Liberals truly believe in the SSSM

Liberals in the American sense, that is; in the UK we tend to use the term 'Hampstead Socialist'. Here are some English liberals: Ed Miliband, Nick Clegg; the BBC is liberal, so is The Guardian; sadly, so is The Economist.

It's easy to make a list of the things liberals like: internationalism, multiculturalism, immigration, diversity .. and the things they don't: anti-drug laws, state powers of surveillance, long prison terms, war. In general, liberals tend to the politically-correct.

I'm not interested in either backing or knocking any of these things: I don't like war either, especially when it's gratuitous. In any case, my wife is liberal. Instead, I'm trying to really get to the bottom of what makes a liberal, what really is the fundamental defining quality of liberalism.

I think it's adherence to the 'blank slate' Standard Social-Science Model.

You will recall that this model proposes that human are all nurture, with no important genetic differences when it comes to physical, social, personality or cognitive traits. In this model there is an ideal to which all may/should aspire: it is the person who is intelligent, healthy, agreeable, friendly, tolerant and generally pro-social. Someone very much like a liberal in fact.

The Standard Social-Science Model is spectacularly inconsistent with both everyday observation and the relevant theories (evolution, genetics and genomics, psychometrics, anatomy, etc). So how do liberals deal with such ubiquitous empirical refutation?

• If the unpleasant fact is small and relatively non-threatening (e.g. it happens not to be the case that everyone is of equal potential intelligence) the fact is distanced and ignored.


• If the fact is deeply destabilising to the SSSM and has 'difficult' public policy implications (e.g. the empirical fact that the distribution of intelligence differs between ethnic groups) then this is seen as a major threat and 'repressive tolerance' kicks in (the arguments are ignored and the perpetrators are excluded from the domain of rational dispute by a distancing epithet such as 'racist' - distancing labels generally end in '-ist').

Next question. Why do liberals believe in a model which is empirically wrong - and obviously so? I think there are two reasons.

1. Liberals believe in the SSSM not for intellectual reasons, which would be empirically refutable as discussed, but viscerally. The liberal worldview emerges from the limbic system, not the prefrontal cortex. In this, liberalism is a statement of faith and not of rationality. I hate to say it, but the propensity to be liberal is actually somewhat genetic.


2. Liberal thinking is encapsulated in Enlightenment political thinking, and in its normative social form is the underpinning for the universal franchise and liberal democracy. Après moi le déluge, they surely think. They're wrong about that, by the way, but it's certainly true that a political philosophy based on being generally nice, and having high hopes for personal development, is a big improvement on most prior political philosophies: there are worse things than Patrician Benevolence.

Believing in a Pollyanna-ish, benign and perfectible view of the human condition - and life in general - is a common failing of writers and is particularly damaging if you write science-fiction. Take David Brin, who is ferociously smart, well-connected in US technocratic circles and liberal to the core.

I have just read his door-stopper Existence. Here's an excerpt from this review.

"Starting with a series of snapshots of a world thirty years or so hence, Brin creates a picture where most of today's great threats have occurred and have been, if not overcome, then at least lived through. The seas have risen, nuclear terrorism has been perpetrated and the Yellowstone supervolcano has burped. It's a tomorrow where social and technological change have reshaped the world, and where a new social order is trying to put the brakes on progress, to end the Enlightenment. Beneath the optimism, though, there's danger. The world seems doomed to stagnation, unable to respond to any of a growing list of existential threats.

"But then an astronaut on the last space station, clearing space garbage, finds something strange — something not of this earth. And that means everything is about to change, once again.

"Brin draws on themes he's written about and discussed over much of the last decade, exploring a society shaped by ubiquitous surveillance (and equally ubiquitous sousveillance), where governments and ad hoc social media groups can use the same tools to draw their own conclusions and solve their own problems. It's the scenario he shaped in The Transparent Society, where little brother is the antidote to Big Brother (and that he elaborated on in a conversation with ZDNet earlier this year). But in Existence Brin also shows the downside of radical transparency, exploring how demagogues and propagandists can manipulate transparency to their own ends, using targeted disinformation.

