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In his September 1, 2017 speech to incoming Russian schoolchildren, Putin made waves by proclaiming that whoever becomes the leader in AI will become “ruler of the world.” This provoked a variety of reactions, from Elon Musk commenting on his belief that competition for AI superiority will be the likeliest cause of World War III to discussions of the geopolitical aspects of the “control problem” at the more esoteric rationalist venues like /r/slatestarcodex. Many of the reactions were skeptical, citing Russia’s traditional weaknesses at commercializing its inventions. Nonetheless, Bloomberg columnist Leonid Bershidsky, who can hardly be called a Russia optimist, cautioned that Musk’s concerns be taken seriously, citing a range of civilian and military AI applications being developed in Russia.

But here’s another story that happened to unfold on the same day. Back in 2015, Sergey Chemezov, the head of Rostekh state technology corporation – one of Putin’s KGB chums from their time in 1980s East Germany – proudly presented the Russian President with one of his company’s latest “innovatory” offerings: A thin, double-screen, YotaPad-based tablet which was “of entirely Russian make”, meant to be used as an electronic textbook in schools. But they were actually made in Taiwan, and when the devices were distributed to some Russian schoolchildren at the start of the school year, it emerged that they took three minutes to start up, only worked with a stylus, and weighed 1.5 kilograms. According to an investigation by the online journal Znak, the device in question was actually a slightly rebranded version of the American device enTourage eDGe, an outdated and unsuccessful product from 2009 that could be bought wholesale for $20 apiece as of 2015 (you can still get it for $30 on Ebay today). Meanwhile, the official cost of the 8,000 tablets in the trial electronic textbook program was 24o million rubles, which translates to around $500 apiece. This isn’t even very impressive innovation so far as siphoning away taxpayer money into private pockets is concerned, to say nothing of technology.

So which of these stories best reflects the real state of Russian science and technology?

The one in which a technologically adept elite are seriously driving the development of things like strong AI and pondering on its world-historical consequences – or the one in which a clique of kleptocrats pay lip service to innovation while skimming off even the modest resources they bother investing into science and technology?

As per usual, I believe that the best guide aren’t anecdotes, which are the singular of “statistics,” but numbers, numbers, and more numbers in international comparison, as I did in 2006 with respect to China’s scientific/technological convergence with the United States in terms of indicators like published scientific articles published, the prevalence of industrial robots, and the number of supercomputers. I will repeat the same exercise, but with Russia.

Scientific Articles

The SJR maintains a database of scientific publications by country and subject for the past 20 years.


The Soviet Union in 1986 produced around 7.6% of the world’s scientific articles, which was a quarter of the American rate and comparable to other leading industrialized countries like the UK, Japan, West Germany, and France. In the wake of the brain drain and financial collapse in the wake of the USSR’s dissolution, this figure plummeted to below 3% by the mid-1990s and below 2% by the mid-2000s, in a drop made all the more remarkable by the absence of a “publish or perish” scientific culture in the erstwhile USSR. It was only in 2014 that Russia’s relative standing began to recover.

However, with 73,000 articles published in 2016, Russia remains far below the United States (602,000) and China (471,000), as well the bigger European countries like the UK (183,000), Germany (166,000), and France (113,000). As the 13th most scientifically productive country in the world, it is wedged in between South Korea and Brazil. This is true across the board. For instance, even in the sphere where Russia does best, in the Soviet mainstay of “Physics and Astronomy”, it is still only fourth in the world with 23,000 articles, well behind both China (79,000) and the United States (59,000).

Moreover, even the very modest overall figures conceal a yawning gap in some of the most recent and prospective spheres of modern science. Before worrying about the dangers of AI “eating us” – let alone fantasizing about “sharing this know-how with the entire world” – it would have perhaps served Putin better to first concern himself with the question of why Russia only published 552 papers in the field of AI in 2016, relative to 11,800 in China and 6,700 in the US. Another important sphere that is seeing blistering progress are the genomic sciences, some of whose applications – for instance, human germline engineering for higher IQ – will be world-transforming. Could Russia lead the world in producing “[genetically] spellchecked supermen“? With 690 published papers on Genetics to America’s 13,600 and China’s 9,600, 386 in Biotechnology to China’s 7,100 and America’s 6,400, and 350 in Bioengineering to China’s 6,600 and America’s 4,900, this question answers itself.

The state of affairs in the social sciences is even worse. While Russia’s two (sic) published articles in Women’s Studies in 2016 are nothing to worry about – sooner the converse – that’s about where the happy news ends. Not only do the social sciences suffer from all the other weaknesses of Russian science, but the Soviet legacy there is, if anything, negative value added.

For instance, one sphere that I am personally highly familiar with, psychometrics – the science of measuring mental capacities and processes – was declared a “bourgeois pseudoscience” in 1936, with research in it banned up until the 1970s (though they, unlike the geneticists, seem to have at least largely escaped Stalin’s murderous gaze). Consequently, pretty much all of it had to be re-imported wholesale from the West. While there are now some very good people working on psychometrics in Russia, they have to do it on ageing computers in a creaking building, and financed almost exclusively by European grants.

Far from atypical, this is a steady pattern in the social sciences. To take another example, consider Sinology. Many of the USSR’s leading Orientalists were executed in the late 1930s on spying charges (trumped up ones, I hope it goes without saying). Today, as China expert Alexander Gabuev explained in a couple of articles in Kommersant several years ago, which I summarized in a recent article for The Unz Review (The State of Russian Sinology: Past Chequered, Present Dismal, Future Uncertain), the field of China Studies in Russia is a minnow relative both to China Studies in the West, and to Russia Studies in China. And why should it be otherwise? As of when Gabuev wrote his overviews, the average salary of a docent at the prestigious Moscow State University’s Institute of Asian and African Studies was around $500. Consequently, there is a near total lack of expertise in the country that Kremlin talking points describe as Russia’s “strategic partner.” Though one can cite any number of amazing anecdotes from Gabuev’s articles, I will limit myself to just one. During the Russian-Chinese military exercises “Maritime Cooperation 2012,” the Chinese had nearly 200 young officers with a solid knowledge of Russian at hand to provide linguistic support; the Russians could only muster three translators, and presumably, the Russian GRU intelligence service’s sole China analyst wasn’t one of them. Consequently, not only is the Russian military’s degree of China expertise incomparably lower than America’s, but it is also likely far lower than the PLA’s understanding of the Russian military.

One observes a catastrophic lack of understanding of China across the entirety of the Russian ideological spectrum, not least as regards the extent to which their own country is falling behind.

Scientific Articles: Adjusted for Quality

But if Russia’s raw research output is nothing to write home about, it diminishes to near irrelevance when adjusted for quality.

Here’s one important thing you should know about our world if it were a Civilization playthrough: The Anglo-Saxons have won the Cultural Victory. The majority of cultural output in the world happens in the English language, and this rises to at least 95% so far as science and technology are concerned. The Germans were competitive earlier in the century, before the Nazis (and American demographics) ruined everything, and the Soviet Union maintained a technical mini-civilization partly secluded from the global mainstream, but since its collapse, the Anglo system has become the only game in town.

Most of the really important scientific research gets published in a handful of high-impact factor journals. If there is a proxy for modern day scientific productivity adjusted for quality, and without the generational lag problems that you encounter with the Nobel Prizes, then it is the number of articles an institution or country manages to publish in those elite journals, which are proxied by the Nature Index.

# Country Physics Chem Life Total
1 USA 4307 4567 6674 15157
2 China 1970 4025 795 6380
3 Germany 1411 1372 940 3593
4 UK 965 947 1126 3039
5 Japan 879 1116 581 2538
6 France 755 542 468 1811
7 Canada 315 421 483 1229
8 Switzerland 400 345 319 1019
9 South Korea 462 542 141 990
10 Spain 373 442 190 980
11 Italy 503 234 171 909
12 Australia 243 268 280 835
13 India 300 408 81 804
14 Netherlands 275 234 245 744
15 Sweden 152 140 181 452
16 Israel 175 132 162 442
17 Singapore 150 232 80 404
18 Russia 252 98 27 377
19 Belgium 123 114 112 336
20 Taiwan 134 157 57 332
21 Denmark 108 79 111 299
22 Austria 110 82 105 285
23 Brazil 144 34 57 246
24 Poland 114 74 18 204
25 Finland 70 42 52 160

Source: Nature Index, WFC 2016

The US absolutely dominates high-quality research, producing about a third of the world’s total, even though China has gained considerably ground, going from 9% of the global total in WFC 2012 to 14% as of today.


Despite modest improvements since 2012, Russia remains a complete minnow, accounting for less than 1% of elite global scientific research. It is worth noting that it lags China not only absolutely, but in per capita terms as well. In total, Russia produces as much elite level science as does Singapore, Belgium… and the University of Cambridge.

It is hard to imagine any plausible adjustment which would cardinally improve its position. Although it is possible that Russia’s scientific potential is somewhat underestimated by linguistic insularity and its incomplete integration with the global science scene, this is unlikely to be a major factor; since Russia is not actually a world scientific leader in any sphere but a few rather narrow areas of metallurgy and nuclear physics, much of the conversations that take place in exclusively Russian language journals will be outdated and useless. It is also likely that a significantly larger chunk of Russian scientific research relates to military applications than in most other countries, and is effectively “black.” That said, even we assume – very generously – that this underestimation is on the order of 50%, that would still mean that 146 million Russians produce fewer Science Points than the 8 million citizens of Switzerland. Even in Physics, its area of greatest relative strength, Russia barely manages to match Australia; as for the Life Sciences, it is nestled in between Czechia and Argentina.

This analysis is backed up by the performance of individual Russian institutions and scientists.


The most productive (and elite) Russian university, Moscow State University, is in 254th place on the Nature Index, alongside the likes of Oregon State University and the University of Liverpool; fine institutions though they might well be, they do not have a reputation as academic powerhouses. Although Russians tend to complain about the low positions of their universities on international rankings – and I will admit to having once espoused such beliefs myself – it is worth noting that since Moscow State University is 93rd on the latest ARWU Shanghai Ranking and 194th on the THES ranking, it would seem that if anything, the rankings overstate Russia’s performance.

There are a grand total of three Russia-based researchers in Clarivate Analytics’ database of highly cited researchers (of whom only one, Sergey V. Morozov, has his primary affiliation there; the other two primarily work in Spain and the United States). Amazingly, this means that there are as many Russian highly cited researchers in just one American university, U.C. Berkeley – Alexey Filippenko, Igor Grigoriev, Natalia Ivanova – as there are in the whole of Russia! In fairness, Russia’s BRICs rivals Brazil and India don’t do substantially better. However, China has long left its colleagues behind; there are almost 200 highly cited researchers who have their primary affiliation in the Heavenly Kingdom, who are producing 20% of the world’s high-impact academic publications as of 2016.

R&D/Academic Personnel

Russia spends a relatively low but far from catastrophic 1.1% of its GDP on R&D, which is similar to the Mediterranean and Visegrad countries. It also used to have one of the highest concentrations of researchers in the world, with almost 8/1,000 workers employed in R&D, which was higher than the equivalent figures in all the major OECD countries except Japan. Since then, this figure has declined to 6/1,000 even as the average OECD figures went up, so here Russia, too, now keeps company with the Mediterranean and Visegrad. Even so, this was hardly a disaster – the USSR overproduced “researchers” in the same way as it overproduced “doctors” and “engineers”, many of whom would have been mere nurses or technicians in the West. So the thinning out of a good fraction of those fake “researchers” should in theory have been a good thing, assuming that the system was purging itself of dead wood. But the reality was sooner the other way round. Due to the utter lack of prospects in Russian academia, the most talented either continued to emigrate West (with the bulk of that outflow occuring in the 1990s), or went into the private sector.

Many explanations have been proposed as to why Russian science has been in an unending death spiral. Some of the more ideological works cite factors such as the lack of democracy and human rights, and its estrangement from the West – as if Yeltsin’s Russia was a fount of innovation (or democracy, for that matter), while the scientific explosion in modern day China is a mirage (not to mention countless historical counterexamples, e.g. the most scientifically dynamic country in the world prior to World War I was authoritarian Wilhelmine Germany). In Becky Ferreira’s recent profile of Russian science for VICE, one researcher is quoted as saying the following: “If people really only went to countries which do not invade other countries and respect human rights, then they would stick to countries like Andorra or Bhutan… Maybe it sounds a bit cynical, but in my observation, most people in science are driven by opportunities. Regardless of whether such an attitude is moral or not, it is clear that science should be free of any politics.

No, the real reasons are much more banal: Money, or rather the lack thereof.

According to an exhaustive study of global academic salaries published in 2012, the average Russian academic received 2-4x less money than his equivalents in Visegrad, the Baltics, and even Kazakhstan, and an order of magnitude less than in the developed world.


Source: Paying the Professoriate by Philip G. Altbach et al. (2012).

Here is what the authors have to say about the practical consequences of this breadcrumbs-based approach to scientific funding:

In Russia, young faculty earn approximately 70 percent of the average wage in the workforce; professors’ salaries often fall 10 percent below the average wage of others in the workforce who have completed higher education. In most countries, a middle-class income generally depends on additional employment, either within the same institution, at another academic institution, or in nonacademic employment. All of this added pressure decreases the attractiveness of the academic career and will further deter the “best and brightest” from choosing academe.

Finally, it would be remiss not to mention the astounding prevalence of corruption in Russian academia. According to a Slate article by Leon Neyfakh, the Russian plagiarism detection project Dissernet has found improper borrowing in around 4% of all the dissertations defended in Russia. This doesn’t include plagiarism-free ghostwritten work: Ararat Osipian, a specialist in academic corruption, estimates that around a quarter of all dissertations written in Russia after the collapse of the Soviet Union were purchased.

There have also been private complaints of “ethnic capture” of certain Russian academic departments, primarily by Caucasians. To the best of my knowledge, this is an unquantified phenomenon (though it would not surprise me if this was true, since such a pattern has been confirmed in Italy, where as you go south – which is more corrupt – the incidence of identical surnames within university departments increases, indicating rising nepotism). However, consider the case of the Ingush. They produced six times fewer scientists per capita than Russians during the less corrupt Soviet period; today, their homeland is the highest unemployment, most subsidized region in Russia. And yet they somehow manage to have the highest concentration of postgrads per capita in all of Russia, around 50% more than in second-place Moscow. I will leave readers to draw their own conclusions.

As if the poverty level wages were not enough, the corruption and cronyism also cannot help but discourage the more talented and conscientious from academic careers.

R&D Equipment

The age when enthusiasts could jerry-rig their own scientific equipment are long gone. You need powerful supercomputers to simulate protein folding, climate change, and the integrity of your nuclear arsenal. You need high throughput sequencers to do serious experimental work in genetics.

But money isn’t any more forthcoming here than it is for salaries.


Twice a year, the Top 500 website compiles a list of the world’s five hundred most powerful supercomputers. Since 2010, China has exploded out of the margins to overtake the United States – as of November 2017, it had 202 top supercomputers to America’s 143, and that included the world’s most powerful supercomputer, the Sunway TaihuLight, which runs on entirely Chinese processors.


Table: Country Share of Top 500 supercomputers in November 2017

Russia’s performance is… rather underwhelming – its measly 0.6% global share of the world’s top 500 supercomputers is equivalent to Switzerland, and lower than that of Sweden, Ireland, and Saudi Arabia.


Nor are the trends encouraging. While there was an uptick in Russia’s numbers of top 500 supercomputers to around 2% of the world total around 2010-2011, those figures have been dwindling ever since.

High Throughput sequencers

James Hadfield maintains a reasonably up to date map of the world’s high throughput DNA sequencers. The current version of the map isn’t easily readable, but here is a screenshot from 2013.


This is a very typical picture: A modest cluster in Moscow, while the rest of North Eurasia is a scientific desert.


Russia’s performance in patent applications isn’t too bad by global standards – comparable in per capita terms to the UK and France, much higher than in the BRICS minus China (and it’s not exactly a secret that many East Asian patents are of a spurious nature).

Patent applications (2015)
China 968,252
United States 288,335
Japan 258,839
Korea, Rep. 167,275
Germany 47,384
Russian Federation 29,269
United Kingdom 14,867
France 14,306
India 12,579
Turkey 5,352
Poland 4,676
Brazil 4,641

But you can’t realize ideas without money, and despite growing by leaps and bounds in the past decade, the Russian venture capital industry remains tiny from a global perspective.


