Why are there so few women in tech? The truth behind the Google memo

An engineer at the company has suggested male domination of Silicon Valley is down to biological differences between the sexes. But the root causes are much more complicated

It is time to be open about the science of human nature. This was the assertion of software engineer James Damore to his colleagues at Google, in an internal memo that has since led to his sacking. Im simply stating, Damore wrote, that the distribution of preferences and abilities of men and women differ in part due to biological causes and that these differences may explain why we dont see equal representation of women in tech and leadership. He went on to imply that womens stronger interest in people and neuroticism might make them less naturally suited to being coders at Google.

The companys leadership viewed the matter differently, firing Damore and sparing his female colleagues the need to prove their biological aptitude for working with computers.

Sacking one errant employee doesnt alter an awkward fact, though. Only 20% of Google engineers are women a statistic that is matched roughly across big tech companies. So, does Damore have a point? Is there an underlying biological explanation for why so few women work at a company that prides itself on its progressive ideals and family-friendly ethos?

There are countless scientific studies that claim to identify differences between male and female cognitive aptitudes and, in the UK, far fewer girls choose to study computer science at GCSE level (20% of the total number of students), at degree level (16%) and beyond. There is something seductive about the idea that professional success springs from our innate abilities, rather than the degree to which society tips the odds in our favour.

After the contents of the memo became public, through a leak to tech site Gizmodo, the scientific argument for innate biological differences quickly found favour with some tech insiders, albeit those writing anonymously on sites such as Hacker News and the gossip app Blind.

Students
Students at the Indian Institute of Management Lucknow. Far more women study computing in India than in the UK. Photograph: Hindustan Times/Getty Images

On Blind which requires users to prove who they work for before posting one Google employee wrote: Can we go back to the time when Silicon Valley were [sic] about nerds and geeks, thats why I applied [to] Google and came to the US. I mean this industry used to be a safe place for people like us, why so fking complicated now. I used to dislike conservatives until I started working in tech, wrote another. Now I sympathise with them due to the hostility and groupthink, as well as the fact that they are the only ones standing up for classical liberal values.

While the biological hypothesis seems to appeal to some tech workers, the notion that Silicon Valleys gender gap can be explained away by such factors is questionable. Prof Dame Wendy Hall, a director of the Web Science Institute at the University of Southampton, points to the wide variation in gender ratios in computing internationally, which she argues would not be seen if there were a universal biological difference in ability between the sexes. While only 16% of computer science undergraduates in the UK and a similar proportion in the US are female, the balance is different in India, Malaysia and Nigeria.

I walk into a classroom in India and its more than 50% girls, the same in Malaysia, says Hall. They are so passionate about coding, Lots of women love coding. There just arent these gender differences there.

In fact, in the west, female participation in computer science has plunged since the mid-80s, while female participation in medicine and other scientific fields has increased steadily.

Over the past decade, even with a number of initiatives being set up to boost girls participation in coding and computer science, the proportion of female computer science undergraduates has continued to fall 10 years ago, the proportion was 19% of the UK total.

Hall believes that the gender gap and the male computer geek stereotype can be dated back to the advent of the home computer in the early 80s, when the machines were marketed heavily as gaming systems for men. She suspects this might be more culpable for womens low participation than men having evolved a mindset better suited to writing lines of code.

Women were turned off computing in the 80s, she says. Computers were sold as toys for the boys. Somehow that cultural stigma has stuck in the west in a way that we cant get rid of and its just getting worse. The skills gap is going to get huge.

Jane Margolis, a psychologist at the University of California, Los Angeles, agrees. Margolis interviewed hundreds of computer science students in the 90s at Carnegie Mellon University, which had one of the top programmes in the country at the time.

Many of the women at Carnegie Mellon talked about computers being in [their brothers] bedroom and there were a lot of father-son internships around the computer that werent happening with the girls, she says. There was a cultural assumption that the norms of being in computer science were that you would do it 24/7, were obsessed with it, wanted nothing in your life but computers and that was very much associated with male adolescents, she added. It was very much based around a male norm. Females were made to think that, if they didnt dream in code and if it wasnt their full obsession, they didnt belong or were not capable of being in the field.

Former
Former Tinder vice-president Whitney Wolfe, who sued the company over atrocious misogyny in 2014. Photograph: Jeff Wilson for the Observer

Prof Gina Rippon, a neuroscientist at Aston University in Birmingham, has studied extensively cognitive differences between men and women. She says that, while Damore pointed to scientific evidence for men and women having different aptitudes and personality traits, he seemed to miss the point that, even if there were well-established sex differences at any level, theyre always very tiny. Certainly not enough to explain the gender ratios of Google programmers even if you didnt want to get into the nitty-gritty of arguing about the science.

Rippons work suggests that, in many cases, the differences between male and female performance, if present, are very small, can disappear with training and are not consistent across cultures.

