Anarcho-Primitivism or Transhumanism?

[Luther Blissett]

Transhumanism is definitely already sneaking up, and will hit a runaway course sooner than we expect. Hence the threat.

[Luther Blissett]

Oiiiiiiiii vey.

There’s a Gene for That
History is littered with horrifying examples of the misuse of evolutionary theory to justify power and inequality. Welcome to a new age of biological determinism.
by Pankaj Mehta


If you want to understand why humans wage wars, there is a gene for that. Want to understand why men rape women? There is a gene for that. Want to understand why the “national characters” of East Asia, the West, and Africa are different? We have those genes covered too. Indeed, if we are to believe most popular media, there is a gene for just about every inequality and inequity in modern society.

Genetic determinism and its uglier cousin, social Darwinism, are making a comeback. Armed with large genomic datasets and an arsenal of statistical techniques, a small but vocal band of scientists are determined to hunt down the genetic basis of all we are and all we do.

The relationship between genetics and biological determinism is almost as old as the field itself. After all, one of the foremost modern institutes of genetics, the Cold Spring Harbor Laboratory, began as a eugenics institute whose activities included “lobbying for eugenic legislation to restrict immigration and sterilize `defectives,’ educating the public on eugenic health, and disseminating eugenic ideas widely.”

The latest wave of biological determinism continues this long history, but differs in a crucial way from the past. We are at the beginning of the genomics era — an era in which advances in molecular biology make it possible to precisely measure minute genetic differences between humans. Combined with the fact that we live in a new Gilded Age where a small global elite has access to, and needs justification for, unprecedented amounts of wealth and power, the conditions are ripe for a dangerous resurgence of biological determinism.

Today it costs $5000 to sequence a genome, to identify the 6 billion As, Cs, Ts, and Gs that define an individual’s DNA. Soon it will cost less — much less. We are told that this is a revolutionary moment. With access to detailed genetic information, medical professionals and genetic counselors will soon be able to identify the diseases we’re predisposed to, and help prevent or minimize their impact through “personalized medicine.”

The scientific knowledge extracted from these data is priceless. We are beginning to understand how viruses evolve, the genetic mutations that give rise to cancers, and the genetic basis of cellular identity. The sequencing revolution has allowed us to study the molecular basis of genetic regulation and identify amazing new players like non-coding RNAs and chromatin modifications. All of our ideas about biology are being reshaped.

One of the most striking results of the new sequencing studies is how similar humans actually are — we differ from each other only in 0.1% of our DNA. Yet this 0.1% of the genome gives rise to the variations we see between people in traits such as skin color, height, and proclivity for disease. An important goal of modern genetics is to relate a particular genomic variant to a specific trait or disease. To do so, scientists are developing powerful new statistical tools to analyze a wealth of sequence data from populations around the world.

The relationship between genes and observable traits is indisputable. Tall parents tend to have tall kids. Dark-haired parents have dark-haired kids. That traits are inherited has been clear since Mendel codified his famous Laws of Inheritance, inferred from statistical observations of over 29,000 pea plants. In classical Mendelian genetics, separate genes encoding for separate traits are passed independently from each other to their offspring. Thus, there is a clear mapping between genetic information, or genotype, and observable traits, or phenotype. A single gene (technically a locus or genetic location) encodes for a single trait and is not influenced by the other traits a person possesses. Furthermore, environmental factors have little influence on most Mendelian traits. Famous examples that fall into this framework include sickle-cell anemia and cystic fibrosis, each caused by a mutation to a specific gene.

However, it is now clear that the simple assumptions underlying Mendelian genetics are not applicable to most traits and diseases. Nearly all phenotypes, from height and eye color to diseases such as diabetes, emerge from extremely complex interactions between multiple genes (loci) and the environment. In contrast to Mendelian genetics, where one can easily identify the gene that encodes for a particular trait, for many traits there is no simple mapping from genotype to phenotype.

