Futurology, Science, Technology

Bioprinted fairy drones

As Arthur C. Clarke wrote, any sufficiently advanced technology is indistinguishable from magic. In the case of bioprinted fairy drones, the tech only looks like magic because it isn’t advanced enough.

Bioprinting is the 3D printing of organic material. It’s been demonstrated for years in various different capacities, but the current state-of-the-art suggests that we’re as far from printing a fully-functional organ as a we are from inorganic 3D printers printing a fully-functional car — you can do something that superficially looks right, but doesn’t have all (or even a bare minimum) of the functionality.

Some of the problems bioprinting has are even the same problems that inorganic 3D printing has: There are a lot of different cell (/material) types, and you can’t get away with using the wrong thing. Just as jet engines don’t work too well when 3D printed out of pure plastic, you don’t want to mix up kidney cell types (plural: there are multiple types) with artery cell types.

Other problems are unique to bioprinting: while houses and boats (or rather, the empty shells of houses and boats) are limited only by the range of the printer, organic material has a tendency to die very quickly if it doesn’t get any oxygen, and getting oxygen into tissue without a heart is very difficult. Difficult, but for small things, possible, and that’s where fairies come in.

Fairies, at least in their Victorian-era depictions, are tiny. Not actually small enough to deal with all the oxygen diffusion issues by themselves, but small enough that it’s plausible tissue could be printed in a cryo-preserved state (which does work, just not for human-sized creatures), and then the complete organism thawed out alive when printing is finished. Their diminutive size also makes their wings actually plausible, whereas a human-sized biodrone would need ridiculous wings to fly.

At this point, normal people will be asking ethical questions about their brains and lifespan. As they’ve been printed, this is absolutely the wrong question: you absolutely should not even try to print a brain into them in the first place — and not just because of the ethical dimension! We couldn’t even design a functional brain yet because we don’t actually understand brains very well (if we did, every A.I. question from self-driving cars to social media moderation would already be solved), but even if we understood brains perfectly, the brain and nerve tissues are particularly awkward one to print as axons and dendrites give them pointy bits which go all over the place in ways which directly matter to them being useful.

So, instead of giving them brains, give them WiFi. Instead of eyes, give them cameras. Congratulations, you now have a bioprinted fairy drone.

You may ask: Why?

Fair question. Other than size-fetishists, who benefits from a tiny flying humanoid robot? Well, pretty much everyone. While they couldn’t do any heavy lifting, the entire history of human invention all the way back to the inclined plane, the wheel, and fire, has been to minimise our heavy lifting. What tiny flying human-shaped organic robots can do is not limited to themselves, but part of the entire ecosystem of machines in our world, one of which is swarm robotics that lets them work together much more effectively than a mere team of humans, and at basically the same range of tasks.

So, my answer to “why” is a slight variant on an old meme of a question: Would you rather compete against a single 1.8m tall human, or a thousand pocket-sized fairies all working together?

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Futurology, Minds, Philosophy, Politics, SciFi, Technology, Transhumanism

Sufficient technology

Let’s hypothesise sufficient brain scans. As far as I know, we don’t have better than either very low resolution full-brain imaging (millions of synapses per voxel), or very limited high resolution imaging (thousands of synapses total), at least not for living brains. Let’s just pretend for the sake of argument that we have synapse-resolution full-brain scans of living subjects.

What are the implications?

  • Is a backup of your mind protected by the right to avoid self-incrimination? What about the minds of your pets?
  • Does a backup need to be punished (e.g. prison) if the person it is made from is punished? What if the offence occurred after the backup was made?
  • If the mind state is running rather than offline cold-storage, how many votes do all the copies get? What if they’re allowed to diverge? Which of them is allowed to access the bank accounts or other assets of the original? Is the original entitled to money earned by the copies?
  • If you memorise something and then get backed up, is that copyright infringement?
  • If a mind can run on silicon for less than the cost of food to keep a human healthy, can anyone other than the foremost mind in their respective field ever be employed?
  • If someone is backed up then the original is killed by someone who knows the person was backed up, is that murder, or is it the equivalent of a serious assault that causes a small duration of amnesia?
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AI, Futurology, Opinion, Philosophy

Memetic monocultures

Brief kernel of an idea:

  1. Societies deem certain ideas “dangerous”.
  2. If it possible to technologically eliminate perceived dangers, we can be tempted to do so, even when we perceived wrongly.
  3. Group-think has lead to catastrophic misjudgments.
  4. This represents a potential future “great filter” for the Fermi paradox. It does not apply to previous attempts at eliminating dissenting views, as they were social, not technological, in nature, and limited in geographical scope.
  5. This risk has not yet become practical, but we shouldn’t feel complacent just because brain-computer-interfaces are basic and indoctrinal viruses are fictional, as universal surveillance is sufficient and affordable, limited only by sufficiently advanced AI to assist human overseers (perfect AI not required).
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AI, Futurology

Pocket brains

  • Total iPhone sales between Q4 2017 and Q4 2018: 217.52 million
  • Performance of Neural Engine, component of Apple A11 SoC used in iPhone 8, 8 Plus, and X: 600 billion operations per second
  • Estimated computational power required to simulate a human brain in real time: 36.8×1015
  • Total compute power of all iPhones sold between Q4 2017 and Q4 2018, assuming 50% were A11’s (I’m not looking for more detailed stats right now): 6525.6×1015
  • Number of simultaneous, real-time, simulations of complete human brains that can be supported by 2017-18 sales of iPhones: 177

 

  • Performance of “Next-generation Neural Engine” in Apple A12 SoC used in Phone XR, XS, XS Max: 5 trillion operations per second
  • Assuming next year’s sales are unchanged (and given that all current models use this chip and I therefore shouldn’t discount by 50% the way I did previously) number of simultaneous, real-time, simulations of complete human brains that can be supported by 2018-19 sales of iPhones: 1.30512×1021/36.8×1015 = 35,465

 

  • Speedup required before one iPhone’s Neural Engine is sufficient to simulate a human brain in real time: 36.8×1015/5×1012 = 7,360
  • When this will happen, assuming Moore’s Law continues: log2(7360)×1.5 = 19.268… years = January, 2038
  • Reason to not expect this: A12 feature size is 7nm, silicon diameter is ~0.234nm, size may only reduce by a linear factor of about 30 or an areal factor of about 900 before features are atomic. (Oh no, you’ll have to buy a whole eight iPhones to equal your whole brain).

 

  • Purchase cost of existing hardware to simulate one human brain: <7,360×$749 → <$5,512,640
  • Power requirements of simulating one human brain in real time using existing hardware, assuming the vague estimates of ~5W TDP for an A12 SoC are correct: 7,360×~5W → ~36.8kW
  • Annual electricity bill from simulating one human brain in real time: 36.8kW * 1 year * $0.1/kWh = 32,200 US dollars
  • Reasons to be cautious about previous number: it ignores the cost of hardware failure, and I don’t know the MTBF of an A12 SoC so I can’t even calculate that
  • Fermi estimate of MTBF of Apple SoC: between myself, my coworkers, my friends, and my family, I have experience of at least 10 devices, and none have failed before being upgraded over a year later, so assume hardware replacement <10%/year → <$551,264/year
  • Assuming hardware replacement currently costs $551,264/year, and that Moore’s law continues, then expected date that the annual replacement cost of hardware required to simulate a human brain in real time becomes equal to median personal US annual income in 2016 ($31,099): log2($551,264/$31,099) = 6.22… years = late December, 2024
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