To emulate a biological signal processor, one need only identify its key internal signal dimensions and their internal mappings – how input signals are mapped to output signals for each part of the system. These key dimensions are typically a tiny fraction of its physical degrees of freedom. Reproducing such dimensions and mappings with sufficient accuracy will reproduce the function of the system.
This is proven daily by the 200,000 people with artificial ears, and will be proven soon when artificial eyes are fielded. Artificial ears and eyes do not require a detailed weather-forecasting-like simulation of the vast complex physical systems that are our ears and eyes. Yes, such artificial organs do not exactly reproduce the input-output relations of their biological counterparts. I expect someone with one artificial ear and one real ear could tell the difference. But the reproduction is close enough to allow the artificial versions to perform most of the same practical functions.
This response confuses me because Hanson seems to be making a different claim here than he made in his EconTalk interview. There his claim seemed to be that we didn’t need to understand how the brain works in any detail because we could simply scan a brain’s neurons and “port” them to a silicon substrate. Here, in contrast, he’s suggesting that we determine the brain’s “key internal signal dimensions and their internal mappings” and then build a digital system that replicates these higher-level functions. Which is to say we do need to understand how the brain works in some detail before we can duplicate it computationally.
The example of artificial ears seems particularly inapt here because the ears perform a function—converting sound waves to electrical signals—that we’ve known how to do with electronics for more than a century. The way we make an artificial ear is not to “scan” a natural ear and “port” it to digital hardware, rather it’s to understand its high-level functional requirements and build a new device that achieves the same goal in a very different way from a natural ear. We had to know how to build an artificial “ear” (e.g. a microphone) from scratch before we could build a replacement for a natural ear.
This obviously gets us nowhere when we try to apply it to the brain. We don’t understand the brain’s functional specifications in any detail, nor do we know how to build an artificial system to perform any of these functions.
With that said, the fundamental conceptual mistake he makes here is the same one he made in his EconTalk interview: relying too heavily on an analogy between the brain and manmade electronic devices. A number of commenters on my last post thought was I defending the view that there was something special, maybe even supernatural, about the brain. Actually, I was making the opposite claim: that there’s something special about computers. Because computers are designed by human beings, they’re inherently amenable to simulation by other manmade computing devices. My claim isn’t that brains are uniquely hard to simulate, it’s that the brain is like lots of other natural systems that computers aren’t good at simulating.
People didn’t seem to like my weather example (largely for good reasons) so let’s talk about proteins. Biologists know a ton about proteins. They know how DNA works and have successfully sequenced the genomes of a number of organisms. They have a detailed understanding of how our cells build proteins from the sequences in our DNA. And they know a great deal about the physics and chemistry of how proteins “fold” into a variety of three-dimensional shapes that make them useful for a vast array of functions inside cells.
Yet despite all our knowledge, simulating the behavior of proteins continues to be really difficult. General protein folding is believed to be computationally intractible (NP-hard in computer science jargon), which means that if I give you an arbitrary sequence of amino acids even the most powerful computers are unlikely to be able to predict the shape of the folded protein within our lifetimes. And simulating how various proteins will interact with one another inside a cell is even more difficult—so difficult that biologists generally don’t even try. Instead, they rely on real-world observations of how proteins behave inside of actual cells and then perform a variety of statistical techniques to figure out which proteins are affecting one another.
My point here isn’t that we’d necessarily have to solve the protein-interaction problem before we can solve the brain-simulation problem—though that’s possible. Rather my point is that even detailed micro-level knowledge of a system doesn’t necessarily give us the capacity to efficiently predict its macro-level behavior. Even in cases where we know all of the components of a system (amino acid sequences in this case) and all the rules for how the interact (the chemistry of amino acids is fairly well understood), that doesn’t mean a computer can tell us what the system will do next. This is because, among other things, nature often does things in a “massively parallel” way that we simply don’t know how to simulate efficiently.
By the same token, even if we had a pristine brain scan and a detailed understanding of the micro-level properties of neurons, there’s no good reason to think that simulating the behavior of 100 billion neurons will ever be computationally tractable. And by that I don’t just mean “on today’s hardware.” The problems computer scientists call “NP-hard” are often so complex that even many more decades of Moore’s Law wouldn’t allow us to solve them efficiently.
Emulating a computer doesn’t involve any of these problems because computers were designed by and for human engineers, and human engineers want systems that are easy to reason about. But evolution faces no such constraint. Natural selection is indifferent between a system that’s mathematically tractable and one that isn’t, and so its probable that evolution has produced human brains with at least some features that are not amenable to efficient simulation in silicon.