The sheer volume of DNA sequence data now available has convinced scientists they can overcome this challenge. To do so, they are developing new scientific and statistical tools geared toward analyzing and extracting genetic information from sequence data. The goal of these genome-wide association studies (GWAS) is to provide a blueprint for decoding the information contained in our DNA, and to identify the genetic basis of complex traits and diseases. GWAS are now a staple of modern population genetics. This is reflected in the astronomical increase in the number of published GWAS in the last decade, from single digits in 2005 to more than thirteen hundred to date. There are GWAS on body height, birth weight, Inflammatory Bowel Disease, how people respond to particular drugs or vaccines, cancers, diabetes, Parkinson’s disease, and more. There are actually so many GWAS that specialized viewers have been created to help scientists visualize the results of all these studies.

Given the increasing prevalence of GWAS, it is useful to explicate the basic logic underlying these studies. The concepts of phenotypic and genetic variations play a central role in GWAS. Phenotypic variation is defined as the variation of a trait in a population (such as the distribution of heights in the population of American men). Note that in order to define phenotypic variation, we must specify a population. This is an a priori choice that must be made to construct a statistical model. The choice of population is often an important source of bias where hidden social assumptions enter GWAS — this is especially true for studies that try to understand genetic variation across “racial” groups.

GWAS try to statistically explain the observed phenotypic variation in terms of the genetic variation in the same population. This is where modern genomics shines. Whereas in the pre-genomic era one had to work hard to measure genetic variation at a single locus, now one can consult a readily available public database to get the genetic variation for thousands of individuals across the entire genome. Most GWAS focus on single-nucleotide polymorphisms (SNPs): DNA sequence variations that occur at a single base in the genome (e.g. AAGGCT vs. AAGTCT). Scientists have observed approximately 12 million SNPs in human populations. This number may seem incredibly large, but there are 6 billion bases in human DNA. So only 0.2% of all DNA bases exhibit any variation across all sampled human populations. For a trait such as height, there are about 180 SNPs known to contribute to human height variation.

The goal of GWAS is to relate genotypic variation to phenotypic variance. This is often expressed in a concept called heritability, which seeks to partition the phenotypic variance into a genetic and an environmental component. Roughly speaking, heritability is defined as the fraction of the phenotypic variation that we can ascribe to genetic variation. A heritability of zero means that all the phenotypic variance is environmental whereas a heritability of one means it is entirely genetic.

Behind the concept of heritability lies a whole world of simplifying assumptions about how biology works and how genes and environment interact, all filtered through increasingly complicated and obtuse statistical models. Heritability depends on the populations chosen and the environments probed by the experiments. Even the clean distinction between environment and genes is at some level artificial. As Richard Lewontin points out:

The very physical nature of the environment as it is relevant to organisms is determined by the organisms themselves. . . . A bacterium living in liquid does not feel gravity because it is so small . . . but its size is determined by its genes, so it is the genetic difference between us and bacteria that determines whether the force of gravity is relevant to us.

All this is to say that though heritability is a useful concept, it is an abstraction — one that depends entirely on the statistical models (with all their assumptions and prejudices) we use to define it.

Most importantly for our purposes, even for an extremely heritable trait such as height, the environment can drastically change the observed traits. For example, during the Guatemalan Civil War, US-backed death squads and paramilitaries brutalized the rural, indigenous population of Guatemala, resulting in widespread malnutrition. Many Mayan refugees fled to the United States to escape the violence. Comparing the heights of six- to twelve-year-old children of Guatemala Maya with American Maya, researchers found that the Americans were 10.24 centimeters taller than their Guatemalan counterparts, largely due to nutrition and access to healthcare. By comparison, the gene known to most influence height, the growth factor gene GDF5, is associated with changes in height of just 0.3 to 0.7 centimeters, and this only for participants with European ancestry.

Such dramatic environmental influences are commonplace. For example, the heritability of Type II diabetes, adjusted for age and Body Mass Index (BMI), is thought to be between 0.5 and 0.75 (a little less than that of height, but as discussed above, this number should be taken with a grain of salt). Currently, GWAS are able to explain only about 6% of this heritability, with no loci (genes) particularly predictive for whether an individual will develop diabetes. In contrast to genetics, an unhealthy BMI — a simple measure of how overweight a person is — increases the odds of developing diabetes nearly eightfold.

The same story holds for IQ — a staple of genetic studies on “intelligence.” Putting the validity of IQ tests aside for a moment, studies show a long and sustained increase in IQ scores over the course of the twentieth century (the Flynn Effect), pointing to the importance of environment rather than genetics in determining IQ.

