Tag Archives: Biology

Signal Mappers Decouple

Andrew Sullivan notes that Tim Lee argues that ems (whole brain emulations) just won’t work:

There’s no reason to think it will ever be possible to scan the human brain and create a functionally equivalent copy in software. Hanson … fails to grasp that the emulation of one computer by another is only possible because digital computers are the products of human designs, and are therefore inherently easier to emulate than natural systems. … Digital computers … were built by a human being based on a top-down specification that explicitly defines which details of their operation are important. The spec says exactly which aspects of the machine must be emulated and which aspects may be safely ignored. This matters because we don’t have anywhere close to enough hardware to model the physical characteristics of digital machines in detail. Rather, emulation involves re-implementing the mathematical model on which the original hardware was based. Because this model is mathematically precise, the original device can be perfectly replicated.

You can’t emulate a natural system because natural systems don’t have designers, and therefore weren’t built to conform to any particular mathematical model. … Creating a simulation of a natural system inherently means means making judgment calls about which aspects of a physical system are the most important. And because there’s no underlying blueprint, these guesses are never perfect: it will always be necessary to leave out some details that affect the behavior of the overall system, which means that simulations are never more than approximately right. Weather simulations, for example, are never going to be able to predict precisely where each raindrop will fall, they only predict general large-scale trends, and only for a limited period of time. … We may have relatively good models for the operation of nerves, but these models are simplifications, and therefore they will differ in subtle ways from the operation of actual nerves. And these subtle micro-level inaccuracies will snowball into large-scale errors when we try to simulate an entire brain, in precisely the same way that small micro-level imperfections in weather models accumulate to make accurate long-range forecasting inaccurate. … Each neuron is itself a complex biological system. I see no reason to think we’ll ever be able to reduce it to a mathematically tractable model. (more; Eli Dourado agrees; Alex Waller disagrees.)

Human brains were not designed by humans, but they were designed. Evolution has imposed huge selection pressures on brains over millions of years, to perform very particular functions. Yes, humans use more math that does natural selection to assist them. But we should expect brain emulation to be feasible because brains function to process signals, and the decoupling of signal dimensions from other system dimensions is central to achieving the function of a signal processor. The weather is not a designed signal processor, so it does not achieve such decoupling. Let me explain.

A signal processor is designed to mantain some intended relation between particular inputs and outputs. All known signal processors are physical systems with vastly more degrees of freedom than are contained in the relevant inputs they seek to receive, the outputs they seek to send, or the sorts of dependencies between input and outputs they seek to maintain. So in order manage its intended input-output relation, a single 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. Really, just ask most any signal-process hardware engineer.

Now sometimes random inputs can be useful in certain signal processing strategies, and this can be implemented by coupling certain parts of the system to most any random degrees of freedom. So signal processors don’t always want to minimize extra couplings. But this is a rare exception to the general need to decouple.

The bottom line is that 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.

We are confident that the number of relevant signal dimensions in a human brain is vastly smaller than its physical degrees of freedom. But we do not know just how many are those dimensions. The more dimensions, the harder it will be to emulate them. But the fact that human brains continue to function with nearly the same effectiveness when they are whacked on the side of the head, or when flooded with various odd chemicals, shows they have been designed to decouple from most other physical brain dimensions.

The brain still functions reasonably well even flooded with chemicals specifically designed to interfere with neurotransmitters, the key chemicals by which neurons send signals to each other! Yes people on “drugs” don’t function exactly the same, but with moderate drug levels people can still perform most of the functions required for most jobs.

Remember, my main claim is that whole brain emulation will let machines substitue for humans through the vast majority of the world economy. The equivalent of human brains on mild drugs should be plenty sufficient for this purpose – we don’t need exact replicas.

Added 7p: Tim Lee responds:

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. …

Biologists know a ton about proteins. … Yet despite all our knowledge, … general protein folding is believed to be computationally intractible. … 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. … 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.

