“Whole Brain Emulation: Reverse Engineering a Mind” By Randal A. Koene

As fiancés know, setting a date is a double-edged sword.  Goals seem more tangible and apt to plan around, but unkept promises usually end with someone looking foolish.

Pushing the envelope and making plans about the future is what was intended at Among the high-profile thinkers speaking at Global Future 2045: Towards a New Strategy for Human Evolution, Randal A. Koene delivered a speech on brain emulation:

“I am going to discuss whole brain emulation, about what it takes to reverse engineer a mind. (…) : How does all this come together? How could you reverse engineer a mind? (…) How do you actually determine the goals for something like that?

(…) So, I think when you’re talking about what are the goals of something like Whole Brain Emulation (WBE), the real challenge is to first determine what is it that we’re trying to replicate here when you reverse engineer a mind? (…) . It’s the forces that are then initiating an electrical signal inside my nerve that travels up my arm; and I’m not even aware of that until it’s getting processed in my mind.

So, it’s really this processing, that’s the important part here. We are evolved to be able to experience (…)

Now, it’s not just about this matter of survival. Survival is important, but there’s also the matter of where do we want to go? Where do we want to develop towards? It’s a matter of what can we experience, what is the fullness of where we can go with our ability to take on challenges? (…)

(…) Now, if we think of a software analogy, it’s kind of like what we’re trying to do is we’re trying to build different ways of implementing the same function so that it works in another platform. It’s kind of like building platform independent code. (…)

In the last hundred years, neuroscience has mostly focused on looking at these basic components in the brain(…)

So what I want to make, first of all, is a distinction between simulating and emulating (…)

You’ve got projects there that are taking statistical data from a lot of animals looking at how the cells connect to one another, what sort of neurotransmitters are there, and using stochastic data to put together a model with which you then can simulate dynamics activity.

Now, if you’re looking at a specific piece of tissue and you want to make something like a neuroprosthetic (…) You don’t just want a generic idea. So this, the specific way of putting the neural circuitry together, that’s what we call an emulation.

(…) The most important point I just want to make right now is that you need to have some success criteria. You need to know what are we aiming at here? When do we call it a successful emulation? How far do you have to go with this?

And there are some subjective things going on there. Because when you say, what would you call – what would you still consider your “self” or how do you say that your “awareness”, your “experience” is fully experienced and it is you? Well, I’m not the same person I was when I was five years old. I’m definitely not the same person. And if I jumped from then to now, it probably wouldn’t seem like that was still me. But with all the pieces in between, all the gradual changes that were going on, it seems acceptable somehow. So you could think that there is some degree of change that is acceptable, that is a close enough approximation of whatever you call the system and its experiences.

(…) What you do if you have a big problem like that is you break it down into smaller problems.

(…) The first one, which is here, Validating Scope and Resolution, is really about iterative hypothesis testing.(…)

Then there’s the structural part where we know that we’re breaking the system down into pieces, so we need to know how are these pieces communicating with one another?

The third one is the functional characterization, which is that we need to understand inside of each of the simpler systems, “So how do they work?” (…)

And then we need someplace where you can put all that data together, represent what it means, have a platform on which you could emulate a brain. (…)


Really, what we’re trying to do in this whole process, aside from figuring out how deep do we need to go, what do we need to do to create this experience of “being,” in an emulation, is: Where’s that sweet spot between developing ever-more-complicated technology that needs to get at a higher resolution of data, and where can we go by filling in the system, by building these model parameters out, by computing, by optimizing parameters, which is really something that kind of works in the opposite direction. If you have more measurements, your systems become smaller. The systems become easier to predict. If you have less measurements then you have bigger systems, so you have a larger computational problem, and you also need to observe the system over a longer period of time.

(…) And the thing is, right now we’ve got this wonderful infrastructure here, science infrastructure, an economy that is able to tackle big problems and we don’t know for sure if we’re going to have that in 20, 40, or 60 years. How can you say for sure what you’re going to have then, right? So we have this opportunity now to understand more about ourselves, to learn what it means to exist, and to help us thrive. So I think that we really should grab that opportunity.”

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