Schizophrenia is another example. In his excellent blog Cross-Check, John Horgan discusses CMYA5, touted as the “schizophrenia gene” in the popular press. He points out that if you carry this gene, your risk of schizophrenia rises by just 0.07% to 1.07%. In contrast, “if you have a schizophrenic first-degree relative, such as a sibling, your probability of becoming schizophrenic is about 10%, which is more than 100 times the added risk of having the CMYA5 gene.” Such results are not uncommon. The field is actually very concerned about the lack of predictive power of GWAS (often discussed in the context of the “missing heritability” problem).

Despite the limited success of GWAS, it is doubtful that genetic determinist claims will abate in the near future. The main reason for this is the sheer volume of new genetic data currently being generated. This data deluge is a biological determinist’s wet dream. In case you think I am exaggerating, here’s a quote from a recent study on “the genetic architecture of economic and political preference” published in PNAS, a leading scientific journal. Not surprisingly, the SNPs they identified “explain only a small part of the total variance.” Far from discouraged, the authors conclude their abstract on an optimistic note:

These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPS.

The sheer hubris speaks for itself. Given the difficulty of using GWAS to explain height — an easily quantified, easily measured trait — the absurdity of claiming to identify the genetic basis of ill-defined, temporally variable, hard-to-quantify traits such as intelligence, aggression, or political preference is patently clear.

Regardless, the genetic determinist’s playbook in the genomics era is clear: Collect mass quantities of sequence data. Find an ill-defined trait (like political preference). Find a gene that is statistically overrepresented in the sub-population that “possesses” that trait. Declare victory. Ignore the fact that these genes don’t really explain the phenotypic variance of the trait. Instead, claim that if we only had more data the statistics would all work out. Further generalize these results to the level of societies and claim they explain the fundamental genetic basis of human behavior. Write a press release and wait for the media to write glowing reviews. Repeat with another data set and another trait.

Biological determinism seems plausible precisely because it gives the illusion that it is grounded in scientific observation. No scientist disagrees that the basic building blocks of an organism are encoded in its genetic material, and that evolution, through some combination of genetic drift and selection, has shaped those genes. But trying to ascribe human behavior, whether eating a whole bag of potato chips or waging war, to a set of genes is clearly a quixotic exercise.

As Nigel Goldenfeld and Leo Kadanoff implore in a beautiful article discussing complex systems: “Use the right level of description to catch the phenomena of interest. Don’t model bulldozers with quarks.” While it is certainly true that all the properties of a bulldozer result from the particles that make it up, like quarks and electrons, it is useless to think about the properties of a bulldozer (its shape, its color, its function) in terms of those particles. The shape and function of a bulldozer are emergent properties of the system as a whole. Just as you can’t reduce the properties of a bulldozer to those of quarks, you can’t reduce the complex behaviors and traits of an organism to its genes. Marx made the same point when he stated that “merely quantitative differences beyond a certain point pass into qualitative changes.”

If the philosophical and scientific bases of genetic determinist claims are so problematic, why is such sloppy thinking rewarded with front-page articles in the New York Times Science section? To answer this, we must consider not just science, but politics.

We live in an era in which corporations make unprecedented profits, an elite few accumulate enormous wealth, and inequality is reaching levels approaching those of the Gilded Age. The contradictions between neoliberal capitalism and democratic impulses are continually exposed. The claims of equal opportunity underlying much of liberal thinking are becoming farce. The incongruity between what capitalism professes to be and the reality of capitalism is becoming increasingly apparent.

The appeal of biological determinism is that it offers plausible, scientific explanations for societal contradictions engendered by capitalism. If Type II diabetes is reduced to the problem of genetics (which it surely is to some degree), then we don’t have to think about the rise of obesity and its underlying causes: the agro-business monopoly, income inequality, and class-based disparities in food quality. Combine this with the prevalence of drug-based solutions to disease pushed by the pharmaceutical industry and it is no surprise that we are left with the impression that complex social phenomena are reducible to simple scientific fact.

Biological determinism, to paraphrase the great literary critic Roberto Schwarz, is a socially necessary illusion well-grounded in appearance. Much like art and literature, science “is historically shaped and . . . registers the social process to which it owes its existence.” Scientists inherit the prejudices of the societies in which they live and work. Nowhere is this more obvious than in the modern incarnation of biological determinism with its decidedly neoliberal assumptions about humans and societies.

