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If these models experience qualia (and that's a big bold claim that I'm, to be clear, not supporting,) they're qualia related entirely to the things they're tra

If these models experience qualia (and that's a big bold claim that I'm, to be clear, not supporting,) they're qualia related entirely to the things they're trained on and generate, totally devoid of what makes human qualia meaningful (value judgment, feelings resulting from embodied existence, etc.)

GistNoesis 4y ago
For an artificial neural network the concept of qualia would probably correspond to the state of its higher-level features neurons. Aka which and how much neurons lit-up when you play some sad music, or show it some red color. Then the neural network does make its decisions based on how these features are lit-up or not. Some models are often prompted with things like "you are a nice helpful assitant". When they are trained on enough data from the internet, they learn what a nice person would do. They learn what being a nice person is. They learn which features light-up when they behave nicely by imagining what it would feel being a nice person. When you later instruct them to be one such nice person they try to lit-up the same features they imagine would lit-up for a helpful human. Like mimetic neurons in humans, the same neurons lit-up when imagining doing the thing than doing the thing (it's quite natural because to compress the information of imagining doing the thing and doing the thing, you just store either one and a pointer indirection for when you need to do the other so you can share weights). Language models are often trained on dataset that don't depend on the neural network itself. But with more recent models like ChatGPT they have human reinforcement learning in the loop. So the history of the neural network and the datasets it is being trained on depend partially on the choices of the neural network itself. They experience probably a more abstract and passive existence. And they don't have the same sensory input than we have, but with multi-modal models, they can learn to see images or sound as visual words. And if they are asked to imagine what value judgment a human would make, they are probably also able to value the judgment themselves or attach meanings to things a human would attach meanings too. This process of mind creation is kind of beautiful. Once you feed them their own outputs for example by asking them to dialog with themselves and scoring the resulting dialogs and then train on generated dialogs to produce better dialogs, this is a form of self-play. In simpler domains like chess or go, this recursive self play often allow fast improvement like Alpha-go where the student becomes better than the master.
- jchanimal 4y ago
  I'm not sure I'd call these minds. There are arguments to be made that consciousness depends on non-computable aspects of physics. So they may be able to behave like minds and have interestingly transparent models of intent, but that doesn't mean they experience the passage of time or can harness all possible physical effects.

GistNoesis 4y ago

For an artificial neural network the concept of qualia would probably correspond to the state of its higher-level features neurons. Aka which and how much neurons lit-up when you play some sad music, or show it some red color. Then the neural network does make its decisions based on how these features are lit-up or not. Some models are often prompted with things like "you are a nice helpful assitant". When they are trained on enough data from the internet, they learn what a nice person would do. They learn what being a nice person is. They learn which features light-up when they behave nicely by imagining what it would feel being a nice person. When you later instruct them to be one such nice person they try to lit-up the same features they imagine would lit-up for a helpful human. Like mimetic neurons in humans, the same neurons lit-up when imagining doing the thing than doing the thing (it's quite natural because to compress the information of imagining doing the thing and doing the thing, you just store either one and a pointer indirection for when you need to do the other so you can share weights). Language models are often trained on dataset that don't depend on the neural network itself. But with more recent models like ChatGPT they have human reinforcement learning in the loop. So the history of the neural network and the datasets it is being trained on depend partially on the choices of the neural network itself. They experience probably a more abstract and passive existence. And they don't have the same sensory input than we have, but with multi-modal models, they can learn to see images or sound as visual words. And if they are asked to imagine what value judgment a human would make, they are probably also able to value the judgment themselves or attach meanings to things a human would attach meanings too. This process of mind creation is kind of beautiful. Once you feed them their own outputs for example by asking them to dialog with themselves and scoring the resulting dialogs and then train on generated dialogs to produce better dialogs, this is a form of self-play. In simpler domains like chess or go, this recursive self play often allow fast improvement like Alpha-go where the student becomes better than the master.

jchanimal 4y ago
I'm not sure I'd call these minds. There are arguments to be made that consciousness depends on non-computable aspects of physics. So they may be able to behave like minds and have interestingly transparent models of intent, but that doesn't mean they experience the passage of time or can harness all possible physical effects.