In one study, Rippon found that British men performed significantly better on a spatial rotation task than women. However, when the experiment was repeated with Chinese participants, there was no difference between the male and female participants. Other similar studies have found that gender differences in spatial rotation tasks disappeared when the researchers controlled for video game experience. Rippon points to another study, which showed that differences in personality traits between men and women varied wildly across countries, depending on the status of women in that society.

So, Damores suggestion that women are more prone to anxiety does not imply that this difference is a function of hormones or hardwiring of the brain. Plus, there is compelling evidence that unconscious biases have a powerful effect on what people expect themselves to be good at and how they perform. For instance, girls tend to score worse on a test if they are told their maths skills are being assessed than when they are told they are taking part in a study investigating how people solve problems.

Even assuming that there are fundamental differences between male and female cognition and personality, there is no clear, logical line between such findings in a laboratory setting and performance in the workplace.

Priya Guha, the UK lead of tech incubator RocketSpace and a former UK consul general in San Francisco, argues that, even by its own arguments, Damores memo missed the point. The description of an engineer as somebody who has their head down, focused on developing the next line of code, is the sort of engineer that wont be adding value, she says. We need engineers out there who are both very strong developers, but also people who understand the world around them and are comfortable interacting with society. So, by that description, women would be better engineers even by the stereotypes he proposes.

Unfortunately, many such multiskilled people are likely to be deterred by the perception of hostility engendered by claims like Damores. We have a historical challenge to encourage girls, let alone women, into careers such as engineering, which then creates an imbalance in the people who enter tech industries overall, says Guha. Tech has a particular problem in this area. Wherever there are instances of people creating a hostile environment, companies need to stamp that out quickly. His dismissal sends a really powerful message: the environment in these companies needs to be thought about to ensure that it improves day by day.

But Eileen Burbidge, a partner at venture capital firm Passion Capital, argues that tech does not have a significantly worse gender gap than other high-pressure industries such as finance or the media. I think it comes down to cultural norms and female representation in general, Burbidge says. It is what affects the rest of the business world: its around the same time that women start thinking about having families that they think about the opportunity cost of staying in a work environment, and if its not positive or they get negative influences its going to affect their decisions.

She argues that, in many ways, tech is better placed than most large industries to tackle its gender gaps. I dont think theres anything specific that needs to be done for technology: I think the tech sector is more introspective and likes to think of itself as more progressive, so remedies that work for other sectors will help here, too, she says.

In Stem [science, technology, engineering and mathematics] in particular, were seeing the tech industry trying to be more proactive about outreach. The industry is trying to have this discussion a lot companies dont always follow what they say, but they say it, at least.

Computer
Computing is too important to be left to men … the late computer scientist Karen Sprck Jones. Photograph: Cambridge University

Peter Daly, an associate in the employment team at the law firm Bindmans, agrees with Burbidge. The clients Ive had from the tech world are pretty evenly split by gender, he says. But, because it encourages risk-taking, tech doesnt fit well with maternity and pregnancy, so that can be a source of a lot of friction. You see people in the industry who see pregnancy as a genuine problem. That, he says, is the main cause of gender-specific issues in technology at least, those that reach the stage of requiring a legal recourse.

Internal documents such as Damores are the soft end of the sort of hostile working environment female employees can face at overwhelmingly male tech firms. At the extreme end, as companies such as Uber and Tinder have learned, this environment can result in claims of sexual harassment and illegal discrimination.

At Uber, where 85% of technical employees are male, one engineer, Susan Fowler, wrote a tell-all blogpost that revealed a workplace where managers proposition female employees for sex and human resources does little to stop the issue. Tinder faced a similar scandal when former VP Whitney Wolfe sued the company over atrocious misogyny in 2014, entering into evidence abusive texts allegedly sent by Tinders chairman, Sean Rad.

Beyond the egregious cases, the wider culture of even the most diverse Silicon Valley firms can still end up being offputting to would-be employees: the campus-style culture, which encourages workers to be on site from dawn till dusk, renders it hard for any primary caregiver to be part of the team, while in some companies an antipathy for part-time work or on-site creches can also limit flexibility.

Addressing the gender gap isnt only an issue of perception. Companies with homogenous workforces make worse products and earn less money, argues Guha. We know large numbers of women are struggling to get funding. A female founder is 86% less likely to be funded than a man, she says. Thats crazy when we know the return on investment is higher; it is about 34% higher for companies with a gender diverse leadership. Its not about corporate social responsibility: a diverse range of thinking will bring better value for the company.

As we move into a future in which algorithms have greater influence on our lives from communication to healthcare, transport to the law the gender balance in tech companies goes beyond what is fair for their employees. The result of male domination of tech has led to the development of, for example, voice recognition technologies that, trained and tested solely by men, struggle to understand female voices. It has resulted in virtual reality technologies that disproportionally impose motion sickness on women. At this early moment in its history, the tech industry is already littered with products that have gender bias effectively programmed into them.

