A couple of things come to mind, but I've only been studying the surrounding material for around eight months so I can't guarantee a wholly accurate overview of this. Also, even if accurate, I can't guarantee that you'll take to my explanation. Anyway, the first thing is that brain form computing probably isn't a necessary or likely approach to artificial general intelligence (AGI) unless the first AGI is an upload. There doesn't seem to be good reason to build an AGI in a manner similar to a human brain and in fact, doing so seems like a terrible idea. The issues with opacity of the code would be nightmarish (I can't just look at a massive network of trained neural networks and point to the problem when the code doesn't do what I thought it would). The second is that consciousness is not necessarily even related to the issue of AGI, the AGI certainly doesn't need any code that tries to mimick human thought. As far as I can tell, all it really needs (and really this might be putting more constraints than are necessary) is code that allows it to adapt to general environments (transferability) that have nice computable approximations it can build by using the data it gets through it's sensory modalities (these can be anything from something familiar, like a pair of cameras, or something less so like a geiger counter or some kind of direct feed from thousands of sources at once). Also, a utility function that encodes certain input patterns with certain utilities, some [black box] statistical hierarchical feature extraction [/black box] so it can sort out useful/important features in its environment that it can exploit. Researchers in the areas of machine learning and reinforcement learning are working on all of this sort of stuff, it's fairly mainstream. As far as computing power - the computing power of the human brain is definitely measurable so we can do a pretty straightforward analysis of how much more is possible. As far as raw computing power, I think we're

As far as we know, it easily could require an insanely high amount of computing power. The thing is, there are things out there that have as much computing power as human brains—namely, human brains themselves. So if we ever become capable of building computers out of the same sort of stuff that human brains are built out of (namely, really tiny machines that use chemicals and stuff), we'll certainly be able to create computers with the same amount of raw power as the human brain. How hard will it be to create intelligent software to run on these machines? Well, creating intelligent beings is hard enough that humans haven't managed to do it in a few decades of trying, but easy enough that evolution has done it in three billion years. I don't think we know much else about how hard it is. [...] Well, "bootstrapping" is the idea of AI "pulling itself up by its own bootstraps", or, in this case, "making itself more intelligent using its own intelligence". The idea is that every time the AI makes itself more intelligent, it will be able to use its newfound intelligence to find even more ways to make itself more intelligent. Is it possible that the AI will eventually "hit a wall", and stop finding ways to improve itself? In a word, yes. [...] There's no easy way. If it knows the purpose of each of its parts, then it might be able to look at a part, and come up with a new part that does the same thing better. Maybe it could look at the reasoning that went into designing itself, and think to itself something like, "What they thought here was adequate, but the system would work better if they had known this fact." Then it could change the design, and so change itself.