In 2016, VC funding in Russia (€295 million) was at the level of Ireland (€367 million) and Finland (€324 million) in absolute terms, though a bit above sluggish and overly bureaucratic Italy (€162 million).

And this is relative to Europe, a continent that grossly underperforms relative to its wealth and demographics. According to another source, the old continent had just $14.4 billion worth of VC activity in 2015, relative to $72.3 billion in the United States, $49.2 billion in China, and $8.0 billion in India.

In per capita terms, this means that VC funding in Russia it is at just around 5% of the Chinese level and 1% of the American level.

This expresses itself across the entire range of the hi-tech sphere, but we will just focus on one of the most important and “hip” applications.

Artificial Intelligence Startups

Let’s go back to artificial intelligence, the brains behind the coming wave of automation. How does Russia stack up?


It accounts for 13 of Europe’s estimated 409 AI startups as of mid-2017…


… or just 0.7% of the world’s 1951 total.

The US enjoys near total dominance in this sphere – with more than a thousand AI startups, it accounts for more than half of the world total. China is assuredly moving into second place position, hurtling past Japan and the major European countries.

Meanwhile, Russia is once again in the company of countries like Sweden, Finland, and Switzerland, who have less than 10% of its population.


According to a just released report by CB Insights, in 2017 China leapfrogged past the US to dominate global equity funding to AI startups. They are fast becoming the only two relevant countries in this sphere, with countries that are not China or the US accounting for a mere 13% of the global total.


For all the lunacies of the Soviet economic system, their planners did at least appreciate the importance of robotics and their role in enhancing productivity in manufacturing.


Source: International Federation of Robotics – World Robotics 2005

At the time of its collapse, the USSR had an operational stock of around 60,000 multipurpose industrial robots. In practice, this is a very inflated figure – a large percentage were simple, even hand-operated tools that would not have been counted as industrial robots anywhere in the capitalist world. Still, the Soviet level of industrial robotization in the 1980s was at least broadly comparable to the developed world, and several orders of magnitude higher than in a China just emerging out of its Maoist slumber.

Until the early 2000s, the publicly available databases generally didn’t even include the numbers of industrial robots in Chinese factories, so small and insignificant were their quantities. But from the late 2000s, the robotization of Chinese industry began to explode. As of 2016, it accounted for about 30% of the world industrial robots market, overtook Japan to become the country with the world’s largest operational stock of multipurpose industrial robots, and leveled with the United Kingdom in robot density.

Conversely, it has since become hard to even find any specific data for Russia… According to the World Robotics 2013 – Industrial Robots report, Russia had an operational stock of around 1,771 multipurpose industrial robots as of 2012.


Source: World Robotics 2013 – Industrial Robots


Source: World Robotics 2013 – Industrial Robots (2011 data)

Russia’s (total!) figures are slightly higher than in Slovenia, but lower than in Slovakia. In per capita terms, the rate of robotization per worker in Russia in Russia hovers between that of India and Iran, and is far behind middle-income industrial countries like Turkey, Brazil, and Mexico, to say nothing of a China fast gallivanting its way up to the levels of its super-automated East Asian peers.


Source: International Federation of Robotics – Feb 2018 press release on robot density (2016 data)

The state of affairs today isn’t any better. A 2016 report from the Russian robotics association NAURR presents two different datasets about the rate of introduction of new robots onto the Russian market in recent years.


Sales of robots in Russia, 2005-2014
Graph: World Robotics 2015


Sales of robots in Russia, 2011-2014
Source: FANUC

Although they diverge somewhat in their assessments, the underlying picture is clear – only around 500 industrial robots are introduced into Russian industry per year as of 2014, accounting for a dismal 0.25% of the global total. This is about thrice less even than Brazil’s 1,300, and two orders of magnitude lower than in China, where 57,000 were sold in the same year. It is likewise highly unlikely that Russia saw any improvements since 2014, considering that this was when it fell into a two year recession.

According to the NAURR report, the top five countries for scientific publications about robotics are the United States, followed by China, Japan, Germany, and South Korea. While figures for Russia aren’t given, it is probably safe to say that it is about as irrelevant here as it is in AI.

Machine Tools

It would also be worthwhile to briefly survey the machine tool industry – a sector of special interest not only because of this its inherent technological sophistication, but also because of its strategic importance as the only part of the industrial economy that actually reproduces itself and makes everything else possible.


Source: Gardener Research – World Machine Tool Survey 2016

As you might expect, the lists of countries that dominate industrial robots and machine tools production – Japan, Korea, the Germanic lands, Italy, and increasingly, China – are highly similar. Russia is not an exception, accounting for just 0.6% of world machine tool production.

As with elite level science and robots, China has left Russia in the dust not only in absolute, but even per capita, terms.


Global share of machine tool production 1913-1995 (Brown – USA; Black – Germany; Green – Britain; Red – Russia; Purple – Japan; Yellow – China)
Source: genby

The Russian Federation also massively lags even the late USSR. As an autarkic military-industrial empire, the USSR understood the necessity of being able to make the machines that make all the other machines, bequeathing the Russian Federation with 2.8 million machine tools in 1992 upon its dissolution. Since then, that machine tool stock has inexorably depreciated, and as of 2013 constituted just 760,000 pieces, with the average age almost doubling from 12 years to 21 years.


Since the end of the USSR, it has become clear that a chasm has opened up in in terms of scientific and technological output between Russia and the developed West.

This video juxtaposing the lumbering Robot Fyodor versus the agile Atlas built by Boston Dynamics seems like a good metaphor for what is perhaps the single biggest failure of Putinism in the past 18 years.

In comparison, any successes or failures in the Middle Eastern military adventures that pundits and commenters obsess over are basically irrelevant.

This is not to say that things are unremittingly bleak and getting worse.

The government has a strategic goal to get five of its universities into the global top 100 by 2020, to which end it has lavished significantly greater funding on its 21 most prospective universities. Consequently, academic salaries have greatly improved since 2013, at least in the elite institutions. They still don’t compare to the caviar feasts served up to Western professors, but at least they now constitute solid hunks of bread instead of the measly crumbs that were served up before.

There’s no very obvious reasons why Russia can’t succeed more at science. The average IQ relative to British norms is around 97, which might fall significantly short of Germanic and Anglo-Saxon (native!) averages, but isn’t really out of place relative to Mediterranean or East-Central European standards. Moreover, there are signs that Russia continues to enjoy a Flynn Effect, and besides, surely any minor disadvantage with respect to raw IQs is cancelled out by Russia’s traditionally very strong performance in international programming and mathematics contests.

Meanwhile, as regards industry, it is worth pointing out that Russia does consume around 2.7% of the world’s machine tools – it is, after all, the world’s eighth (or so) manufacturing power, not the gas station masquerading as a country of John McCain’s imagination. Infrastructure – roads, rail, airports – has genuinely gotten much better in the past decade, and with post-Soviet inflation finally tamed, Russia looks set for fairly vigorous growth.

But the problems holding Russia back are severe, and possibly intractable.

There remain strong financial and ultimately institutional barriers to unlocking Russia’s scientific potential. Putin and his clique seem to prefer lavishing resources on expensive status-signalling sporting events and white elephants as opposed to serious science and supercomputers. The former burnishes his prestige amongst simple people and provides endless opportunities to siphon away money to his Ozero chums – the latest lunatic project is to built a bridge for $10 billion to Sakhalin and its 500,000 people (a contract won by Arkady Rotenberg – who else?), which is about what the federal government spends on the Ministry of Education in a year – while the latter will only cause political trouble.

Ending corruption within academia would likewise seem a quixotic endeavor. While one can say much more on this topic, consider that PhD’s are no less a status symbol for the Russian elites than Mercedes cars and English boarding schools for their children. High-flyers found to have plagiarized their doctoral dissertations include no less than one in every nine members of the State Duma, and for that matter, Vladimir Putin himself. Waiting for these people to solve the problem of academic fraud is about as realistic as expecting them to solve corruption, or training foxes to guard hen houses. Nor is it possible to imagine a serious response to ethnic nepotism in academia in the land of Article 282, where you can be prosecuted just for arguing that the Caucasian republics should get fewer federal subsidies.

Finally, the absurdly low levels of robotization in industry raise serious questions about Russia’s political economy and its economic future. Why are Russian businesses loth to make serious moves towards automation in industry, even though Russia is, despite everything, a reasonably high IQ and well educated country? Is it because these require big capital investments that they are not willing to risk because of what they perceive as Russia’s environment of legal nihilism? It is correlated with Russian elites being the most apatride of any major civilization?

The importance of finding good answers and good solutions to these questions will only increase in the coming years and decades, as industry moves towards greater and greater automation. It seems likely that the countries that will be most successful at this will also be those who are succeeding at robotization today. Will Russia fall into a low-income trap where low wages preclude automation, and low automation preclude greater productivity and wages? At any rate, it doesn’t seem to be the case that anyone in Russia is seriously thinking about this, at least beyond empty electoral slogans – even as Putin runs for his fourth and hopefully final term, his promise to create 25 million hi-tech jobs during the 2012 Presidential elections has been conveniently forgotten.

Now this is not to say that the problem is with the Putin regime and that its removal will improve things. The pro-Western liberal elites are at least as rapacious as the kremlins, no less authoritarian in spirit, and far less patriotic to boot. Although this post was primarily about Russia, feel free to go back through the hyperlinks and study the case of the Ukraine, where liberal “lustrators” have repeatedly won; it is almost Sub-Saharan Africa so far as advanced science, native hi-tech (as opposed to offshored work), and any sort of capital-intensive manufacturing that wasn’t bequeathed to it by the USSR is concerned. Even the Visegrad and Baltic nations don’t have much to write home about. While most of them – especially, Czechia, Estonia, and Poland – do substantially better than Russia on most of these metrics, they still hugely lag the developed West and have been left behind in the dust by the Chinese juggernaut.

I don’t propose any great over-arching solution to these problems. “More money for RAN, less money for the Rotenbergs” might be a nice slogan, but as they say, the devil is in the details.

However, a solid start would be to look at the statistics and acknowledge that a very big problem exists, which, unresolved, will continue to degrade Russia’s economic, industrial, and eventually military competitiveness.

• Category: Economics, Science • Tags: Automation, Corruption, Russia, Science, Technology 
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Is now ready for the period 1 January 2017 – 31 December 2017.

The Weighted Fraction Count (WFC) of the Nature Index is is probably the single best proxy for quality-adjusted scientific output in the world today. You can read about the methodology here.

The first publicly accessible Nature Index dates to 2013, and covers the year 2012. In just the past five years, China has gone from having 25% of America’s elite science output to close to 50%.

. Country/territory AC FC WFC
1 United States of America (USA) 25537 17764.24 15791.8
2 China 11136 7870.2 7449.71
3 Germany 9092 4423.53 3785.08
4 United Kingdom (UK) 8146 3747.49 3087.55
5 Japan 4761 2962.21 2679.12
6 France 5345 2330.97 1890.01
7 Canada 3032 1390.92 1236.33
8 Switzerland 3053 1209.41 1080.48
9 South Korea 1985 1092.6 1000.22
10 Spain 3134 1220.04 950.11
11 India 1701 1085.43 935.44
12 Italy 3373 1335.3 923.17
13 Australia 2660 1056.63 833.15
14 Netherlands 2692 943.84 751.98
15 Israel 1202 564.51 484.92
16 Singapore 896 481.74 480.44
17 Sweden 1702 558.94 466.83
18 Russia 1514 497.54 389.54
19 Taiwan 1007 384.24 343.02
20 Belgium 1202 402.86 331.58
21 Austria 1049 355.42 318.08
22 Denmark 1081 316.7 272.51
23 Brazil 1086 341.38 253.22
24 Poland 1088 301.26 211.71
25 Norway 622 183.88 166.96
26 Czech Republic 688 203.8 164.23
27 Finland 708 197.14 160.23
28 Chile 1085 234.94 109.69
29 Portugal 573 128.74 109.11
30 New Zealand 400 118.06 107.87
31 Saudi Arabia 382 104.65 102.23
32 Ireland 484 117.38 101.84
33 Argentina 436 161.01 101.38
34 Iran 282 119.49 90.63
35 Mexico 584 157.72 86.93
36 Hungary 437 98.76 72.01
37 South Africa 588 127.25 71.95
38 Greece 433 86.5 64.35
39 Turkey 346 77.06 57.61
40 Pakistan 179 41.37 37.28
41 Slovenia 198 39.16 36.86
42 Thailand 224 35.98 32.28
43 Iceland 119 27.82 26.6
44 Estonia 167 32.2 24.49
45 Ukraine 309 38.61 23.96
46 Croatia 213 30.46 22.62
47 Romania 229 21.88 19.81
48 Luxembourg 56 14.97 14.97
49 Slovakia 157 26.09 14.95
50 Colombia 253 18.86 13.91
51 United Arab Emirates 110 21.06 12.63
52 Lithuania 118 15.18 11.73
53 Egypt 162 12.64 10.4
54 Serbia 190 16.35 8.91
55 Panama 40 8.64 8.64
56 Armenia 186 11.73 8.41
57 Vietnam 65 10.89 8.27
58 Bulgaria 158 13.22 8.03
59 Kazakhstan 31 9.14 7.71
60 Qatar 89 7.55 7.5
61 Malaysia 139 7.96 6.73
62 Belarus 152 6.48 6.42
63 Indonesia 52 6.51 6.41
64 Uruguay 17 6.03 6.03
65 Lebanon 23 6.57 5.97
66 Ecuador 99 5.99 5.68
67 Cyprus 98 6.02 5.15
68 Malta 20 6.06 4.79
69 Peru 45 4.9 4.54
70 Kenya 17 4.51 4.51
71 Georgia 177 8.54 3.37
72 Tunisia 23 5.03 3.07
73 Latvia 59 3.9 2.91
74 Morocco 88 2.93 2.84
75 Moldova 11 2.79 2.79
76 Oman 11 2.76 2.76
77 Algeria 17 3.23 2.36
78 Philippines 35 2.14 2.14
79 Costa Rica 14 4.09 1.95
80 Benin 7 1.57 1.57
81 Mongolia 12 1.52 1.52
82 Azerbaijan 80 1.63 1.51
83 Mali 9 1.45 1.45
84 Sri Lanka 53 1.36 1.36
85 North Korea 2 1.3 1.3
86 Madagascar 7 1.3 1.3
87 Venezuela 17 2.5 1.24
88 Congo 6 1.11 1.11
89 Nepal 7 1.22 1.1
90 Jordan 9 1.25 1.09
91 Uganda 9 1.76 1.09
92 Iraq 19 1.11 1.05
93 Tanzania 9 1.03 1.03
94 Bosnia and Herzegovina 7 1.12 1.02
95 Nigeria 16 2.44 1.02
96 Ethiopia 14 1.31 1
97 Macedonia 6 1.07 0.93
98 Brunei 5 0.9 0.9
99 Cuba 21 1.1 0.83
100 Bangladesh 10 0.81 0.81
101 Namibia 16 0.91 0.72
102 Monaco 13 0.7 0.7
103 Uzbekistan 8 0.89 0.68
104 Seychelles 3 0.67 0.67
105 Papua New Guinea 4 0.58 0.58
106 Botswana 7 0.63 0.57
107 Tajikistan 3 1.16 0.56
108 Kuwait 7 0.76 0.56
109 Malawi 3 0.55 0.55
110 Angola 4 0.55 0.55
111 Ivory Coast 5 0.53 0.53
112 Cameroon 9 0.65 0.51
113 Niger 4 0.5 0.5
114 Libya 3 0.47 0.47
115 Senegal 3 0.97 0.44
116 Jamaica 5 0.43 0.43
117 Sierra Leone 3 0.41 0.41
118 Guatemala 4 0.45 0.36
119 Sudan 3 0.56 0.36
120 Syria 1 0.33 0.33
121 Gabon 7 0.33 0.33
122 Burkina Faso 6 0.57 0.32
123 Bahamas 3 0.32 0.32
124 Fiji 2 0.28 0.28
125 Cambodia 2 0.27 0.27
126 Ghana 4 0.27 0.27
127 Vatican City State (Holy See) 27 0.96 0.25
128 Faroe Islands 1 0.25 0.25
129 Albania 2 0.24 0.24
130 Greenland 2 0.22 0.22
131 Maldives 1 0.22 0.22
132 East Timor 1 0.2 0.2
133 Rwanda 3 0.6 0.2
134 Palestinian territories 31 0.19 0.19
135 Trinidad and Tobago 2 0.19 0.19
136 Nicaragua 1 0.17 0.17
137 Bolivia 2 0.17 0.17
138 Bahrain 1 0.13 0.13
139 Cape Verde 1 0.13 0.13
140 Swaziland 1 0.13 0.13
141 Saint Kitts and Nevis 1 0.11 0.11
142 Liechtenstein 2 0.07 0.07
143 Paraguay 2 0.07 0.07
144 Honduras 2 0.07 0.07
145 Solomon Islands 1 0.06 0.06
146 Samoa 1 0.06 0.06
147 Gambia 1 0.06 0.06
148 Mozambique 2 0.25 0.05
149 Zambia 1 0.05 0.05
150 Grenada 1 0.05 0.05
151 Central African Republic 1 0.05 0.05
152 Montenegro 3 0.04 0.04
153 Liberia 1 0.04 0.04
154 Myanmar 1 0.04 0.04
155 Suriname 1 0.03 0.03
156 Kyrgyzstan 1 0.03 0.03
157 French Polynesia 2 0.16 0.03
158 Guinea 1 0.03 0.03
159 Laos 1 0.03 0.03
160 Zimbabwe 1 0.02 0.02
161 Guinea-Bissau 1 0.01 0.01


• Category: Science • Tags: Science 
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Look, I realize Elon Musk is really cool and all, but this latest is just not that significant. The Falcon Heavy can carry 63 tons into orbit – but only if the rocket isn’t reused. If it is, it’s just a sad 8 tons [for GTO launches]. That already rules out commercial applications involving very expensive payloads (e.g. most satellites), so long as reliability remains significantly worse than for proven workhorses like the Soyuz (97% success rate) or the Ariane (95%).

