problem: I've read arguments for WBE, but I can't find any against.
Most people agree that WBE is the first step to FAI (EDIT: I mean to say that if we were going to try to build AGI in the safest way possible, WBE would be the first step. I did not mean to imply that I thought WBE would come before AGI). I've read a significant portion of Bostrom's WBE roadmap. My question is, are there any good arguments against the feasibility of WBE? A quick google search did not turn up anything other than
This video. Given that many people consider the scenario in which WBE comes before AGI, to be safer than the converse, shouldn't we be talking about this more? What probability do you guys assign to the likelihood that WBE comes before AGI?
Bostrom's WBE roadmap details what technological advancement is needed to get towards WBE:
Different required technologies have different support and drivers for development. Computers are developed independently of any emulation goal, driven by mass market forces and the need for special high performance hardware. Moore’s law and related exponential trends appear likely to continue some distance into the future, and the feedback loops powering them are unlikely to rapidly disappear (see further discussion in Appendix B: Computer Performance Development). There is independent (and often sizeable) investment into computer games, virtual reality, physics simulation and medical simulations. Like computers, these fields produce their own revenue streams and do not require WBE‐specific or scientific encouragement.
A large number of the other technologies, such as microscopy, image processing, and computational neuroscience are driven by research and niche applications. This means less funding, more variability of the funding, and dependence on smaller groups developing them. Scanning technologies are tied to how much money there is in research (including brain emulation research) unless medical or other applications can be found. Validation techniques are not widely used in neuroscience yet, but could (and should) become standard as systems biology becomes more common and widely applied.
Finally there are a few areas relatively specific to WBE: large‐scale neuroscience, physical handling of large amounts of tissue blocks, achieving high scanning volumes, measuring functional information from the images, automated identification of cell types, synapses, connectivity and parameters. These areas are the ones that need most support in order to enable WBE. The latter group is also the hardest to forecast, since it has weak drivers and a small number of researchers. The first group is easier to extrapolate by using current trends, with the assumption that they remain unbroken sufficiently far into the future.
Implications for those trying to accelerate the future:
Because much of the technological requirements are going to be driven by business-as-usual funding and standard application, anybody who wants to help bring about WBE faster (and hence FAI) should focus on either donating towards the niche applications that won't receive a lot of funding otherwise, or try to become a researcher in those areas (but what good would becoming a researcher be if there's no funding?). Also, how probable is it that once the business-as-usual technologies become more advanced, more government/corporate funding will go towards the niche applications?
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.
problem: I've read arguments for WBE, but I can't find any against.
Most people agree that WBE is the first step to FAI (EDIT: I mean to say that if we were going to try to build AGI in the safest way possible, WBE would be the first step. I did not mean to imply that I thought WBE would come before AGI). I've read a significant portion of Bostrom's WBE roadmap. My question is, are there any good arguments against the feasibility of WBE? A quick google search did not turn up anything other than
This video. Given that many people consider the scenario in which WBE comes before AGI, to be safer than the converse, shouldn't we be talking about this more? What probability do you guys assign to the likelihood that WBE comes before AGI?
Bostrom's WBE roadmap details what technological advancement is needed to get towards WBE:
Implications for those trying to accelerate the future:
Because much of the technological requirements are going to be driven by business-as-usual funding and standard application, anybody who wants to help bring about WBE faster (and hence FAI) should focus on either donating towards the niche applications that won't receive a lot of funding otherwise, or try to become a researcher in those areas (but what good would becoming a researcher be if there's no funding?). Also, how probable is it that once the business-as-usual technologies become more advanced, more government/corporate funding will go towards the niche applications?