I'm scared about biological computing

Ethical analogies with animals and veganism

  • Many comments compare biocomputing ethics to factory farming, breeding animals to want to be eaten, or decerebrated animals.
  • Disagreement over whether vegan ethics are directly relevant: some see the core issue as sentience and suffering; others as “rigging” preferences (e.g., dogs bred to love work, hypothetical pigs bred to want to be eaten).
  • Several note everyone “draws a line” (plants vs animals vs specific animals), and accuse some arguments of being inconsistent or relativistic.
  • Some argue lab-grown or non-sentient substrates would largely dissolve vegan objections; others say the underlying moral questions would persist.

Consciousness and moral status

  • Large subthread on whether silicon AIs and biological computers can be conscious.
  • Thought experiments invoked: “China brain,” Chinese room, split-brain patients, philosophical zombies.
  • Positions range from:
    • Strong materialism (“consciousness emerges from physical processes; in principle anything could be conscious”),
    • To hard skepticism (“consciousness is an incoherent or religiously inherited concept”),
    • To views centering consciousness on emotion/brainstem and “felt homeostasis,” implying petri-dish networks are likely non-conscious.
  • Debate on free will and whether humans themselves are just prediction engines / LLM-like.

Doom-playing neuron experiments

  • Multiple commenters stress the neurons are not receiving raw visual input; a conventional neural network encodes game state into electrode signals and decodes outputs.
  • Skepticism that the neurons are truly “seeing” or “playing Doom” versus adding structured noise; some call popular descriptions misleading or “ghost stories.”
  • Others emphasize that, despite hype, there is substantive neuroscience and interesting proof-of-concept learning, especially in simpler “pong” setups without heavy preprocessing.
  • A researcher-like voice explains electrode-count limitations and defends the use of autoencoders.

Prospects and fears of biological computing

  • Some argue biocomputers are inevitable and vastly more energy-efficient, potentially enabling “brains in jars” and new intelligence substrates.
  • Others dismiss strong extrapolations (e.g., rapid “Claude’s law” scaling, live-animal networks) as speculative or ethically horrific.
  • Concern that, absent clear theories of consciousness, we risk creating suffering systems (biological or AI) without knowing where to draw ethical lines.

Media, hype, and epistemic quality

  • Frustration that YouTube-driven narratives and superficial reading distort public understanding of these experiments.
  • Counterpoint: books and other media can mislead too; the real issue is critical thinking and algorithmic amplification of bad ideas.