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Brain simulation - Matt Bamberger's journal
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mattbamberger
mattbamberger
Brain simulation

There are two basic approaches to building an AI. The traditional approach is to write an artificial intelligence from scratch. A less sophisticated but perhaps more tractable approach is to simply simulate the workings of an actual human brain. For a variety of reasons that I'll detail below, I don't think this is the best approach, but I think it's an excellent fallback. If traditional approaches fail to deliver a working AI in a timely fashion (which they very well may), there's an excellent chance that brain simulation will be able to deliver a good enough AI within the next few decades.

Why brain simulation is likely to succeed

  • Unlike traditional AI, brain simulation requires solving relatively few big problems. In order to simulate a brain, it's probably sufficient to understand in detail how each of the 50 or so different types of neurons work, and how the different parts of the brain are connected. That's a lot of knowledge, but it's a well-constrained, well-understood problem space, and one in which we're making very rapid advances. The key point is that you can completely simulate a human being without necessarily having any deep understanding of how human intelligence actually works.
  • The requisite computational power is almost here. The amount of computational power required to completely simulate a human brain at the neural level is probably about 20 petaflops. Computers with that capacity will be available within a few years, and will be ubiquitous within the next two decades.

Nonetheless, brain simulation is harder than it sounds

Although running a simulation of a brain is hard, I don't think it's the hardest part of making brain simulation actually work. There are three non-obvious but significant obstacles to producing something useful:

  • Assuming that we have the knowledge required to completely simulate all the components of a human brain and all their interactions, we still have to know the exact structure and connectivity map of the brain. That is to say, it's not sufficient to be able to run a simulation: you also have to know the exact starting state. This is difficult because it requires either a complete high-fidelity scan of an existing fully-grown brain, or else the ability to simulate with high fidelity the growth of a human brain (which is a separate and equally complicated field), plus the ability to mature the simulated brain (which presumably involves "raising" it from infancy).
  • By definition, a fully-functional simulation of a human brain is a fully-fledged person, in ways that a traditional AI would not necessarily be. There are a variety of hard ethical issues associated with experimenting on human beings.
  • It's not clear to what extent a human brain is capable of functioning without a body and rich sensory input. It's possible that running a virtual brain will also require running a virtual body and/or virtual world, each of which is a substantial problem in its own right.

Brain simulation is more powerful than many people realize

On the face of it, the ability to create a fully functional human being isn't that profound an accomplishment. My partner and I have accomplished that very thing on two separate occasions, using only common tools we found around the house. However, simulated brains have some very interesting properties that potentially make them far more capable than real humans, even in the absence of any ability to understand or tinker with their core capabilities.

  • Virtual brains can run very quickly. A virtual brain running on a computer doesn't have to run in realtime. If you can run a brain in realtime on a given computer, you can run the same brain a thousand times faster on a computer that's a thousand times faster. A fast AI isn't any smarter than a slow one, but it can accomplish the same amount of work a lot quicker. A computer programmer running at 1000X realtime can accomplish in one year what a real programmer would take 1000 years to accomplish.
  • You can clone AIs. One obvious implication of this is that you can populate your AI population exclusively with geniuses. A less obvious implication has to do with knowledge-sharing and task balancing. When a new version of the flu appears, you can instantly suspend all of your architect and engineer AIs, and reboot them as copies of your leading flu expert. Another use for AI cloning is getting around boredom. A lot of jobs require a high level of expertise, but involve a great deal of boring and repetitive work. By rebooting your doctor AI before each new patient, you get a doctor who's always fresh and focused on the task at hand (and who can instantly morph into whatever specialist the patient turns out to need).

Limitations of brain simulation

The above notwithstanding, there are some serious problems with brain simulation that make it problematic.

  • Simulated people are by definition just like people. That means they're cranky and sneaky and prone to behaving badly. Those are bad properties in an AI.
  • It's hard to extend the core capabilities of a simulated brain. One of the advantages of the simulation approach is that it requires very little deep understanding of how human intelligence actually works. The downside of that is that it makes it very hard to improve the AI. Even fairly simple tasks like increasing the AI's memory or directly transferring knowledge or skills from one AI to another become very hard problems. Virtual brains will be super-fast, super-capable people, but they're unlikely to be genuinely superhuman.

Overall, I see brain simulation as a good backup choice. I'd much rather see a traditional approach succeed, because its end result is likely to be both safer and more powerful. In the eventuality that traditional approaches don't succeed, however, I think that virtual brains will very probably become feasible within the next few decades.

Artificial Development's CCortex project is, as far as I know, the largest and most interesting virtual brain project currently underway.

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