Tag Archives: Design

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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Drexler on Engineering

Most of my economics colleagues know little about engineering. Yet much of what they actually want to do in the world, i.e., get people to adopt new better institutions, is better seen as engineering than as science. To help educate them, I quote from Eric Drexler in his new book, explaining the difference between science and engineering:

The essence of science is inquiry; the essence of engineering is design. Scientific inquiry expands the scope of human perception and understanding; engineering design expands the scope of human plans and results. …

•     Scientists seek unique, correct theories, and if several theories seem plausible, all but one must be wrong, while engineers seek options for working designs, and if several options will work, success is assured.
•     Scientists seek theories that apply across the widest possible range (the Standard Model applies to everything), while engineers seek concepts well-suited to particular domains (liquid-cooled nozzles for engines in liquid-fueled rockets).
•     Scientists seek theories that make precise, hence brittle predictions (like Newton’s), while engineers seek designs that provide a robust margin of safety.
•     In science a single failed prediction can disprove a theory, no matter how many previous tests it has passed, while in engineering one successful design can validate a concept, no matter how many previous versions have failed. ..

Simple systems can behave in ways beyond the reach of predictive calculation. This is true even in classical physics. …. Engineers, however, can constrain and master this sort of unpredictability. A pipe carrying turbulent water is unpredictable inside (despite being like a shielded box), yet can deliver water reliably through a faucet downstream. The details of this turbulent flow are beyond prediction, yet everything about the flow is bounded in magnitude, and in a robust engineering design the unpredictable details won’t matter.  …

The reason that aircraft seldom fall from the sky with a broken wing isn’t that anyone has perfect knowledge of dislocation dynamics and high-cycle fatigue in dispersion-hardened aluminum, nor because of perfect design calculations, nor because of perfection of any other kind. Instead, the reason that wings remain intact is that engineers apply conservative design, specifying structures that will survive even unlikely events, taking account of expected flaws in high-quality components, crack growth in aluminum under high-cycle fatigue, and known inaccuracies in the design calculations themselves. This design discipline provides safety margins, and safety margins explain why disasters are rare. …

The key to designing and managing complexity is to work with design components of a particular kind— components that are complex, yet can be understood and described in a simple way from the outside. … Exotic effects that are hard to discover or measure will almost certainly be easy to avoid or ignore. … Exotic effects that can be discovered and measured can sometimes be exploited for practical purposes. …

When faced with imprecise knowledge, a scientist will be inclined to improve it, yet an engineer will routinely accept it. Might predictions be wrong by as much as 10 percent, and for poorly understood reasons? The reasons may pose a difficult scientific puzzle, yet an engineer might see no problem at all. Add a 50 percent margin of safety, and move on.

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Disorganized collection growth

When I was a teenager, I lived in a nice house with my mother, stepfather and three younger brothers. The contents of the house were what you would expect if you took a normal house, multiplied the number of things in it by ten, then shook it very hard. Almost – a greater proportion of the things were in boxes or containers of some kind than you would expect by chance, and also there were narrow trails cleared along the important thoroughfares. For instance there was a clear path to the first few chairs in the living room, from which the more athletic members of the household could jump to most of the other chairs.

This state of affairs interested me. From what little I had seen of other families’ houses, it was pretty unusual. Yet looking at the details of of the processes which produced it, I couldn’t see what was unusual. I don’t remember my exact thoughts, but I figured it had to be something that affected the relative inflow and outflow of stuff from the house. But it wasn’t that we had way more spending power than other families, or that we kept a lot of garbage. Most of the things in the house were useful, or would be if you had a non-negligible chance of finding them. It seemed like my family bought usual kinds of things for usual kinds of reasons. A set of lego for the children to play with, a blender because sometimes we wanted to blend things, a box of second hand books or two because they were only 50c.

The last one there looks a bit problematic, but is not that unusual. People often buy marginally valuable items because they are cheap. There were a few other things like that that looked a bit problematic – a tendency to keep drawings, an inclination to buy several shirts if you found one that was good. But nothing that should obviously cause this massive phase transition into chaos.

In the end I’m still not sure what the dominant problem was, or if there was one. But I can tell you about one kind of failure that I think contributed, which I also notice in other places.

Suppose you have a collection of things, for instance household items. You want to use one, for instance a pair of scissors. Depending on the organization of your collection of household items, it can be more or less tricky to find the scissors. At a certain level of trickiness, it is cheaper to just buy some new scissors than to find the old ones. So you buy the new scissors.

Once you have the new scissors, you add them to your collection of things. This is both the obvious thing to do with items you possess, and the obvious solution to scissors having apparently been too rare amongst your possessions.

Unfortunately adding more scissors also decreases the density of every other kind of thing in the collection. So next time you are looking for a pen it is just a little bit harder to find. If pens are near the threshold where it’s easier to get new pens than find your old pens, you buy some more pens. Which pushes a couple of other items past the threshold. On it goes, and slowly it again becomes hard to find scissors.

In short, a given amount of organization can only support being able to cheaply find so much stuff. You can respond to this constraint by only keeping that much stuff, for instance borrowing or buying then discarding items if they are beyond what your system can keep track of. Or you can respond by continually trying to push the ratios of different things to something impossible, which leads to a huge disorganized pile of stuff.

