I think AGI requires different topological/conceptual paradigms rather than pure speed/processing capacity. But the latter is necessary to experiment and create recognizable results.
A lot of the current excitement around AI image construction and SD's availability is the intuitive sense that these tools have succeeded in emulating some key aspects of our visual cortex - given a set of object classifiers they can create imaginary views that are recognizable to us. It's sort of an illusion - Stable Diffusion has no aesthetic or experiential preferences of its own and so its activity is reflexive rather than conscious, and we don't understand if or how consciousness is emergent from complex reflexivity.
But the key point is that it's doing such a good job at this 'narrow' task of visual synthesis, and other models are doing such a good job at the 'narrow' tasks of textual or audible synthesis, that it's competitive with a human in an idiot-savant kind of way. And we know from our own experience that skill and learning are protean - we may disagree on the value of different types of learning, but don't question the similarity of the underlying mechanism. Thus I might think that becoming an expert on, say, the fictional universe of Star Wars is a waste of time, but the process of knowledge acquisition, recall, and synthesis are not fundamentally different from those used to learn history or engineering ('experimentation' can exist in terms of consensus establishment in a fandom about whether an innovation is canonical or parodic).
So if we can train models with a billion semantically-tagged media objects and have them generate new media objects that meaningfully reflect the tags we supply, it means we have a decent general environmental-feature detection, recall, and resynthesis tool. Being able to take an existing model and tune it on workstations instead of needing a whole datacenter substantially widens the field of possibilities. So what happens if we connect it to sensors and actuators and train our model to navigate a dynamic landscape, which includes 'internal' signals that can't be directly responded to? Consider a virtual or lab environment which is complex and dynamic, and includes energy units (batteries). Our model has internal batteries and feedback mechanisms, but their state can only be altered through external activity and their signals are heavily weighted. Sensory subsystems attached to the model have some precomputed models of their own.
My idea is that the brain is a 'system of systems' and that consciousness emerges from the instrumentation of the time cost of model tuning vs the rate of environmental variation.
A lot of the current excitement around AI image construction and SD's availability is the intuitive sense that these tools have succeeded in emulating some key aspects of our visual cortex - given a set of object classifiers they can create imaginary views that are recognizable to us. It's sort of an illusion - Stable Diffusion has no aesthetic or experiential preferences of its own and so its activity is reflexive rather than conscious, and we don't understand if or how consciousness is emergent from complex reflexivity.
But the key point is that it's doing such a good job at this 'narrow' task of visual synthesis, and other models are doing such a good job at the 'narrow' tasks of textual or audible synthesis, that it's competitive with a human in an idiot-savant kind of way. And we know from our own experience that skill and learning are protean - we may disagree on the value of different types of learning, but don't question the similarity of the underlying mechanism. Thus I might think that becoming an expert on, say, the fictional universe of Star Wars is a waste of time, but the process of knowledge acquisition, recall, and synthesis are not fundamentally different from those used to learn history or engineering ('experimentation' can exist in terms of consensus establishment in a fandom about whether an innovation is canonical or parodic).
So if we can train models with a billion semantically-tagged media objects and have them generate new media objects that meaningfully reflect the tags we supply, it means we have a decent general environmental-feature detection, recall, and resynthesis tool. Being able to take an existing model and tune it on workstations instead of needing a whole datacenter substantially widens the field of possibilities. So what happens if we connect it to sensors and actuators and train our model to navigate a dynamic landscape, which includes 'internal' signals that can't be directly responded to? Consider a virtual or lab environment which is complex and dynamic, and includes energy units (batteries). Our model has internal batteries and feedback mechanisms, but their state can only be altered through external activity and their signals are heavily weighted. Sensory subsystems attached to the model have some precomputed models of their own.
My idea is that the brain is a 'system of systems' and that consciousness emerges from the instrumentation of the time cost of model tuning vs the rate of environmental variation.