For decades, scientists have tried to create computational imitations of the brain.
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Titled, Intelligenceconsider this and respond!
Raghavachary calls this sphenomics, a term he coined to differentiate from phenomenology, phenomenality, and phenomenalism.
Biological structures can be viewed in the same way, Raghavachary believes.
The phenomena are the structures responses.
Conversely, life processes are based on structures that consider and produce response phenomena.
Digital computation of intelligence haspardon the punno analog in the natural world, Raghavachary said.
I view intelligence as considered response in that sense, emanating from physical structures in our bodies and brains.
CR naturally fits within the S->P paradigm, Raghavachary said.
One of these hurdles is simulating the real world, a hot area of research in robotics andself-driving cars.
Structure->phenomena are computation-free, and can interact with each other with arbitrary complexity, Raghavachary says.
Simulating such complexity in a VR simulation is simply untenable.
Simulation of S->P in a machine will always remain exactly that, a simulation.
In such approaches, the AI software component is separated from the hardware that interacts with the world.
For example,deep learning modelscan be trained on millions of images to detect and classify objects.
including its human inhabitants, their motivations, habits, customs, behavior, etc.
the agent would need to fake all these, Raghavachary writes.
We, made of matter and structures, directly interact with structures, whose phenomena we experience.
Experience cannot be digitally computedit needs to be actively acquired via a body, Raghavachary said.
To me, there is simply no substitute for direct experience.
That said, the most relatable human-level AI would likely be best housed in a human-like agent.
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