Another place I notice this is in writing. Suppose you write a blog post. Sadly it is a bit too long for the average reader to remember a key point in the second paragraph. You suspect they will forget it and just fill in what they would expect, consequently missing the whole point. To avoid this, you emphasize the point again in the second last paragraph. But now the post is even longer, and it is not clear whether they will also remember another key part. So you add some more about that point in the conclusion. But now it’s so long the whole argument is probably too hard to piece together, so you add a bit of an outline. Perhaps this eventually reaches an equilibrium in which all the points have been repeated and emphasized and exemplified so much that nobody can fail to understand. Often it would nonetheless have been better to just quit early on.

I think I had a better list of such examples, in a half written post which I put in my collection of blog drafts. Unfortunately my collection is so sprawling and poorly organized that it seemed easier to just write the post again than to find the old one. So here you have it. It’s tempting to add this post too to my blog draft collection and look for it again when I find some more things to add, but no good lies in this direction.

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Virtual Office Design

Imagine that you have an office job (as most of you do). Full of meetings, memos, reports, proposals, phone and email ping pong, informal gossip in the hall or over lunch, etc.

Now imagine that you work in a virtual office. That is, while you are actually lying at home in your VR pod (or being an em brain in a data center), you experience yourself as sharing a virtual office complex with your work colleagues. Sitting at your desk working at your computer, talking in a meeting, chatting with a neighbor in his doorway, or perhaps walking the cubicles to feel the buzz.

OK, now ask yourself: how could we design more effective virtual offices, for the purpose of making an efficient workplace not needlessly taxing its workers? For example, what features of office spaces today would we jettison if we could, since they mainly deal with physical constraints that need not apply in virtual reality?

Maybe each person would feel the temperature and humidity they like best. Maybe walls would glow, instead of all light coming from glaring overhead lights. Maybe you’d always feel like you were walking barefoot on soft grass. Maybe all surfaces could be of the most luxurious textures and styles. Your computer “screen” might fill up a wall, or be 3D in a vast warehouse-sized space. But what else?

People might just appear in each other’s offices, instead of having to walk there, but that might feel disruptive. Perhaps hallways could be lots shorter, with each person having a huge personal corner office looking out on a spectacular view. But would it be ok if the shapes and views of offices and halls made no sense relative to each other?

In meetings it might be possible to let each person see and hear others in great clear detail, even adding biometrics on if they felt scared, tired, etc. You might even be able hear their thoughts if you wished. Or at the other extreme, each person might instead be able to project a pleasant attentive appearance no matter how they actually felt. You might even appear to be in several meetings at once. Where along this spectrum would typically make for the most productive meetings?

If each person could make the walls etc. look however they want to, then how will other people know what they are seeing in order to interact smoothly with them? Would you like the ability to look out at any time and see dozens of people as they work, if the cost were that dozens of people could you look at you at any time?

I’ve read a lot about speculation about virtual reality over the years, but I’ve not seen much that took these sort of questions seriously.

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Adapt Or Start Over?

Sean Carroll has doubts on nanotech:

Living organisms … can, in a wide variety of circumstances, repair themselves. … Which brings up something that has always worried me about nanotechnology … tiny machines that have been heroically constructed … just seem so darn fragile. … surely one has to worry about the little buggers breaking down. … So what you really want is microscopic machinery that is robust enough to repair itself. Fortunately, this problem has already been solved at least once: it’s called “life.” … This is why my utterly underinformed opinion is that the biggest advances will come not from nanotechnology, but from synthetic biology. (more)

There are four ways to deal with system damage: 1) reliability, 2) redundancy, 3) repair, and 4) replacement. Some designs are less prone to damage; with redundant parts all must fail for a system to fail; sometimes damage can be undone; and the faster a system is replaced the less robust it needs to be. Both artificial and natural systems use all four approaches. Artificial systems often have especially reliable parts, and so rely less on repair. And since they can coordinate better with outside systems, when they do repair they rely more on outside assistance – they have less need for self-repair. So I don’t see artificial systems as failing especially at self-repair.

Nevertheless, Carroll’s basic concern has merit. It can be hard for new approaches to compete with complex tightly integrated approaches that have been adapted over a long time. We humans have succeeded in displacing natural systems with artificial systems in many situations, but in other cases we do better to inherit and adapt natural systems than to try to redesign from scratch. For example, if you hear a song you like, it usually makes more sense to just copy it, and perhaps adapt it to your preferred instruments or style, than to design a whole new song like it.  I’ve argued that we are not up to the task of designing cities from scratch, and that the first human-level artificial intelligences will use better parts but mostly copy structure from biological brains.

So what determines when we can successfully redesign from scratch, and when we are better off copying and adapting existing systems? Redesign makes more sense when we have access to far better parts, and when system designs are relatively simple, making system architecture especially important, especially if we can design better architecture. In contrast, it makes more sense to inherit and adapt existing systems when a few key architectural choices matter less, compared to system “content” (i.e., all the rest). As with songs, cities, and minds. I don’t have a strong opinion about which case applies best for nanotech.

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