6 ms·

This gets at the fundamental issue I have with AI: We're trying to get machines to think, but we only have the barest understanding of how we, as humans, think.

by Kapura 3y ago

This gets at the fundamental issue I have with AI: We're trying to get machines to think, but we only have the barest understanding of how we, as humans, think. The concept of a "neuron" that can activate other neurons comes straight from real neurology, but given how complex the human brain is, it's no surprise that we can only create something that is fundamentally "lesser." However, I think that real neurology and machine-learning can be mutually reinforcing fields: structures discovered in the one could be applied to the other, and vice versa. But thinking we can create "AGI" without first increasing our understanding of "wet" neural nets is the height of hubris.

sxg 3y ago
AGI and human intelligence don't necessarily have to work the same way. While many of us assume that they must be very similar, I don't think there's any basis for that assumption.
- swatcoder 3y ago
  That's correct that they don't need to work the same way. I would say most people that have earnestly thought about it assume they won't. But given the exponentially greater chemical, biological, and neurological complexity of human brains, the millions of years of evolutionary "pre-training" that they get imbued with, and the years of constant multimodal sensory input required for their culmination in what's called human intelligence, it takes an extremely bold assumption to believe that equivalent ends can be met with a stream of fmadds driving through in an array of transistors. It's hard to overstate how big a gulf there is in both the hardware and software between living systems and what we currently see in our silicon projects. Neural network learning in general and LLM's in particular are like crude paper airplanes compared to the flight capabilities of a dextrous bird. You can kinda squint and see that they both move some distance through the air, and even marvel at it for a bit, but there's a long long long long way to go.
  - kelseyfrog 3y ago
    And yet no bird ever broke the sound barrier. Humans make things that exceed anything in nature. Thought will only be another example.
    - bomewish 3y ago
      Exceed in some regards but not others right? Like, let’s not imagine the plane is better at being a bird than the bird. It’s an evocative set of associations though. Makes me think we’ll end up creating the Concorde of intelligence but be no closer to understanding how our own works…
      - NiloCK 3y ago
        It'll explain it to us, at least to the best of our capability, with lots of hand-holding.
        pixl97 3y ago
        Or it won't and you'll wonder why your molecular constituents are being siphoned off to build things it finds more useful.
        Workaccount2 3y ago
        If it sticks my brain in a simulation to keep me occupied, I'm totally ok with this.
        NiloCK 3y ago
        Reminder: a tamagotchi Workaccount2 is not you. A parallel instance of you, no matter how perfect, cannot add to your qualitative experiences or make you less dead.
      - hskalin 3y ago
        I agree that creating something akin to an organic being exceeds capabilities at present. But to be fair we never tried to build a bird, instead we tried to build a machine to fly and we excelled. However I concur that we may end up creating an AI which thinks nothing like us, has no sense of morality, and yet far exceeds us in intelligence. And that would be somewhat unsettling.
- fuy 3y ago
  This basis is our limited data set: we have only 1 example of human level intelligence, so it's natural to assume there's something unique, or at least significant about the way it works.
mitthrowaway2 3y ago
> We're trying to get machines to think, but we only have the barest understanding of how we, as humans, think... But thinking we can create "AGI" without first increasing our understanding of "wet" neural nets is the height of hubris. That depends on the landscape of the solution space. If the techniques that work much better than anything else happen to be the same techniques used within our own brains, then we might find them, through aggressive experimentation and exploration, before we even realize that it's also how our own brains work too.
- TeMPOraL 3y ago
  We do in fact know that whatever makes the brains tick, it must be something 1) simple enough for the extremely dumb, brute-force optimizer that is evolution to stumble on it, 2) incrementally improvable by the same mechanism, and 3) conferring some kind of advantage at every step. Those are requirements for some feature to evolve.
  - bratbag 3y ago
    3 is not required. Multiple steps with no negatives is sufficient.
dboreham 3y ago
Hard disagre. Imho humans suffer under a delusion that their thinking is like a computer whereas it is actually much more like an LLM. So if you ask humans[1] to figure our how they think, the answer will be a) long time coming and b) wrong. [1] possibly bhudist monks excepted.
hibikir 3y ago
All the efforts to get computers to play chess mostly like humans do were made to look insufficient by a large enough neural network that took zero knowledge of human position evaluation, which just played against itself an outrageous number of games. The plane is in many ways lesser than birds, and we barely understood aerodynamics when the wright brothers gave us a working plane: While the bird is efficient, we had a whole lot more thrust. Our planes are far better than they were back then, but not because we understand bird better, but because we focused on efficiency of the simpler designs we could build. The Formula 1 car doesn't come from deep understanding of the efficient, graceful running movements of the cheetah. Our results in medicine have been far and ahead of our understanding of biochemistry, DNA, and in general, how the human body works. The discovery of penicilin didn't require a lot of understanding: It required luck. Ozempic and Viagra didn't come from deep understanding of the human body: Resarchers were looking for one thing, and ended up with something useful that was straight out unintended. We might be able to build AGI with what we know of brains, we might not. But either way, it's not because we need to understand the human mind better: Do we have enough compute for a very crude systems that we know how to build to overcome our relative lack of understanding? So if you ask me, what is the height of hubris is to think that working engineering solutions have to come from deep understanding of how nature solves the problem. The only reasonable bet, given the massive improvements in AI in the last decade, is to assume that the error bars of any prediction here are just huge. Will we get stuck, the way self-driving cars seem to have gotten stuck for a bit? Possibly. Will be go from a stochastic parrot into something better than humans, the same way that Go AIs went from pretty weak to better than any human in the blink of an eye? I'd not discount it either. I expect to be surprised either way.
- galaxyLogic 3y ago
  > height of hubris is to think that working engineering solutions have to come from deep understanding of how nature solves the problem. It is basically understood how "nature solves the problem". It does that by evolution. And what is machine learning and neural networks but artificial evolution?
  - valval 3y ago
throwuwu 3y ago
I see no reason why this is a problem. We built analogs of many things before we fully understood how they work. Most of human progress has worked this way through an empirical approach. Makes sense since the real world is the ultimate sandbox and the final judge of an idea’s applicability.
DemocracyFTW2 3y ago
> thinking we can create "AGI" without first increasing our understanding of "wet" neural nets is the height of hubris Already ~40 years ago I read in a popular science book that scientists had tried to apply genetic algorithms to finding a better nozzle for a given purpose (jet engine? I don't remember that). That was then done by prefabricating a number of ring-shaped metal parts the inside of which had different widths and different slopes; a stack of these could form a wide variety of shapes for the conduit. They started with an initial configuration, did a measurement, then threw the dice to determine what part should be changed; if the new measurement was better (according to their metric) than the preceding one, they kept it, otherwise reversed it. They did find a nozzle shape that was not only significantly better than any known configuration; it was also significantly weirder, seemingly completely ad-hoc. There's no computers in this experiment, no intelligent design; people were only necessitated to procure the initial parts and setup, do the dice-throwing and so on; the design process proper was a very basic, randomized, mechanical procedure, no thinking required, no understanding necessary.