My claim is that, in order to create economically-sufficient substitutes for human workers, we don’t need to understand how the brain works beyond having decent models of each cell type as a signal processor. Like the weather, protein folding is not designed to process signals and so does not have the decoupling feature I describe above. Brain cells are designed to process signals in the brain, and so should have a much simplified description in signal processing terms. We already have pretty good signal-processing models of some cell types; we just need to do the same for all the other cell types.

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Ban Mirror Cells

Imagine a mirror reversed cell, made of mirror-reversed molecules. If it gained energy via photosynthesis, or via special adaptations that enable it to eat ordinary life, the fact that it was immune to ordinary predators and disease would give it a huge advantage; it could take over much of the biosphere. Sounds like a good reason not to make mirror cells right? Unfortunately, there are now big efforts to develop mirror cells, because they’d be a handy biotech tool for pumping out lucrative mirror proteins. Yes this is a real gain, and yes there are ways to try to stop mirror cells from getting loose and destroying the biosphere. But really, the gains here seem easily outweighed by the risks. This is a pretty clear case justifying strong global regulation or bans. Alas, I can find no movement in this direction. Details:

A life-form … based on mirror-image versions of earthly proteins and DNA. … If it worked, those new cells … might also open up new avenues of discovery in materials science, fuel synthesis, and pharmaceutical research. On the down side, though, mirror life wouldn’t have any predators or diseases to limit its reproduction. …

A catastrophe was under way across the Charles River at Genzyme, one of the largest biotech companies in the world. … A virus that disrupts cell reproduction infected one of the bioreactors. The entire plant had to be shut down. … When Church talks about mirror life’s quirky advantages, invulnerability to this kind of mishap is high on his list. “Viruses can’t touch a mirror cell,” … This makes mirror life a potential workhorse for biotech. … Church has been hacking the ribosome. … His plan is to make one that reads regular RNA transcripts of genes but can string together wrong-handed amino acids to form mirror proteins. … Church and his team have cracked the first step. … Last year his team got a synthetic ribosome to self-assemble and produce luciferase, the protein that makes fireflies glow. And he has a library of mutant ribosomes that have the right kind of sockets—they’ll accept mirror amino acids. This is where the money comes in. Some of the most valuable drugs are actually tiny proteins that include wrong-handed amino acids—like the immunosuppressant cyclosporine. To manufacture it, pharmaceutical companies have to rely on an inefficient and expensive fungus. A hacked ribosome modified to handle both normal and mirror amino acids could crank out the stuff on an industrial scale. …

Church thinks even bigger. A manufacturing ribosome would be great, but a fully domesticated mirror cell—able to synthesize more-complicated stuff—would change everything. … vats of virus-proof mirror cells could pump out biofuel, lay down nano-size organic circuitry, and even extrude organic cement foundations for skyscrapers. …

Of course, mirror life could also kill us all. … Just as viruses from our side of the mirror can’t infect it, mirror pathogens can’t infect us. … They might be poisonous, though. … To a mirror cell, … there’s just not enough nutrition for them in the wild. … On the other hand, if mirror cells somehow evolved—or were engineered—to consume normal fats, sugars, and proteins, we might have a problem. … Mirror cells would slowly convert edible matter into more of themselves. … If mirror cells acquired the ability to photosynthesize, we’d be screwed. … All it would take would be a droplet of mirror cyanobacteria squirted into the ocean. Cyanobacteria are at the base of the ocean’s food pyramid, converting sunlight and carbon dioxide into more of themselves … That would wipe out the global ocean ecology. …

“I would be the first to say that we shouldn’t make a photosynthetic mirror cell,” Church says. “But I’m reluctant to have a moratorium on something that doesn’t exist yet.” He says he’d build safeguards into his mirror cells so they’d perish without constant care. And the advances in synthetic biology required to transform those first delicate mirror cells into anything that could survive in the wild are even more remote.