"The picture Brin draws is one of a densely networked world that's easy for us to recognise. Ubiquitous augmented reality layers information on everything we see and do, and a networked society pulls together in clusters, joining together in smart mobs to interpret information and solve problems. The rich and the poor share access to an ocean of information, and understanding is the key to everything. It's also a world where machine learning and artificial intelligence have become everyday tools, and there's an uploaded rat living in the interstices of the internet.

"Of course, as in much of Brin's fiction, there's more. It's a story that travels the world, observing it through the eyes of a crusading journalist, a polemical novelist, an ageing astronaut, an aristocrat (or two) and a peasant shoresteading the ruins under a rising ocean. And as we leave the cradle there's also an answer to the Fermi Paradox, plus a tip of the hat to his popular Uplift novels."

In every fibre of his liberal being, David Brin believes that people are fundamentally friendly and nice, uplifted dolphins are friendly and nice (if a bit boisterous), aliens can be tricksy and disingenuous but Brin writes indulgently of them .. and the heartfelt message of his novel is that the universe awaits those who venture out in the spirit of friendliness and niceness.

Makes you nostalgic for the sheer malevolence of the Inhibitors!

The Cybercity concept

Def. Cybercity (n). A town or city with high levels of automation and artificial intelligence designed to run and maintain itself autonomously for the benefit of its inhabitants.

History

The  concept of the self-maintaining city was extensively developed in twentieth century science-fiction [refs]. A typical plot-line features an automated city deserted for thousands or millions of years, which springs back into activity when the protagonists rediscover it.

In the early twenty first century the concept was elaborated into a detailed vision of a cheap 'machine system' which could 'terraform' an extended area of wilderness into a fully-formed and functional city. Visionaries imagined automated-machines digging sewage and communications tunnels, laying down foundations, erecting high-tech solar-powered houses and setting up communications networks, self-driving transport systems and shops without staff. Some thinkers imagined the main application of the cybercity concept to be that of colonizing Mars.

Realism soon set in, however. It became understood that a town or city is a number of different 'machines for living' and that each subsystem had to be treated separately. Indeed, a major issue was systems integration. It was further understood that the act of initial creation, 'breaking the ground', was fundamentally distinct from the demands of ongoing management and maintenance. And even here, full automation was not required.

Contemporary Developments

The modern theory of cybercities envisages an initial act of city-creation, relying on heavy equipment, usually remote-operated, and prefabricated structures. Once the elements of the city are in place and system-integrated, inhabitants can be allocated to their dwellings and can begin their new cybercity life.

The cybercity's management is remotely-operated. Surveillance systems are ubiquitous and a number of drone-like 'effector' systems perform tasks such as repair, transport, personal and medical care, and security. Some of these are fully-automated and operated by on-board AI systems, others are remotely operated from the cybercity control centre (which can be on a different continent), and some are hybrid.

Cybercity Economics

Cybercities deploy some of the most advanced automation and telecommunications systems currently available, and were initially deployed in first-world settings where they had a competitive advantage. The three main drivers were military bases in dangerous theatres, overspill towns in poor or run-down areas and refugee camps. As costs came down, it became possible to deploy cybercities for the first time in countries such as India, Latin America and Africa where they have had a huge impact, particularly in poor rural areas and urban slum-sprawls.

Although costs are born by that fraction of the world population which is both highly-educated and highly-paid, it is generally felt that the tax-burden is manageable, and that all alternatives are worse.

Security Issues

Where most of the inhabitants of a cybercity are relatively uneducated with poor job prospects, there are inevitably issues with gangs, criminality and violence. The cybercity approach to this problem features both carrot and stick. The stick is comprised of ubiquitous surveillance and pro-active security drones capable of apprehending an offender; the carrot features numerous diversions including VR and simulations, sports and leisure centres as well as facilities for clubs and competitions.