More importantly, 10 ton or even 100 ton payloads aren’t gonna cut it if we are serious about establishing a LARGE, autonomous Mars colony that could credibly serve as a long-term refuge from terrestrial existential risks.

I.e. we need something like this:


Instead of some crappy campsite at 0.376g:


So how do we go about this?

Today, there are just two more or less realistic (for now) methods to move millions of tons of material into space.

Space Elevator

Cool and all, but discussions begin and end with an intractable problem: The materials needed to build it are either too weak, or too ridiculously expensive.

Highly vulnerable to accident and sabotage (and sabotage disguised as accident) as well. At orbital speeds, even a small wayward satellite can really wreck your day.

superorion-7 Nuclear pulse propulsion

Has been technically feasible since when it was first proposed in the 1946 by Stanislaw Ulam, and developed into workable designs by Ted Taylor and Freeman Dyson in the late 1950s.

Basic idea: Mount spacecraft/payload onto a pusher plate, and explode a series of shaped nuclear charges to accelerate the thing into space. You could explode them in rapid succession if sudden acceleration is of no concern (cargo only), or in spaced out intervals if carrying human crews. There was a good description of how riding an Orion craft might feel like in Stephen Baxter’s Ark.

Atomic Rockets has an extremely comprehensive article on Project Orion.

8 miserly tons? Fuhgeddaboutit! Even the most modest Project Orion design from 1959 could support 1,300 tons, which is an order of magnitude greater than the most powerful heavy launch rockets either then or now.

There are almost no limits to what can be achieved – if anything, it is small Orion craft that are more of a challenge than large ones.

Orion drive spacecraft scale up quite easily. However, unlike other propulsion systems, they do not scale down gracefully. Surprisingly it is much more of an engineering challenge to make a small Orion. It is difficult to make a nuclear explosive below a certain yield in kilotons, and small nuclear explosives waste most of their uranium or plutonium. But it is relatively easy to make them as huge as you want, just pile on the megatons. So in the 1960′s when General Atomic made their first pass at a design, it was for a titanic 4,000 metric ton monster.

At the extreme end, there was the Super Orion design, able to carry a payload of 8 million tons (including 3 million tons of cargo) – that’s six orders of magnitude greater than the Falcon “Heavy”. Brian Wang notes that this is equivalent to about 30 supercarriers. Supercarriers are small towns in themselves, able to autonomously support thousands of human lives for months on end. The equivalent of 30 of them might be enough for a viable generation ship.

In between these extremes, there were a wide variety of possible configurations and propulsion methods.

One particularly crazed individual even made a design for propulsion based on a continuously detonating stream of radioactive water. (Yes you read that right).

Then there are the military applications:

When the Orion nuclear pulse propulsion concept was being developed, the researchers at General Atomic were interested in an interplanetary research vessel. But the US Air Force was not. They thought the 4,000 ton version of the Orion would be right sized for an interplanetary warship, armed to the teeth.

And when they said armed, they meant ARMED. It had enough nuclear bombs to devastate an entire continent (500 twenty-megaton city-killer warheads), 5-inch Naval cannon turrets, six hypersonic landing boats, and several hundred of the dreaded Casaba Howitzer weapons — which are basically ray guns that shoot nuclear flame (the technical term is “nuclear shaped charge”).

This basically a 4,000 ton Orion with the entire payload shell jam-packed with as many weapons as they could possibly stuff inside.

Keep in mind that this is a realistic design. It could actually be built.

This never came to be thanks to a cabal of Communists sapping the nation’s precious bodily fluids the very weak President Kennedy getting horrified by this assortment of weaponry… somehow I don’t think Trump would have had this problem.

Anyhow, the main problem is ofc fallout. Or rather, the hysterical propaganda around it.

However, there is a recent report that suggests ways of minimizing the fallout from an ORION doing a ground lift-off (or a, wait for it, “blast-off” {rimshot}). Apparently if the launch pad is a large piece of armor plate with a coating of graphite there is little or no fallout.

By which they mean, little or no ground dirt irradiated by neutrons and transformed into deadly fallout and spread the the four winds.

There is another problem, though, ironically because the pulse units use small low-yield nuclear devices.

Large devices can be made very efficient, pretty much 100% of the uranium or plutonium is consumed in the nuclear reaction. It is much more difficult with low-yield devices, especially sub-kiloton devices. Some of the plutonium is not consumed, it is merely vaporized and sprayed into the atmosphere. Fallout, in other words. You will need to develop low-yield devices with 100% plutonium burn-up, or use fusion devices (with 100% burn-up fission triggers or with laser inertial confinement fusion triggers).

Wikipedia notes that the USSR achieved 98% fusion yield in its experiments with nuclear canal excavation:

A 100% pure fusion explosive has yet to be successfully developed, according to declassified US government documents, although relatively clean PNEs (Peaceful nuclear explosions) were tested for canal excavation by the Soviet Union in the 1970s with 98% fusion yield in the Taiga test’s 15 kiloton devices, 0.3 kilotons fission, which excavated part of the proposed Pechora–Kama Canal.

In the end, a combination of Cold War nuclear proliferation treaties and environmentalist hysteria about all things nuclear killed all these beautiful 1950s visions of nuclear trains and trucks and interstellar spaceships dead.

Considering that the nuclear taboo is now greater than ever – there are many demented national leaderships who are banning nuclear power – the chances of anyone resurrecting Project Orion must be considered very small. If anyone does it, it will most likely be either China, which doesn’t answer to demotist whining, or Russia, where the construction of floating nuclear power stations suggests that the anti-nuclear taboo is less than overwhelming.

Otherwise, the chances of us getting off this sad clump of rock in bulk and on a sustainable basis – and these two things are interlinked – must be close to zero for the foreseeable future.

• Category: Science • Tags: Nuclear Power, Space Exploration 
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naked-mole-rat It was already known that these adorable critters tend to live ten times as long as other mammals of their size.

What is more surprising is that according to a new study funded by Calico, Google’s life extension research branch, they seem to defy the Gompertz Law outright (i.e., the tendency of mortality to increase with age). Consequently, their life expectancy in controlled environments might be far higher even than the oft stated 35 years.

This seems to be pretty remarkable, since although there is a wide diversity of ageing/mortality patterns across the tree of life, outright defiance of the Gompertz Law was thought to be limited to the really primitive creatures, like hydra.



This is encouraging for a couple of reasons.

1. Calico is an infamously secretive company that has hovered up a substantial fraction of the world’s ageing experts. At least this is confirmation that it’s still doing work on those lines instead of just more pharmacological money-grubbing.

2. It has long seemed to me that radical IQ augmentation would be easier than radical life extension, since the range of human genetic variation is far greater in the former (an IQ of 175 is cardinally different from 100 in a way that a life expectancy of 120 years is not cardinally different from 80), while the only animals that had really “solved” the ageing problem were presumably too primitive to provide ideas for human longevity. Naked mole rats at least prove that indefinitely extended lifespans are feasible in mammals.

• Category: Science • Tags: Longevity, Paper Review 
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London Student: Exposed: London’s eugenics conference and its neo-Nazi links

A eugenics conference held annually at University College London by an honorary professor, the London Conference on Intelligence, is dominated by a secretive group of white supremacists with neo-Nazi links, London Student can exclusively reveal.

Content note: This article contains references to racism, anti-Semitism and child abuse.

The conference has taken place at UCL four times since its inception in 2014, and now even boasts its own YouTube channel bearing the UCL logo.

UCL have told London Student that they are investigating the conference. A spokesperson said: “We are an institution that is committed to free speech but also to combatting racism and sexism in all forms.”

Some background: This scandal broke out when Toby Young, a conservative British political figure with a colorful history of Twitter controversies, was appointed to a government board on education – and removed almost instantaneously, after this story broke in The London Student, and was subsequently reported across all of Britain’s major newspapers, including The Telegraph, The Guardian, and The Daily Mail.

Surprising enough – though perhaps not, for people familiar with the venom establishment conservatives are capable of – The Telegraph’s account is even more downright nasty and libellous than The Guardian’s, despite the former being considered to be the mouthpiece of The Conservative Party, while the latter is but a slightly higher end version of VICE and the late Gawker. In contrast, the usually tabloidy Daily Mail, representing Britain’s “Red Tribe,” has the most measured tone.

So what is there to say?

First, I really need to emphasize that James Thompson’s views are not controversial in academic psychometrics.


Yes, I suppose that by SJW standards, 83% of all IQ experts are white supremacists, since the only politically correct position is that 0% of group IQ differences are due to genes.

Incidentally, this would include people like Richard Haier, Jelte Wicherts and James Flynn, who all attended the ISIR conference conference in Canada last year, which according to The Guardian was “a similar conference” to the London Conference on Intelligence.

Customary reminder that IQ studies are the only major branch of psychology that is not afflicted by the replication crisis.


So hey, you want to defenestrate Thompson et al.? You would have to ban pretty much all of academic psychometrics for consistency.

UCL professor David Colquhoun expressed disbelief that the university would host such “pseudoscience” and stated that the organiser, Professor James Thompson, “clearly doesn’t understand genetics.”

“The actual genetic difference between humans, with respect to race or sex, is absolutely miniscule compared to what they have in common,” he told London Student.

This is just a direct example of Lewontin’s fallacy. Greg Cochran deconstructs it very concisely here.

Or if you need a higher profile name, here is what Dawkins has to say about it:

If any outside readers are interested in what typically gets presented at the conferences of this cabal white supremacists with Neo-Nazi links, a few of the speeches from 2017 are available online have been (hopefully temporarily) taken down, but you can still view Emil Kirkegaard’s speech.

As regards the allegations (frankly smears) against Emil Kirkegaard, they are based on the fantasies of a demented and unusually dedicated stalker, the contents of which he has addressed in some detail at his blog.

See also the video Emil has just released with Tara McCarthy about this affair.

One amusing thing jumps out in particular – the apparent inability of the “journalist” behind this piece to read the texts he links to.

Thompson is a frequent contributor to the Unz Review, which has been described as “a mix of far-right and far-left anti-Semitic crackpottery,” and features articles such as ‘America’s Jews are Driving America’s Wars’ and ‘What to do with Latinos?’. His own articles include frequent defences of the idea that women are innately less intelligent than men (1, 2, 3, and 4), and an analysis of the racial wage gap which concludes that “some ethnicities contribute relatively little,” namely “blacks.

It is safe to say that most people reading this on The London Student would come away with the impression that Fred Reed, the author of the article “What to do with Latinos?“, is some sort of hardcore anti-Latino fanatic.

Here’s a few quotes from that article:

The embittered anti-immigration people, readers of sites like VDare, would not care. Screw the vile brown scum, rapists and welfare parasites, murderers, drug peddlers, low-IQ nasty unevolved human flatworms. The bastards came illegally, so to hell with them. …

Many of the white nationalists exhibit an almost effeminate squeamishness at the thought of their precious bodily essences being polluted by oozing dark sludge. Well, as you will. There are reasons why this view isn’t going to prevail. See below.

Much hate so racism wow.

Meanwhile, in the world of reality, as opposed to the make-believe world of Britbong SJWs who don’t read their own links and don’t think anybody else would either, Fred Reed is married to a Mexican, voluntarily (and apparently happily) lives in Mexico, and is actually disliked in White Nationalist circles, to the extent that they are aware of him.

stop-iq-health-research Anyhow, continue to read The Unz Review – apart from the content, which stands for itself, you will also be entertained by neverending media drama and flame wars with the collective Society 282 and their “powerful takes” (see right for an especially powerful example).

Jokes aside, though, it’s pretty sad. Considering the very close relationship between intelligence and economic development, understanding it better should be one of the largest priorities in terms of alleviating global poverty and suffering – especially since we might be on the cusp of technological solutions (voluntary bioengineering).

It’s also in the end futile. The West may well stick its head in the sand on this issue, but China surely won’t, and will be all the more dominant in the 21st century on account of it.

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Commenter Polish Perspective draws attention to a startling new statistic:

Total spending on R&D in China (as a percentage of GDP) more than doubled from 0.9% in 2000 to 2.1% in 2016… China’s share of high-impact academic publications (the top 0.1% of papers in Scopus, which rates by citations) has grown, from less than 1% in 1997 to about 20% in 2016.

In my 2016 longread, I pointed out that China was converging with America on a broad range of hi-tech economy indicators.

Now yes, Chinese papers have a reputation for shoddiness, being worse on average than Western ones, but absolute values do matter, and quality is rapidly improving anyway.

Incidentally, this is confirmed by China’s performance on Nature’s WFC index, where it rose from 24% of the US level in 2013 to 40% in 2016, and 46% as of just the Oct 2016-Sep 2017 period.

Clearly it is well on the road to becoming a global innovation power, in addition to its already extant strengths in basic manufacturing.

Note that this will not be evident in Nobel Prize statistics until the middle of the century, since they now have a 20-30 year lag time (the Japanese, for instance, only started winning substantial numbers of them from around 2000).

The most interesting question is whether China will converge to Japan/Korea’s level of per capita elite scientific output, or go on to hurtle past them to the Anglo/Germanic level.

If the former, it will still end up the world’s premier scientific power, with around 50% higher Science Point production than the US.

If the latter, its scientific dominance will be commensurate to its demographic preponderance, and as complete as its economic (and probable military) dominance.

Incidentally, Russia is a complete failure on these metrics – it is considerably less productive than a high-functioning small country like Switzerland. I have a 4,000 word post on that ready to go in due course.

• Category: Science • Tags: China, Science 
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I want to gather most of my arguments for skepticism (or, optimism) about a superintelligence apocalypse in one place.

(1) I appreciate that the mindspace of unexplored superintelligences is both vast and something we have had absolutely zero experience with or access to. This argument is also the most speculative one.

That said, here are the big reasons why I don’t expect superintelligences to tend towards “psychotic” mindstates:

(a) They probably won’t have the human evolutionary suite that would incline them to such actions – status maximization, mate seeking, survival instinct, etc;

(b) They will (by definition) be very intelligent, and higher intelligence tends to be associated with greater cooperative and tit-for-that behavior.

Yes, there are too many fail points to count above, so the core of my skepticism concerns the very likelihood of a “hard” takeoff scenario (and consequently, the capacity of an emergent superintelligence to become a singleton):

(2) The first observation is that problems tend to become harder as you climb up the technological ladder, and there is no good reason to expect that intelligence augmentation is going to be a singular exception. Even an incipient superintelligence is going to continue having to rely on elite human intelligence, perhaps supercharged by genetic IQ augmentation, to keep going forwards for some time. Consequently, I think an oligopoly of incipient superintelligences developed in parallel by the big players is likelier than a monopoly, i.e. a potential singleton.

