Neural networks in AI barely relate to any biological neural network.
Neural networks in AI are essentially a “scored” pachinko machine, with each peg having different numbers, which cause a “score” to go up for the “right” answer.
Basically, just a really fast, and expensive sieve filter.
I said “inspired by” and not “exact digital replicas”.
In classical MLP networks a neuron is modeled as an activation function depending on its inputs. Connections between those are “learned”, basically weights which determine the influence of one neuron’s output on the next neuron’s input. This is indeed Inspired by biological neural networks.
Interestingly, in some computer vision deep learning architectures, we have found structures after the training procedure which are even similar to how human vision works.
There are a bunch of different artificial neural network types, most – if not all – inspired by biology. I wouldn’t be so bold to reduce them in that absurd manner you did.
I said “inspired by” and not “exact digital replicas”
its not, though. Its best described as inspired by a big pachinko machine, with weighted pegs.
It is almost in no way inspired by. Thats just propaganda being put out to make AI more palatable, and personable.
There are a bunch of different artificial neural network types, most – if not all – inspired by biology. I wouldn’t be so bold to reduce them in that absurd manner you did.
I would be, because it’s factual.
If it was “inspired by” it would be able to tell the difference between running over a person, and avoiding a car, by example. It wouldn’t start hallucinating when asked simple questions, because a biological brain acts in congruence with it’s inputs.
Which happens because of a web of interconnections and spanning of multiple sphere’s, with two major ones acting as checks on the other. Which is nothing like any current AI model.
In current models, each token has a limited number of interconnects, and always to a neighbor node. That is nothing like a biological neuronal network.
Welcome to Microsoft 476! With new features such as fly! That’s right! A real simulated fly will help you. Introducing, “the wall” it’s where the fly lives! It will stay out of your way unless you call it. Setup your own buzzing noises! It will remind you in perfect stereo fly sounds about your incoming meeting!
This hypes me so much more than GPT-based tools
As neural networks in AI are inspired by nature, new techniques will surely follow the insights gained by such brain mapping research.
Neural networks in AI barely relate to any biological neural network.
Neural networks in AI are essentially a “scored” pachinko machine, with each peg having different numbers, which cause a “score” to go up for the “right” answer.
Basically, just a really fast, and expensive sieve filter.
I said “inspired by” and not “exact digital replicas”.
In classical MLP networks a neuron is modeled as an activation function depending on its inputs. Connections between those are “learned”, basically weights which determine the influence of one neuron’s output on the next neuron’s input. This is indeed Inspired by biological neural networks.
Interestingly, in some computer vision deep learning architectures, we have found structures after the training procedure which are even similar to how human vision works.
There are a bunch of different artificial neural network types, most – if not all – inspired by biology. I wouldn’t be so bold to reduce them in that absurd manner you did.
its not, though. Its best described as inspired by a big pachinko machine, with weighted pegs.
It is almost in no way inspired by. Thats just propaganda being put out to make AI more palatable, and personable.
I would be, because it’s factual.
If it was “inspired by” it would be able to tell the difference between running over a person, and avoiding a car, by example. It wouldn’t start hallucinating when asked simple questions, because a biological brain acts in congruence with it’s inputs.
Which happens because of a web of interconnections and spanning of multiple sphere’s, with two major ones acting as checks on the other. Which is nothing like any current AI model.
In current models, each token has a limited number of interconnects, and always to a neighbor node. That is nothing like a biological neuronal network.
Welcome to Microsoft 476! With new features such as fly! That’s right! A real simulated fly will help you. Introducing, “the wall” it’s where the fly lives! It will stay out of your way unless you call it. Setup your own buzzing noises! It will remind you in perfect stereo fly sounds about your incoming meeting!