a rich mental model of what a bridge can be."" - https://cacm.acm.org/news/artificial-intelligence-still-cant-form-concepts/ https://cacm.acm.org/news/artificial-intelligence-still-cant... Asking ChatGPT: Me: "Hi, a bridge is a big metal thing over a road or river; what does it mean to "bridge the gender gap"? Can you explain briefly, please?" ChatGPT: "To "bridge the gender gap" means to reduce the differences or inequalities between men and women, particularly in areas like pay, opportunities, education, or representation. It's about creating a more equal and fair situation where both genders have the same access and opportunities. Just like a physical bridge connects two places, "bridging the gender gap" connects the unequal sides, making them more balanced." Me: "Interesting; can you suggest other non-physical things which could be "bridged" please?" ChatGPT: "Certainly! Here are some examples of non-physical things that can be "bridged": Cultural gap – Reducing misunderstandings or differences between people from different cultures. Communication gap – Improving understanding between people who are having difficulty communicating. Generation gap – Narrowing the differences in attitudes and perspectives between older and younger generations. Wealth gap – Reducing economic inequality between rich and poor. Knowledge gap – Closing the gap in access to information or education between different groups. Digital divide – Bridging the gap between those who have access to technology and the internet and those who don’t. Trust gap – Rebuilding or enhancing trust between individuals or groups, like companies and their customers. In each case, "bridging" is about connecting or bringing closer two sides that are separated by some form of difference or inequality. Seems to have some grasp of the general concept of "bridging" more than Eliza or Eugene Goostman, doesn't it? "I have tried some letter-string problems in GPT-3, and in some cases it could solve them. It learned, for example, the concept of successorship. Not perfect, not robust, but I found it still surprising that it can do this. Therefore, I don't agree that these systems are only 'stochastic parrots', as some scientists have called them. I have seen evidence of GPT building simple internal models of situations."" says the expert in the article. I'm not saying this is an AGI or will lead directly to AGI, but stubbornly refusing to accept this obvious progress as any progress towards machine intelligence at all, calling it "absolutely zero" evidence of progress seems wilfully blinkered. Do you genuinely put us absolutely no closer, not a single step closer, to AGI than the Mechanical Turk or the people of 50k BC?
marjancek 2y ago
Viruses are not super intelligent, yet one could kill us all. The biggest danger I see is a widespread AI with a set of badly defined goals, not a particularly smart and evil one.

Jabrov 2y ago

Why do we have to simulate real neurons to achieve intelligence? We don't simulate a bird's bones and muscles to achieve flight. LLMs are a huge step forward. Sure, they might not be the thing to ultimately deliver superhuman intelligence. But it's unfair to say that we're not any closer at all.

