Formerly u/CanadaPlus101 on Reddit.

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Cake day: June 12th, 2023

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  • My wild guess on how they do that industrially is macerated pulp in water and a big nasty press to squeeze the water back out. Maybe some really cheap adhesive goes in there too, I don’t know.

    For arbitrary molding, you need a certain level of fluidity that will be hard to achieve with a fairly pure pulp. You’ll notice all those packaging materials - or at least the ones I can think of - are shaped to be suitable for stamping. However, it can be used as reinforcement in concrete or a resin!










  • The thing being, it’s kind of an inflexible blackbox technology, and that’s easier said than done. In one fell swoop we’ve gotten all that soft, fuzzy common sense stuff that people were chasing for decades inside a computer, but it’s ironically still beyond our reach to fully use.

    From here, I either expect that steady progress will be made in finding more clever and constrained ways of using the raw neural net output, or we’re back to an AI winter. I suppose it’s possible a new architecture and/or training scheme will come along, but it doesn’t seem imminent.



  • Agreed. The started out trying to make artificial nerves, but then made something totally different. The fact we see the same biases and failure mechanisms emerging in them, now that we’re measuring them at scale, is actually a huge surprise. It probably says something deep and fundamental about the geometry of randomly chosen high-dimensional function spaces, regardless of how they’re implemented.

    Like you said we have no understanding of what exactly a neuron in the brain is actually doing when it’s fired, and that’s not considering the chemical component of the brain.

    I wouldn’t say none. What the axons, dendrites and synapses are doing is very well understood down to the molecular level - so that’s the input and output part. I’m aware knowledge of the biological equivalents of the other stuff (ReLU function and backpropagation) is incomplete. I do assume some things are clear even there, although you’d have to ask a neurologist for details.



  • Both have neurons with synapses linking them to other neurons. In the artificial case, synapse activation can be any floating point number, and outgoing synapses are calculated from incoming synapses all at once (there’s no notion of time, it’s not dynamic). Biological neurons are binary, they either fire or do not fire, during a firing cycle they ramp up to a peak potential and then drop down in a predictable fashion. But, it’s dynamic; they can peak at any time and downstream neurons can begin to fire “early”.

    They do seem to be equivalent in some way, although AFAIK it’s unclear how at this point, and the exact activation function of each brain neuron is a bit mysterious.



  • Coherent originality does not point to the machine’s understanding; the human is the one capable of finding a result coherent and weighting their program to produce more results in that vein.

    You got the “originality” part there, right? I’m talking about tasks that never came close to being in the training data. Would you like me to link some of the research?

    Your brain does not function in the same way as an artificial neural network, nor are they even in the same neighborhood of capability. John Carmack estimates the brain to be four orders of magnitude more efficient in its thinking; Andrej Karpathy says six.

    Given that both biological and computer neural nets very by orders of magnitude in size, that means pretty little. It’s true that one is based on continuous floats and the other is dynamic peaks, but the end result is often remarkably similar in function and behavior.


























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