The history of biology is littered with horrifying examples of the misuse of genetics (and evolutionary theory) to justify power and inequality: evolutionary justifications for slavery and colonialism, scientific explanations for rape and patriarchy, and genetic explanations for the inherent superiority of the ruling elite. We must work tirelessly to ensure that history does not repeat itself in the genomics era.

[FourthBase]

http://www.newscientist.com/article/dn2 ... puter.html

You can't predict hell, but you can invent it.

[FourthBase]

http://www.wired.com/2015/07/science-ca ... -together/

Via Diabolica, per usual, paved with good intentions.
Or, good excuses, at least.

[zangtang]

"Things could get even more interesting once we are able to connect human brains.
This will probably only be possible when better non-invasive methods for monitoring
Movie Camera and stimulating the brain have been developed."

- or just make sure noone's looking & go ahead connect 3 human brains together anyway......
- you know you want to, & you know you're going to......................

[Wombaticus Rex]

Very relevant rant here:
http://runesoup.com/2015/07/competing-schools-of-magic/

[Sounder]

Thanks for that Wombat, that is some fine work from Gordon.

[stillrobertpaulsen]

Very relevant rant here:
http://runesoup.com/2015/07/competing-schools-of-magic/

Thanks Wombaticus, that had two bust-a-gut laughing moments: "Tulip Mania bullshit" and "Dances With Smurfs."

I just wanted to include a link that Rory brought to my attention on another thread. It's got some interesting stuff about anarcho-primitivism and how what Paul Kingsnorth calls the "neo-environmentalists" are heading in the opposite direction. But there was one section that really stood out for me because it completely inverted my understanding of how civilization came to be:

In his book A Short History of Progress, Ronald Wright coins the term ‘progress trap’. A progress trap, says Wright, is a short-term social or technological improvement which turns out in the longer term to be a backward step. By the time this is realised – if it ever is – it is too late to change course.

The earliest example he gives is the improvement in hunting techniques in the Upper Palaeolithic era, around 15 000 years ago. Wright tracks the disappearance of wildlife on a vast scale whenever prehistoric humans arrived on a new continent. As Wright explains: ‘Some of their slaughter sites were almost industrial in size: 1000 mammoths at one; more than 100 000 horses at another.’ But there was a catch:

The perfection of hunting spelled the end of hunting as a way of life. Easy meat meant more babies. More babies meant more hunters. More hunters, sooner or later, meant less game. Most of the great human migrations across the world at this time must have been driven by want, as we bankrupted the land with our moveable feasts.

This is the progress trap. Each improvement in our knowledge or in our technology will create new problems which require new improvements. Each of these improvements tends to make society bigger, more complex, less human-scale, more destructive of non-human life and more likely to collapse under its own weight.

Spencer Wells takes up the story in his book Pandora’s Seed, a revisionist history of the development of agriculture. The story we were all taught at school – or I was, anyway – is that humans ‘developed’ or ‘invented’ agriculture, because they were clever enough to see that it would form the basis of a better way of living than hunting and gathering. This is the same attitude that makes us assume that a brushcutter is a better way of mowing grass than a scythe, and it seems to be equally erroneous. As Wells demonstrates, analysis of the skeletal remains of people living before and after the transition to agriculture during the Palaeolithic demonstrate something remarkable: an all-round collapse in quality-of-life when farming was adopted.

Hunter-gatherers living during the Palaeolithic period, between 30 000 and 9000 BCE, were on average taller – and thus, by implication healthier – than any people since, including people living in late twentieth-century America. Their median lifespan was higher than at any period for the next six thousand years, and their health, as estimated by measuring the pelvic inlet depth of their skeletons, appears to have been better, again, than at any period since – including the present day. This collapse in individual well-being was likely due to the fact that settled agricultural life is physically harder and more disease-ridden than the life of a shifting hunter-gatherer community.

So much for progress. But why in this case, Wells asks, would any community move from hunting-gathering to agriculture? The answer seems to be: not because they wanted to, but because they had to. They had spelled the end of their hunting-gathering lifestyle by getting too good at it. They had killed off most of their prey and expanded their numbers beyond the point at which they could all survive. They had fallen into a progress trap.