Prospects for the Cybercity

As automation continues to increase in capability and decrease in price, it is expected that the remaining human involvement in both building and operating cybercities will continue to decline. At first sight this would appear to create an ever-growing problem of un- and under-employment. However, with a concomitant rise in the sophistication of diversions which are inherently more interesting than work, this problem is generally felt to be manageable.

Idiocracy: a glimmer of hope

From Mike Judge’s film "Idiocracy" (2006):

 “Most science fiction of the day predicted a future that was more civilized and more intelligent. But as time went on things seemed to be heading in the opposite direction. The years passed and mankind became stupider at a frightening rate. Some had high hopes that genetic engineering would correct this trend in evolution. But sadly, the greatest minds and resources were focused on conquering hair loss and prolonging erections.”

'Idiocracy' portrayed a society where the left-hand side of the bell curve (those with IQ less than 100, lack of conscientiousness, lack of impulse control, .etc, etc) had reproduced uncontrollably while those folk with the smarts hadn't bothered. In the quantitative genetics literature, this would be called truncation selection, in this case breeding for stupidity.

The results were that nothing worked. The complex infrastructure of logistics, power, water, sanitation - even keeping the streets clean - had broken down. People lived in decaying hovels and lived on scraps. The other, less obvious, feature of 'Idiocracy' was that innovation was now impossible: the extremely bright people who drive innovation either didn't exist, or lacked an infrastructure to do anything with their ideas.

The mean IQ of "Idiocracy" society was pretty low, I would guess well south of 90, but there are societies around today which are not dissimilar. Turn on the News.

Well-meaning people - which is most of us - typically have three main responses to these continuing human tragedies:

• Hope for some unspecified 'development' which will fix things - a sadly illusory prospect.
• Rely on a discredited neo-colonialism from outside to fix/run things (Ebola care is the latest manifestation of this).
• Hope for future genetic engineering to increase the cognitive capabilities of these populations (as in the quote above). Good luck with that.

Yes, it looks hopeless.

You're may think I'm referring to the societies of sub-Saharan Africa but even in advanced western countries we have our areas of 'Idiocracy' - our bleak, welfare-ridden estates.

So what would an answer look like?

The low-IQ societies of today and of the past were (and in some cases, still are) equatorial hunter-gatherers, operating in a relatively non-seasonal environment, in smallish groups and mostly in the here-and-now*. In that environment they didn't experience any 'cognitive limitations' - they were adapted. We can't go back to that situation, which was hardly ideal in terms of lifestyle difficulty anyway. Besides which, the numbers are now too great and we've largely lost the ecological environment which made that lifestyle possible.

No, we have to create a simulated environment which pushes similar ecological buttons, but which operates at much higher population densities. It may sound ridiculous, but the model which comes to mind is that of a very-robust, highly-automated and free theme park.

• The activities there would permit a variety of inter-personal relationship types including, for example, status-contests for the males (e.g. sports competitions).
• Basic human requirements such as food, shelter, entertainment and medical care would be provided free-of-charge.
• The mil-spec automated systems would fulfill a social role of 'slaves' - something we couldn't do with real, technically-competent people.**

I would guess we're less than a hundred years from being able to create such an effective and affordable infrastructure***. In that time we're not going to lose the many very smart people we need to create and maintain it. Let's get on with it.

---
* Sub-Saharan agricultural and pastoralism is fairly recent (cf the Bantu) in evolutionary terms.

** As last seen with the Romans and their Hellenic slaves.

*** You may be thinking 'zoo' but this would not be for the benefit of spectators: such a competent social environment would be pretty utopian for all of us.

LinkedIn: greedy, evil or just plain stupid?

Prof. Matt Strassler writes: (my emphasis)

I use LinkedIn sparingly, though I have found it beneficial on occasion. But one of its features is that its software is constantly asking you if you want to make LinkedIn-contact with people whom it thinks you might know. That’s understandable; LinkedIn has to make money, and information and contacts are money for them.

But there’s one LinkedIn request that you have to be careful with, in which they ask for permission to import your contacts lists and send LinkedIn invitations to make contact with every single person on that list. 

