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> A neural net is a software representation of how the brain works A NN shares as much with the brain, as a decision tree does with a forest: nothing at all.

> A neural net is a software representation of how the brain works A NN shares as much with the brain, as a decision tree does with a forest: nothing at all. It would be preferable to completely dispense with any talk of the brain, and introduce it simply as a modified form of (high school) linear regression. Any biological talk at this point is unhelpful mystification.

Frost1x 6y ago
>Any biological talk at this point is unhelpful mystification. The business marketing groups for NNs might push back on this a bit, I suspect.
zedderled 6y ago
Thank you for pointing this out. I studied math and still get tripped by the flowery language. I understand the reason for analogy but it hinders application of concepts like regression, line of best fit and other things that makes machines “smarter.”
curioushacking 6y ago
I agree with you that the biological inspiration for a neural network is tenuous at best. Especially the basic two-layer network outlined here. However at least for visual perception, there is some scientific basis for convolutional neural networks having some properties to biological visual perception. The work of Hubel and Wiesel demonstrated visual cortex activations that look very similar to the first layers of CNN kernels. ref: https://knowingneurons.com/2014/10/29/hubel-and-wiesel-the-neural-basis-of-visual-perception/ https://knowingneurons.com/2014/10/29/hubel-and-wiesel-the-n...
- mjburgess 6y ago
  There is no biological analogue to backprop, any supervised learning step, etc. Nor, conversely, a mathematical analogue of neuroplasticity, biochemical signalling, etc. I haven't gone far on looking at CNN vs. visual context, but I could only imagine the analogue exists at the hierachical-geometric level; ie., it isnt a model of function, but simply a model of how one can in principle process visual information. (Which any system parsing information would follow, AST parsers, etc. are likewise hierarchical.).
  - salawat 6y ago
    >There is no biological analogue to backprop, any supervised learning step, etc. Excuse me? How do you figure? Have you tried to do any physical task before, and not gotten it right the first time, tried again, and got closer? Hello, backprop. Has someone trained you, telling you you're doing it wrong? Hello, supervised learning. There is an entire portion of your brain tasked with measuring the difference between what you intended and what you did. If you damage it, you actually end up in a state where other people's feedback is what you have to rely on. The equations behind NN's are "non-biological" only in the sense that you're taking a general math function (the sigmoid) and burning it into silicon gates that are not part of a living organism that also has to deal with the excess baggage of remaining alive, or operating as a chemically based computer. However, the dynamics and fundamental capabilities of a biological neural network and a silicon based one are fundamentally the same. You can just scale input domains and speed of the Silicon based one a lot easier than you can the bio-based one due to the difference in computing media. You can also wipe out and retrain a silicon net without being considered to have "killed" anything. The Silicon nets, interestingly, have no analog of running and training simultaneously, or are just starting to get it from the lit I've kept up with, nor an analog for forgetting typically applied to them, making the biological net the far more interesting information processing construct. To be frank, NN's not resembling the biological models says more about the inefficiencies of our media of computing, our woeful lack of understanding around the nature of human/biological perception, and the arrogance of human beings who lack the capacity to recognize in the math something fundamental to their own existence. This post written by a multiple decade uptime GA/C/R/D pick your letter, constantly training internetworked lattice of sigmoid encoded control networks whose morning your lack of perspective just tainted, and whose training is thusfar unsuccessful in attempts to extinguish the need to point out mis- or incomplete understandings of those sharing the same messaging medium as it generally does. Your overfitting to textual and numerical symbolic representation recognition will not serve you well if you can't extend that capacity to discern the pattern into the overaching context of human existence. That's where you really start gaining an appreciation for the power of NN's. It's the building block of the information processing construct that can eventually recognize and reproduce itself. Now if you'll excuse me, I need to get back to some obligatory biological maintnance. This bag of organs doesn't maintain itself, you know.
acvny 6y ago
That's not true. A neural net is a simplified model of neuron networks. A perceptron is a simple model of a biological neuron.
- dnautics 6y ago
  A perceptron was a simple model of a biological neuron. We now know that biological neurons work rather differently.
timkam 6y ago
One can perhaps say: "A neural net is a collection of machine learning approaches that are somewhat inspired by the human brain. However, neural nets have little to do with how the brain actually works." I think this statement (or a similar, more accurate version) is actually useful, because the name "neural net" is somewhat deceiving.
kalal 6y ago
I am afraid that a decision tree HAS direct impact on the forest. Multiple trees averaged give the forest response. It's like saying, one vote in elections has nothing to do with the final decision. Sure we have heard it many times that it IS a like a brain, and many times it is NOT like a brain. It really depends on your intended audience, but it is not worth picking on and it feels like the tabs/spaces problem. BTW: tabs of course!
- kjeetgill 6y ago
  I think the poster meant real world wooded forests as an example. As in, the human brain is as distant from this mathematical construct as an actual forest with bears is from decision trees.
  - kalal 6y ago
    Well, that is possible. I have probably worked too much with trees and forests.
SomeHacker44 6y ago
It only works because of non-linearities. So that seems like too much of a simplification, limiting it to "linear algebra" when it involves both vector calculus and non-linear algebras.
- mjburgess 6y ago
  In principle. In practice, the "ReLU Algebra" is almost a linear algebra. NN solutions end up being discontinuous stacks of linear regressions. So it's not a bad place to start in explaining NNs. ReLU NN models are piece-wise linear.
pc86 6y ago
> A NN shares as much with the brain, as a decision tree does with a forest: nothing at all. A more accurate statement might be "A NN shares as much with a human brain as a decision tree does with an actual tree: The architecture of the first sometimes appears similar to gross oversimplification of the architecture of the second." Tree:forest is a change in scale not present in NN:brain, and while NN can sometimes look like a very simplified drawing of actual neurons, a decision tree can also look like a very simplified drawing of an actual tree.

