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https://onlinelibrary.wiley.com/doi/10.1002/anie.202008228

He published it in the age when the concept of structure was being worked out, and supported his rule with experiments (no NMR, but classical methods of identification).

Um no. References 10-13 don't establish the fact, if you look at them. Convolution kernels long predate their use in neural networks as a convolution layer.

The sliding NxM convolution window is the "receptive field" but it isn't analogous to the field in the eye. The kernel matrix existed long before it was used in NNs, and is the mapping mechanism to the fully connected convolution input layer.

https://en.wikipedia.org/wiki/Kernel_(image_processing)

Thanks for being interested, but there is a lot of fluffery in ML discussions. The neurons of a NN are not remotely the same as biological neurons - the only thing they share in common is the activation function, and even then they are only symbolically similar.

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It's more that he worked out through trial and error a principle that hadn't been thoroughly explored, and published the findings as a theory only.

When technology advanced enough to detect what was happening and prove the theory correct, he was given the credit with the name of the Rule

> Convolutional networks are simply fully connected a

uhm no.

https://en.wikipedia.org/wiki/Convolutional_neural_network

Convolutional networks were inspired by biological processes[10][11][12][13] in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.

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In short, based on what you are describing, a LLM is a terrible tool for the compression of its training data in comparison to virtually any other reasonable compression technique one could think of, by any metric.

When you talk about compression, you're generally talking about some raw data that you run through an algorithm which compresses it into a more manageable form, and then you run it through another algorithm to recover the raw data again with some amount of lossiness (or it can be lossless). AI models can do that, sure, but they are not designed to be data structures for storage and retrieval -- in a simplified ANN model they take new training data that is given to them, and in adjusting their weights the model can now interpolate between this new data and previous training data. That might, however, make it so that now asking this model to recall a specific piece of old training data will result in an even fuzzier, less-faithful output, the tradeoff being that the model can now be asked about hypothetical data between what it's been trained on. (I'll have to find a good intro guide for a simple ANN model that illustrates this with diagrams.) None of this gets into space, time, or resource efficiency, but those are all guaranteed to be worse than a dedicated compression algorithm in any practical as well.

I suppose you can look at a broad overview of how data compression works in general. There are ANN/AI algorithms for compression -- they use the predictive network to essentially tune an existing deterministic compression algorithm, optimizing it for the data that's being compressed. That's not anywhere close to similar to taking an ANN like a large language model and locating the compressed data entirely in the ANN's weights.

I don't know if this helps -- I can try to clarify stuff or provide some better articles if you like.

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A vaccine is a weakened or incapacitated version of a disease that teaches your body how to fight of a real infection. This can be both a virus or a bacteria.

Penicilin is a medicine that kills bacteria.

Vaccinations prevent sickness Penecilin (or other antibiotics) cure bacterial infections

Vaccines are not a treatment for sick people You cant prevent whats already there

No. Convolutional networks are simply fully connected all-combinations of every pixel in the image (under a sliding window, usually). They are not modeled after any brain, they are modeled after signal processing convolution filters (pre-neural network) for 2D signals. The learning epochs of the convolution network teach the network which pixels to pay attention to at the meta level (features), and the further levels combine those features.

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Replying to my earlier post because the we-can't-see-the-decayed-structures-that-were-made-from-trees is speculation extrapolated from history. Greeks started out with temples made of trees. The vertical lines/grooves of the stylized marble columns we are left with were made to mimic tree trunks. The Egyptians started out with leather and chests for crypt or burial funerary goods made from real animal hides and real wood. As materials became more scarce, faux leather was used and wood grain-like grooves made from woven reeds for the chests that we're left with.

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It's actually a very important distinction. A vaccine just exposes a specific protein target to the host's immune system.

Thing is, each host immune system is going to create a completely different cocktail of antibodies to bind that protein. Suppose you and I got vaccinated for covid with the same vaccine. If you compared our resulting antibodies they would be completely different and also likely bind to different parts of the target protein.

The process of developing immunity is a pretty big subject and it's important to emphasize that.

Larger groups of people to create and cooperatively share or use the built environment, yes. This requires communication of a certain mutual sophistication. But it does not mean necessarily they were all living together, all the time. We have churches standing today that have taken hundreds of years to build from start to finish. This is even with dense populations, the technology, tools and know-how to construct it in a particular style. Many intervening events delayed the build.

What the changes were that led to larger cooperation, presumably over long spans of time across dozens of generations, are where historical record meets speculation. Some agroforestry researchers believe balanophagy (acorns as the main dietary source of calories and nutrients) declined as demand for trees for other uses became prevalent, which pushed the rise of the less-nutritive and more effortful agriculture. Numerous other options have been suggested. Perhaps structures were initially created from trees for thousands of years before megaliths became the norm.

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Thank you. Really tired of people not understanding that permanent immunity is a property of our immune system, not the virus.