splwjs 2y ago
We've had markov chain generators for a while, having enough computing power to grant them the power to regurgitate wikipedia reddit and stackoverflow content is not "a huge step towards agi"
- Jabrov 2y ago
  I disagree. It's true that Markov chain generators have existed for years. But historically their output was usually just this cute thing that gave you a chuckle; they were seldomly as useful in a general sense like LLMs currently are. I think that the increase you mention in compute power and data is itself a huge step forward. But also transformers have been super important. Transformer-based LLMs are orders of magnitude more powerful, smarter, trained on more data, etc. than previous types of models because of how they can scale. The attention mechanism also allows them to pay attention to way more of the input, not just the few preceding tokens.
  - dijit 2y ago
    I think you missed OPs point. If you want something useful, then we're getting closer. AGI is something specific, as a requisite, it must understand what is being asked, and what we have now is a puppet show that makes us humans think that the machine is thinking, similar to Markov chains. There is absolutely some utility in this- but it's about as close to AGI as the horse-cart is to commercial aircraft. Some AI hype people are really uncomfortable with that fact, I'm sorry, but that reality will hit you sooner rather than later. It does not mean what we have is perfect, cannot be improved in the short term, or that it has no practical applications already. EDIT: downvoting me wont change this, go study the field of academic AI properly please
    - drdeca 2y ago
      AGI is something fairly specific, yes, but depending on what you mean by “understand”, I don’t think it necessarily needs to “understand”? To behave (for all practical purposes) as if it “understands” is good enough. For some senses of “understand” this may be the same thing as for it to “understand”, so for those senses of “understand”, yes it needs to “understand”. It seems clear to me that, if we could programmatically sample from a satisfactory conditional probability distribution, that this would be sufficient for it to, for all practical purposes, behave as if it “understands”, and moreover for it to count as AGI. (For it to do so at a fast enough rate would make it both AGI and practically relevant.) So, the question as I see it, is whether the developments with ANNs trained as they have been, is progress towards producing something that can sample from a conditional probability distribution in a way that would be satisfactory for AGI. I don’t see much reason to conclude that they are not? I suppose your claim is that the conditional probability distributions are not getting closer to being such that they are practically as if they exhibit understanding? I guess this might be true… It does seem like some things would be better served by having variables with a fixed identity but a changing value, rather than just producing more variables? I guess that’s kind of like the “pure functional programming vs not-that” distinction, and of course as pure functional programming shows, one can still compute whatever one wants while only using immutable values, but one still usually uses something that is as if a value is changing. And of course, for transformer models, tasks that take more than O(N^2) or whatever (… maybe O(N^3) because on N tokens, each is processed in ways depending on each pair of the results of processing previous ones?) can’t be done in producing a single output token, so that’s a limitation there.. I suppose that the thing that is supposed to make transformers faster to train, by making it so that the predictions for each of the tokens in a sequence can be done in parallel, kinda only makes sense if you have a ground truth sequence of tokens… though there is the RLHF (and similar) where the fine-tuning is done based on estimation of a score on the final output… which I suppose possibly neither is great at getting behavior sufficiently similar to reasoning? (Note: when I say “satisfactory probability distribution” I don’t mean to imply that we have a nice specification of a conditional probability distribution which we merely need to produce a method that can sample from it. But there should exist (in the abstract (non-constructive) mathematical sense) probability distributions which would be satisfactory.)
blastro 2y ago
Not trying to be pedantic, but: > We don't simulate a bird's bones and muscles Isn't really true, is it? We simulate the bones with an airframe, and we simulate the muscles with a prop/jet. The wings are similarly simulated.
- exe34 2y ago
  yes, the wings flap. the airframes are made of bone.
- suriya-ganesh 2y ago
  The type of propulsion is not, though. Nothing in nature has 360° rotatory motion. Airplane is not an exact simulation of a bird. Only an approximation
  - dboreham 2y ago
    https://phys.org/news/2018-04-bacterial-propeller.html https://phys.org/news/2018-04-bacterial-propeller.html
- r2_pilot 2y ago
  A more suitable word would be that we "emulate" the bones, etc., rather than simulate, which to me implies that you are testing against a replica of a thing. But I don't really think emulate works here because they're not especially analogous; these are fundamentally different approaches to flight so any form following the function would be incidental at best.
  - blastro 2y ago
    That's fair.
- Jabrov 2y ago
  I was thinking OP meant more simulate as in https://en.wikipedia.org/wiki/OpenWorm https://en.wikipedia.org/wiki/OpenWorm ie. a complete bottom-up simulation at the cellular level By your definition of simulate, I think artificial neural networks are absolutely on their way to simulate intelligence
a_wild_dandan 2y ago
We don't. That person has no idea what they're talking about. Virtually no AI researcher would require "neuron simulations" for intelligence, nor agree with their AGI claims. (See: recent machine learning expert surveys, for example.) Use HN comments for comic relief. When you see one worth taking seriously, you'll know it. Otherwise we're just giving oxygen to contrarians.
- semi-extrinsic 2y ago
  Happy to be providing some comic relief! Just to clarify, I'm definitely not saying "neuron simulation" is required in any way. I'm just asking, how can we be very close to "solving" a significant part of the most complex brains, yet miles away from solving the simplest brains? You should be able to answer that question (or a steelmanned version of it), not just ridicule strawmen.
  - kwhitefoot 2y ago
    Ai in practice doesn't have anything to do with simulation, nor with 'solving a significant part of the most complex brains'. The immediate threat is that humans will use the leverage that LLMs give to replace and to influence other humans, in other words to gain power over us. Whether this is AGI or not is beside the point.
    - semi-extrinsic 2y ago
      Ah, but the replacing humans issue is a completely different "threat", and one we've overcome many many times since the Luddites and even before. Every time we make something more efficient we face this exact problem, and we eventually solve it. As for the influencing part, what specific actions to gain power over us can be achieved now with LLMs, that could not be achieved before using a few tens of thousands of paid humans?
      - kwhitefoot 2y ago
        > what specific actions to gain power over us can be achieved now with LLMs, that could not be achieved before using a few tens of thousands of paid humans? It's being able to do it without having to employ the tens of thousands of humans that makes it different. With the LLM you are able to react much faster and pay fewer people more money.
gambiting 2y ago
>>Why do we have to simulate real neurons to achieve intelligence? We don't simulate a bird's bones and muscles to achieve flight. Is this.....a reference to Greg Egan's Permutation City? Because that was the exact argument some characters used against real AGI(which let's assume they had, it's a Sci-Fi book). Basically it went along the lines of "even though we can simulate digestion at molecular level, nothing is actually digested. Why should simulating neuron activity create actual thoughts and intelligence?"