In this light, Transhumanism is the Grand Guignol of progress traps. The apex or nadir, depending on your pov.

[JackRiddler]

The most obvious progress trap strictly defined of our own age is automobilism. The world has been restocked with inedible mammoths.

[FourthBase]

http://www.theglobeandmail.com/news/nat ... e25811702/

I am ashamed of Boston, and I congratulate Philly.

[FourthBase]

http://paleofuture.gizmodo.com/heres-vi ... 1721797093

Whoever this glorious Philly bro is, I genuflect in his direction.

All the idiots and autistic robo-cuckolds mourning this abomination today make me want to vomit, slap them in the face, and then vomit some more.

If there is ever another Hitchbot roaming around Boston, someone please alert me. I will relish destroying it and going to jail for vandalism. I'd smash it into a thousand pieces in a hundred different ways and then throw that pile of inhuman shit into the Harbor.

[Luther Blissett]

http://paleofuture.gizmodo.com/heres-video-of-the-jerk-who-killed-hitchbot-1721797093

Whoever this glorious Philly bro is, I genuflect in his direction.

All the idiots and autistic robo-cuckolds mourning this abomination today make me want to vomit, slap them in the face, and then vomit some more.

If there is ever another Hitchbot roaming around Boston, someone please alert me. I will relish destroying it and going to jail for vandalism. I'd smash it into a thousand pieces in a hundred different ways and then throw that pile of inhuman shit into the Harbor.

SnitchBOT had it coming.



My friends and I have had such a delightful time over the weekend laughing about this. I've had one or two dissenters, and I am quick to point out that SnitchBOT = true gentrification, and that our beloved Old Philadelphia is still alive and well. A lot of great Meek Mill vs. Drake puns too.

All that being said, I'm also delighted that a friend is rebuilding it at the Hacktory. She's going to do a much better job and it will probably serve some better public good that that bucket of trash.

[Luther Blissett]

No surprise to anyone here.

Artificial intelligence: ‘Homo sapiens will be split into a handful of gods and the rest of us’

A new report suggests that the marriage of AI and robotics could replace so many jobs that the era of mass employment could come to an end


If you wanted relief from stories about tyre factories and steel plants closing, you could try relaxing with a new 300-page report from Bank of America Merrill Lynch which looks at the likely effects of a robot revolution.

But you might not end up reassured. Though it promises robot carers for an ageing population, it also forecasts huge numbers of jobs being wiped out: up to 35% of all workers in the UK and 47% of those in the US, including white-collar jobs, seeing their livelihoods taken away by machines.

Haven’t we heard all this before, though? From the luddites of the 19th century to print unions protesting in the 1980s about computers, there have always been people fearful about the march of mechanisation. And yet we keep on creating new job categories.

However, there are still concerns that the combination of artificial intelligence (AI) – which is able to make logical inferences about its surroundings and experience – married to ever-improving robotics, will wipe away entire swaths of work and radically reshape society.

“The poster child for automation is agriculture,” says Calum Chace, author of Surviving AI and the novel Pandora’s Brain. “In 1900, 40% of the US labour force worked in agriculture. By 1960, the figure was a few per cent. And yet people had jobs; the nature of the jobs had changed.

“But then again, there were 21 million horses in the US in 1900. By 1960, there were just three million. The difference was that humans have cognitive skills – we could learn to do new things. But that might not always be the case as machines get smarter and smarter.”

What if we’re the horses to AI’s humans? To those who don’t watch the industry closely, it’s hard to see how quickly the combination of robotics and artificial intelligence is advancing. Last week a team from the Massachusetts Institute of Technology released a video showing a tiny drone flying through a lightly forested area at 30mph, avoiding the trees – all without a pilot, using only its onboard processors. Of course it can outrun a human-piloted one.

MIT has also built a “robot cheetah” which can jump over obstacles of up to 40cm without help. Add to that the standard progress of computing, where processing power doubles roughly every 18 months (or, equally, prices for capability halve), and you can see why people like Chace are getting worried.

But the incursion of AI into our daily life won’t begin with robot cheetahs. In fact, it began long ago; the edge is thin, but the wedge is long. Cooking systems with vision processors can decide whether burgers are properly cooked. Restaurants can give customers access to tablets with the menu and let people choose without needing service staff.

