| >I'm quite sure I've read somewhere that information cannot be lost in the absolute sense, Quantum information is, in some decently well-regarded theories, a conserved quantity. Classical information is not, in any major theory. >In short: if you're smart(fast, precise, determined) enough to look at the individual molecules of a puddle of brain-goo. And if you can infer the way it has collapsed by ray tracing those molecules back through how they collided with each other/the walls of your mold then it should be possible to reconstruct the spatial form of the brains at least. That's a pretty big IF obviously, but equally obviously not impossible. If only you can look deep/far/fast enough. Again: classical information is not a conserved quantity. A puddle of brain-goo will likely tell you more than a puddle of non-brain goo about the person who used to be that brain, but there is a very strong limit to what it can tell you. You cannot, so to speak, extrapolate the universe from one small piece of fairy cake. (Disclaimer: I have previously donated to the Brain Preservation Foundation precisely because I think the issue deserves investigation by mainstream, non-wishful scientists so that people who want to... whatever it is they're planning on, can do it.) >Many knowledgeable people are making guesses based on our current understanding of intelligence/computation/AI, and then extrapolating. The paradoxical thing is that on the one hand AI-doomsday speakers tell us no to anthropomorphise (for good reasons) with the motives of an AI, but on the other hand apply human reasoning/understanding to predict such machines/patterns. The thing about "sufficiently advanced AI" is that it dodges the basic issues. A sufficiently advanced AI is just a machine for crunching data into generalized theories. It can only learn theories in the presence of data. Admittedly, the more data it gets from a broader variety of domains, the more it can form abstract theories that give usefully informed prior knowledge about what it can expect to find in new domains. But if it can use detailed knowledge about brains-in-general to reconstruct a puddle of brain-goo into a solid model of a human brain, solid enough to "make it live", that's not because of some ontologically basic "smartness" about the AI, it's because the AI has the right kind of learning machinery for crunching data about specific and general things together to allow it to learn and utilize very large sums of domain knowledge. These sums could possibly larger than any individual human might obtain in the course of a single 20-year education, from kindergarten to PhD, but the key factor in "AI's" understanding of the natural sciences will ultimately be experimental data and domain knowledge derived from experimental data. |