Reply to Jones on Ems

In response to Richard Jones’ book review, I said:

So according to Jones, we can’t trust anthropologists to describe foragers they’ve met, we can’t trust economics when tech changes society, and familiar design principles fail for understanding brains and tiny chemical systems. Apparently only his field, physics, can be trusted well outside current experience. In reply, I say I’d rather rely on experts in each field, relative to his generic skepticism. Brain scientists see familiar design principles as applying to brains, even when designed by evolution, economists see economics as applying to past and distant societies with different tech, and anthropologists think they can understand cultures they visit.

Jones complained on twitter that I “prefer to argue from authority rather than engage with their substance.” I replied “There can’t be much specific response to generic skepticism,” to which he replied, “Well, there’s more than 4000 words of quite technical argument on the mind uploading question in the post I reference.” He’s right that he wrote 4400 words. But let me explain why I see them more as generic skepticism than technical argument.

For context, note that there are whole fields of biological engineering, wherein standard engineering principles are used to understand the engineering of biological systems. These include the design of many specific systems with organisms, such as lungs, blood, muscles, bone, and skin, and also specific subsystems within cells, and also standard behaviors, such as gait rhythms and foraging patterns. Standard design principles are also used to understand why cells are split into different modules that perform distinct functions, instead of having each cell try to contribute to all functions, and why only a few degrees of freedom for each cell matters for that cell’s contribution to its system. Such design principles can also be used to understand why systems are abstract, in the sense of as having only one main type of muscle, for creating forces used for many purposes, one main type of blood system, to move most everything around, or only one main fast signal system, for sending signals of many types.

Our models of the function of many key organs have in fact often enabled us to create functional replacements for them. In addition, we already have good models of, and successful physical emulations of, key parts of the brain’s input and out, such, as input from eyes and ears, and output to arms and legs.

Okay, now here are Jones’ key words:

This separation between the physical and the digital in an integrated circuit isn’t an accident or something pre-ordained – it happens because we’ve designed it to be that way. For those of us who don’t accept the idea of intelligent design in biology, that’s not true for brains. There is no clean “digital abstraction layer” in a brain – why should there be, unless someone designed it that way?

But evolution does design, and its designs do respect standard design principles. Evolution has gained by using both abstraction and modularity. Organs exist. Humans may be better in some ways than evolution at searching large design spaces, but biology definitely designs.

In a brain, for example, the digital is continually remodelling the physical – we see changes in connectivity and changes in synaptic strength as a consequence of the information being processed, changes, that as we see, are the manifestation of substantial physical changes, at the molecular level, in the neurons and synapses.

We have programmable logic devices, such as FPGAs, which can do exactly this.

Underlying all these phenomena are processes of macromolecular shape change in response to a changing local environment. .. This emphasizes that the fundamental unit of biological information processing is not the neuron or the synapse, it’s the molecule.

But you could make that same sort of argument about all organs, such as bones, muscles, lungs, blood, etc., and say we also can’t understand or emulate them without measuring and modeling them them in molecular detail. Similarly for the brain input/output systems that we have already emulated.

Determining the location and connectivity of individual neurons .. is necessary, but far from sufficient condition for specifying the informational state of the brain. .. The molecular basis of biological computation means that it isn’t deterministic, it’s stochastic, it’s random.

Randomness is quite easy to emulate, and most who see ems as possible expect to need brain scans with substantial chemical, in addition to spatial, resolution.

And that’s it, that is Jones’ “technical” critique. Since biological systems are made by evolution human design principles don’t apply, and since they are made of molecules one can’t emulate them without measuring and modeling at the molecular level. Never mind that we have actually seen design principles apply, and emulated while ignoring molecules. That’s what I call “generic skepticism”.

In contrast, I say brains are signal processing systems, and applying standard design principles to such systems tells us:

To manage its intended input-output relation, a signal processor simply must be designed to minimize the coupling between its designed input, output, and internal channels, and all of its other “extra” physical degrees of freedom. ..  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.

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  • 4hodmt

    Adrian Thompson’s research into evolving circuits in FPGAs found no digital abstraction layer. The evolved circuit was very sensitive to analog implementation details, to the point of reliance on parasitic circuit elements for correct function.

    An Evolved Circuit, Intrinsic in Silicon, Entwined with Physics:
    http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.9691&rep=rep1&type=pdf

  • http://juridicalcoherence.blogspot.com/ Stephen Diamond

    Does your argument depend on cognition being a form of signal transmission? The signal-transmission model probably applies to sensation, but even perception goes beyond the data. (In Ulric Neisser’s phrase, analysis is accomplished by synthesis.)

    • http://overcomingbias.com RobinHanson

      Processing is very different from transmission. Processing can go well beyond the data.

      • http://juridicalcoherence.blogspot.com/ Stephen Diamond

        “Signal Mappers Decouple” (which you linked) seems intended to address intuitions like mine. There you define signal processing: “A signal processor is designed to maintain some intended relation between particular inputs and outputs.”

        But human cognition (unlike sensation) doesn’t seem designed to maintain some intended input-output relation. Instead it serves to optimize the relationship between output and the (probable) environment. How certain are we that the brain accomplishes (or would best accomplish) optimization to the environment through signal processing?

      • http://overcomingbias.com RobinHanson

        There isn’t any other option we know of other than to collect signals from the environment, infer that environment from those signals, and then calculate actions to benefit us in that environment.

  • Silent Cal

    A point that I sometimes suspect em-skeptics of missing is that emulations don’t need to be exact. If we discard a given feature as noise, the question isn’t whether we maintain perfect fidelity, it’s whether we retain substantial functionality.

    For instance, it seems possible that the effects of a concussion are mediated by effects most neural models would discard. But being unable to model a concussion would not hinder the em revolution in the slightest.

    • Peter David Jones

      How much you can discard depends on how much you understand. If you understood everything, you could build an AI.

  • arch1

    1) I think you meant “…since they are made of molecules one can’t emulate them withOUT measuring and modeling at the molecular level.”

    2) While bio *does* employ modularity, isn’t it also true that bio is much more casual about modularity than engineering is? My impression is that in bio, one-to-many and many-to-many mappings of function to subsystem are common; while in engineering, modularity has to date been absolutely essential to e.g. large-team development, efficient maintenance, and efficient enhancement.

    • http://overcomingbias.com RobinHanson

      Typo fixed; thanks.

  • http://www.asansor.gen.tr celikermakina
  • brianholtz

    Jones’ objection to Hanson is indeed unfounded. But there is a more fundamental technological problem with Whole Brain Emulation: http://blog.knowinghumans.net/2016/07/why-age-of-em-will-not-happen.html