by Kapura 3y ago

sxg 3y ago

AGI and human intelligence don't necessarily have to work the same way. While many of us assume that they must be very similar, I don't think there's any basis for that assumption.

swatcoder 3y ago
That's correct that they don't need to work the same way. I would say most people that have earnestly thought about it assume they won't. But given the exponentially greater chemical, biological, and neurological complexity of human brains, the millions of years of evolutionary "pre-training" that they get imbued with, and the years of constant multimodal sensory input required for their culmination in what's called human intelligence, it takes an extremely bold assumption to believe that equivalent ends can be met with a stream of fmadds driving through in an array of transistors. It's hard to overstate how big a gulf there is in both the hardware and software between living systems and what we currently see in our silicon projects. Neural network learning in general and LLM's in particular are like crude paper airplanes compared to the flight capabilities of a dextrous bird. You can kinda squint and see that they both move some distance through the air, and even marvel at it for a bit, but there's a long long long long way to go.
- kelseyfrog 3y ago
  And yet no bird ever broke the sound barrier. Humans make things that exceed anything in nature. Thought will only be another example.
  - bomewish 3y ago
    Exceed in some regards but not others right? Like, let’s not imagine the plane is better at being a bird than the bird. It’s an evocative set of associations though. Makes me think we’ll end up creating the Concorde of intelligence but be no closer to understanding how our own works…
    - NiloCK 3y ago
      It'll explain it to us, at least to the best of our capability, with lots of hand-holding.
      - pixl97 3y ago
        Or it won't and you'll wonder why your molecular constituents are being siphoned off to build things it finds more useful.
        Workaccount2 3y ago
        If it sticks my brain in a simulation to keep me occupied, I'm totally ok with this.
        NiloCK 3y ago
        Reminder: a tamagotchi Workaccount2 is not you. A parallel instance of you, no matter how perfect, cannot add to your qualitative experiences or make you less dead.
    - hskalin 3y ago
      I agree that creating something akin to an organic being exceeds capabilities at present. But to be fair we never tried to build a bird, instead we tried to build a machine to fly and we excelled. However I concur that we may end up creating an AI which thinks nothing like us, has no sense of morality, and yet far exceeds us in intelligence. And that would be somewhat unsettling.
fuy 3y ago
This basis is our limited data set: we have only 1 example of human level intelligence, so it's natural to assume there's something unique, or at least significant about the way it works.