I don’t have to explain to you why this would typically be undesirable… it’s obvious. Just think of one person you’d rather not talk to, or who’d rather not hear from you, who might be down in a forgotten corner of your list of contacts.

In the old days, if you were to plan something so drastic as contacting hundreds of unrelated people on a single day about joining your professional network, you’d be discussing it on the phone or at a desk with a company representative, having a conversation. And you’d probably have to sign a piece of paper. Moreover, you’d have at least a few minutes, if not days, to consider what you’d done and change your mind.

In the modern age ... clicking is enough. But everyone knows that it is easy to misread something and click on it, or do something through accidental clicking of a touch-pad, a slip of a mouse, or a bump of a touchscreen. I don’t know which of these happened to me yesterday. In any case, in order to take an action as outlandish and irrevocable as sending blanket faux-personalized email invitations to everyone I have ever known, it is essential for a company to have a warning pop-up: “This action will send email invitations to 452 individuals. Are you sure you want to do this?” The default should be “No“, and you have to click on “Yes” for the action to go ahead.

But for LinkedIn, as I discovered yesterday, a single click is apparently all it takes, with no warning screen.  In my opinion, this is somewhere between unethical, negligent, and sneaky.

Actually let’s just call it evil.

I guess like most people on LinkedIn, I've also almost been a victim of this scam. My email-contacts list has more than a thousand entries, based on years of working in high-tech companies and as a technology consultant. I read about Prof. Strassler's disaster and thought, 'There but for the grace of God' ...

Now that I'm out of that, I think LinkedIn is just too high-risk. Goodbye, guys - you brought it on yourselves.

"What's my IQ?" (You ask yourself)

Lots of people haven't taken IQ tests, or maybe they did but their company declined to share the results. But calm yourselves: there is a way. Recall that one of the major utilities of IQ test results is that they correlate strongly with competence in occupations requiring intelligence. So naturally, you can reverse-engineer your IQ from your occupation or education, or at least establish a personal floor level.

Let's start with university. In the UK, along with many industrialised societies, something like 40% of the cohort go to university. This sets the entrance level at the 60th percentile. Looking up the value of 0.1 (corresponds to 0.6 = 0.5 + 0.1) in a normal distribution table tells us that this occurs at 0.25 standard deviations to the right of the mean. Since IQ is normed at mean = 100, standard deviation = 15, the minimum IQ to get into university these days is about 104.

Obviously if this is your IQ you're going to be doing media studies or drama, not economics, mathematics or physics. It's a floor.

Now we turn to another useful chart, this time from Steve Hsu via Lubos Motl. I reproduce part of Lubos' rather satirical post below. Steve Hsu's original data was GRE (Graduate Record Examinations) scores, which he then translated into IQ scores. The GRE are taken by most American university graduates seeking to do post-graduate study to PhD level. Anyway, here's Lubos:

"Steve Hsu has found a very interesting table with the average GRE scores computed for various concentrations. He has also defined a linear map translating the average V-Q-A scores into a more familiar IQ scale. This convention looks natural to me and I will follow his scale although it is not guaranteed that it is equally calibrated as other IQ measurements.

"Disclaimer: these cold numbers expressing typical IQ for different occupations must be interpreted very carefully. They don't necessarily imply anything. The outcome depends on the character of the question, discrimination, etc. Despite different numbers, all of us are equal. Blah blah blah. And so on.

"The results are: [A PhD student's typical IQ for this subject]

130.0 Physics
129.0 Mathematics
128.5 Computer Science
128.0 Economics
127.5 Chemical engineering
127.0 Material science
126.0 Electrical engineering
125.5 Mechanical engineering
125.0 Philosophy
124.0 Chemistry
123.0 Earth sciences
122.0 Industrial engineering
122.0 Civil engineering
121.5 Biology
120.1 English/literature
120.0 Religion/theology
119.8 Political science
119.7 History
118.0 Art history
117.7 Anthropology/archeology
116.5 Architecture
116.0 Business
115.0 Sociology
114.0 Psychology
114.0 Medicine
112.0 Communication
109.0 Education
106.0 Public administration"

Since these are graduate entry levels, they're creaming off the top end of undergraduates. So it's kind of an 'undergraduate good 2:1' floor we're seeing here. Oh, and scary & surprising about US doctors, don't you think? In the UK, the average IQ of medical students is reckoned to be 125 (page 61).