Lawyers who used to slog through giant files for the “discovery” phase of a trial can turn it over to a computer. An “intelligent assistant” called Amy will, via email, set up meetings autonomously. Google announced last week that you can get Gmail to write appropriate responses to incoming emails. (You still have to act on your responses, of course.)

Further afield, Foxconn, the Taiwanese company which assembles devices for Apple and others, aims to replace much of its workforce with automated systems. The AP news agency gets news stories written automatically about sports and business by a system developed by Automated Insights. The longer you look, the more you find computers displacing simple work. And the harder it becomes to find jobs for everyone.

So how much impact will robotics and AI have on jobs, and on society? Carl Benedikt Frey, who with Michael Osborne in 2013 published the seminal paper The Future of Employment: How Susceptible Are Jobs to Computerisation? – on which the BoA report draws heavily – says that he doesn’t like to be labelled a “doomsday predictor”.

He points out that even while some jobs are replaced, new ones spring up that focus more on services and interaction with and between people. “The fastest-growing occupations in the past five years are all related to services,” he tells the Observer. “The two biggest are Zumba instructor and personal trainer.”

Frey observes that technology is leading to a rarification of leading-edge employment, where fewer and fewer people have the necessary skills to work in the frontline of its advances. “In the 1980s, 8.2% of the US workforce were employed in new technologies introduced in that decade,” he notes. “By the 1990s, it was 4.2%. For the 2000s, our estimate is that it’s just 0.5%. That tells me that, on the one hand, the potential for automation is expanding – but also that technology doesn’t create that many new jobs now compared to the past.”

This worries Chace. “There will be people who own the AI, and therefore own everything else,” he says. “Which means homo sapiens will be split into a handful of ‘gods’, and then the rest of us.

“I think our best hope going forward is figuring out how to live in an economy of radical abundance, where machines do all the work, and we basically play.”

Arguably, we might be part of the way there already; is a dance fitness programme like Zumba anything more than adult play? But, as Chace says, a workless lifestyle also means “you have to think about a universal income” – a basic, unconditional level of state support.

Perhaps the biggest problem is that there has been so little examination of the social effects of AI. Frey and Osborne are contributing to Oxford University’s programme on the future impacts of technology; at Cambridge, Observer columnist John Naughton and David Runciman are leading a project to map the social impacts of such change. But technology moves fast; it’s hard enough figuring out what happened in the past, let alone what the future will bring.

But some jobs probably won’t be vulnerable. Does Frey, now 31, think that he will still have a job in 20 years’ time? There’s a brief laugh. “Yes.” Academia, at least, looks safe for now – at least in the view of the academics.

The danger of change is not destitution, but inequality
Productivity is the secret ingredient in economic growth. In the late 18th century, the cleric and scholar Thomas Malthus notoriously predicted that a rapidly rising human population would result in misery and starvation.

But Malthus failed to anticipate the drastic technological changes - from the steam-powered loom to the combine harvester - that would allow the production of food and the other necessities of life to expand even more rapidly than the number of hungry mouths. The key to economic progress is this ability to do more with the same investment of capital and labour.

The latest round of rapid innovation, driven by the advance of robots and AI, is likely to power continued improvements.

Recent research led by Guy Michaels at the London School of Economics looked at detailed data across 14 industries and 17 countries over more than a decade, and found that the adoption of robots boosted productivity and wages without significantly undermining jobs.

Robotisation has reduced the number of working hours needed to make things; but at the same time as workers have been laid off from production lines, new jobs have been created elsewhere, many of them more creative and less dirty. So far, fears of mass layoffs as the machines take over have proven almost as unfounded as those that have always accompanied other great technological leaps forward.

There is an important caveat to this reassuring picture, however. The relatively low-skilled factory workers who have been displaced by robots are rarely the same people who land up as app developers or analysts, and technological progress is already being blamed for exacerbating inequality, a trend Bank of America Merrill Lynch believes may continue in future.

So the rise of the machines may generate huge economic benefits; but unless it is carefully managed, those gains may be captured by shareholders and highly educated knowledge workers, exacerbating inequality and leaving some groups out in the cold. Heather Stewart

[FourthBase]

Watched "Chappie", funny but unnerving transhumanist propaganda, a case where Vigilant Citizen is spot-the-fuck-on. If society can't bring itself to destroy robots when necessary it'll be because generations have been raised to empathize with Chappies, Wall-E's, Rosies, Johnnies, et al. -- let alone the uncannier robots to come shortly.