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Brain Size Is Not A Filter

We relate brain size to appearance time for 511 fossil and extant mammalian species to test for temporal changes in relative brain size over time. We show that there is wide variation across groups in encephalization slopes across groups and that encephalization is not universal in mammals. … Encephalization [vs. time] trends are associated with sociality in extant species. These findings … highlight the role sociality may play in driving the evolution of large brains. (more; HT Razib Khan)

The biggest brains have consistently gotten bigger over the last half billion years since multi-cellular life appeared. Big brains seem to be a necessary precondition for human level intelligence and civilization, and human size brains appeared only very recently. These facts strongly suggest that achieving human level intelligence is just not a big component of the great filter.  It appeared quickly after big brains, and big brains seem likely given enough time and sociality, and sociality seems likely.

This unfortunately means that it is very difficult to collect data on all steps of the great filter.  It is big and real and matters enormously, but we can hardly see it.

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Green Is Far, Mostly Pink

I’m teaching environmental econ again for the first time in six years, and reviewing some standard texts two things stand out:

  1. Green Is Mostly Pink – Weighted by public or private efforts, most environmental policies are focused on limiting the harm some “pink” humans do to others via intermediaries of air, water, food, light, or sound. Whether that harm passes through green stuff is incidental to such policies. Much of the rest focuses on vague concerns that current human ways are not “sustainable.” What little concern there is about green stuff out there is mostly to ensure humans have nice green places to visit when they want, and that humans avoid guilt for stuff that happens out there due to their intervention. Actually concern for the welfare of green stuff from its own point of view is pretty minimal.
  2. Green Is Far – At first, I found it hard to see what the various “environmental” topics have in common. Air purity, food locality, future human population, animal experiments, oil & mineral depletion, energy efficiency, sea levels, urban sprawl, landfills, consumerism, – what unites such diverse topics? And then it hit me: they are mostly rather “far“. That is, “environmental” concerns tend to be at unusually large distances in space, time, and social relation from ordinary folks and concerns. The common theme seems to be how we here now relate to much larger contexts, and the oddities of far-mode thinking go a long way to explain odd enviro thoughts.  Cosmology would be super-green, if folks thought we had a non-trivial relation to non-Earth life.
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More Diversity Or Less?

Nature reviews Biology’s First Law: The Tendency for Diversity and Complexity to Increase in Evolutionary Systems:

McShea and Brandon state that diversity and complexity tend to increase over time in biological systems. It is, the authors argue, a universal law, applicable to all taxa, at all hierarchical levels and at all times. They use the analogy of Newton’s law of inertia — just as it tells us that a body will move with a constant velocity if no forces act on it, this zero-force evolutionary law seeks to capture how a biological system will behave in the absence of other influences. Although the trend they describe may not manifest itself in cases when it is counteracted by constraints, it provides the background against which other evolutionary pressures should be understood.

The authors adopt a simplified measure of complexity that considers only the degree of differentiation among the parts of a biological system, not the various functions of those parts. … The authors argue persuasively that their simpler definition of complexity is more scientifically useful … because function is hard to quantify. … Diversity at one level of the hierarchy equates to complexity one level higher. Both diversity and complexity will increase over time through the accumulation of mutations, they suggest. …

The tendency for increasing diversity has been recognized previously in specific situations. … The authors aim to encompass these various findings in a single theory that covers all of the fields in which the principle has been seen. … They make a good case for their argument that a single principle is at work. …

Their theory suggests new research questions, such as whether the tendency for diversity to increase will usually be overcome by natural selection, and it advances our philosophical understanding of evolution. The law also makes testable predictions: for example, that diversity and complexity will increase fastest in ecological circumstances and taxa where selection is weak.

This is a deliciously vast topic, with huge long term implications. Overall, diversity has clearly increased within biology on average over time. Very recently, humans have displaced other biology diversity with human diversity. Within the human realm, many kinds of diversity have also increased, though some kinds have decreased as well. The big open question: will diversity continue to increase, or at least not greatly decrease, into the distant future?

One the one hand you might think that physics is the same everywhere, matter doesn’t vary that much, and there is only one very best way to arrange atoms for any particular purpose. So within a million years we’ll figure out the most competitive local designs and from then on everyone will use them.  Surely there is a lot of truth in this.