At the very top-end, Steve Hsu has observed:

"I doubt that the average among eminent scientists (averaging over all fields) is 160; probably a bit lower like 145."

Finally I draw your attention to "Smart Fraction" Theory, which examines the notion that to run a modern, complex, industrialised society requires a mass of people with (verbal) IQs greater than around 106. Our best societies today have a smart fraction just under 50% (see graph at the bottom of the linked article).

Estimating IQ from genotype

This post is a simple back of the envelope calculation based on Davide Piffer's paper as discussed in my earlier post.

First a quick reminder about opinion polls and sampling.

Opinion Polls

We assume a large population of interest and we sample n individuals (often 1,000) with a yes-no question. Something like "Are you going to vote for the Labour Party in the forthcoming election?" We want to know how likely it is that the population as a whole votes in the same proportions as found in our survey. Suppose p is the fraction of the sample-population who tell us they will vote yes (example: 0.32).

This is just the same as throwing a biased coin (Heads with probability 0.32) a thousand times and seeing how many Heads we actually get. Clearly on average we'll get 320 Heads [the mean of our sample is np]. Of more interest, however, is the standard deviation of the mean if we took sample after sample (or coin-throwing exercise after coin-throwing exercise). We would like to know the upper and lower bounds of 'yes' respondents we would get in, say, 95% of the samples we took, corresponding to +/- 1.96 standard deviations. We can be pretty confident that those bounds would play out in real life (nineteen times out of 20).

The standard deviation of a binomial distribution, which is what we have here, is √(npq) where n is the size of the sample (example, 1,000), p is the probability of the 'yes' outcome (example: 0.32) and q is the probability of the 'no' outcome (0.68 = 1-p).

The 95% confidence interval around the mean np is +/- 1.96 standard deviations - which we approximate here to 2. We also approximate p and q to 0.5 as this is the largest value of √(pq).

Plugging the numbers in, we get the 95% confidence interval as: 2 times √(0.5 x 0.5 x n) = √n.

In our running example with 1,000 people sampled (√1000 equals around 32), this tells us that the interval 320 +/- 32 will  contain the number of 'yes' answers we'll get 95% of the time. We sometime prefer to have the results as a proportion, usually written as a percentage, in which case we divide everything by n.

The mean number of 'yes' voters here is 320/1,000 = np/n = p (0.32 or 32%).

The 95% confidence interval here is 32/1,000 = √n/n = 1/√n  (usually described as +/- 3%).

Note that if we had sampled just 100 voters, we would have a 95% confidence interval of +/- 1/√100 = +/- 10%. We're already losing quite a bit of predictive power.

Asking just 10 people, the 95% confidence interval is 1/√10 = 0.32 = approx. 30%. So the three people who said they'd vote 'yes' .. in multiple surveys that number could dip as low as zero and as high as six. Pretty much worthless in forecasting the election.

---

To apply this to IQ I'm going to use the data in Davide Piffer's paper, as discussed in my earlier post - to which you may need to refer.

Looking at my own results I had 16 alleles to play with, of which 7 were 'good for intelligence'. So this is an opinion poll where I was able to survey only 16 people. Duh!

My computed allele frequency was 44% against a European average of 35.5% so I'm 8.5 percentage points up from the average.

Looking at the Chinese/Japanese figures we see an allele frequency score of 39.1% (a difference of 3.6% from the European mean) which corresponds to an IQ difference of 5 IQ points from Europeans. I'm going to assume a linear relation - an additive model.