(I do not think a scenario of many superintelligences is realistic, at least in the early stages of intelligence takeoff, since only a few large organizations (e.g. Google, the PLA) will be able to bear the massive capital and R&D expenditures of developing one).

(3) Many agents are just better at solving very complex problems than a single one. (This has been rigorously shown to be the case for resource distribution with respect to free markets vs. central planning). Therefore, even a superintelligence that has exhausted everything that human intelligence could offer would have an incentive to “branch off.”

But those new agents will develop their own separate interests, values, etc.- they would have to in order to maximize their own problem-solving potential (rigid ideologues are not effective in a complex and dynamic environment). But then you’ll get a true multiplicity of powerful superintelligent actors, in addition to the implicit balance of power situation created by the initial superintelligence oligopoly, and even stronger incentives to institute new legal frameworks to avoid wars of all against all.

A world of many superintelligences jockeying for influence, angling for advantage, and trading for favors would seem to be better for humans than a face-off against a single God-like superintelligence.

I do of course realize I could be existentially-catastrophically wrong about this.

And I am a big supporter of MIRI and other efforts to study the value alignment problem, though I am skeptical about its chances of success.

legg-algorithms-ai DeepMind’s Shane Legg proved in his 2008 dissertation (pp.106-108) that simple but powerful AI algorithms do not exist, while an upper bound exists on “how powerful an algorithm can be before it can no longer be proven to be a powerful algorithm” (the area on the graph to the right where any superintelligence will probably lie). That is, the developers of a future superintelligence will not be able to predict its behavior without actually running it.

This is why I don’t really share Nick Bostrom’s fears about a “risk-race to the bottom” that neglects AI safety considerations in the rush to the first superintelligence. I am skeptical that the problem is at all solvable.

Actually, the collaborative alternative he advocates for instead – by institutionalizing a monopoly on superintelligence development – may have the perverse result of increasing existential risk due to a lack of competitor superintelligences that could keep their “fellows” in check.

• Category: Science • Tags: Existential Risks, Futurism, Superintelligence 
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Grigoriev, Andrey & Lynn 2009
Studies of Socioeconomic and Ethnic Differences in Intelligence in the Former Soviet Union in the Early Twentieth Century


This paper reviews the studies of socioeconomic and ethnic and racial differences in intelligence carried out in Russia/USSR during the late 1920s and early 1930s. In these studies the IQs of social classes and of ethnic minorities were tested. These included Tatars (a Caucasoid people), Chuvash and Altai (mixed Caucasoid-Mongoloid peoples), Evenk (a mixed Caucasoid-Arctic people), and Uzbeks (a Central-South Asian people). The results of these studies showed socioeconomic differences of 12 IQ points between the children of white collar and blue collar workers, and that with the exception of the Tartars the ethnic minorities obtained lower IQs than European Russians.

This is essentially a short history of psychometrics in the USSR/Russia.

(1) The first measurement of Russian IQ was performed in 1909 by A.M. Schubert, who used the French Binet test with n=229 children: “She concluded that the Binet test appeared to be too difficult for Russian children and the scale should be moved on 1 to 2 ages to be appropriate for them.” Since Mental age ÷ Physical age × 100 = IQ, this implies their average IQ was perhaps one S.D. lower than that of the French, though later researchers pointed out those children were drawn from lower socio-economic strata.

In 1930, now in the USSR, another study found the following:

They tested 414 children aged between 8½ and 11½ with the American Stanford– Binet (administered in Russian translation). The sample consisted of 200 children of peasants,141 children of blue collar workers, and 73 children of white-collar workers. All children were from Moscow or the Moscow region. The results were that the children of peasants obtained a mean IQ of 87 (the standard deviation=10), the children of blue-collar workers a mean IQ of 91 (SD=8.6) and the children of white-collar workers a mean IQ 98 (SD=8.4). The mean IQ (unweighted) for three groups was 92… Thus, the total weighted mean for Russian children in this study was 90.3 (these IQs are in relation to American Stanford–Binet norms).

Capture This brings to mind a 1920s study quoted by Anne Anastasi in her book Differential Psychology (pp.524), in which Russian immigrant children to the US got 90.

This 10 point difference was presumably there because Russia was a more economically backwards country, with a more repressed average IQ due to gaps in schooling, malnutrition, parasitic load, etc.

(2) As in the West, consistent differences were found in the IQs of people from different socio-economic strata.

Another study of relation of IQ to social class was carried out by M. Syrkin (М.Сыркин)(Сыркин,1929) who compared the intelligence of fourth grade children (N=338, age approximately 10 years) belonging to six socio-economic groups. The lowest group was described as “ blue collar workers and at least one of parents illiterate”and the highest group was described as “white-collar workers and at least one parent educated in an institute of higher education”. Intelligence was assessed with five verbal tests measuring comprehension and verbal reasoning. There was a difference of 1.42d(equivalent to 21.3 IQ points) between the lowest and highest socioeconomic groups.

The USSR really did expel, kill off, or otherwise limit the reproductive fitness of its best and brightest.

In 1928, E.I. Zverev (Е.И. Зверев)(Зверев, 1931) tested the IQ of 114 children just admitted to school and aged about 7½– 8 years, in and around the city of Kursk, about 500 km south of Moscow. The children were tested with the Binet– Bert test (a Russian adaptation of the Binet). The mean IQ of these children was 80.8. This is much lower than the IQ of children obtained by Gurjanov, Smirnov, Sokolov, & Shevarev (Гурьянов, Смирнов, Соколов,&Шеварев, 1930) for Moscow and the Moscow region. Probably this difference was due to methodological and sample differences, but there is a possibility that the regional factor was also involved.

The latter hypothesis is likely the correct one.

In the 2009 PISA test, there was a 12 IQ point difference between Kursk and Moscow, which is an incredibly concentrated cognitive cluster.

(3) Now we go on to the most “controversial” part – ethnic differences in IQ.

Central Russia

There were also some studies of the IQs of non-Slavonic but predominantly Caucasoid peoples.I. Bektchentay (И .Бикчентай) and Z. Carimowa (З.Каримова )(Бикчентай &Каримова, 1930) tested the IQs of 380 Tartar children aged 8– 18 in fi ve Tartar schools in Moscow with the Boltunow–Binettest(aRussian adaptation of the Binet). The Tartars are indigenous to the Caucasus in the far south of Russia and the former Soviet Union, but a number of them live in central Russian towns and cities. The mean IQ of the Tartar children in this study was approximately the same as that of Russian children. The correlation between the Boltunow– Binet test and school achievements (assessed by teachers’ estimates) in their study was 0.84.

Yes, this is a pretty major distinction.

The Volga Tatars – the Muslim and Christianized Tatars of central Russia – have an average IQ of around 100 (about equal to modern Russia/Europe). Population genetics studies have found them to be basically acculturated Slavs.

The first of these was reported by F.P. Petrov (Е.П. Петров) (Петров, 1928) who tested the IQs of 1398 Chuvash children aged 3–13 in 1926–1927 with the French Binet–Simon test… The figures inTable 2 show a median IQ of 87 for boys and 84 for girls, and means (unweighted) of 89 for boys and 86 for girls. These are in relation to 100 for French norms, but no normative data are reported for Russian children. The IQs of the Chuvash children show a decline with age, with the lowest IQs among the 12 and 13 year olds.

Chuvashia is currently about average for the Russian regions.


Also tests carried out on indigenous tundric peoples, such as the Evenks (Bulanov 1930):

The results are presented as typical for Evenk children, but because of the small samples, their IQs may not be regarded as reliable. The results are as follows. For the Binet test the mean IQ was 70.16 (for 5 children, and in relation to French norms). The results obtained with the Rossolimo test showed lower average IQs of the Evenk (Tungus) compared with a Moscow sample on some abilities, namely, memory for pictures and words, ability to comprehend combined pictures, ability to comprehend visual incongruities, and, according to Bulanow’s interpreta- tion, ability to retain a high level of attention. As regards memory for pictures, the results contradicted the sometimes described capacity of Evenk (Tungus) to remember exactly long routes on wild territory (Encyclopedic Dictionary by Brockhaus & Efron (Энциклопедический словарь Ф .А . Брок – гауза и И.А .Ефрона ), 1902, vol. 67, p. 66)….

Bulanow also reported some observations on Evenk (Tungus) children and adults concerning their great difficulty in understanding the concepts of measurement and number. He reported that when Evenk children were questioned about devices for measurement, they did not have the concept of an absolute unit of measurement. They thought that the unit changed with the material measured. Bulanow reported further that when he asked Evenk adults how many children they had “ It was difficult, almost impossible, to get from parents precise information as to how many of their children were alive, how many of their children had died, what was the age of their children, and so on.” (p. 198).

… and on the Altai (Zaporochets 1930):

The results for the Binet test were as follows: mean IQ for total group was 66.9 (sd. 8.5), mean IQ for children aged 8– 12 was 69.15, and the mean IQ for children aged 13–16 years was 64.8. As noted by Zaporojets, this test was tedious for the Altai children. Some tasks were especially difficult for them. These were tasks involving calculation, logical operations, and the fluency task to name as many as words as possible during 3 min. As for the Rossolimo test, the most diffi cult tests for Altai children were those requiring the ability to retain a high level of attention and to comprehend visual incongruities. Their mean IQ for the Pintner–Peterson test was 75.

Zaporojets noted that the Altai children did not have a clear understanding of units of measurement. He observed that when they were questioned about the length of a meter, the Altai would often ask: “Which meter?”They thought that the meter in one shop could be longer than in another. An adult Altai said about distance: “It is 100 big versts (approximately 100 kilometers)” (he apparently thought that the number of small versts must be more).

Zaporojets’ paper contains some interesting observations on adult Altai. Although adult Altai performed calculations poorly at the time of study, they showed a remarkable ability for visual estimation of large quantities. A herdsman, who could count only to 20–30, noticed very well the absence of one horse, cow or sheep in a herd of many hundreds. He looked at a huge herd and noted that a particular cow was absent. Another example of the great visualization ability of the Altai was that they could remember and showed the way through wild territory, where they had been only once many years previously.

Common theme: No numeracy (they’d have a very bad Whipple’s index), very premodern and non-abstract ways of thinking, but quite well suited for their environment.

In PISA 2009, Yakutia had the lowest score of any tested Russian region, including Dagestan (though Chechnya and Ingushetia were not included). Ethnic Yakuts, who probably have similar IQs to the Altai and Evenks, constitute 50% of its population, though probably more like 2/3 amongst the children taking PISA due to their higher fertility rates. This might imply that the average Yakut IQ is in the low-to-mid 80s.

Central Asia

First test was carried out in 1926 by A. Schtelerman: He did not give IQs but reported that the scores of the Uzbek children were lower than those of children in Moscow.

A series of studies by V.K. Soloviev on Russian and Uzbek army cadets and professionals found that “the test scores and the educational level of the Uzbeks were lower than those of the Europeans.”

The third study of the intelligence of the Uzbeks was carried out in 1931 by A.R. Luria (А.Р . Лурия ), at that time at the Institute of Psychology in Moscow. Luria did not use intelligence tests but gave a descriptive analysis of the Uzbeks’ cognitive abilities. He distinguished two modes of thought designated graphic recall (memories of how objects in the individual’s personal experience are related) and ca- tegorical relationships (categorisation by abstract concepts). He found that the thought processes of illiterate Uzbek peasants were confined to graphic recall and that they were not able to form abstract concepts. For example, they were shown a hammer, an axe, a log and a saw, and asked which of these did not belong. The typical Uzbek answer was that they all belonged together because they are all needed to make firewood. People who are able to think in terms of categorical relationships identify the log as the answer because the other three are tools (an abstract concept). Illiterate Uzbeks peasants were unable to form concepts of this kind. They were also unable to solve syllogisms. For instance, given the syllogism “There are no camels in Germany; the city of B is in Germany; are there camels there?” Luria gave as a typical Uzbeks answer “I don’t know, I have never seen German cities. If B is a large city, there should be camels there.” Similarly, Luria asked “In the far north, where there is snow, all bears are white; Novia Zemlya is in the far north; what color are the bears in Novia Zemlya?”. A typical Uzbek answer was “I’ve never been to the far north and never seen bears”(Luria,1979, p. 77–8). Thus, Luria concluded that these peoples were not capable of abstract thought: “ the processes of abstraction and generalization are not invariant at all stages of socioeconomic and cultural development. Rather, such processes are pro- ducts of the cultural environment” (Luria, 1979, p. 74). Luria proposed that the ability to think in terms of categorical relationships is acquired through education. He did not suggest that the Uzbeks have any genetic cognitive deficiency.

I wrote about Luria back in the late 2000s when I still agnostic about genetic racial differences in IQ.

Today those factors no longer really hold, but Central Asians do very poorly on international standardized tests.

Kyrgyzstan came at the very bottom of PISA 2009, with a PISA-equivalent IQ of around 75.

Table below is from David Becker’s database of national IQs:

National Ethnic Age N Test IQ Study
Kazakhstan 8 to 16 617 SPM+ 87.30 Grigoriev & Lynn (2014)
Kyrgyz 85.60 Lynn & Cheng (2014)
Tajikistan 13 to 15 674 SPM+ 88.00 Khosimov & Lynn (2017)
Uzbekistan 10 to 15 51 SPM+ 86.00 Grigoriev & Lynn (2014 )
Uzbekistan 11 to 13 614 SPM+ 85.00 Salahodjaev et al. (2017)

Still, Luria has some of the best arguments against that position, so its a bit surprising that the blank slatists don’t cite him more.

stalin-the-tajik(4) Or maybe not, because it still didn’t save him him from the SJWs’ ideological predecessors, Sovok Justice Warriors:

These early studies carried out in the years 1926– 1931 found that there were substantial socioeconomic and ethnic/ racial differences in intelligence in the Soviet Union. These conclusions were not consistent with Marxist orthodoxy which held that these differences would disappear under communism. Accordingly, these studies, particularly that of Luria, attracted a great deal of criticism in the Soviet Union in the early 1930s. This has been described by Kozulin (1984): “Critics accused Luria of insulting the national minorities of Soviet Asia whom he had ostensibly depicted as an inferior race. The results of the expedition were refused publication and the very theme of cultural development was forbidden” . In 1936 intelligence testing was banned in the Soviet Union. It was not until the 1960s and early 1970s that this prohibition was progressively relaxed (Grigorenko & Kornilova, 1997). Luria’s work was not published in Russian until 1974 and English translations were published in 1976 and 1979 (Luria, 1976, 1979).

As Lynn and Grigoriev point out, this was closely correlated to the suppression of genetics research, though at least Luria and Co. weren’t outright murdered like Vavilov.

The history of work on intelligence in the former Soviet Union parallels that of genetics, where mainstream Mendelian theory represented by Nikolai Vavilov in the 1920s was likewise suppressed in the 1930s and replaced by the environmentalist pseudo-genetics of Trofi m Lysenko. The domination of science by political theory was relaxed in the 1960s and 1970s, and in recent decades both intelligence research and Mendelian genetics have been rehabilitated in Russia.

Scientifically, there is real work being done on psychometrics in Russia, though in comparison to the US it is very meager and basically inconsequential.

Since it is not politicized in the US, it is neither promoted nor prosecuted.

If psychometric considerations were to move closer to politics, e.g. by tying them to the hot potato that is Central Asian immigration, things can go any which way. Although Russians have a more commonsense take on these matters – if 25% of Americans seriously think intelligence is a “social construct,” it’s probably more like 5% in Russia. On the other hand, the Leftists, Stalinists, and even many Eurasianists are aggressively opposed to the idea that intelligence is heritable and differs significantly between races, and in the event that the authorities side with them, Russian scientists don’t have the First Amendment or an fair and impartial court system to hide behind.

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Here we report the development of a system that incorporates a pumpless oxygenator circuit connected to the fetus of a lamb via an umbilical cord interface that is maintained within a closed ‘amniotic fluid’ circuit that closely reproduces the environment of the womb. We show that fetal lambs that are developmentally equivalent to the extreme premature human infant can be physiologically supported in this extra-uterine device for up to 4 weeks. Lambs on support maintain stable haemodynamics, have normal blood gas and oxygenation parameters and maintain patency of the fetal circulation. With appropriate nutritional support, lambs on the system demonstrate normal somatic growth, lung maturation and brain growth and myelination.

This is really cool.

twitter-artificial-wombs I have been advocating this technology since I started blogging in 2008.