mitthrowaway2 3y ago

> We're trying to get machines to think, but we only have the barest understanding of how we, as humans, think... But thinking we can create "AGI" without first increasing our understanding of "wet" neural nets is the height of hubris. That depends on the landscape of the solution space. If the techniques that work much better than anything else happen to be the same techniques used within our own brains, then we might find them, through aggressive experimentation and exploration, before we even realize that it's also how our own brains work too.

TeMPOraL 3y ago
We do in fact know that whatever makes the brains tick, it must be something 1) simple enough for the extremely dumb, brute-force optimizer that is evolution to stumble on it, 2) incrementally improvable by the same mechanism, and 3) conferring some kind of advantage at every step. Those are requirements for some feature to evolve.
- bratbag 3y ago
  3 is not required. Multiple steps with no negatives is sufficient.

dboreham 3y ago

Hard disagre. Imho humans suffer under a delusion that their thinking is like a computer whereas it is actually much more like an LLM. So if you ask humans[1] to figure our how they think, the answer will be a) long time coming and b) wrong. [1] possibly bhudist monks excepted.

hibikir 3y ago

All the efforts to get computers to play chess mostly like humans do were made to look insufficient by a large enough neural network that took zero knowledge of human position evaluation, which just played against itself an outrageous number of games. The plane is in many ways lesser than birds, and we barely understood aerodynamics when the wright brothers gave us a working plane: While the bird is efficient, we had a whole lot more thrust. Our planes are far better than they were back then, but not because we understand bird better, but because we focused on efficiency of the simpler designs we could build. The Formula 1 car doesn't come from deep understanding of the efficient, graceful running movements of the cheetah. Our results in medicine have been far and ahead of our understanding of biochemistry, DNA, and in general, how the human body works. The discovery of penicilin didn't require a lot of understanding: It required luck. Ozempic and Viagra didn't come from deep understanding of the human body: Resarchers were looking for one thing, and ended up with something useful that was straight out unintended. We might be able to build AGI with what we know of brains, we might not. But either way, it's not because we need to understand the human mind better: Do we have enough compute for a very crude systems that we know how to build to overcome our relative lack of understanding? So if you ask me, what is the height of hubris is to think that working engineering solutions have to come from deep understanding of how nature solves the problem. The only reasonable bet, given the massive improvements in AI in the last decade, is to assume that the error bars of any prediction here are just huge. Will we get stuck, the way self-driving cars seem to have gotten stuck for a bit? Possibly. Will be go from a stochastic parrot into something better than humans, the same way that Go AIs went from pretty weak to better than any human in the blink of an eye? I'd not discount it either. I expect to be surprised either way.

galaxyLogic 3y ago
> height of hubris is to think that working engineering solutions have to come from deep understanding of how nature solves the problem. It is basically understood how "nature solves the problem". It does that by evolution. And what is machine learning and neural networks but artificial evolution?
- valval 3y ago

throwuwu 3y ago

I see no reason why this is a problem. We built analogs of many things before we fully understood how they work. Most of human progress has worked this way through an empirical approach. Makes sense since the real world is the ultimate sandbox and the final judge of an idea’s applicability.

DemocracyFTW2 3y ago

> thinking we can create "AGI" without first increasing our understanding of "wet" neural nets is the height of hubris Already ~40 years ago I read in a popular science book that scientists had tried to apply genetic algorithms to finding a better nozzle for a given purpose (jet engine? I don't remember that). That was then done by prefabricating a number of ring-shaped metal parts the inside of which had different widths and different slopes; a stack of these could form a wide variety of shapes for the conduit. They started with an initial configuration, did a measurement, then threw the dice to determine what part should be changed; if the new measurement was better (according to their metric) than the preceding one, they kept it, otherwise reversed it. They did find a nozzle shape that was not only significantly better than any known configuration; it was also significantly weirder, seemingly completely ad-hoc. There's no computers in this experiment, no intelligent design; people were only necessitated to procure the initial parts and setup, do the dice-throwing and so on; the design process proper was a very basic, randomized, mechanical procedure, no thinking required, no understanding necessary.