are there any good arguments against the feasibility of WBE
Against the feasibility? Heck yes. It’s a ridiculously hard technical problem. Building a mind from scratch sounds much easier. Airplanes were invented in 1903 and we still don’t have a scanner that can copy birds.
Storage requirements for example. There are about 10^11 neurons and 10^14 synapses. If we don’t model them all, there is absolutely no guarantee that you’ll get a human, let alone a specific human. And there’s some news that glial cells play an important role in cognition, too, so that’s another couple hundred billion cells and more connections.
Even if saying everything important about a cell or synapse only takes 10 bytes, that still a few internetworths of data to keep running in parallel. In contrast, there are plenty of optimizing tricks that you can play with something made out of code rather than opaque physical data. Even if the first seed AI is as complicated as a human brain, it could be smushed down to smaller processing requirements because it’s in an easily manipulable language. So if computing power requirements end up determining which comes first, it seems quite likely that seed AI comes first.
Airplanes were invented in 1903 and we still don’t have a scanner that can copy birds.
To keep the metaphor precise, we’d need only to copy the bird’s technology of flight, which is its physical shape and its flying technique, not the whole internal structure. The reason we don’t do it is that we already have better means of air transportation, rather than unfeasibility of bird emulation.
I disagree—I would argue that, in principle, simulating/emulating a mind would be much easier than building a mind from scratch. My main justification is that simulating a brain is much more straightforward than building one from scratch. They are both undoubtedly extremely difficult tasks, but we are much closer to being able to accomplish the simulation. As a rough measure of this, you can try to look at where current companies and researchers are placing their bets on the problem. For example, brain simulation is a field which is already maturing rapidly (IBM’s project being a keynote example), whereas the state of the art of “mind design from scratch”, as it were, is still essentially speculative. Some groups like Goertzel’s team and others are looking at it, but no big company is taking on the task.
If you count IBM’s simulation of networks (made out of point nodes, not simulated neurons, unless you’re thinking of a different project) as “betting on emulating whole humans,” then why not also count all their work on AI as “betting on building minds from scratch?” And, of course, Google. And, of course, birds.
Against the feasibility? Heck yes. It’s a ridiculously hard technical problem. Building a mind from scratch sounds much easier. Airplanes were invented in 1903 and we still don’t have a scanner that can copy birds.
Storage requirements for example. There are about 10^11 neurons and 10^14 synapses. If we don’t model them all, there is absolutely no guarantee that you’ll get a human, let alone a specific human. And there’s some news that glial cells play an important role in cognition, too, so that’s another couple hundred billion cells and more connections.
Even if saying everything important about a cell or synapse only takes 10 bytes, that still a few internetworths of data to keep running in parallel. In contrast, there are plenty of optimizing tricks that you can play with something made out of code rather than opaque physical data. Even if the first seed AI is as complicated as a human brain, it could be smushed down to smaller processing requirements because it’s in an easily manipulable language. So if computing power requirements end up determining which comes first, it seems quite likely that seed AI comes first.
To keep the metaphor precise, we’d need only to copy the bird’s technology of flight, which is its physical shape and its flying technique, not the whole internal structure. The reason we don’t do it is that we already have better means of air transportation, rather than unfeasibility of bird emulation.
And we do, in fact, have toys that achieve flight by being bird-shaped and flapping their wings.
I disagree—I would argue that, in principle, simulating/emulating a mind would be much easier than building a mind from scratch. My main justification is that simulating a brain is much more straightforward than building one from scratch. They are both undoubtedly extremely difficult tasks, but we are much closer to being able to accomplish the simulation. As a rough measure of this, you can try to look at where current companies and researchers are placing their bets on the problem. For example, brain simulation is a field which is already maturing rapidly (IBM’s project being a keynote example), whereas the state of the art of “mind design from scratch”, as it were, is still essentially speculative. Some groups like Goertzel’s team and others are looking at it, but no big company is taking on the task.
If you count IBM’s simulation of networks (made out of point nodes, not simulated neurons, unless you’re thinking of a different project) as “betting on emulating whole humans,” then why not also count all their work on AI as “betting on building minds from scratch?” And, of course, Google. And, of course, birds.