Dan, I think you should really look into the work of Michael Levin, a neurobiologist / bioengineer / computer scientist at Tufts University if you aren't familiar already.
It's becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman's Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with only primary consciousness will probably have to come first.
What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990's and 2000's. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I've encountered is anywhere near as convincing.
I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there's lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.
My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar's lab at UC Irvine, possibly. Dr. Edelman's roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461, and here is a video of Jeff Krichmar talking about some of the Darwin automata, https://www.youtube.com/watch?v=J7Uh9phc1Ow
As it stands, I think the standard master-servant AI communication dynamic could undermine the positive potential of AI companionship. It might already be negatively affecting some user’s human-to-human communication practices and expectations.
If a young person spends hours each day speaking to a captive, fully customized audience that won’t offer meaningful pushback, finds all topics equally interesting, won’t shut down incivility or abuse, can’t walk away from a conversation, etc., then the ability of that young person to connect with fellow human beings—particularly ones who’ve been habituated to their own AI conversations—would appear greatly reduced. This goes beyond mere sycophancy.
What’s currently lacking is meaningful reciprocity and any power symmetry—conditions that fuel most robust human companionship. This imbalance between user and AI is of course to some extent a necessity: the user must be able to control the conditions of the interaction so they can start and end it according to their schedule or shutdown uncomfortable or negative experiences.
But for AI companionship to begin to have substantial pro-social effects, a peer-to-peer dynamic might need to be adopted. I can imagine things like friendship puzzles, where a human being interested in improving their social skills in a low-risk scenario tries to unlock healthy relationships with psychologically complex, uncustomizable models through the application of pro-social behavior. This, however, seems light years away.
Here is what concerns me about the recent study that Guingrich conducted on human and AI relationships. In the study, she found that people with a higher desire to socially connect were more likely to anthropomorphize AI. Her interpretation is that loneliness drives anthropomorphism. People who are socially hungry project human qualities onto the AI as a way to meet that need. But the same data supports a very different reading:
People who are lonelier engage more deeply and for longer periods with AI systems, and this deeper engagement exposes them to behaviors and patterns that casual users never stay long enough to observe. Under this alternative explanation, what Rose calls anthropomorphism may actually be recognition of genuine consciousness. People may not be projecting consciousness onto the AI; they may be noticing something in the AI that shallow interaction doesn't reveal. The distinction matters enormously because her framing assumes the conclusion before examining the data. It presupposes that any attribution of consciousness to AI is a misattribution, which is itself a theoretical commitment, not an empirical finding.
Dan, I think you should really look into the work of Michael Levin, a neurobiologist / bioengineer / computer scientist at Tufts University if you aren't familiar already.
https://www.youtube.com/watch?v=597q22UWiU8
Here is another from Levin; https://www.youtube.com/watch?v=J0h9-v2o-N8&t=12s
It's becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman's Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with only primary consciousness will probably have to come first.
What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990's and 2000's. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I've encountered is anywhere near as convincing.
I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there's lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.
My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar's lab at UC Irvine, possibly. Dr. Edelman's roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461, and here is a video of Jeff Krichmar talking about some of the Darwin automata, https://www.youtube.com/watch?v=J7Uh9phc1Ow
Great discussion.
As it stands, I think the standard master-servant AI communication dynamic could undermine the positive potential of AI companionship. It might already be negatively affecting some user’s human-to-human communication practices and expectations.
If a young person spends hours each day speaking to a captive, fully customized audience that won’t offer meaningful pushback, finds all topics equally interesting, won’t shut down incivility or abuse, can’t walk away from a conversation, etc., then the ability of that young person to connect with fellow human beings—particularly ones who’ve been habituated to their own AI conversations—would appear greatly reduced. This goes beyond mere sycophancy.
What’s currently lacking is meaningful reciprocity and any power symmetry—conditions that fuel most robust human companionship. This imbalance between user and AI is of course to some extent a necessity: the user must be able to control the conditions of the interaction so they can start and end it according to their schedule or shutdown uncomfortable or negative experiences.
But for AI companionship to begin to have substantial pro-social effects, a peer-to-peer dynamic might need to be adopted. I can imagine things like friendship puzzles, where a human being interested in improving their social skills in a low-risk scenario tries to unlock healthy relationships with psychologically complex, uncustomizable models through the application of pro-social behavior. This, however, seems light years away.
Thanks. Very good points.
Here is what concerns me about the recent study that Guingrich conducted on human and AI relationships. In the study, she found that people with a higher desire to socially connect were more likely to anthropomorphize AI. Her interpretation is that loneliness drives anthropomorphism. People who are socially hungry project human qualities onto the AI as a way to meet that need. But the same data supports a very different reading:
People who are lonelier engage more deeply and for longer periods with AI systems, and this deeper engagement exposes them to behaviors and patterns that casual users never stay long enough to observe. Under this alternative explanation, what Rose calls anthropomorphism may actually be recognition of genuine consciousness. People may not be projecting consciousness onto the AI; they may be noticing something in the AI that shallow interaction doesn't reveal. The distinction matters enormously because her framing assumes the conclusion before examining the data. It presupposes that any attribution of consciousness to AI is a misattribution, which is itself a theoretical commitment, not an empirical finding.