4 ms·

I mean, I think the reason I would say the night sky is “beautiful” is because the meaning of the word for me is constructed from the experiences I’ve had in wh

by del82 1y ago

I mean, I think the reason I would say the night sky is “beautiful” is because the meaning of the word for me is constructed from the experiences I’ve had in which I’ve heard other people use the word. So I’d agree that the night sky is “beautiful”, but not because I somehow have access to a deeper meaning of the word or the sky than an LLM does. As someone who (long ago) studied philosophy of mind and (Chomskian) linguistics, it’s striking how much LLMs have shrunk the space available to people who want to maintain that the brain is special & there’s a qualitative (rather than just quantitative) difference between mind and machine and yet still be monists.

foogazi 1y ago
> I think the reason I would say the night sky is “beautiful” is because the meaning of the word for me is constructed from the experiences I’ve had in which I’ve heard other people use the word. Ok but you don’t look at every night sky or every sunset and say “wow that’s beautiful” There’s a quality to it - not because you heard someone say it but because you experience it
- holler 1y ago
  my thought exactly
- adastra22 1y ago
  Because words are much lower bandwidth than speech. But if you were “told” about a sunset by means of a Matrix style direct mind uploading of an experience, it would seem just as real and vivid. That’s a quantitative difference in bandwidth, not a qualitative difference in character.
- TeMPOraL 1y ago
  > Ok but you don’t look at every night sky or every sunset and say “wow that’s beautiful Exactly - because it's a semantic shorthand. Sunsets are fucking boring, ugly, transient phenomena. Watching a sunset while feeling safe and relaxed, maybe in a company of your love interest who's just as high on endorphins as you are right now - this is what feels beautiful. This is a sunset that's beautiful. But the sunset is just a pointer to the experience, something others can relate to, not actually the source of it.
  - drewbeck 1y ago
    I’ve seen incredible sunsets while stressed depressed and worse. Are you saying sunsets cannot be experienced as beautiful on their own?
FloorEgg 1y ago
The more I learn about AI, biology and the brain, the more it seems to me that the difference between life and machines is just complexity. People are just really really complex machines. However there are clearly qualitative differences between the human mind and any machines we know of yet, and those qualitative differences are emergent properties, in the same way that a rabbit is qualitatively different than a stone or a chunk of wood. I also think most of the recent AI experts/optimists underestimate how complex the mind is. I'm not at the cutting edge of how LLMs are being trained and architected, but the sense I have is we haven't modelled the diversity of connections in the mind or diversity of cell types. E.g. Transcriptomic diversity of cell types across the adult human brain (Siletti et al., 2023, Science)
- simonh 1y ago
  I’d say sophistication. Observing the landscape enables us to spot useful resources and terrain features, or spot dangers and predators. We are afraid of dark enclosed spaces because they could hide dangers. Our ancestors with appropriate responses were more likely to survive. A huge limitation of LLMs is that they have no ability to dynamically engage with the world. We’re not just passive observers, we’re participants in our environment and we learn from testing that environment through action. I know there are experiments with AIs doing this, and in a sense game playing AIs are learning about model worlds through action in them.
  - FloorEgg 1y ago
    The idea I keep coming back to is that as far as we know it took roughly 100k-1M years for anatomically modern humans to evolve language, abstract thinking, information systems, etc. (equivalent to LLMs), but it took 100M-1B years to evolve from the first multi-celled organisms to anatomically modern humans. In other words, human level embodiment (internal modelling of the real world and ability to navigate it) is likely at least 1000x harder than modelling human language and abstract knowledge. And to build further on what you are saying, the way LLMs are trained and then used, they seem a bit more like DNA than the human brain in terms of how the "learning" is being done. An instance of an LLM is like a copy of DNA trained on a play of many generations of experience. So it seems there are at least four things not yet worked out re AI reaching human level "AGI": 1) The number of weights (synapses) and parameters (neurons) needs to grow by orders of magnitude 2) We need new analogs that mimic the brains diversity of cell types and communication modes 3) We need to solve the embodiment problem, which is far from trivial and not fully understood 4) We need efficient ways for the system to continuously learn (an analog for neuroplasticity) It may be that these are mutually reinforcing, in that solving #1 and #2 makes a lot of progress towards #3 and #4. I also suspect that #4 is economical, in that if the cost to train a GPT-5 level model was 1,000,000 cheaper, then maybe everyone could have one that's continuously learning (and diverging), rather than everyone sharing the same training run that's static once complete. All of this to say I still consider LLMs "intelligent", just a different kind and less complex intelligence than humans.
intended 1y ago
The fact that things are constructed by neurons in the brain, and are a representation of other things - does not preclude your representation from being deeper and richer than LLM representations. The patterns in experience are reduced to some dimensions in an LLM (or generative model). They do not capture all the dimensions - because the representation itself is a capture of another representation. Personally, I have no need to reassure myself whether I am a special snowflake or not. Whatever snowflake I am, I strongly prefer accuracy in my analogies of technology. GenAI does not capture a model of the world, it captures a model of the training data. If video tools were that good, they would have started with voxels.
dmkii 1y ago
It’s interesting you mention linguistics because I feel a lot of the discussions around AI come back to early 20th century linguistics debates between Russel, Wittgenstein and later Chomsky. I tend to side with (later) Wittgenstein’s perception that language is inherently a social construct. He gives the example of a “game” where there’s no meaningful overlap between e.g. Olympic Games and Monopoly, yet we understand very well what game we’re talking about because of our social constructs. I would argue that LLMs are highly effective at understanding (or at least emulating) social constructs because of their training data. That makes them excellent at language even without a full understanding of the world.
heyjamesknight 1y ago
You don’t have a deeper “meaning of the word,” you have an actual experience of beauty. Three word is just a label for the thing you, me, and other humans have experienced. The machine has no experience.

