You are now arguing that statistical CGI is human because its “neural networks” are inspired by biological neurons, which is an entirely different argument than the one I answered on. But fine.
As your article says:
the architecture of deep neural networks has undergone significant transformations.
The functions and achitecture has been so optimized and simplified, that it is just matrix multiplication now. It’s just math now. Math that is a lot simpler than the math that would be required to describe and simulate human brains.
The functions and achitecture has been so optimized and simplified, that it is just matrix multiplication now. It’s just math now.
“Just math” is used to describe essentially everything in science. You’re implying that a mathematical model can’t predict reality which is just incorrect.
We use math to accurately describe all kinds of natural processes and phenomenon. Mathematical models are the foundation of most fields of science because they accurately model reality.
And, because matrices a useful mathematical tool for describing complex systems (here, the connections between large numbers of neurons) they’re often used in many fields.
This is why we can predict time dilation in the GPS satellites used to locate your phone or how air will flow over the blades in jet turbines: because mathematical models of a process completely describe the process.
my argument is that the mathematical model for machine learning is in no way close to human minds anymore
And I’m saying that you can’t know that, because science doesn’t know that.
There is a reason that these are called neural networks. The atomic unit that they’re built on is a model of actual neurons and the information encoded in the network (connection strength and activation threshold) is based on observational studies of brains and how they process information.
Making a claim like ‘it isn’t the same as a human mind’ is simply not supported by evidence because there are no studies that try to correlate neural structures with the subjective ‘mind’ (i.e. the software running on the brain hardware).
However, we do know how neurons accept inputs based on weighting and apply linear transformations of their inputs into their outputs and we can create mathematical neurons that match observed neurons. We can train these networks and the way that they adjust their weights also matches cultured physical neurons. We know based on observational data that the mathematical model matches the physical neurons.
Obviously we don’t have Transformer networks in our brains, because we don’t learn to predict next tokens or to denoise images. But the underlying hardware that these systems run on is an exact analog of the neurons that make up the brains of everything on Earth. There’s nothing magical about human minds, they’re built out of neurons just as much as transformer networks.
You’re trying to split hairs but not explaining how any of that applies to the definition of art.
You are now arguing that statistical CGI is human because its “neural networks” are inspired by biological neurons, which is an entirely different argument than the one I answered on. But fine.
As your article says:
The functions and achitecture has been so optimized and simplified, that it is just matrix multiplication now. It’s just math now. Math that is a lot simpler than the math that would be required to describe and simulate human brains.
“Just math” is used to describe essentially everything in science. You’re implying that a mathematical model can’t predict reality which is just incorrect.
We use math to accurately describe all kinds of natural processes and phenomenon. Mathematical models are the foundation of most fields of science because they accurately model reality.
And, because matrices a useful mathematical tool for describing complex systems (here, the connections between large numbers of neurons) they’re often used in many fields.
This is why we can predict time dilation in the GPS satellites used to locate your phone or how air will flow over the blades in jet turbines: because mathematical models of a process completely describe the process.
bruh
of course math can predict and model reality, but that was not my argument
my argument is that the mathematical model for machine learning is in no way close to human minds anymore
And I’m saying that you can’t know that, because science doesn’t know that.
There is a reason that these are called neural networks. The atomic unit that they’re built on is a model of actual neurons and the information encoded in the network (connection strength and activation threshold) is based on observational studies of brains and how they process information.
Making a claim like ‘it isn’t the same as a human mind’ is simply not supported by evidence because there are no studies that try to correlate neural structures with the subjective ‘mind’ (i.e. the software running on the brain hardware).
However, we do know how neurons accept inputs based on weighting and apply linear transformations of their inputs into their outputs and we can create mathematical neurons that match observed neurons. We can train these networks and the way that they adjust their weights also matches cultured physical neurons. We know based on observational data that the mathematical model matches the physical neurons.
Obviously we don’t have Transformer networks in our brains, because we don’t learn to predict next tokens or to denoise images. But the underlying hardware that these systems run on is an exact analog of the neurons that make up the brains of everything on Earth. There’s nothing magical about human minds, they’re built out of neurons just as much as transformer networks.
You’re trying to split hairs but not explaining how any of that applies to the definition of art.