The immediate benefits, which the authors cite, are a reduction in infant mortality caused by extreme prematurity. This is good, though not that big of a deal, since it is very low in First World countries anyway, while poorer countries will probably not be able to afford the technology anyway.

The real promise is in its eugenic potential.

It is common knowledge that the well-educated reproduce less than the poorly educated, and that has resulted in decades of dysgenic decline throughout the developed world. This dysgenic effect has overtaken the Flynn effect. One of the reasons the well-educated, and especially well-educated women, have few or zero children is because it is bad for their career prospects. There are also some women who are just uncomfortable with the idea of pregnancy and childbirth.

There are essentially just a few solutions to this problem:

(1) Do nothing, deny heritability of IQ. Import Afro-Muslims to breed the next generation of doctors and engineers.

(2) Do nothing, hope for a literal deus ex machina solution, such as Musk’s neural lace or superintelligence.

(3) The Alt Right solution: Send the women back to the kitchen.

Ethical considerations aside, there’s also the matter of practicality – you’d have to be really hardcore at enforcing your “White Sharia” to make any substantive difference. Even most conservative Muslim societies, where female labor participation is very low, have seen plummeting fertility rates. And, needless to say, it does nothing about the dysgenic aspect of modern fertility patterns, which are a significantly bigger problem than falling fertility rates anyway.

(4) Develop artificial wombs.

This is a good idea from all sorts of ideological perspectives.

Everyone: Immediate higher fertility rates in the countries that develop them, especially amongst well-educated women. This might cancel out dysgenic decline at a single stroke.

Liberals: Alternate option for women who don’t want to undergo pregnancy/childbirth for whatever reason. No more market for surrogate mothers – an end to a particularly icky form of Third World exploitation.

Libertarians: People with the means to pay – that is, millionaires and especially billionaires – will no longer be bounded in their reproductive capacity by the biology of their female partner or by the culture of their society (generally, no polygamy). Since wealth is moderately correlated with IQ, this will be eugenic. That said, this might strike some as dystopian. Maybe one could start taxing additional artificial womb-grown offspring past the first five or ten? Then you’d get “offshore hatcheries.” Okay, I suppose that’s even more dystopian.

Zensunnis: I suppose cultures that really dislike women can just gradually start making do without them by replacing them with the equivalent of Axlotl tanks. Conversely, (almost) all female “Amazonian” societies will also become possible. Let’s make sci-fi tropes real.

Futurists: Combining artificial wombs with CRISPR gene-editing for IQ on a mass scale pretty much directly leads to a biosingularity.

As I pointed out, a biosingularity may be preferable to one born of machine superintelligence because it bypasses the AI alignment problem and doesn’t risk the end of conscious experience.

• Category: Science • Tags: Fertility, Paper Review, Transhumanism 
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Tang, Lichun et al. 2017
CRISPR/Cas9-mediated gene editing in human zygotes using Cas9 protein


Previous works using human tripronuclear zygotes suggested that the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system could be a tool in correcting disease-causing mutations. However, whether this system was applicable in normal human (dual pronuclear, 2PN) zygotes was unclear. Here we demonstrate that CRISPR/Cas9 is also effective as a gene-editing tool in human 2PN zygotes. By injection of Cas9 protein complexed with the appropriate sgRNAs and homology donors into one-cell human embryos, we demonstrated efficient homologous recombination-mediated correction of point mutations in HBB and G6PD. However, our results also reveal limitations of this correction procedure and highlight the need for further research.

Gwern Branwen’s comments:

Even nicer: another human-embryo CRISPR paper. Some old 2015 work – results: no off-target mutations and efficiencies of 20/50/100% for various edits. (As I predicted, the older papers, Liang et al 2015 / Kang et al 2016 / Komor et al 2016, were not state of the art and would be improved on considerably.)

Back in February 2015, qualia researcher Mike Johnson predicted that dedicated billionaire with scant regard for legalistic regulations could start genetically “spellchecking” their offspring within 5-7 years.

But if anything, he might have overestimated the timeframe.


• Category: Science • Tags: Crispr, Genetic Load, Paper Review, Transhumanism 
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Silicon Valley’s tech oligarchs are becoming increasingly interested in brain-computer interfaces.

The WSJ is now reporting that Elon Musk is entering the game with a new company, Neuralink.

At the low end, they could improve function in patients suffering from diseases such as Parkinson’s, which is the modest aim that the first such companies like Kernel are aiming for. However, in the most “techno-utopian” visions, they could be used to raise general IQ in healthy people, integrating people directly into the Internet of Things and perhaps even helping bridge the gap between biological and potentially runaway machine intelligence (Elon Musk is known to be concerned about the dangers of unfriendly superintelligence).

Well, best of luck to them. Deus Ex is a cool universe, and in ours, it doesn’t even look like the buildup of glial nerve tissues is going to be an issue in ours.

So, no Neuropozyne addicts, at least. But there are other, more directly technical, reasons why implants are going to be really hard to get right, as summed up by Nick Bostrom in his book on Superintelligence.

This brings us to the second reason to doubt that superintelligence will be achieved through cyborgization, namely that enhancement is likely to be far more difficult than therapy. Patients who suffer from paralysis might benefit from an implant that replaces their severed nerves or activates spinal motion pattern generators. Patients who are deaf or blind might benefit from artificial cochleae and retinas. Patients with Parkinson’s disease or chronic pain might benefit from deep brain stimulation that excites or inhibits activity in a particular area of the brain. What seems far more difficult to achieve is a high-bandwidth direct interaction between brain and computer to provide substantial increases in intelligence of a form that could not be more readily attained by other means. Most of the potential benefits that brain implants could provide in healthy subjects could be obtained at far less risk, expense, and inconvenience by using our regular motor and sensory organs to interact with computers located outside of our bodies. We do not need to plug a fiber optic cable into our brains in order to access the Internet. Not only can the human retina transmit data at an impressive rate of nearly 10 million bits per second, but it comes pre-packaged with a massive amount of dedicated wetware, the visual cortex, that is highly adapted to extracting meaning from this information torrent and to interfacing with other brain areas for further processing. Even if there were an easy way of pumping more information into our brains, the extra data inflow would do little to increase the rate at which we think and learn unless all the neural machinery necessary for making sense of the data were similarly upgraded. Since this includes almost all of the brain, what would really be needed is a “whole brain prosthesis–—which is just another way of saying artificial general intelligence. Yet if one had a human-level AI, one could dispense with neurosurgery: a computer might as well have a metal casing as one of bone.

Not only is there this seemingly insurmountable computing capacity problem, but there is also an equally daunting translation problem.

But what about the dream of bypassing words altogether and establishing a connection between two brains that enables concepts, thoughts, or entire areas of expertise to be “downloaded” from one mind to another? We can download large files to our computers, including libraries with millions of books and articles, and this can be done over the course of seconds: could something similar be done with our brains? The apparent plausibility of this idea probably derives from an incorrect view of how information is stored and represented in the brain. As noted, the rate-limiting step in human intelligence is not how fast raw data can be fed into the brain but rather how quickly the brain can extract meaning and make sense of the data. Perhaps it will be suggested that we transmit meanings directly, rather than package them into sensory data that must be decoded by the recipient. There are two problems with this. The first is that brains, by contrast to the kinds of program we typically run on our computers, do not use standardized data storage and representation formats. Rather, each brain develops its own idiosyncratic representations of higher-level content. Which particular neuronal assemblies are recruited to represent a particular concept depends on the unique experiences of the brain in question (along with various genetic factors and stochastic physiological processes). Just as in artificial neural nets, meaning in biological neural networks is likely represented holistically in the structure and activity patterns of sizeable overlapping regions, not in discrete memory cells laid out in neat arrays. It would therefore not be possible to establish a simple mapping between the neurons in one brain and those in another in such a way that thoughts could automatically slide over from one to the other. In order for the thoughts of one brain to be intelligible to another, the thoughts need to be decomposed and packaged into symbols according to some shared convention that allows the symbols to be correctly interpreted by the receiving brain. This is the job of language.

In principle, one could imagine offloading the cognitive work of articulation and interpretation to an interface that would somehow read out the neural states in the sender’s brain and somehow feed in a bespoke pattern of activation to the receiver’s brain. But this brings us to the second problem with the cyborg scenario. Even setting aside the (quite immense) technical challenge of how to reliably read and write simultaneously from perhaps billions of individually addressable neurons, creating the requisite interface is probably an AI-complete problem. The interface would need to include a component able (in real-time) to map firing patterns in one brain onto semantically equivalent firing patterns in the other brain. The detailed multilevel understanding of the neural computation needed to accomplish such a task would seem to directly enable neuromorphic AI.

As for learning a mapping using the brain’s native capacities… well, we sort of already do that, and through methods that have the advantage of not being evolutionarily novel.

One hope for the cyborg route is that the brain, if permanently implanted with a device connecting it to some external resource, would over time learn an effective mapping between its own internal cognitive states and the inputs it receives from, or the outputs accepted by, the device. Then the implant itself would not need to be intelligent; rather, the brain would intelligently adapt to the interface, much as the brain of an infant gradually learns to interpret the signals arriving from receptors in its eyes and ears. But here again one must question how much would really be gained. Suppose that the brain’s plasticity were such that it could learn to detect patterns in some new input stream arbitrary projected onto some part of the cortex by means of a brain–computer interface: why not project the same information onto the retina instead, as a visual pattern, or onto the cochlea as sounds? The low-tech alternative avoids a thousand complications, and in either case the brain could deploy its pattern-recognition mechanisms and plasticity to learn to make sense of the information.

Unless and until Elon Musk clearly explains how his “neural lace” is going to get around these issues, we should treat it with the skepticism it warrants.

Contra /pol/, Musk’s achievements are indeed tall, but contra /r/Futurology, the hype around him is ten times taller.

• Category: Science • Tags: Futurism, Neuroscience 
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This blog post by Sarah Constantin has an impressively comprehensive tally of performance trends in AI across multiple domains.

chess-elo-humans-vs-computers Three main things to do take away:

  • In games performance, e.g. chess (see right, based on Swedish Chess Computer Association data) “exponential growth in data and computation power yields exponential improvements in raw performance.” So the relation between them is linear.
  • This relationship may be sublinear in non-game domains, such as natural language processing (NLP).
  • “Deep learning” only created discontinuous (but one time) improvements in image and speech recognition, but not in strategy games or NLP. Its record on machine translation and arcade games (see below right) is ambiguous.

arcade-games-human-vs-computer So “deep learning” might not have been as transformational as the tech press would have had you believe, and as Miles Brundage observed, has largely been about “general approaches for building narrow systems rather than general approaches for building general systems.”

And we also know that Moore’s Law has been slowing down of late.

If this is basically accurate, then the spate of highly visible AI successes we have been seeing in quick succession of late – peak human performance in go in 2016; in No Limit poker with multiple players a couple of months ago – could end up being a one-off coincidence that will be followed by another AI winter.

And we will have to do something cleverer than naively projecting Kurzweil’s graphs forwards to get to the singularity.

• Category: Science • Tags: Artificial Intelligence, Futurism 
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Ashburn-Nardo, Leslie 2017
Parenthood as a Moral Imperative? Moral Outrage and the Stigmatization of Voluntarily Childfree Women and Men


Nationally representative data indicate that adults in the United States are increasingly delaying the decision to have children or are forgoing parenthood entirely. Although some empirical research has examined the social consequences of adults’ decision to be childfree, few studies have identified explanatory mechanisms for the stigma this population experiences. Based on the logic of backlash theory and research on retributive justice, the present research examined moral outrage as a mechanism through which voluntarily childfree targets are perceived less favorably than are targets with children for violating the prescribed social role of parenthood. In a between-subjects experiment, 197 undergraduates (147 women, 49 men, 1 participant with missing gender data) from a large U.S. Midwestern urban university were randomly assigned to evaluate a male or female married target who had chosen to have zero or two children. Participants completed measures of the target’s perceived psychological fulfillment and their affective reactions to the target. Consistent with earlier studies, voluntarily childfree targets were perceived as significantly less psychologically fulfilled than targets with two children. Extending past research, voluntarily childfree targets elicited significantly greater moral outrage than did targets with two children. My findings were not qualified by targets’ gender. Moral outrage mediated the effect of target parenthood status on perceived fulfillment. Collectively, these findings offer the first known empirical evidence of perceptions of parenthood as a moral imperative.

The author herself doesn’t seem to be happy with her own findings:

Practically speaking, the present findings have some troubling potential implications for howpeople transition to parenthood. For example, the present findings, obtained with college students in the Midwestern United States, suggest that many young people view children as a necessary ingredient for fulfilling lives. Thus, they may feel tremendous pressure to have children, not only from others as this literature suggests (Mueller and Yoder 1999), but also internally. Ironically, these perceptions have absolutely no basis in reality. Meta-analyses reveal that parents report significantly less marital satisfaction than do non-parents, and as their number of children increases, marital satisfaction decreases (Twenge et al. 2003).

That maybe so, but reality definitely seems to have a basis in those perceptions.

For instance, people without those perceptions didn’t tend to pass on their genes.

• Category: Science • Tags: Demographics, Paper Review, Psychology 
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Fundamentally solve the “intelligence problem,” and all other problems become trivial.

The problem is that this problem is a very hard one, and our native wit is unlikely to suffice. Moreover, because problems tend to get harder, not easier, as you advance up the technological ladder (Karlin, 2015), in a “business as usual” scenario with no substantial intelligence augmentation we will effectively only have a 100-200 year “window” to effect this breakthrough before global dysgenic fertility patterns rule it out entirely for a large part of the next millennium.

To avoid a period of prolonged technological and scientific stagnation, with its attendant risks of collapse, our global “hive mind” (or “noosphere”) will at a minimum have to sustain and preferably sustainably augment its own intelligence. The end goal is to create (or become) a machine, or network of machines, that recursively augment their own intelligence – “the last invention that man need ever make” (Good, 1965).

In light of this, there are five main distinct ways in which human (or posthuman) civilization could develop in the next millennium.


(1) Direct Technosingularity

kurzweil-singularity-is-near The development of artificial general intelligence (AGI), which should quickly bootstrap itself into a superintelligence – defined by Nick Bostrom as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest” (Bostrom, 2014). Especially if this is a “hard” takeoff, the superintelligence will also likely become a singleton, an entity with global hegemony (Bostrom, 2006).

Many experts predict AGI could appear by the middle of the 21st century (Kurzweil, 2005; Müller & Bostrom, 2016). This should quickly auto-translate into a technological singularity, henceforth “technosingularity,” whose utilitarian value for humanity will depend on whether we manage to solve the AI alignment problem (i.e., whether we manage to figure out how to persuade the robots not to kill us all).

The technosingularity will creep up on us, and then radically transform absolutely everything, including the very possibility of any further meaningful prognostication – it will be “a throwing away of all the previous rules, perhaps in the blink of an eye, an exponential runaway beyond any hope of control” (Vinge, 1993). The “direct technosingularity” scenario is likely if AGI turns out to be relatively easy, as the futurist Ray Kurzweil and DeepMind CEO Demis Hassabis believe.

(2) The Age of Em

The development of Whole Brain Emulation (WBE) could accelerate the technosingularity, if it is relatively easy and is developed before AGI. As the economist Robin Hanson argues in his book The Age of Em, untold quintillions of emulated human minds, or “ems,” running trillions of times faster than biological wetware, should be able to effect a transition to true superintelligence and the technosingularity within a couple of human years (Hanson, 2016). This assumes that em civilization does not self-destruct, and that AGI does not ultimately prove to be an intractable problem. A simple Monte Carlo simulation by Anders Sandberg hints that WBE might be achieved by the 2060s (Sandberg, 2014).


Deus Ex: Human Revolution.

(3) Biosingularity

We still haven’t come close to exhausting our biological and biomechatronic potential for intelligence augmentation. The level of biological complexity has increased hyperbolically since the appearance of life on Earth (Markov & Korotayev, 2007), so even if both WBE and AGI turn out to be very hard, it might still be perfectly possible for human civilization to continue eking out huge further increases in aggregate cognitive power. Enough, perhaps, to kickstart the technosingularity.

There are many possible paths to a biosingularity.