On the other hand, the very best design for any one thing may depend greatly on other choices made nearby, and ancient legacies, choices made long ago that are too expensive to change, may vary greatly from place to place. And there should be far far more places out there, only weakly connected to each other due to vast distances and light-speed limits.

On a third hand (oh someone will have them), the future might not be competitive, if a stable world government arises before our descendants radiate rapidly out into the cosmos, and if there are no aliens that matter out there. Such a stable central power might work to reduce diversity, to cement its hold on power. (More on world govt here, here, and here.) Or perhaps it will have stable preferences, unchallengeable power, and prefer to create diversity.

So, will diversity increase in the long run?

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Ancient Hobbits

It remains one of the greatest human fossil discoveries of all time. The bones of a race of tiny primitive people, who used stone tools to hunt pony-sized elephants and battle huge Komodo dragons, were discovered on the Indonesian island of Flores in 2004. …

According to a growing number of scientists, Homo floresiensis is probably a direct descendant of some of the first apemen to evolve on the African savannah three million years ago. …  It sounds improbable but the basic physical similarity between the two species is striking. … Analysis of Lucy’s skeleton shows it has great similarities with the bones of H. floresiensis, although her species died out millions of years ago while the hobbits hung on in Flores until about 17,000 years ago. …

The crucial point about this interpretation is that it explains why the Flores people had such minuscule proportions. … In research that provides further support for this idea, scientists have recently dated some stone tools on Flores as being around 1.1 million years old, far older than had been previously supposed. … He has now uncovered stone tools on nearby Sulawesi. These could be almost two million years old.

More here.  HT Tyler.

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Animal Smoking Studies

Some seem to think experiments show smoking causes cancer in animals.  Not so, for mice or rats:

I review the results of a representative selection of chronic inhalation studies with rats and mice exposed to mainstream cigarette smoke. … Smoke-induced epithelial hypertrophy, hyperplasia, and squamous metaplasia were reported in the conducting airways in most of the studies, along with increased numbers of intra-alveolar macrophages that were occasionally associated with alveolar metaplasia. Lung adenomas and adenocarcinomas were reported in only a few of the studies. No statistically significant increase in the incidence of malignant lung tumors was seen. …

The 14 studies reviewed … [showed] significant increases in the numbers of malignant tumors were not produced in the respiratory tracts of rats or mice exposed chronically by inhalation to cigarette smoke.  The studies clearly involved the inhalation of very large amounts of smoke (usually from unfiltered, high-tar cigarettes) …  The results of this work clearly indicate that maximal amounts of smoke were inhaled into the lungs of the animals (blood COHb concentrations very close to those associated with lethality) daily for up to 2 yr with no carcinogenic effect noted.

Nor for hamsters, dogs, or primates:

This paper makes an identical evaluation as before, but, restricting the species being evaluated to representative studies of smoke-exposed hamsters, dogs (both by tracheostomy and by direct inhalation), and nonhuman primates. As was seen previously, no statistically significant increase in the incidence of malignant tumors of the respiratory tract was found in any of the 3 species, even though very long exposures and high doses of smoke were used.

Now the number of animals in these studies is a few thousand at most, and their duration is less than decades, but experimenters did have complete control over making animals smoke heavily.  Yes this review author works for a tobacco firm, but his papers seem professional.

Searching for “animal smoking experiments,” I found many sources admitting we haven’t found much evidence smoking hurts animals, and none saying the opposite.  Here is a ’97 Scientific American article “Animal Research is Wasteful and Misleading”: Continue reading "Animal Smoking Studies" »

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Monogamy Is Human

It easier to maintain pair bonding in larger primate social groups if males can’t easily tell when females are fertile.  In turn, monogamy makes it easier to keep the peace in larger groups.  And since folks in large groups have more uses for big brains, and more resources to pay for them, monogamous social apes should have bigger brains.  So monogamy encouraged by hidden female fertility may have been the key to humans succeeding far beyond other apes.

