You can't judge a theory just by reading about it.
If the theory is well-formulated, rests on vetted facts, and you understand the context sufficiently, you can say: "This is a good theory." If it's not well-formulated or does not rest vetted facts, and your understanding is sufficent, you can say: "Bad theory."
There's an entire world of variation between those 2 extremes, but there's no reason implicitly why you can't understand a theory just by reading about it.
If the goal is to evaluate a theory's virtues, there's nothing stopping you if you have access to the correct documentation (provided it exists). Not sure what you think is missing.
"You can't judge a theory just by reading about it."
Also, can't see why it should matter one way or the other.
"You can't judge a theory just by reading about it."
But I don't see that. When I read James or Spinoza, I consider the same factors she includes from her book. They all generally believe in the bi-directional power of the mind-body connection and how that relates to someone's driving philosophical system.
This predicts that basically ChatGPT has emotions.
It's just scale at this point. At this point, maybe human neurons and connections are more complex than what can be done in a machine, but not for long.
Humans only think they are special, that a machine can't feel.
But that is only our subjective experience of ourselves, which itself is also just interpretation. It seems like everyone is in agreement that our perception of the outer world is fallible. But so is our perception of our internal processes. Our inner thoughts are just as opaque to us as the outer world.
"Man can do what he wills but he cannot will what he wills." Schopenhauer.
Maybe if you cut open a brain, you wont find a printout with backpropagate code.
But you do find neurons, in a network.
Humans do learn right? They take in information and encode it in their brain.
Why does it have to be back propagation to qualify. Bayesian? Minimum Entropy?
There are a lot of forms a machine neural network can take. There are a lot of theories on exactly how the brain 'calculates'/'processes'. With all of the advancements in Neuroscience and AI in last 5 years, it is bit hubris to say we'll never be able to figure out the brain, and also be able to model it.
There are a lot more like this. The field is moving too rapidly for me to go find every paper today. But it is dozens, and not even so cutting edge there isn't already books on it.
https://www.quantamagazine.org/some-neural-networks-learn-la...
1. 2020 PREDICTIVE CODING APPROXIMATES BACKPROP ALONG ARBITRARY COMPUTATION GRAPHS https://arxiv.org/pdf/2006.04182.pdf
Agree with embodied issue. Humans have a large amount of sensory input, from the 'body'. I'd disagree that AI will never have this, considering the large number of sensory technologies that already exists and are being developed.
It only takes wiring them together. Eyes, smells, touch, these are all existing and being refined.
2.
LLMs? AI research is far more vast than just LLM's. LLM's just happen to be the latest shiny thing.
"never"?. That is bold, 5 years ago people said the abilities in current LLM's were a "never", yet here we are.
It's counter-intuitive, but it fits better with what we know about how the nervous system works than the commonly accepted fingerprint idea.
Emotions are the brain's attempt at constructing the concepts that best explain and account for our body's energy budget.
I don’t see why this theory contradicts the one in the article, in your own examples you’re using bodily states as the input to emotional processes.
The fingerprint idea is that emotions are caused by body states without that layer of interpretation/construction, so you could look at bare metrics like blood pressure, arousal, and facial expression and derive which emotion the person must be feeling directly from them.