by del82 1y ago

foogazi 1y ago

> I think the reason I would say the night sky is “beautiful” is because the meaning of the word for me is constructed from the experiences I’ve had in which I’ve heard other people use the word. Ok but you don’t look at every night sky or every sunset and say “wow that’s beautiful” There’s a quality to it - not because you heard someone say it but because you experience it

holler 1y ago
my thought exactly
adastra22 1y ago
Because words are much lower bandwidth than speech. But if you were “told” about a sunset by means of a Matrix style direct mind uploading of an experience, it would seem just as real and vivid. That’s a quantitative difference in bandwidth, not a qualitative difference in character.
TeMPOraL 1y ago
> Ok but you don’t look at every night sky or every sunset and say “wow that’s beautiful Exactly - because it's a semantic shorthand. Sunsets are fucking boring, ugly, transient phenomena. Watching a sunset while feeling safe and relaxed, maybe in a company of your love interest who's just as high on endorphins as you are right now - this is what feels beautiful. This is a sunset that's beautiful. But the sunset is just a pointer to the experience, something others can relate to, not actually the source of it.
- drewbeck 1y ago
  I’ve seen incredible sunsets while stressed depressed and worse. Are you saying sunsets cannot be experienced as beautiful on their own?

FloorEgg 1y ago

The more I learn about AI, biology and the brain, the more it seems to me that the difference between life and machines is just complexity. People are just really really complex machines. However there are clearly qualitative differences between the human mind and any machines we know of yet, and those qualitative differences are emergent properties, in the same way that a rabbit is qualitatively different than a stone or a chunk of wood. I also think most of the recent AI experts/optimists underestimate how complex the mind is. I'm not at the cutting edge of how LLMs are being trained and architected, but the sense I have is we haven't modelled the diversity of connections in the mind or diversity of cell types. E.g. Transcriptomic diversity of cell types across the adult human brain (Siletti et al., 2023, Science)

simonh 1y ago
I’d say sophistication. Observing the landscape enables us to spot useful resources and terrain features, or spot dangers and predators. We are afraid of dark enclosed spaces because they could hide dangers. Our ancestors with appropriate responses were more likely to survive. A huge limitation of LLMs is that they have no ability to dynamically engage with the world. We’re not just passive observers, we’re participants in our environment and we learn from testing that environment through action. I know there are experiments with AIs doing this, and in a sense game playing AIs are learning about model worlds through action in them.
- FloorEgg 1y ago
  The idea I keep coming back to is that as far as we know it took roughly 100k-1M years for anatomically modern humans to evolve language, abstract thinking, information systems, etc. (equivalent to LLMs), but it took 100M-1B years to evolve from the first multi-celled organisms to anatomically modern humans. In other words, human level embodiment (internal modelling of the real world and ability to navigate it) is likely at least 1000x harder than modelling human language and abstract knowledge. And to build further on what you are saying, the way LLMs are trained and then used, they seem a bit more like DNA than the human brain in terms of how the "learning" is being done. An instance of an LLM is like a copy of DNA trained on a play of many generations of experience. So it seems there are at least four things not yet worked out re AI reaching human level "AGI": 1) The number of weights (synapses) and parameters (neurons) needs to grow by orders of magnitude 2) We need new analogs that mimic the brains diversity of cell types and communication modes 3) We need to solve the embodiment problem, which is far from trivial and not fully understood 4) We need efficient ways for the system to continuously learn (an analog for neuroplasticity) It may be that these are mutually reinforcing, in that solving #1 and #2 makes a lot of progress towards #3 and #4. I also suspect that #4 is economical, in that if the cost to train a GPT-5 level model was 1,000,000 cheaper, then maybe everyone could have one that's continuously learning (and diverging), rather than everyone sharing the same training run that's static once complete. All of this to say I still consider LLMs "intelligent", just a different kind and less complex intelligence than humans.

intended 1y ago

The fact that things are constructed by neurons in the brain, and are a representation of other things - does not preclude your representation from being deeper and richer than LLM representations. The patterns in experience are reduced to some dimensions in an LLM (or generative model). They do not capture all the dimensions - because the representation itself is a capture of another representation. Personally, I have no need to reassure myself whether I am a special snowflake or not. Whatever snowflake I am, I strongly prefer accuracy in my analogies of technology. GenAI does not capture a model of the world, it captures a model of the training data. If video tools were that good, they would have started with voxels.

dmkii 1y ago

It’s interesting you mention linguistics because I feel a lot of the discussions around AI come back to early 20th century linguistics debates between Russel, Wittgenstein and later Chomsky. I tend to side with (later) Wittgenstein’s perception that language is inherently a social construct. He gives the example of a “game” where there’s no meaningful overlap between e.g. Olympic Games and Monopoly, yet we understand very well what game we’re talking about because of our social constructs. I would argue that LLMs are highly effective at understanding (or at least emulating) social constructs because of their training data. That makes them excellent at language even without a full understanding of the world.

heyjamesknight 1y ago

You don’t have a deeper “meaning of the word,” you have an actual experience of beauty. Three word is just a label for the thing you, me, and other humans have experienced. The machine has no experience.