The simplest one is through demographics: The tried and tested method of population growth (Korotaev & Khaltourina, 2006). As “technocornucopians” like Julian Simon argue, more people equals more potential innovators. However, only a tiny “smart fraction” can meaningfully contribute to technological progress, and global dysgenic fertility patterns imply that its share of the world population is going to go down inexorably now that the FLynn effect of environmental IQ increases is petering out across the world, especially in the high IQ nations responsible for most technological progress in the first place (Dutton, Van Der Linden, & Lynn, 2016). In the longterm “business as usual” scenario, this will result in an Idiocracy incapable of any further technological progress and at permanent risk of a Malthusian population crash should average IQ fall below the level necessary to sustain technological civilization.

As such, dysgenic fertility will have to be countered by eugenic policies or technological interventions. The former are either too mild to make a cardinal difference, or too coercive to seriously advocate. This leaves us with the technological solutions, which in turn largely fall into two bins: Genomics and biomechatronics.

The simplest route, already on the cusp of technological feasibility, is embryo selection for IQ. This could result in gains of one standard deviation per generation, and an eventual increase of as much as 300 IQ points over baseline once all IQ-affecting alleles have been discovered and optimized for (Hsu, 2014; Shulman & Bostrom, 2014). That is perhaps overoptimistic, since it assumes that the effects will remain strictly additive and will not run into diminishing returns.

Even so, a world with a thousand or a million times as many John von Neumanns running about will be more civilized, far richer, and orders of magnitude more technologically dynamic than what we have now (just compare the differences in civility, prosperity, and social cohesion between regions in the same country separated by a mere half of a standard deviation in average IQ, such as Massachussetts and West Virginia). This hyperintelligent civilization’s chances of solving the WBE and/or AGI problem will be correspondingly much higher.

The problem is that getting to the promised land will take about a dozen generations, that is, at least 200-300 years. Do we really want to wait that long? We needn’t. Once technologies such as CRISPR/Cas9 maturate, we can drastically accelerate the process and accomplish the same thing through direct gene editing. All this of course assumes that a concert of the world’s most powerful states doesn’t coordinate to vigorously clamp down on the new technologies.

Even so, we would still remain “bounded” by human biology. For instance, womb size and metabolic load are a crimper on brain size, and the specificities of our neural substrate places an ultimate ceiling even on “genetically corrected” human intellectual potential.

There are four potential ways to go beyond biology, presented below from “most realistic” to “most sci-fi”:

Neuropharmocology: Nootropics already exist, but they do not increase IQ by any significant amount and are unlikely to do so in the future (Bostrom, 2014).

Biomechatronics: The development of neural implants to augment human cognition beyond its peak biological potential. The first start-ups, based for now on treatment as opposed to enhancement, are beginning to appear, such as Kernel, where the futurist Randal Koene is the head scientist. This “cyborg” approach promises a more seamless, and likely safer, integration with ems and/or intelligent machines, whensoever they might appear – this is the reason why Elon Musk is a proponent of this approach. However, there’s a good chance that meaningful brain-machine interfaces will be very hard to implement (Bostrom, 2014).

Nanotechnology: Nanobots could potentially optimize neural pathways, or even create their own foglet-based neural nets.

Direct Biosingularity: If WBE and/or superintelligence prove to be very hard or intractable, or come with “minor” issues such as a lack of rigorous solutions to the AI alignment problem or the permanent loss of conscious experience (Johnson, 2016), then we might attempt a direct biosingularity – for instance, Nick Bostrom suggests the development of novel synthetic genes, and even more “exotic possibilities” such as vats full of complexly structured cortical tissue or “uplifted” transgenic animals, especially elephants or whales that can support very large brains (Bostrom, 2014). The terminal result of a true biosingularity could might be some kind of “ecotechnic singleton,” e.g. Stanisław Lem’s Solaris, a planet dominated by a globe-spanning sentient ocean.

Bounded by the speed of neuronal chemical reactions, it is safe to say that the biosingularity will be a much slower affair than The Age of Em or a superintelligence explosion, not to mention the technosingularity that would likely soon follow either of those two events. However, human civilization in this scenario might still eventually achieve the critical mass of cognitive power needed to solve WBE or AGI, thus setting off the chain reaction that leads to the technosingularity.


(4) Eschaton

Nick Bostrom defined existential risk thus: “One where an adverse outcome would either annihilate Earth-originating intelligent life or permanently and drastically curtail its potential.(Bostrom, 2002)

We can divide existential risks into four main bins: Geoplanetary; Anthropic; Technological; and Philosophical.

In any given decade, a gamma ray burst or even a very big asteroid could snuff us out in our earthly cradle. However, the background risk is both constant and extremely low, so it would be cosmically bad luck for a geoplanetary Götterdämmerung to do us in just as we are about to enter the posthuman era.

There are three big sources of “anthropic” existential risk: Nuclear war, climate change, and the exhaustion of high-EROEI energy sources.

Fears of atomic annihilation are understandable, but even a full-scale thermonuclear exchange between Russia and the US is survivable, and will not result in the collapse of industrial civilization ala A Canticle for Leibowitz or the Fallout video games, let alone human extinction (Kahn, 1960; Kearny, 1979). This was true during the Cold War and it is doubly true today, when nuclear weapons stocks are much lower. To be sure, some modest percentage of the world population will die, and a majority of the capital stock in the warring nations will be destroyed, but as Herman Kahn might have said, this is a tragic but nonetheless distinguishable outcome compared to a true “existential risk.”

Much the same can be said of anthropogenic climate change. While it would probably do more harm than good, at least in the medium-term (Stager, 2011), even the worst outcomes like a clathrate collapse will most likely not translate into James Lovelock’s apocalyptic visions of “breeding pairs” desperately eking out a hardscrabble survival in the Arctic. The only truly terminal outcome would be a runaway greenhouse effect that turns Earth into Venus, but there is simply nowhere near enough carbon on our planetary surface for that to happen.

As regards global energy supplies, while the end of high-density fossil fuels might somewhat reduce living standards relative to what they would have otherwise been, there is no evidence it would cause economic decline, let alone technological regression back to the Olduvai Gorge conditions as some of the most alarmist “doomers” have claimed. We still have a lot of fat to cut! Ultimately, the material culture even of an energy-starved country like Cuba compares very positively to those of 95% of all humans who have ever lived. Besides, there are still centuries’ worth of coal reserves left on the planet, and nuclear and solar power have been exploited to only a small fraction of their potential.

By far the biggest technological risk is malevolent AGI, so much so that entire research outfits such as MIRI have sprung up to work on it. However, it is so tightly coupled to the Technosingularity scenario that I will refrain from further commentary on it here.

This leaves mostly just the “philosophical,” or logically derived, existential risks. For instance, the computer simulation we are in might end (Bostrom, 2003) – perhaps because we are not interesting enough (if we fail to reach technosingularity), or for lack of hardware to simulate an intelligence explosion (if we do). Another disquieting possibility is implied by the foreboding silence all around as – as Enrico Fermi asked, “Where is everyone?” Perhaps we are truly alone. Or perhaps alien post-singularity civilizations stay silent for a good reason.

We began to blithely broadcast our presence to the void more than a century ago, so if there is indeed a “superpredator” civilization keeping watch over the galaxy, ready to swoop down at the first sign of a potential rival (e.g. for the simulation’s limited computing resources), then our doom may have already long been written onto the stars. However, unless they have figured out how to subvert the laws of physics, their response will be bounded by the speed of light. As such, the question of whether it takes us half a century or a millenium to solve the intelligence problem – and by extension, all other problems, including space colonization – assumes the most cardinal importance!


Vladimir Manyukhin, Tower of Sin.

(5) The Age of Malthusian Industrialism (or, “Business as Usual”)

The 21st century turns out to be a disappointment in all respects. We do not merge with the Machine God, nor do we descend back into the Olduvai Gorge by way of the Fury Road. Instead, we get to experience the true torture of seeing the conventional, mainstream forecasts of all the boring, besuited economists, businessmen, and sundry beigeocrats pan out.

Human genetic editing is banned by government edict around the world, to “protect human dignity” in the religious countries and “prevent inequality” in the religiously progressive ones. The 1% predictably flout these regulations at will, improving their progeny while keeping the rest of the human biomass down where they believe it belongs, but the elites do not have the demographic weight to compensate for plummeting average IQs as dysgenics decisively overtakes the FLynn Effect.

We discover that Kurzweil’s cake is a lie. Moore’s Law stalls, and the current buzz over deep learning turns into a permanent AI winter. Robin Hanson dies a disappointed man, though not before cryogenically freezing himself in the hope that he would be revived as an em. But Alcor goes bankrupt in 2145, and when it is discovered that somebody had embezzled the funds set aside for just such a contingency, nobody can be found to pay to keep those weird ice mummies around. They are perfunctorily tossed into a ditch, and whatever vestigial consciousness their frozen husks might have still possessed seeps and dissolves into the dirt along with their thawing lifeblood. A supermall is build on their bones around what is now an extremely crowded location in the Phoenix megapolis.

For the old concerns about graying populations and pensions are now ancient history. Because fertility preferences, like all aspects of personality, are heritable – and thus ultracompetitive in a world where the old Malthusian constraints have been relaxed – the “breeders” have long overtaken the “rearers” as a percentage of the population, and humanity is now in the midst of an epochal baby boom that will last centuries. Just as the human population rose tenfold from 1 billion in 1800 to 10 billion by 2100, so it will rise by yet another order of magnitude in the next two or three centuries. But this demographic expansion is highly dysgenic, so global average IQ falls by a standard deviation and technology stagnates. Sometime towards the middle of the millenium, the population will approach 100 billion souls and will soar past the carrying capacity of the global industrial economy.

Then things will get pretty awful.

But as they say, every problem contains the seed of its own solution. Gnon sets to winnowing the population, culling the sickly, the stupid, and the spendthrift. As the neoreactionary philosopher Nick Land notes, waxing Lovecraftian, “There is no machinery extant, or even rigorously imaginable, that can sustain a single iota of attained value outside the forges of Hell.”

In the harsh new world of Malthusian industrialism, Idiocracy starts giving way to A Farewell to Alms, the eugenic fertility patterns that undergirded IQ gains in Early Modern Britain and paved the way to the industrial revolution. A few more centuries of the most intelligent and hard-working having more surviving grandchildren, and we will be back to where we are now today, capable of having a second stab at solving the intelligence problem but able to draw from a vastly bigger population for the task.

Assuming that a Tyranid hive fleet hadn’t gobbled up Terra in the intervening millennium…

2061su-longing-for-home, Longing for Home

The Forking Paths of the Third Millennium

In response to criticism that he was wasting his time on an unlikely scenario, Robin Hanson pointed out that even if there was just a 1% chance of The Age of Em coming about, studying it was well worth his while considering the sheer amount of future consciences and potential suffering at stake.

Although I can imagine some readers considering some of these scenarios as less likely than others, I think it’s fair to say that all of them are at least minimally plausible, and that most people would also assign a greater than 1% likelihood to a majority of them. As such, they are legitimate objects of serious consideration.

My own probability assessment is as follows:

(1) (a) Direct Technosingularity – 25%, if Kurzweil/MIRI/DeepMind are correct, with a probability peak around 2045, and most likely to be implemented via neural networks (Lin & Tegmark, 2016).

(2) The Age of Em – <1%, since we cannot obtain functional models even of 40 year old microchips from scanning them, to say nothing of biological organisms (Jonas & Kording, 2016)

(3) (a) Biosingularity to Technosingularity – 50%, since the genomics revolution is just getting started and governments are unlikely to either want to, let alone be successful at, rigorously suppressing it. And if AGI is harder than the optimists say, and will take considerably longer than mid-century to develop, then it’s a safe bet that IQ-augmented humans will come to play a critical role in eventually developing it. I would put the probability peak for a technosingularity from a biosingularity at around 2100.

(3) (b) Direct Biosingularity – 5%, if we decide that proceeding with AGI is too risky, or that consciousness both has cardinal inherent value and is only possible with a biological substrate.

(4) Eschaton – 10%, of which: (a) Philosophical existential risks – 5%; (b) Malevolent AGI – 1%; (c) Other existential risks, primarily technological ones: 4%.

(5) The Age of Malthusian Industrialism – 10%, with about even odds on whether we manage to launch the technosingularity the second time round.

There is a huge amount of literature on four of these five scenarios. The most famous book on the technosingularity is Ray Kurzweil’s The Singularity is Near, though you could make do with Vernor Vinge’s classic article The Coming Technological Singularity. Robin Hanson’s The Age of Em is the book on its subject. Some of the components of a potential biosingularity are already within our technological horizon – Stephen Hsu is worth following on this topic, though as regards biomechatronics, for now it remains more sci-fi than science (obligatory nod to the Deus Ex video game franchise). The popular literature on existential risks of all kinds is vast, with Nick Bostrom’s Superintelligence being the definitional work on AGI risks. It is also well worth reading his many articles on philosophical existential risks.

Ironically, by far the biggest lacuna is with regards to the “business as usual” scenario. It’s as if the world’s futurist thinkers have been so consumed with the most exotic and “interesting” scenarios (e.g. superintelligence, ems, socio-economic collapse, etc.) that they have neglected to consider what will happen if we take all the standard economic and demographic projections for this century, apply our understanding of economics, psychometrics, technology, and evolutionary psychology to them, and stretch them out to their logical conclusions.

The resultant Age of Industrial Malthusianism is not only something that’s easier to imagine than many of the other scenarios, and by extension easier for modern people to connect with, but it is also something that is genuinely interesting in its own right. It is also very important to understand well. That is because it is by no means a “good scenario,” even if it is perhaps the most “natural” one, since it will eventually entail unimaginable amounts of suffering for untold billions a few centuries down the line, when the time comes to balance the Malthusian equation. We will also have to spend an extended amount of time under an elevated level of philosophical existential risk. This would be the price we will have to pay for state regulations that block the path to a biosingularity today.


Bostrom, N. (2002). Existential risks. Journal of Evolution and Technology / WTA, 9(1), 1–31.

Bostrom, N. (2003). Are We Living in a Computer Simulation? The Philosophical Quarterly, 53(211), 243–255.

Bostrom, N. (2006). What is a Singleton. Linguistic and Philosophical Investigations, 5(2), 48–54.

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

Dutton, E., Van Der Linden, D., & Lynn, R. (2016). The negative Flynn Effect: A systematic literature review. Intelligence, 59, 163–169.

Good, I. J. (1965). Speculations Concerning the First Ultraintelligent Machine. In F. Alt & M. Ruminoff (Eds.), Advances in Computers, volume 6. Academic Press.

Hanson, R. (2016). The Age of Em: Work, Love, and Life when Robots Rule the Earth. Oxford University Press.

Hsu, S. D. H. (2014, August 14). On the genetic architecture of intelligence and other quantitative traits. arXiv [q-bio.GN]. Retrieved from

Johnson, M. (2016). Principia Qualia: the executive summary. Open Theory. Retrieved from

Jonas, E., & Kording, K. (2016). Could a neuroscientist understand a microprocessor? bioRxiv. Retrieved from

Kahn, H. (1960). On thermonuclear war (Vol. 141). Cambridge Univ Press.

Karlin, A. (2015). Introduction to Apollo’s Ascent. The Unz Review. Retrieved from

Kearny, C. H. (1979). Nuclear war survival skills. NWS Research Bureau.

Korotaev, A. V., & Khaltourina, D. (2006). Introduction to Social Macrodynamics: Secular Cycles and Millennial Trends in Africa. Editorial URSS.

Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Penguin.

Lin, H. W., & Tegmark, M. (2016, August 29). Why does deep and cheap learning work so well?arXiv [cond-mat.dis-nn]. Retrieved from

Markov, A. V., & Korotayev, A. V. (2007). Phanerozoic marine biodiversity follows a hyperbolic trend. Palaeoworld, 16(4), 311–318.

Müller, V. C., & Bostrom, N. (2016). Future Progress in Artificial Intelligence: A Survey of Expert Opinion. In V. C. Müller (Ed.), Fundamental Issues of Artificial Intelligence (pp. 555–572). Springer International Publishing.

Sandberg, A. (2014). Monte Carlo model of brain emulation development. Retrieved from

Shulman, C., & Bostrom, N. (2014). Embryo Selection for Cognitive Enhancement: Curiosity or Game-changer? Global Policy, 5(1), 85–92.

Stager, C. (2011). Deep Future: The Next 100,000 Years of Life on Earth. Macmillan.

Vinge, V. (1993). The coming technological singularity: How to survive in the post-human era. In Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace. Retrieved from

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Today I was at a talk with Robin Hanson to promote his book THE AGE OF EM hosted by the Bay Area Futurists.