andyjohnson0 2y ago

I disagree. Machine AGI does not have to be based on the same foundations as biological intelligence.

semi-extrinsic 2y ago
Sure, that's OK. But taking a step back, how can we be close to solving one system, yet far away from solving another system that is a milion times simpler in every metric?
- gizmo 2y ago
  We cannot perfectly replicate how real life worms behave because we don't have the necessary training data. We can create AI worms based on fake behavioral data, of course, but that will not result in prefect AI replicas of real world worms. We do have untold terabytes of the written word and that's sufficient to bootstrap intelligence with. It turns out that going to space, splitting the atom, and creating general superintelligence is easier than understanding insect behavior or replicating exactly how a bumblebee flies. Our intuitions about the relative difficulty of unsolved scientific questions are pretty much useless. Some seemingly easy things are in fact impossibly difficult and the inverse is true as well.
Jabrov 2y ago
Very relevant quote from the article: > In my view comparing Ai’s cognitive, creative or intellectual powers to those of the human brain is not especially helpful. Think of the car. Humans can’t run as fast as horses. But we can build machines that far outpace them. We do not achieve this by imitation. We don’t engineer mechanical legs and hooves of the kind that took evolution 34 million years of tinkering and modification from eohippus to the present day. We go a completely different way and we come up with something that doesn’t at all exist in nature: the wheel. And instead of a mechanical heart and mechanical muscles Karl Benz offers us the internal combustion engine and crankshaft. Ditto with flying, and travelling across or under the waves. The commonly held idea that the best engineering mimics nature is largely misguided. Yes, we look sometimes look to the natural world for inspiration but in the big things, structurally, we go our own way. And as a result we can fly higher and faster than birds, move over land quicker than a cheetah, swim over and under the water faster and further than a salmon or a whale and so on.

dboreham 2y ago

Very long time AI skeptic (since 1980) but I believe you're wrong. It turns out we don't need to figure out exactly how a biological brain works. It's just nature's solution for implementing matrix operations (greatly simplified take, obviously). Having hit on approaches that produce similar results to biological systems, we now don't need to focus so much on emulating them. I do suspect however that there's something to the biological experience of "being the life support system" for the brain, that significantly affects the training process. It might be challenging to simulate that.