To convert a difference of mean allele frequency to IQ difference we multiple by 5/3.6 = 1.4. So the estimate of my IQ is 8.5 * 1.4 = 12 points above the European average of 100. In my incorrigible vanity I'd like to believe that 112 is rather on the low side! What is the 95% confidence interval for this calculation?

Since n = 16, and following the path described above, the 95% confidence interval is +/- 1/√16 = 25%.

That's the allele frequency limits so my true allele frequency (of those hundreds or thousands of 'good alleles driving IQ') is probably in the range 44% +/- 25% or [19%, 69%]. To change these limits into IQ scores multiply the confidence interval of +/-25%  by 1.4 giving +/- 35 IQ points

We may be 95% confident that my IQ is in the range [77, 147].

So I guess we can be 95% confident that I'm neither extremely educationally subnormal nor Albert Einstein!

The take-home message is that we need hundreds of alleles to give us a big enough sample to get the error bounds down. The concordance of twins brought up together for IQ is around 0.86 so non-genetic factors will still prevent us getting all the way.

BTW we're just a few years from getting to that 'hundreds of IQ-affecting alleles' point, so although this is a fun exercise, reality will be along soon enough.

The genetics of intelligence - early results

Refer to text below to understand this

A somewhat overlooked paper: "Factor Analysis of Population Allele Frequencies as a Simple, Novel Method of Detecting Signals of Recent Polygenic Selection: The Example of Educational Attainment and IQ" available here.

This is how Peter Frost summarises the paper.

"We know that human intellectual capacity has risen through small incremental changes at very many genes, probably hundreds if not thousands. Have these changes been the same in all populations?

"Davide Piffer (2013) has tried to answer this question by using a small subset of these genes. He began with seven SNPs whose different alleles are associated with differences in performance on PISA or IQ tests. Then, for fifty human populations, he looked up the prevalence of each allele that seems to increase performance. Finally, for each population, he calculated the average prevalence of these alleles at all seven genes.

"The average prevalence was 39% among East Asians, 36% among Europeans, 32% among Amerindians, 24% among Melanesians and Papuan-New Guineans, and 16% among sub-Saharan Africans. The lowest scores were among San Bushmen (6%) and Mbuti Pygmies (5%). A related finding is that all but one of the alleles are specific to humans and not shared with ancestral primates.

"Yes, he was using a small subset of genes that influence intellectual capacity. But you don't need a big number to get the big picture. If you dip your hand into a barrel of differently colored jelly beans, the colors you see in your hand will match well enough what's in the barrel. In any case, if the same trend holds up with a subset of 50 or so genes, it will be hard to say it's all due to chance."

The alleles which Piffer frequency-analysed differentially code for things like:

"... the regulation of neuronal morphology in neurons, including hippocampal neurons and developing brains"

"... neuronal excitability, synaptic plasticity and feedback regulation of acetylcholine release."

Ten or so SNPs don't determine very much of a person's intelligence, which depends upon the actions of hundreds or thousands of genes as well as environmental effects. But even a small sample - if representative and correlated - can be quite predictive, as Piffer explains.

"As the effect size of each SNP is typically very low (around 0.1%), even 10 SNPs would not account for more than 1% of the variance in IQ or educational attainment scores across populations. The likely explanation for why the effect size for the 10 SNPs at a cross population level detected in this study is so high (around 80%), is that the alleles are not randomly distributed across human races, so that the combined frequency of a few alleles predicts the frequencies of many other alleles affecting the same phenotype. This inflates the correlation with the phenotype well beyond anything that would be explainable by the modest effect sizes of the examined SNPs.

"This is nothing more than the principle applied to psychometric instruments, such as IQ tests or personality scales, where a handful of items produce a reliable score, precisely because these items represent an underlying, latent factor and are thus correlated among each other. Even reliable psychometric scales are usually composed of around 10 items, equal to the number of SNPs examined in the present study, which in turn showed good internal reliability (Cronbach’s α= 0.84).

"A model based on random evolution or genetic drift alone cannot account for such a pattern."