Why might we think this?  Chimps are humans’ closest living relatives, splitting apart 5-7 million years ago.  The Ardipithecus ramidus proto-humans of 4.4 million years ago were bipeds with a broad diet in woods and grasslands, and with a brain

about the same size as a modern bonobo or female common chimpanzee brain … The less pronounced nature of [their] upper canine teeth … has been used to suggest that the last common ancestor of homonids and African apes was characterized by relatively little aggression between males and between groups.

A recent Science article persuasively elaborated this argument:

Elimination of the [upper canine teeth] in hominids is unique among all higher primates and occurred long before Australopithecus. … Available evidence now suggests [it] was, as is theoretically most likely, a social adaptation … consistent with a strategy of increasingly targeted provisioning. …. Males would benefit from enhanced male-to-male cooperation …. Foraging could be achieved most productively by cooperative male patrols … Provisioning would reduce female-to-female competition … and would improve (or maintain) social cohesion. …

A large brain is not our most unique characteristic. … The combination of [upper canine teeth] elimination, habitual bipedality, and reproductive crypsis (each in itself an extreme rarity) is unique among all known mammals. Conversely, simple brain enlargement is readily explicable in myriad ways.

They plausibly suggest that these three key uniquely human features appeared together over 4 million years ago, leading over time to our uniquely large human social groups and brains, and all else they imply.

If monogamy is this essential to human success, that does make me a bit more concerned about current trends away from monogamy.  Of course hunter-gatherer monogamy may only have been for 4+ year periods, and we are in some ways moving more toward that.  But still, it gives me pause.

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Humans Are Evolving

The team studied 2238 women who had passed menopause and so completed their reproductive lives. For this group, Stearns’s team tested whether a woman’s height, weight, blood pressure, cholesterol or other traits correlated with the number of children she had borne. They controlled for changes due to social and cultural factors to calculate how strongly natural selection is shaping these traits.

Quite a lot, it turns out. Shorter, heavier women tended to have more children, on average, than taller, lighter ones. Women with lower blood pressure and lower cholesterol levels likewise reared more children, and – not surprisingly – so did women who had their first child at a younger age or who entered menopause later. Strikingly, these traits were passed on to their daughters, who in turn also had more children.

If these trends continue for 10 generations, Stearns calculates, the average woman in 2409 will be 2 centimetres shorter and 1 kilogram heavier than she is today. She will bear her first child about 5 months earlier and enter menopause 10 months later.

More here. And this is just for the few parameters tested in this study; no doubt many more features are evolving as well.

Our culture respects taller thinner women who wait longer before having kids, but in fact we are evolving short heavy women who have kids earlier.  Shades of Idiocracy – in many ways we are evolving to become less of what we now respect.

In principle humans could implement strong central regulations to ensure that they evolved to become the sort of creatures they respect, at least regarding a few features of regulatory focus.  But it is far from clear that we are willing, or even able, to achieve this.  And it is far from clear to me that we would be better off achieving such far ideals. Perhaps short plump early moms are happier, after all.

Of course I expect that within a century the main dynamic will be even faster robot evolution, but the same principle will apply – without strong central coordination they are unlikely to evolve to become what we or they most respect.

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Bad News On Human Extinction

Disasters that destroy all but a thousand humans are more likely than disasters that destroy all but a hundred humans.  So this news says human extinction is more likely than we thought:

Conservation biologists may be deluding themselves. An analysis of the minimum number of individuals needed for a species to survive in the long term has found that current conservation practices underestimate the risk of extinction by not fully allowing for the dangers posed by the loss of genetic diversity. If correct, it means the number of individuals in endangered species are being allowed to dwindle too far.

Lochran Traill at the University of Adelaide, Australia, and colleagues found that for thousands of species the minimum viable population size (MVP) – where a species has a 90 per cent chance of surviving the next 100 years – comes in at thousands rather than hundreds of individuals. Many biologists, Traill says, work with lower numbers and so allow unacceptably high extinction risks.

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