11–20 of 183 posts
If I had a nickel for every time some neurologist tried to compare brains to neural networks. It's a surefire way to tell someone is either desperate for grant money or has been smoking crack. (previously: comparing brains and "electronic computers") Their entire article hinges on the complaint "brain seems shallow and neural networks are deep, ergo neural networks are doing it wrong." Neurologists seem to have a rea…
If I had a nickel for every time some neurologist tried to compare brains to neural networks. It's a surefire way to tell someone is either desperate for grant money or has been smoking crack. (previously: comparing brains and "electronic computers") Their entire article hinges on the complaint "brain seems shallow and neural networks are deep, ergo neural networks are doing it wrong." Neurologists seem to have a rea…
If I had a nickel for every time some neurologist tried to compare brains to neural networks. It's a surefire way to tell someone is either desperate for grant money or has been smoking crack. (previously: comparing brains and "electronic computers") Their entire article hinges on the complaint "brain seems shallow and neural networks are deep, ergo neural networks are doing it wrong." Neurologists seem to have a rea…
There are insights that can come from studying the brain, that do indeed apply. Some researchers may not glean anything from such studies, and some may. I have no doubt that as neural networks get more an more powerful, we will continue to find more ways they are similar to the brain, and apply things we've learned about the brain to them.
I certainly prefer to see people making comparisons of neural networks to the brain, that the old "it's just a glorified autocomplete" and the like.
Relax.
If I had a nickel for every time some neurologist tried to compare brains to neural networks. It's a surefire way to tell someone is either desperate for grant money or has been smoking crack. (previously: comparing brains and "electronic computers") Their entire article hinges on the complaint "brain seems shallow and neural networks are deep, ergo neural networks are doing it wrong." Neurologists seem to have a rea…
If I had a nickel for every time some neurologist tried to compare brains to neural networks. It's a surefire way to tell someone is either desperate for grant money or has been smoking crack. (previously: comparing brains and "electronic computers") Their entire article hinges on the complaint "brain seems shallow and neural networks are deep, ergo neural networks are doing it wrong." Neurologists seem to have a rea…
[1] - https://en.wikipedia.org/wiki/Convolutional_neural_network
Earlier quoted context omitted.
Yes, it's odd that sled dogs make terrible housepets. /s Neural networks fundamentally aren't designed to be otherwise. The workflow that has guided their entire development for over a decade is based around expensive training and static inference.
Why then all the talk about AGI when fundamentals don’t even allow for it to emerge.
It’s indeed odd that current dnn’s require massive amount of energy to retrain and lack any kind of practical continuous adaptation and learning.
When you say “massive amount of energy” are you comparing the energy requirements to a single human or to the billions of years of solar and geothermal energy that went into producing the human species?
If I had a nickel for every time some neurologist tried to compare brains to neural networks. It's a surefire way to tell someone is either desperate for grant money or has been smoking crack. (previously: comparing brains and "electronic computers") Their entire article hinges on the complaint "brain seems shallow and neural networks are deep, ergo neural networks are doing it wrong." Neurologists seem to have a rea…
> The concept of CNNs didn't come from biology
I just opened a survey paper on CNNs and literally the first sentence of the paper reads:
> “Convolutional Neural Network (CNN) is a well-known deep learning architecture inspired by the natural visual perception mechanism of the living creatures. In 1959, Hubel & Wiesel [1] found that cells in animal visual cortex are responsible for detecting light in receptive fields. Inspired by this discovery…”
Source: https://arxiv.org/pdf/1512.07108.pdf%C3%A3%E2%82%AC%E2%80%9A
If I had a nickel for every time some neurologist tried to compare brains to neural networks. It's a surefire way to tell someone is either desperate for grant money or has been smoking crack. (previously: comparing brains and "electronic computers") Their entire article hinges on the complaint "brain seems shallow and neural networks are deep, ergo neural networks are doing it wrong." Neurologists seem to have a rea…
CNNs actually are biologically inspired. The receptive field in a CNN mimics the way that cortical neurons only respond to stimuli in a restricted region of the visual field. Different cortical neurons have receptive fields that partially overlap to cover the whole visual field [1]. [1] - https://en.wikipedia.org/wiki/Convolutional_neural_network
It's not surprising that we found out later the brain also uses such a fundamental element of signal theory.