> We aren’t that different from machines, we just need to know more about the CPU and all the co-processors and how the logic gates interact
Except it is unfortunately not that simple, because it assumes that distinct components such as CPU, co-processors and even logic gates exist in that context, as is totally reasonable to assume on devices created by humans. Abstracting complex machines into distinct components is a proven strategy to engineer a system, but it's not a necessity for functioning systems to exist.
In the case of natural organism, they "just" need to work. They don't have a blueprint, and they don't need to be organized in a way that allows for easy understanding by looking at individual parts in separation.
Consider also the difference between machine learning through neural networks ("we stuff a lot of training data in there and get what we want eventually, we hardly understand what the model does or why it fails"), and a QR code reader ("we carefully designed the format from the top down, including e.g. framing, error correction, and several invariants like rotation; if a QR code does not get recognized, we can usually tell exactly where and why it failed").
I am not able to draw the distinction you are trying to make. The more we make machines, especially ones to interact with inputs from our world, the more easy it is to understand our bodies.
Correct, because there is no blueprint then we don't know about how the brains and neurons interact. But if there is a problem with a heart valve we know exactly where and why it failed.
I expect greater convergence in these fields, and as such I can't agree with you.
> But if there is a problem with a heart valve we know exactly where and why it failed.
I highly doubt that. Even for heart valves, which seem less complicated than plenty of other body parts (at first glance, I'm sure they are plenty complicated in detail), because they are comparatively mechanical. For example, Wikipedia says (with citation): "Causes of aortic insufficiency in the majority of cases are unknown, or idiopathic."
Try a kidney or something related to the nervous system next.
> The more we make machines, especially ones to interact with inputs from our world, the more easy it is to understand our bodies.
FWIW I am making machines, and the more I do, the more amazed I am about how intensely complicated our body in general and our nervous system specifically is.
“Imagine a being like nature, wasteful beyond measure, indifferent beyond measure, without mercy and justice, fertile and desolate and uncertain at the same time; imagine indifference itself as a power, how could you live according to this indifference?"
There is a theory "endosymbiotic hypothesis" that mitochondria were wholesale absorbed bacteria into our DNA line. Which I think is pretty wild, and explains how neatly they're compartmentalized.
No doubt, and separate organs with (sometimes rough, sometimes very clear) separate functions are also a thing, so evolution seems to favor compartmentalization for more complex systems. But that does not mean that there are not complex overarching interactions that make full understanding really hard. The sort of interactions you would stay away from when designing a computer, not because it would not work, but because it would make design, debugging, and iteration upon your system prohibitively hard.
Except it is unfortunately not that simple, because it assumes that distinct components such as CPU, co-processors and even logic gates exist in that context, as is totally reasonable to assume on devices created by humans. Abstracting complex machines into distinct components is a proven strategy to engineer a system, but it's not a necessity for functioning systems to exist.
In the case of natural organism, they "just" need to work. They don't have a blueprint, and they don't need to be organized in a way that allows for easy understanding by looking at individual parts in separation.
Consider also the difference between machine learning through neural networks ("we stuff a lot of training data in there and get what we want eventually, we hardly understand what the model does or why it fails"), and a QR code reader ("we carefully designed the format from the top down, including e.g. framing, error correction, and several invariants like rotation; if a QR code does not get recognized, we can usually tell exactly where and why it failed").