jodrellblank 2y ago

The human brain needs 80 billion neurons to hold 5-7 numbers in memory and add and multiply them. How can a MOS integrated circuit from 1965 with only 250 transistors possibly be turned into a faster and more capable arithmetic calculator when it is so woefully underpowered by comparison? > "there is absolutely zero evidence indicating that we are any closer to AGI than what James Watt was to realizing nuclear fusion" James Watt lived before Rutherford split the atom, he didn't know they could be split or fused, he was not trying for nuclear fusion. We do know that information exists and can be processed. Still, James Watt was closer to large scale controlled release of energy than humans before the control of fire. We know that human level intelligence is possible, in a way that Watt didn't know fusion was possible. We have looked for other mechanisms hiding in the brain - Penrose and Hameroff's ideas of quantum tubules for one - and rejected them. We've pretty closely bounded the amount of energy a brain uses, what it can sense, how it can input and output, what it's made of, and through what mechanisms of electric pulses and neurotransmitters it seems likely to operate. We've identified brain regions which cause predictable effects when damaged (on language, memory, executive decision making). We've dissected it, photographed it, x-rayed it, imaged it in layers, imaged it while active. We've represented facts as data, written code which uses genetic algorithms to solve problems, written code which writes code to solve problems, written code which crunches data in large quantities to pull out higher dimensional patterns, code which appears to identify concepts from that, code which does specialised tasks such as face recognition, language recognition, language generation, person recognition. It's pretty indefensible to say "absolutely zero evidence indicating we are any closer than people of 10k BC, people of 0 AD, people of 1800, or people of 1900". You're basically doing the God of the gaps argument, as we map the brain physically in more and more detail, and encroach from the data an information processing side, the gap between them where intelligence could be hiding appears to be shrinking. But with no evidence or support, you suggest something else is hiding there which makes the gap a Jaunt so large that encroachment from either side is immeasurably tiny. And you hide it behind the "G" for general. Twenty years ago you could talk to a nonsense chatbot. Ten-ish years ago you could talk to a somewhat coherent GPT. Five years ago you could talk to a surprisingly coherent ChatGPT. Today people collaborate with Claude, ChatGPT, Gemini, ask them to explain concepts, provide examples and get them to follow on, offload work to them, learn from them, have them proof read documents, have them rewrite in different styles. I think Hofstadter said he wasn't interested in computers because they don't have concepts. Here's an article in the ACM last year saying similar: ""Neural networks can look at a picture and tell whether it contains a dog, a cat, or a car, but they do not have a rich understanding of any of those categories. Take the concept of a bridge. Humans can extend the notion of a bridge to abstract levels. We can talk about a bridge between people or bridging the gender gap. We can instantly understand what these expressions mean because we have a rich mental model of what a bridge can be."" - https://cacm.acm.org/news/artificial-intelligence-still-cant-form-concepts/ https://cacm.acm.org/news/artificial-intelligence-still-cant... Asking ChatGPT: Me: "Hi, a bridge is a big metal thing over a road or river; what does it mean to "bridge the gender gap"? Can you explain briefly, please?" ChatGPT: "To "bridge the gender gap" means to reduce the differences or inequalities between men and women, particularly in areas like pay, opportunities, education, or representation. It's about creating a more equal and fair situation where both genders have the same access and opportunities. Just like a physical bridge connects two places, "bridging the gender gap" connects the unequal sides, making them more balanced." Me: "Interesting; can you suggest other non-physical things which could be "bridged" please?" ChatGPT: "Certainly! Here are some examples of non-physical things that can be "bridged": Cultural gap – Reducing misunderstandings or differences between people from different cultures. Communication gap – Improving understanding between people who are having difficulty communicating. Generation gap – Narrowing the differences in attitudes and perspectives between older and younger generations. Wealth gap – Reducing economic inequality between rich and poor. Knowledge gap – Closing the gap in access to information or education between different groups. Digital divide – Bridging the gap between those who have access to technology and the internet and those who don’t. Trust gap – Rebuilding or enhancing trust between individuals or groups, like companies and their customers. In each case, "bridging" is about connecting or bringing closer two sides that are separated by some form of difference or inequality. Seems to have some grasp of the general concept of "bridging" more than Eliza or Eugene Goostman, doesn't it? "I have tried some letter-string problems in GPT-3, and in some cases it could solve them. It learned, for example, the concept of successorship. Not perfect, not robust, but I found it still surprising that it can do this. Therefore, I don't agree that these systems are only 'stochastic parrots', as some scientists have called them. I have seen evidence of GPT building simple internal models of situations."" says the expert in the article. I'm not saying this is an AGI or will lead directly to AGI, but stubbornly refusing to accept this obvious progress as any progress towards machine intelligence at all, calling it "absolutely zero" evidence of progress seems wilfully blinkered. Do you genuinely put us absolutely no closer, not a single step closer, to AGI than the Mechanical Turk or the people of 50k BC?

marjancek 2y ago

Viruses are not super intelligent, yet one could kill us all. The biggest danger I see is a widespread AI with a set of badly defined goals, not a particularly smart and evil one.