As an academic polymath with interests in physics, computer science, and economics, Hanson draws upon his extensive reading across these fields to try to piece together what such a society will look like.

His argument is that in 30 years to a century, there will be a phase transition as mind uploading takes off and the world economy rapidly becomes dominated by “ems” (emulations); human brains running on a silicon substrate, and potentially millions of times faster. Since transport congestion costs aren’t a factor, this em civilization will live in a few very densely populated cities largely composed of cooling pipes and computer hardware. The economy will double once every month, and in a year or two, it will transition to yet another, cardinally different, growth phase and social structure.

I might or might not eventually do a book review, but for now, here is a link to Scott Alexander’s.

Alternatively, this lecture slide summarizes the main points.


A few observations, arguments, and counterarguments from the meeting:

(1) This struck many people as the most counterintuitive assetion, but I agree that wages in the em world should quickly plummet to subsistence levels (which are much lower than for biological organisms). This is probably what will happen eventually with our civilization if there is no “singularity”/transition to a higher growth phase, since fertility preferences are an aspect of personality, and as such, highly heritable. (Come to think of it this is basically what happens to the Imperium of Man in Warhammer 40k, down to the hive cities in which most citizens eke out “lives of quiet desperation,” though ones which “can still be worth living.”)

Since Ctrl-C Ctrl-V is much easier and quicker than biological reproduction, a regression to the historical (and zoological) norm that that is the Malthusian trap seems – barring some kind of singleton enforcing global restrictions on reproduction – seems inevitable.

(2) A more questionable claim is Hanson’s prediction that ems will tend to be more religious than humans, on the basis that hardworking people – that is, the sorts of people whose minds are most likely to be uploaded and then copied far and wide – tend to be more religious. This is true enough, but there is also a strong and well known negative correlation between religiosity and intelligence. Which wins out?

(3) The marginal return on intelligence is extremely high, in both economics and scientific dynamism (Apollo’s Ascent theory). As such, raising the intelligence of individual ems will be of the utmost priority. However, Hanson makes a great deal of the idea that em minds will be a black box, at least in the beginning, and as such largely impenetrable to significant improvement.

My intuition is that this is unlikely. If we develop technology to a level where we can not only copy and upload human minds but provide them with internally consistent virtual reality environments that they can perceive and interact within, it would probably be relatively trivial to build brains with, say 250 billion neurons, instead of the ~86 billion we are currently endowed with and largely limited to by biology (the circulatory system, the birth canal, etc). There is a moderate correlation between just brain volume and intelligence, so its quite likely that drastic gains on the order of multiple S.D.’s can be attained just by the (relatively cheap) method of doubling or tripling the size of the connectome. The creative and scientific potential of billions of 300 IQ minds computing millions of times faster than biological brains might be greater than the gap between our current world and that of a chimpanzee troupe in the Central African rainforest.

Two consequences to this. First, progress will if anything be even faster than what Hanson projects; direct intelligence amplification in tandem with electronic reproduction might mean going straight to the technological singularity. Second, it might even help ems avoid the Malthusian trap, which is probably a good thing from an ethical perspective. If waiting for technological developments that augment your own intelligence turns out to be more adaptive than making copies of yourself like Agent Smith in The Matrix until us ems are all on a subsistence wage, then the Malthusian trap could be avoided.

(4) I find this entire scenario to be extremely unlikely. In both his book and his lecture, Hanson discusses and then quickly dismisses the likelihood of superintelligence first being attained through research in AI and neural nets.

There are two problems with this assertion:

(a) The median forecast in Bostrom’s Superintelligence is for High Level Machine Intelligence to be attained at around 2050. (I am skeptical about this for reasons intrinsic to Apollo’s Ascent theory, but absolutely the same constraints would apply to developing brain emulation technology).

(b) The current state of AI research is much more impressive than brain emulation. The apex of modern AI research can beat the world’s best Go players, several years ahead of schedule. In contrast, we only finished modeling the 302 neuron brain of the c. elegans worm a few years ago. Even today, we cannot obtain functional models even of 40 year old microchips from scanning them, to say nothing of biological organisms. That the gap will not only be closed but for the brain emulation route to take the lead is a rather formidable leap of faith.

Now to be fair to Hanson, he did explicitly state that he does not regard the Age of Em as a certain or even a highly probable future. His criterion for analyzing a future scenario is for it to have at least a 1% chance of happening, and he believes that the Age of Em easily fulfills that condition. Personally I suspect it’s a lot less than 1%. Then again, Hanson knows a lot more computer science than I do, and in any case even if the predictions fail to pan out he has still managed to provide ample fodder for science fiction writers.

(5) My question to Hanson during the Q&A section of the talk: Which regions/entities do you expect to form the first em communities? And what are the geopolitical ramifications in these last years of “human” civilization?

(a) The big factors he lists are the following:

  • Access to cold water, or a cold climate in general, for cooling purposes.
  • Proximity to big human cities for servicing human customers (at least in the initial stages before the em economy becomes largely autonomous).
  • Low regulations.

So plausible candidates (according to Hanson) would be Scandinavia, or the “northern regions of China.”

As he also noted at another point, in the early stages of em creation technology, mind uploading is likely to be “destructive,” i.e. resulting in the biological death of the person who is to be emulated. So there might be an extra selection filter for state or corporate ruthlessness.

(b) In domestic and social terms, during the transition period, humans can be expected to “retire” as the em economy explodes and soon far exceeds the scope of the old human economy. Those humans who control a slice of the em economy will become very rich, while those who don’t… fare less well.

However, Hanson doesn’t have anything to say on the geopolitical aspects of the transition period because it is much less predictable than the “equilibrium state” of the em economy that he set out to describe. As such, he does not think it is worthwhile for someone who is not a sci-fi writer to delve into that particular issue. That makes sense.

(6) As a couple of people pointed out, atomic weapons can wipe out an entire em “city,” which contain billions of ems.

What would em warfare be like? The obvious answer is cyber-cyber-cyber we gotta hack the mainframe style stuff. But surely, sometimes, the easiest move is to just knock over the table and beat your opponent to death with the chessboard.

If Pinker gets pwned during the em era and global nuclear wars between em hive cities ruled by Gandhi emulations break out, could this make em hive cities unviable and result in a radical decentralization?

(7) How did Hanson become Hanson?

He repeated the Talebian argument (which I sympathize with) that following the news is a pointless waste of time.

It is much more productive to read books, especially textbooks, and to take introductory classes in a wide range of subjects. To try to get a good grasp on our civilization’s system of knowledge, so that you might be able to make productive observations once you reach your 50s.

Confirmation bias? Regardless, it’s one more small piece of evidence in favor of my decision to log off.

• Category: Science • Tags: Futurism, Superintelligence, The AK 
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In recent years there has been a surge in interest in gut flora in the wake of research on its substantial effects on personality, so much so that researchers have even taken to describing it as a neutral network.

And much like humans, and even their brains, they are not going to be an exception to recent evolution.

As Chris Kresser writes:

In other words, evolution does not act solely on your 23,000 human genes. Rather, it acts on the 9.02 million genes (both host and microbial) that are present in and on your body, as a single entity.

Moreover, the microbiome can introduce genetic variation and evolve through methods specific to it, such as sharing genes with each other and acquisition of new strains from the environment. And even the borders between bacterial genes and “human” genes are surprisingly porous.

The really interesting observation is yet to come:

Social behavior in primates is also thought to be a critical factor in the evolution of human intelligence (32). Access to microbes may have been a driving force in the evolution of animal sociality, since microbes confer many benefits to the host (33). Social behaviors like grooming, kissing, and sex increased the transfer of microbes from one organism to another. Studies in social mammals have found that development of the forebrain and neocortex in social mammals depends on signals from the microbiota (34), and germ-free mice that lack a microbiota also lack social behavior and show deficits in social cognitive abilities (35).

Depending on the size of these effects there could be some pretty important implications and confounds for psychometrics and genetics of IQ research.

Bacterial composition, for instance, though strongly hereditary, is also going to be affected by the food one eats (a cultural factor), the people with whom one has close contacts with (kissing, certain intimate contacts, and one supposes, effluence in non-hygienic countries), and the local geography, elevation, and climate. Could intelligence be a matter of not just blood and chance, but of soil?

Best not to get too carried away with yet. This paper finds that spousal partners did not have significantly more microbiome similarity than unrelated invididuals (though the sample sizes were small). Still, it might be worth bearing in mind.

• Category: Science • Tags: Ancestral Health, Intelligence 
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Last month there was an interview with Eliezer Yudkowsky, the rationalist philosopher and successful Harry Potter fanfic writer who heads the world’s foremost research outfit dedicated to figuring out ways in which a future runaway computer superintelligence could be made to refrain from murdering us all.

It’s really pretty interestingl. It contains a nice explication of Bayes, what Eliezer would do if he were to be World Dictator, his thoughts on the Singularity, justification of immortality, and thoughts on how to balance mosquito nets against the risk of genocidal Skynet from an Effective Altruism perspective.

That said, the reason I am making a separate post for this is that here at last Yudkowsky gives a more more or less concrete definition of what conditions a superintelligence “explosion” would have to satisfy in order to be considered as such:

Suppose we get to the point where there’s an AI smart enough to do the same kind of work that humans do in making the AI smarter; it can tweak itself, it can do computer science, it can invent new algorithms. It can self-improve. What happens after that — does it become even smarter, see even more improvements, and rapidly gain capability up to some very high limit? Or does nothing much exciting happen?

It could be that, (A), self-improvements of size δ tend to make the AI sufficiently smarter that it can go back and find new potential self-improvements of size k ⋅ δ and that k is greater than one, and this continues for a sufficiently extended regime that there’s a rapid cascade of self-improvements leading up to superintelligence; what I. J. Good called the intelligence explosion. Or it could be that, (B), k is less than one or that all regimes like this are small and don’t lead up to superintelligence, or that superintelligence is impossible, and you get a fizzle instead of an explosion. Which is true, A or B? If you actually built an AI at some particular level of intelligence and it actually tried to do that, something would actually happen out there in the empirical real world, and that event would be determined by background facts about the landscape of algorithms and attainable improvements.

You can’t get solid information about that event by psychoanalyzing people. It’s exactly the sort of thing that Bayes’s Theorem tells us is the equivalent of trying to run a car without fuel. Some people will be escapist regardless of the true values on the hidden variables of computer science, so observing some people being escapist isn’t strong evidence, even if it might make you feel like you want to disaffiliate with a belief or something.

Psychoanalyzing people might not be so useful, but trying to understand the relationship between cognitive capacity and technological progress is another matter.

I am fairly sure that k<1 for the banal reason that more advanced technologies need exponentially more and more cognitive capacity – intelligence, IQ – to develop. Critically, there is no reason this wouldn’t apply to cognitive-enhancing technologies themselves. In fact, it would be extremely strange – and extremely dangerous, admittedly – if this consistent pattern in the history of science ceased to hold. (In other words, this is merely an extension of Apollo’s Ascent theory. Technological progress invariably gets harder as you climb up the tech tree, which works against sustained runaway dynamics).

Any putative superintelligence, to continue making breakthoughs at an increasing rate, would have to not only solve ever harder problems as part of the process of constantly upgrading itself but to also create and/or “enslave” an exponentially increasing amount of computing power and task it to the near exclusive goal of improving itself and prevent rival superintelligences from copying its advances in what will surely be a far more integrated noosphere by 2050 or 2100 or if/whenever this scenario happens. I just don’t find it very plausible our malevolent superintelligence will be able to fulfill all of those conditions. Though admittedly, if this theory is wrong, then there will be nobody left to point it out anyway.

• Category: Science • Tags: Apollo's Ascent, Rationality, Superintelligence 
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Latest data from NASA:


At +1.35C, this is the biggest monthly temperature anomaly (measured from the base period of 1951-1980) ever measured, and it is a near certainty now that 2016 will be warmer overall than 2015, making for a third-time consecutive record breaking year.

There are several reasons for this:

(1) The El Nino effect. This year’s is a pretty strong one as far as they go, but not quite as strong as the one in 1997-1998, which produced the last major local peak and formed the lynchpin of GW denier arguments throughout the 2000s. Nonetheless, average global temperatures in February 2016 were almost half a degree higher than the +0.88C anomaly seen in February 1998. The most comparably strong El Nino before that was the 1982-1983 one, but the February 1983 anomaly was fairly unremarkle at +0.40C. That’s a difference of almost a degree between then and now.

solar-irradiance(2) Solar irradiance is actually pretty weak relative to its average in the 1950-2000 period so that can’t be part of the explanation.

(3) I wonder to what extent if any the major recent uptick in methane emissions from melting permafrost, which has expressed itself in the form of some spectacular new craters in Northern Siberia last year, has contributed to this.

All in all, this is very bad news for the international community’s target of limiting global warming to the IPCC’s two degrees injunction.

There have been some encouraging counter developments – for instance, global carbon emissions actually fel l in 2015 – but celebrations are premature since there have been plenty of prior periods when global CO2 emissions fell not just for one year but several years in a row: 1973-1975 (first oil shock), 1980-83 (second oil shock), 1989-1994 (collapse of the highly energy-inefficient Communist economies), and 2008-2009 (the Great Recession).

In any case, if the aforementioned methane release scenario is at or close to the runaway threshold, that wouldn’t really matter all that much anyway.

For myself I have always been skeptical that this particular drifting oil tanker could be stopped in time to avert serious levels of warming. I still stand by my 2010 prediction that “geoengineering” is going to start appearing on normies’ vocabularies sooner rather than later, and perhaps implementation of some geoengineering schemes will begin as early as the 2030s. It’s unlikely to be a happy project that brings everyone together. I suspect it’s more likely to either take the form of a ruinous geopolitical free-for-all, or to catalyze the consolidation of today’s already incipient globalist elite into a stiffling singleton.

• Category: Science • Tags: Geoengineering, Global Warming 
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The heroes of Hikaru’s Go were off by 86 years.

As some of you might have heard, the word of go – or weiqi as it is known in its homeland of China – is currently undergoing its Deep Blue moment as one of the world’s strongest players Lee Sedol faces off against Google’s DeepMind AlphaGo project. Deep Blue was the IBM/Carnegie Mellon supercomputer that in 1997 beat the world’s top grandmaster Gary Kasparov in a series of 6 chess games. But the computer’s margin of victory at 3.5 to 2.5 was modest, and the event was dogged by Kasparov’s allegations that the IBM team had underhandedly helped the computer. It would be an entire decade before the top computer chess programs decisively overtook the top human players. As of today, there is a 563 point difference between the Elo rating of Magnus Carlsen, the current highest rated human player on the FIDE’s database, and the world’s most powerful chess program, the open source Stockfish 7. In practical terms, this means that Carlsen can expect to win fewer than one in a hundred games against the Stockfish running on a contemporary 64-bit quadcore CPU.

In terms of game complexity, more orders of magnitude separate go from chess than chess from draughts, a game that has been fully solved. The aim is to capture territory and enemy stones by encircling them while defending your own turf, both of which are tallied up at the end of the game with the winner being the one with the most points. It is played on a 19×19 board, a lot larger than the 8×8 arrangement of chess, and you can position your pieces – or stones – on any empty space not occupied by or completely encircled by the enemy, whereas the range of possible moves in chess is strongly constricted. Chess is tactics, go is logistics; chess is combined arms, go is encirclements; chess draws strongly upon algorithmic and combinatorial thinking, whereas go is more about pattern matching and “intuition.” Therefore it is not surprising that until recently it was common wisdom that it would be many decades before computers would start beating the world’s top human players. The unimpressive performance of existing go computer programs, and the slowdown or end of Moore’s Law in the past few years, would have only given weight to that pessimistic assessment. (Or perhaps optimistic one, if you’re with MIRI). Lee Sedol himself thought the main question would be whether he would beat AlphaGo by 5-0 or 4-1.

Which makes it all the more remarkable that Lee Sedol is not just behind but having lost all of his three games so far is getting positively rekt.

But apparently Lee’s confidence was more rational than hubris. He had watched AlphaGo playing against weaker players, in which it made some apparent mistakes. But as a DeepMind research scientist noted, this was actually feature, not bug:

As Graepel explained, AlphaGo does not attempt to maximize its points or its margin of victory. It tries to maximize its probability of winning. So, Graepel said, if AlphaGo must choose between a scenario where it will win by 20 points with 80 percent probability and another where it will win by 1 and a half points with 99 percent probability, it will choose the latter. Thus, late in Game One, the system made some moves that Redmond considered mistakes—“slow” in his terminology. These moves seemed to give up points, but from where Graepel was sitting, AlphaGo was merely trying to maximize its chances.