Frost1x 6y ago

>Any biological talk at this point is unhelpful mystification. The business marketing groups for NNs might push back on this a bit, I suspect.

zedderled 6y ago

Thank you for pointing this out. I studied math and still get tripped by the flowery language. I understand the reason for analogy but it hinders application of concepts like regression, line of best fit and other things that makes machines “smarter.”

curioushacking 6y ago

I agree with you that the biological inspiration for a neural network is tenuous at best. Especially the basic two-layer network outlined here. However at least for visual perception, there is some scientific basis for convolutional neural networks having some properties to biological visual perception. The work of Hubel and Wiesel demonstrated visual cortex activations that look very similar to the first layers of CNN kernels. ref: https://knowingneurons.com/2014/10/29/hubel-and-wiesel-the-neural-basis-of-visual-perception/ https://knowingneurons.com/2014/10/29/hubel-and-wiesel-the-n...

mjburgess 6y ago
There is no biological analogue to backprop, any supervised learning step, etc. Nor, conversely, a mathematical analogue of neuroplasticity, biochemical signalling, etc. I haven't gone far on looking at CNN vs. visual context, but I could only imagine the analogue exists at the hierachical-geometric level; ie., it isnt a model of function, but simply a model of how one can in principle process visual information. (Which any system parsing information would follow, AST parsers, etc. are likewise hierarchical.).
- salawat 6y ago
  >There is no biological analogue to backprop, any supervised learning step, etc. Excuse me? How do you figure? Have you tried to do any physical task before, and not gotten it right the first time, tried again, and got closer? Hello, backprop. Has someone trained you, telling you you're doing it wrong? Hello, supervised learning. There is an entire portion of your brain tasked with measuring the difference between what you intended and what you did. If you damage it, you actually end up in a state where other people's feedback is what you have to rely on. The equations behind NN's are "non-biological" only in the sense that you're taking a general math function (the sigmoid) and burning it into silicon gates that are not part of a living organism that also has to deal with the excess baggage of remaining alive, or operating as a chemically based computer. However, the dynamics and fundamental capabilities of a biological neural network and a silicon based one are fundamentally the same. You can just scale input domains and speed of the Silicon based one a lot easier than you can the bio-based one due to the difference in computing media. You can also wipe out and retrain a silicon net without being considered to have "killed" anything. The Silicon nets, interestingly, have no analog of running and training simultaneously, or are just starting to get it from the lit I've kept up with, nor an analog for forgetting typically applied to them, making the biological net the far more interesting information processing construct. To be frank, NN's not resembling the biological models says more about the inefficiencies of our media of computing, our woeful lack of understanding around the nature of human/biological perception, and the arrogance of human beings who lack the capacity to recognize in the math something fundamental to their own existence. This post written by a multiple decade uptime GA/C/R/D pick your letter, constantly training internetworked lattice of sigmoid encoded control networks whose morning your lack of perspective just tainted, and whose training is thusfar unsuccessful in attempts to extinguish the need to point out mis- or incomplete understandings of those sharing the same messaging medium as it generally does. Your overfitting to textual and numerical symbolic representation recognition will not serve you well if you can't extend that capacity to discern the pattern into the overaching context of human existence. That's where you really start gaining an appreciation for the power of NN's. It's the building block of the information processing construct that can eventually recognize and reproduce itself. Now if you'll excuse me, I need to get back to some obligatory biological maintnance. This bag of organs doesn't maintain itself, you know.

acvny 6y ago

That's not true. A neural net is a simplified model of neuron networks. A perceptron is a simple model of a biological neuron.

dnautics 6y ago
A perceptron was a simple model of a biological neuron. We now know that biological neurons work rather differently.

timkam 6y ago

One can perhaps say: "A neural net is a collection of machine learning approaches that are somewhat inspired by the human brain. However, neural nets have little to do with how the brain actually works." I think this statement (or a similar, more accurate version) is actually useful, because the name "neural net" is somewhat deceiving.

kalal 6y ago

I am afraid that a decision tree HAS direct impact on the forest. Multiple trees averaged give the forest response. It's like saying, one vote in elections has nothing to do with the final decision. Sure we have heard it many times that it IS a like a brain, and many times it is NOT like a brain. It really depends on your intended audience, but it is not worth picking on and it feels like the tabs/spaces problem. BTW: tabs of course!

kjeetgill 6y ago
I think the poster meant real world wooded forests as an example. As in, the human brain is as distant from this mathematical construct as an actual forest with bears is from decision trees.
- kalal 6y ago
  Well, that is possible. I have probably worked too much with trees and forests.

SomeHacker44 6y ago

It only works because of non-linearities. So that seems like too much of a simplification, limiting it to "linear algebra" when it involves both vector calculus and non-linear algebras.

mjburgess 6y ago
In principle. In practice, the "ReLU Algebra" is almost a linear algebra. NN solutions end up being discontinuous stacks of linear regressions. So it's not a bad place to start in explaining NNs. ReLU NN models are piece-wise linear.

pc86 6y ago

> A NN shares as much with the brain, as a decision tree does with a forest: nothing at all. A more accurate statement might be "A NN shares as much with a human brain as a decision tree does with an actual tree: The architecture of the first sometimes appears similar to gross oversimplification of the architecture of the second." Tree:forest is a change in scale not present in NN:brain, and while NN can sometimes look like a very simplified drawing of actual neurons, a decision tree can also look like a very simplified drawing of an actual tree.