[Luther Blissett]

From a couple weeks ago, but hasn't been posted yet and is relevant to many of our conversations about the philosophical conundrum surrounding the enhancement project. Here's the Popular Mechanics propaganda take.

How the Pentagon Will Build Super-Soldiers
Russia and China's "Enhanced Human Operations" Terrify the Pentagon
"We're going to have to have a big, big decision on whether we're comfortable going that way."

U.S. adversaries are already working on something America is reluctant to: Enhanced Human Operations (EHO).

EHOs entail modifying the body and the brain itself, creating what some have called "super soldiers." At a press conference laying the Defense Department's future research and development strategy on Monday, Deputy Defense Secretary Bob Work warned that America would soon lose its military competitive advantage if it does not pursue technologies such employing artificial intelligence.

"Now our adversaries, quite frankly, are pursuing enhanced human operations, and it scares the crap out of us," Work said.

Altering human beings from the inside to more effectively fight in combat presents ethical dilemmas for American scientists and military planners. Work says those ethical concerns typically don't apply to authoritarian governments like Russia's or China's, but their lack of hesitation in developing EHOs may force America's hand.

"We're going to have to have a big, big decision on whether we're comfortable going that way," Work admits.

What sort of enhancement are we talking about? As yet, the Pentagon has not been specific, but we can infer several likely human mods from previous work done by DARPA, and our own imagination.

Exoskeletons

As early as 1985, retired four-star Army General Paul F. Gorman sketched out a "SuperTroop" exoskeleton for DARPA that would protect soldiers from chemical, biological, electromagnetic, and ballistic threats, including direct fire from a .50-caliber bullet. Audio, visual, and haptic (touch) sensors were part of the design.

More recently, the Navy has explored the use of exoskeletons for lifting heavy loads and enhancing shipboard firefighting/damage control operations. In 2013, DARPA partnered with U.S. Special Operations Command on a light exoskeleton suit called TALOS (Tactical Assault Light Operator Suit). Every time the discussion comes up, someone mentions Iron Man.

Drugs and Vaccines

You could argue that humans have already been boosted, at least primitively. ISIS and other terrorist organizations have an extensive history of handing their fighters hallucinogens and other drugs to bolster their savagery.

Apparently DARPA's Defense Sciences Office (DSO) investigated a mind-altering pain vaccine. Once injected into a soldier who had been shot, the vaccine would theoretically reduce the pain from inflammation and swelling. After 30 seconds of agony, the soldier would feel no pain for 30 days. As long as the bleeding was contained the soldier could keep fighting.

The government's research into halting the bleeding yielded another program that involved injecting millions of microscopic magnets into a person, which could later be brought together into a single area to stop bleeding with the wave of a wand.

DARPA has also studied whales and dolphins—mammals that don't need to sleep in long chunks like humans do—as inspiration for creating a soldier who requires little to no sleep for a week or more. Whales and dolphins independently control the left and right lobes of their brains, keeping one alert while the other sleeps. It's not known if DAPRA scientists made any progress with human lobe control, but they did explore powerful anti-sleep drugs like Modafinil.

Brain Implants

Enhancing soldiers' endurance isn't limited to exoskeletons. Under a program called the Brain-Machine Interface, DSO investigated how brain implants improve cognitive ability. Scientists sought to implant a computer chip in a rat's brain to see if they could remotely control the animal's movements. If success could be replicated in humans, it might eventually lead to remote guidance or control of a human being on the battlefield. Seriously. The program also explored the possibility of enabling soldiers to communicate with each other by thought alone.

Enhanced Human Operations would also theoretically include psychological or cognitive programming. Again, it could be argued that humans have long been psychologically programmed for war, from Nazi soldiers to Al Qaeda terrorists. But brain implants that trigger specific thought patterns or even instructions might be in the cards. So too might be extending the human being as an information, surveillance, and reconnaissance node by infusing the body with a variety of sensors and communications relays.

The possibilities for EHOs may be limited only by our imagination—and how much our own creations scare the crap out of us.