In other words, while the projected points on the board – territory held plus stones captured – might for a long time appear to be roughly equal, at the same time the probability of ultimate victory would inexorably shift against Lee Sedol. And capped as our human IQs are, not only Lee but all the rest of us might be simply incapable of discerning the deeper strategies in play: “And so we boldly go – into the whirling knives” (to borrow from Nick Bostrom’s book on the risks of computer superintelligence).

Those are in fact the exact terms in which AI scientist/existential risks researcher Eliezer Yudkowsky analyzed this game in a lengthy Facebook post:

At this point it seems likely that Sedol is actually far outclassed by a superhuman player. The suspicion is that since AlphaGo plays purely for *probability of long-term victory* rather than playing for points, the fight against Sedol generates boards that can falsely appear to a human to be balanced even as Sedol’s probability of victory diminishes. The 8p and 9p pros who analyzed games 1 and 2 and thought the flow of a seemingly Sedol-favoring game ‘eventually’ shifted to AlphaGo later, may simply have failed to read the board’s true state. The reality may be a slow, steady diminishment of Sedol’s win probability as the game goes on and Sedol makes subtly imperfect moves that *humans* think result in even-looking boards.

For all we know from what we’ve seen, AlphaGo could win even if Sedol were allowed a one-stone handicap. But AlphaGo’s strength isn’t visible to us – because human pros don’t understand the meaning of AlphaGo’s moves; and because AlphaGo doesn’t care how many points it wins by, it just wants to be utterly certain of winning by at least 0.5 points.

In the third game, which finished just a few hours ago – by the way, you can watch the remaining two games live at the DeepMind YouTube channel, though make sure to learn the rules beforehand or it will be very boring – Lee Sedol, by then far behind on points, made a desperate ploy to salvage the game (or more likely just use the opportunity to test AlphaGo’s capabilities) by initiating a ko fight. A ko is a special case in go in which a local altercation sharply becomes the fulcrum around which the outcome of the entire game might be decided. Making the winning moves requires perfect, precise play as opposed to AlphaGo’s key method of playing out billions of random games and choosing the one which results in the most captured territory after n moves.

But AlphaGo handled the ko situation with aplomb, and Lee had to resign.

The Korean Lee Sedol is the fourth highest rated go player on the planet. But even as of March 9, were it a person, AlphaGo would have already displaced him. The top player in the world is the Chinese Ke Jie, who is currently 100 Elo points higher than Lee. According to my calculations, this implies that Lee should win slightly more than a third of his matches against Ke Jie. His actual record is 2/8, or 25%. Not only is his current tally against AlphaGo is 0/3, but he was beaten by a considerable number of points by an entity that is perfectly content to minimize its lead in order to to maximize its winning probability.

will-lee-sedol-defeat-alphago Finally, a live predictions market on whether Lee Sedol would defeat AlphaGo in any of the three games remaining (that is, before the third match) varied between 20%-25%, implying that the probability of him winning any one game against the the DeepMind monster was less than 10%. (If anything, those probabilities would be even lower now that AlphaGo has demonstrated ko isn’t its Achilles heel, but let us set that aside).

According to my calculations, IF this predictions market is accurate, it would imply that AlphaGo has a ~400-450 Elo point superiority over Lee Sedol based on its performance up to and including the first two games against him.

It would also mean it would be far ahead of Ke Jie, who is the highest ranked human player ever and is currently virtually at his peak. Whereas Lee can only be expected to win 7%-9% of his games against AlphaGo, for Ke Jie this figure would be only modestly higher at 12%-15%. But in principle I see no reason why AlphaGo’s capabilities couldn’t be even higher than that. It’s a long tail – and we can’t see all that far ahead!

But really the most astounding element of this is that what took chess computing a decade to accomplish increasingly appears to have occured in the space of a few days with AlphaGo – despite the slowdown in Moore’s Law in recent years, and the problems of go being far more challenging than those of chess in terms of traditional AI approaches.

For all intents and purposes AI has entered the superhuman realm in a problem space where merely human intelligence had hitherto ruled supreme, and even though we are as far away as ever from discovering the “Hand of God” – the metaphorical perfect game, which will take longer than the lifetime of the universe to compute if all of the universe were to become computronium – we might well be starting the construction of a Sliver of Him.

Update -

Lee won the fourth game!

A win rate of 25% means that AlphaGo’s Elo likely superiority over Lee’s current 3519 points has just plummeted from 400-450 (based on predictions market) to 191, i.e. 3710. Still higher than top player Ke Jie at 3621.

If Lee loses the next game, that Elo difference goes up to 241; if he wins, it gets reduced further to 120. Regardless, we can now say with considerable confidence that AlphaGo is peak human level but decidedly not superhuman level.

Update 2 -

Final remarks:

Was writing article instead of watching final Lee-AlphaGo game but final score is 4:1. Reverse of what Lee had originally predicted! ;)

Anyhow 4:1 score (w/out looking into details) implies Alpha has *probabilistic* ~240 point higher Elo rating than Lee Sedol i.e. ~3760.

That means its likely ~140 points higher than first ranked human Ke Jie and should beat him about 70% of the time.

I had a look at go bots historic performance other day. Looks like they move up by 1 S.D. every two years or so. Treating AlphaGo as the new base, humans should be *completely* outclassed by computer in go by around 2020.

• Category: Science • Tags: Game, Supercomputers 
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Prolific IQ researcher Richard Lynn together with two Russian collaborators have recently published arguing that multiple aspects of socio-economic development – infant mortality, fertility, stature, and literacy-as-a-proxy for intelligence were significantly intercorrelated in late Tsarist Russia.


Literate rate of the European part of the Russian Empire in 1897.

Here is the link to the paper – Regional differences in intelligence, infant mortality, stature and fertility in European Russia in the late nineteenth century

And here is a summary by James Thompson – 50 Russian oblasts.

To the right: Here’s your map, JayMan. You’re welcome.

The main potential sticking point:

There are no data for regional intelligence in the nineteenth century and we have therefore adopted rates of literacy as a proxy for intelligence. This is justified on the grounds that a high correlation between literacy rates and intelligence have been reported in a number of studies. For example, a correlation of .861 between literacy rates for Italian regions in 1880 and early twenty-first century IQs has been reported by Lynn (2010); a correlation of .83 between literacy rates for Spanish regions in the early twenty-first century has been reported by Lynn (2010); (Lynn, 2012); and a correlation of 0.56 between literacy rates and IQs for the states and union territories of India in 2011 has been reported by Lynn and Yadav (2015). There is additional support for using literacy in the nineteenth century as a proxy for intelligence in the results of a study by Grigoriev, Lapteva and Ushakov (Григорьев, Лаптева, Ушаков, 2015) showing a correlation of .58 between literacy rates of the peasant populations of the districts (uezds) of the Moscow province in 1883 and the results of the Unified State Exam and State Certification on Russian Language in the districts of the contemporary Moscow oblast.

The methodology at first struck me as being rather problematic.

I’ve read a bit about Russian state literacy programs in the 19th century (National Literacy Campaigns and Movements) and one of their main features is that they tended to spread out from the central European Russian provinces due to cost effectiveness reasons, hence the low literacy rates of e.g. Siberia in Lynn’s data set. However, there is no particular evidence that Siberian Russians are any duller than average Russians. To the contrary, some 3% of Siberian schoolchildren become “Olympians” – high performers who qualify for highly subsidized higher education. This proportion is lower than the 15% of the central region (which hosts Moscow, Russia’s main cognitive cluster with a 107 average IQ), and the 14% of the north-west region (which hosts Russia’s second cognitive cluster with a 103 average IQ Saint-Petersburg, plus the Russians there are probably slightly brighter in general on account of Finno-Ugric admixture), but is considerably higher than in any other Russian Federal District: The Urals and Volga (both about 2%), and the Far Eastern, Southern, and Caucasus (all considerably below 1%).

In other words, would such a historical literacy – modern intelligence correlation apply to Russia as it does to Italy, Spain, and to a lesser extent, India?


Average 2009 PISA results by Russian region.

Fortunately, we don’t have to postulate, since we do actually have PISA data for many Russian provinces that I revealed back in 2012.

This allows us to test if Lynn’s assumptions apply.

There are difficulties, to be sure. Not all Russian provinces were tested in PISA, and there is, needless to say, no data for any of the Ukrainian and Polish oblasts, or for Belarus. As such, only 20 Russian provinces could be tested in this manner (26 if you also include now independent countries excluding Russia itself).

In some cases, names have changed, typically to honor some faceless Soviet bureaucrat; in more problematic cases, borders have changed significantly (e.g. the five provinces of Estonia, Livonia, Courland, Kovno, and Vilna have become the three countries of Estonia, Latvia, and Lithuania – I have tried to average the literacy figures between them in a common sense but back of the envelope way). The Moscow Governorate has been split into the City of Moscow (with its 107 average IQ) and Moscow oblast (with a modest 96 average IQ). Which of those should be attached to Moscow’s 1897 literacy rate of 40%? (As it happens, I went with just the City of Moscow instead of figuring out how to weigh the populations and adjust and so forth. I’m not trying to writea formal paper, after all).


There is an exponential correlation of R=0.75 between average PISA derived IQs of Russian regions and of now independent countries, and their literacy rate according to the 1897 Census. Therefore, this bears out Lynn’s assumptions.

The two downwards outliers – more relatively intelligent than literate – are Moscow, Tatarstan, Tula, Samara, and Tambov. Moscow is easily explainable – the city itself in Tsarist times would have been more literate than the Moscow Governorate, while its average IQ was artificially boosted in Soviet times since it became not just the empire’s political but also its cognitive (artistic, scientific) capital. Getting a Moscow propiska required considerable intelligence.

The three very major upwards outliers – more relatively literate than intelligent – are the Finno-Ugric Baltic states: Finland, Estonia, and Latvia. This can’t have been a non-Orthodox/Muslim thing: Both Poland (On-The-Vistula Governorate) and Lithuania (Kovno and Vilna) lie neatly on the correlation curve. Nor was it something Finno-Ugric; Karelia (then Olonets) is not an exception either. It must have been something specific just to them and the most obvious explanation is Protestantism. There is a lot of literature on the independent literacy-raising effects of Protestantism and I see no reasons why Estonia, Latvia, and Finland should have been exceptions to that.

Another outlier, though this one is at the bottom of the IQ scale, is Moldova. To be fair I think Moldova’s PISA-derived IQ is artificially lowered by a third to half of an S.D. due to the massive brain drain it has experienced after the collapse of the Soviet Union (something like half the working age population are Gastarbeiters in the EU and Russia). We see similar drops in other countries so afflicted, such as (possibly) Puerto Rico, and (almost certainly) Ireland during most of the 20th century, when it repeatedly reported IQs in the ~90 range (and ironically one of the reasons Richard Lynn himself abandoned it to move to Northern Ireland, thus getting stuck in the most depressed region of the UK and missing out on the rise of the Celtic Tiger a few years later).


The correlation improves further to R=0.80 when we consider only those Tsarist-era provinces which are still part of the Russian Federation. This is accomplished (more than) entirely just by removing the Protestant Baltic nations (Finland, Estonia, and Latvia) and Moldova (whose current day average IQ is depressed due to massive brain drain as per above).

As usual Lynn does his north/south IQ gradient analysis, finding it to be a real thing but diminishing to nothing once the Baltic states of Estonia, Livonia, and Courland are accounted for.

Quoting from Thompson’s summary:

The Russian provinces differed significantly by geographical location. The positive correlations with latitude (r= .33, p<.05) and the negative correlation with longitude (r=−.43, p<.01) show that the rates of literacy were higher in the northand west than in the south and east. These trends were partly determined by the rates of literacy being highest in the north-western provinces of St. Petersburg and the three Baltic states of Estland, Livland and Kourland (correspondingapproximately but not precisely to contemporary Estonia and Latvia; Livland consisted of southern part of contemporary Estonia and eastern part of contemporary Latvia). Removing these four regions makes both correlations non-significant (.21 and −.23).


The Pale of Settlement in 1897.

One additional issue worth bearing in mind: The influence of the Jews. Namely, their concentration in the Pale of Settlement, which correlates to modern day Poland, Belarus, and right-bank Ukraine (west of the Dnieper). There were more than 5.2 million Jews, and their literacy rates were very high (according to the 1926 Soviet Census, Jews over the age of 50 – i.e., who had been educated under the Empire – had a literacy rate of 63% versus 28% for ethnic Russians).

This must have “artificially” raised the literacy rates in this area – as pertains to those regions’ 21st century average IQs, anyway, since the vast majority of those Jews are no longer there due to the trifecta of the Holocaust, Jackson-Vanik, and Aliyah. The effect would probably be to reduce the “indigenous” literacy rates in Lithuania and Poland closer to those of European Russia, while pushing the already low literacy rates of strongly ethnic Malorossiyan and Belorussian provinces considerably lower still. Not a single province of modern Ukraine outside historical Novorossiya (with its strong Great Russian admixture) had a literacy rate above 20% in 1897, despite highly literate Jews helping them out with the statistics.

Unfortunately, there is a severe paucity of usable psychometric data from Ukraine – for instance, it is one of the very few European countries that doesn’t participate in PISA. So its average IQ has to be estimated through generally more indirect means. It does the converted equivalent of 9 IQ points worse than Russia on the TIMSS standardized test. Ukrainians spend less than half as much time as Russians reading, and those from the western parts at least spend a lot more time participating in torchlit processions and chanting “Putin Khuylo.” Some of those activities are considerably more g loaded than others. The low literacy rates in late Tsarist Malorossiya, coupled with the finding of a close correlation between those literacy rates and modern day average IQ across both Russian provinces and today’s independent post-Soviet states, constitutes further evidence of a modest average IQ in Ukraine. Higher than in Moldova to be sure, but probably closer to the level of the Balkans than to Poland.


Sources: Grigoriev, Lapteva, and Lynn 2015; Karlin 2012 (derived from PISA 2009).

IQ Literacy in 1897
Astrakhan 94.8 15.5%
Bashkortostan 93.4 16.7%
ESTONIA 102.1 77.9%
FINLAND 106.6 75.6%
Kaluga 91.7 19.4%
Karelia 98.1 25.3%
Kursk 94.6 16.3%
LATVIA 98.0 74.3%
LITHUANIA 99.0 35.4%
MOLDOVA 84.9 15.6%
Moscow 106.6 40.2%
N. Novgorod 93.1 22.0%
Orenburg 92.7 20.4%
Perm 93.3 19.2%
POLAND 100.2 30.5%
RUSSIA 96.0 21.1%
Ryazan 94.7 20.3%
Saint-Petersburg 102.6 51.5%
Samara 99.2 22.1%
Saratov 96.0 23.8%
Tambov 95.9 16.6%
Tatarstan 98.1 17.9%
Tula 98.6 20.7%
Ulyanovsk 91.5 15.6%
Vladimir 98.9 27.0%
Vologda 95.3 19.1%
Voronezh 92.7 16.3%

Literacy and Social Development in 1890s Russia (from Grigoriev et al. 2015)

Incidentally, I am not surprised to see Yaroslavl being the top non-Baltic/non-capital Russian region by literacy rate in 1897. It struck me as by far the cleanest and most civilized provincial Russian town on the Golden Ring when I visited it in 2002 (a time when Russia was still shaking off the hangover of the Soviet collapse). Curiously enough, it also hosted one of the most vigorous insurrections against the Bolshevik regime in central Russia. Although it was not one of the regions covered by PISA, I would not be surprised if Yaroslavl oblast was to get a 100-102 score on it should it be carried out there (and as would be implied by the correlation curve).


Anatoly Karlin
About Anatoly Karlin

I am a blogger, thinker, and businessman in the SF Bay Area. I’m originally from Russia, spent many years in Britain, and studied at U.C. Berkeley.

One of my tenets is that ideologies tend to suck. As such, I hesitate about attaching labels to myself. That said, if it’s really necessary, I suppose “liberal-conservative neoreactionary” would be close enough.

Though I consider myself part of the Orthodox Church, my philosophy and spiritual views are more influenced by digital physics, Gnosticism, and Russian cosmism than anything specifically Judeo-Christian.