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Cake day: June 30th, 2025

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  • I am only going to respond to you once and I will only address the food issue because there’s other stuff I want to do with my evening than debate with someone so clearly firm in a position.

    Are you familiar with the founding if the FDA in America? Its a very interesting story and if you’re open minded enough I’d highly suggest you read The Poison Squad. Before the FDA was created businesses were adulterating their food products with dangerous substances (something that has happened throughout history) in order to make more money. One example is they were adding formaldehyde to milk so it could sit on the shelf longer without smelling spoiled.

    Businesses don’t care about producing a good product, they care about making more money and they will do that in every way they possibly can regardless of how many people it hurts or kills. You may say that “then those businesses will go out of business” but what if every possible option is doing it? What if you can’t tell just from using it? (Good example is asbestos in talc powder). Regulations save lives.

    I’m not trying to say governments are perfect but at least we, as citizens, get more of a say in what it does than we do with any company. That goes 10x for mega corporations who own entire supply lines or industries. I understand you have a problem with government corruption, as everyone should, but to pretend that corporate corruption isn’t just as bad if not worse is fooling yourself.




  • Good question! Unfortunately, I haven’t found a good source myself. I’ve been trying to piece it together based on my theoretical/mathematical neuroscience background and self-study about transformer architectures. The problem is the point neurons in transformers don’t map to neurons in cortex. You have to instead look at the bigger functions that are being computed by each system.

    The best single source to learn more about theoretical neuroscience is Neuronal Dynamics: From Single Neurons to Networks and Models of cognition. But its super math heavy. If you want to easier to digest videos I recommend Artem Kirsanov on youtube. For transformer architecture 3 Blue 1 Brown has a great series of videos about it on YouTube as well.


  • There are positives and negatives to using cells for computing. There are very complex systems that are responsible for making sure cells grow in the correct architecture and are supported property via blood vessels and glial cells. There is also definitely something to be said about glial cells themselves having a role in computation (whole other topic I won’t get into) not just neurons.

    If were just looking at the input output relationship with neurons we can model it pretty well using neuromorphic hardware which is designed to respond like a neuron. There are still gaps but and there are scientists who believe it isn’t close enough.

    Our current hardware can model neurons closely as well but its less efficient at it. The current AI models use point neurons which takes a lot less computing power but I don’t think they’re the way to AGI. Personally I’m a fan of the adaptive exponential leaky integrate and fire model or the spike response model


  • I’m just going to throw in my 2 cents on the AGI issue since I have a better understanding than average. I’m currently a computational neuroscience PhD student. I don’t work in theoretical neuroscience currently but that’s my hope after I finish my doctorate.

    I don’t believe current transformer architectures are capable of AGI and all big AI companies just seem to be adding more parameters to established transformer architectures.

    Tap for spoiler

    From what I understand transformer architectures act similarly to primary sensory cortices, they model complex probability densities. These companies appear to be banking on the idea that adding more training data, having smaller networks working together with different expertise, and having the train of thought (phonological loop) modeled as part of the PDF is enough to reach AGI. I disagree.

    While all of those things are interesting solutions to parts of the problem I don’t think they address some of the bigger issues.

    1. in biological intelligence memory recall is built into the same system as information processing. Associations between things are learned by simultaneously recalling and processing
    2. decision making and higher order processing do not fit into a transformer architecture
    3. temporal encodings are only made, in the transformer system, via the attention mechanism which is much less efficient than a more biological mechanism.