The particular SNPs used in the study are listed in the tables at the back of the paper. I was naturally interested in checking which of these SNPs are analysed by 23andMe. It turns out that about half are. So in the graphic above you see the 'good for intelligence' SNPs in the first column, the gene name (where available) in the second and the chromosome it's on (from 23andMe) in the third. The fourth column is the specific nucleotide which marks this as a 'good-for-intelligence' allele, and the fifth is the database where the source-data came from (refer to the paper for details). The final column is my own genotype at these alleles, as downloaded from 23andMe.

Here is an Excel workbook for you to try yourself. Hint: download your 23andMe results and load into an Excel spreadsheet; then search on the rs SNP identifiers.

There are sixteen alleles (2 x 8) which are both 'good' and available from 23andMe. Of these 16, you will see that I have 7 'good' ones, so my personal 'frequency' is 7/16 = 0.44. This sounds terrible, but in fact the European average frequency for these 'good alleles' is 35.5% and the East Asian (Chinese, Japanese) average frequency is 39.1. This number of alleles is too small to be a good estimator of anyone's overall IQ though.

The message of Piffer's paper is that, as humans radiated out of Africa, a rising tide of natural selection drove novel alleles coding for increased intelligence to higher and higher population frequencies. This emerges clearly from the  SNPs analysed in the paper, and is by hypothesis true for the rest too. It appears that selection for higher intelligence has been more, rather than less, intense over the last 10,000 years - possibly reflecting the cognitive demands of agrarian, pastoral and yet more complex modern civilisations.

More to come, undoubtedly as the larger scale GWAS studies begin to deliver.

A garden saga

We saw our first woodpecker this morning, on the sunflower hearts ... and then,

A hungry Mr Squirrel eventually climbed down onto the very same sunflower-heart feeder, which promptly collapsed under his weight and fell into the bushes ... as did he.

Snippets

War Dogs by Greg Bear. First of a new trilogy. Tired rip-off of Starship Troopers lacking ideas, authorial engagement and sadly, even much action.

Love Anthony by Lisa Geneva. Her third book (remember Still Alice?). She claims this is her first proper novel. Actually it's a superficial examination of humdrum and conventional setbacks in the lives of two middle-class female literary types. Plus her somewhat clunky, sentimental and rather tedious theory of what it's like to be autistic.

Dominic Cummings' blog. Frankly self-serving account of his time as Michael Gove's spad at the Department of Education. Cummings is somewhat familiar with scientific culture: he knows some stats, a bit of quantum theory, some economics, evolution, genetics and psychometrics. Amongst politicians and civil servants this makes him the one-eyed man blah blah. Sadly, he is of the opinion that this makes people like him better candidates to rule us. Even though he states it himself, he hasn't noticed that successful political leadership is not founded on expertise in formal systems and that the political track record of brilliant academics is abysmal.

Three good pieces

1. Best review of Iain M. Banks's Culture sequence

Matt Hilliard, a smart and insightful reviewer at Strange Horizons wrote this piece on his blog: the best assessment of what on earth Banks was trying to do with his Culture novels. His take on Peter Watts' Echopraxia is equally superior.

2. Steve Hsu's popular and accessible article on genetic engineering for super-intelligence

Steve Hsu in action

"Super-Intelligent Humans Are Coming - genetic engineering will one day create the smartest humans who have ever lived".

'Stephen Hsu is Vice-President for Research and Professor of Theoretical Physics at Michigan State University. He is also a scientific advisor to BGI (formerly, Beijing Genomics Institute) and a founder of its Cognitive Genomics Lab.'

His blog is here.

3. What makes the quadcopter design so great for small drones?

Remember all those camera drones with four rotors arranged in a square? Amazon's PR stunt for drone delivery? Why do we still go for helicopters in the large then? Is the future the quadcopter?

Here's why not.

A helicopter drone

Thanks to Jess Riedel's blog for the last two links.
---
Oh, and here's a laugh-out-loud bonus from The War Nerd.

Take me to Istanbul ..