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> it is unlikely to be qualitatively different from a neural network I'm sorry but this shows a profound misunderstanding of what a NN is, and what the brain i
by mjburgess 6y ago
> it is unlikely to be qualitatively different from a neural network
I'm sorry but this shows a profound misunderstanding of what a NN is, and what the brain is.
There are no "neural network"s. The NN algorithm is a method for optimizing the parameters of a piece-wise linear regression model.
These regression models have no homology to any brain structure and the process of producing them ("training") has no neurological analogue either. They can be produced with a variety of algorithms.
Here's one: f(x) = max(0, 2max(0, 3x - 0.1) - 0.5)
The phrase "neural network" is, as peddled by the media and poorly informed lecturers, a lie. It is neither neural nor a network. It's just gradient descent with more parameters.
A model of the brain would model, at least: neuroplasticity, biochemical signalling, activation frequency, etc.
There is nothing about a peice-wise linear regression model which does any of that. No matter how many circles and lines you draw. (NB. essentially any mathematical function can be drawn as a "neural network". The "network" is just a way of diagramming function application & dot-products).
Aside from all that, the body-brain system of animals is a physical process whose properties are not abstract. The reason we are intelligent is because we have bodies capable of causal analysis; and that capability is biophysical.
To put it another way, no algorithm which runs on a digital computer will turn it into gold. Not even one called, "the midas algorithm".
- tsimionescu 6y agoVery good points! I would add that we don't yet have a complete model of the computational properties of even a non-neural cell, even though we know that all cells exhibit simplistic perception and decision-making. This is not to say that we should expect some non-turing weirdness or anything 'mystical' like that, but that we just haven't taken the time to analyze the exact mechanisms and to model cells at this level well enough to be able to say that they are similar to any particular computational structure. Thus, to claim that the brain in its entirety is similar to any particular computational structure such as an NN is either wrong or trivial - the only level at which 'similarity' can be claimed is that they are both useful in computation, and likely turing complete. Anything more specific is fanciful thinking.
- i-am-curious 6y agoIt's amazing just how many people believe that neural networks have a close resemblance to the human brain.
- plutonorm 6y agoCausal. Imagine a video. Each frame is plotted in space, so instead of a sequence of 2d images, you have a cube. The z axis of the cube shows each frame of the video in turn. This is an equivalent representation to the representation that we are used to. We have just switched the time dimension for a physical dimension. What you are reverently calling causality, is only correlation along the z spacial dimension. In the same way an image can be compressed due to the similarities across it's 2 dimensions, so can a 3d representation of video be compressed along all 3 of it's axis. An appreciation of causality is only induction and induction is only a correlation, if a then probably b. Finding correlations in one of the dimensions you are more familiar with, like the pixels of an image is no different to finding it in time. Within language the elements of time are encoded, just as they are within the video represented as a cube. "The boy threw the ball, the ball landed and then rolled." Just because the time dimension is represented to the neural network within the input parameters of a single iteration of the neural network, does not make it any less able to understand correlations across time.
- mjburgess 6y agoSo this is to repeat hume and mistake causal analysis as a kind of induction or inference. It isnt. Our bodies are the primary site of "causal analysis". Eg., when I touch the hot surface of a stove I do not infer that temperature is a cause of my hand being hot. Such an inference, as the basis for our models of the world, would be -- as you/hume/etc. say -- deeply insufficient. The operation of the world on our bodies is already laden with causal information. A hand striking a face does not just create a "painful sense impression"... rather the body encodes it as caused by the object you also saw. It is the action of the world upon the body that is the bedrock of our causal model building. Scientific/inferential processes sit on top of that to disambiguate between possible distal causes. A machine, as a person, given "mere data" cannot hope to do much.
- hackinthebochs 6y agoNone of this explains how modelling time sequences of events does not sufficiently approximate the kind of causal knowledge you mention.