The most objectionable point about that memo was the notion that there are biological differences that make women less capable, said Burbidge. Obviously, I have an issue with that and I think its fundamentally incorrect. The thing I cant answer is how, in 2017, do you stop people thinking that? I dont know how you change peoples minds.

As we go into the world of AI, when people are designing algorithms that help us live our lives, it will be very bad if thats all done by men, says Hall. Social care, looking after kids, so many aspects of our lives. We really need as many people as possible doing this. Its really important and its going to get more important.

Hall invokes her late mentor Karen Sprck Jones, a pioneering British computer scientist who campaigned hard to encourage more women into the field. As she used to say: Computing is too important to be left to men.

Read more: https://www.theguardian.com/lifeandstyle/2017/aug/08/why-are-there-so-few-women-in-tech-the-truth-behind-the-google-memo

Tech has become another wayfor men to oppress women | Lizzie OShea

We act as if technology were neutral but its not. The challenge now is to remove the gender bias, says human rights lawyer and writer Lizzie OShea

Most women in the Bay Area are soft and weak, cosseted and naive, despite their claims of worldliness, and generally full of shit, wrote former Facebook product manager Antonio Garca Martnez in 2016. They have their self-regarding entitlement feminism, and ceaselessly vaunt their independence. But the reality is, come the epidemic plague or foreign invasion, theyd become precisely the sort of useless baggage youd trade for a box of shotgun shells or a jerry can of diesel. This is from his insider account of Silicon Valley, Chaos Monkeys. The book was a bestseller. The New York Times called it an irresistible and indispensable 360-degree guide to the new technology establishment. Anyone who is surprised by the recent revelations of sexism spreading like wildfire through the technology industry has not been paying attention.

When Susan Fowler wrote about her experience of being sexually harassed at Uber, it prompted a chain of events that seemed unimaginable months ago, including an investigation led by former attorney general Eric Holder, and the departure of a number of key members of the companys leadership team. Venture capitalist Justin Caldbeck faced allegations of harassing behaviour, and when he offered an unimpressive denial, companies funded by his firm banded together to condemn his tepidity. He subsequently resigned, and the future of his former firm is unclear. Since then, dozens of women have come forward to reveal the sexist culture in numerous Silicon Valley technology and venture capital firms. It is increasingly clear from these accounts that the problem for women in the tech industry is not a failure to lean in, it is a cultureof harassment and discrimination that makes many of their workplaces unsafe and unpleasant.

At least this issue is being discussed in ways that open up the possibility that it will be addressed. But the problem of sexism in the tech industry goes much deeper and wider. Technological development is undermining the cause of womens equality in other ways.

American academic Melvin Kranzbergs first law of technology tells us that technology is neither inherently good nor bad, nor is it neutral. As a black mirror it reflects the problems that exist in society including the oppression of women. Millions of people bark orders at Alexa, every day, but rarely are we encouraged to wonder why the domestic organiser is voiced by a woman. The entry system for a womens locker room in a gym recently refused entry to a female member because her title was Dr, and it categorised her as male.

But the issue is not only that technology products reflect a backward view of the role of women. They often also appear ignorant or indifferent to womens lived experience. As the internet of things expands, more devices in our homes and on our bodies are collecting data about us and sending it to networks, a process over which we often have little control. This presents profound problems for vulnerable members of society, including survivors of domestic violence. Wearable technology can be hacked, cars and phones can be tracked, and data from a thermostat can reveal whether someone is at home. This potential is frightening for people who have experienced rape, violence or stalking.

Unsurprisingly, technology is used by abusers: in a survey of domestic violence services organisations, 97% reported that the survivors who use them have experienced harassment, monitoring, and threats by abusers through the misuse of technology. This often happens on phones, but 60% of those surveyed also reported that abusers have spied or eavesdropped on the survivor or children using other forms of technology, including toys and other gifts. Many shelters have resorted to banning the use of Facebook because of fears about revealing information about their location to stalkers. There are ways to make devices give control to users and limit the capacity for abuse. But there is little evidence that this has been a priority for the technology industry.

Products that are more responsive to the needs of women would be a great start. But we should also be thinking bigger: we must avoid reproducing sexism in system design. The word-embedding models used in things like conversation bots and word searches provide an instructive example. These models operate by feeding huge amounts of text into a computer so it learns how words relate to each other in space. It is based on the premise that words which appear near each other in texts share meaning. These spatial relationships are used in natural language-processing so that computers can engage with us conversationally. By reading a lot of text, a computer can learn that Paris is to France as Tokyo is to Japan. It develops a dictionary by association.

But this can create problems when the world is not exactly as it ought to be. For instance, researchers have experimented with one of these word-embedding models, Word2vec, a popular and freely available model trained on three million words from Google News. They found that it produces highly gendered analogies. For instance, when asked Man is to woman as computer programmer is to ?, the model will answer homemaker. Or for father is to mother as doctor is to ?, the answer is nurse. Of course the model reflects a certain reality: it is true that there are more male computer programmers, and nurses are more often women. But this bias, reflecting social discrimination, will now be reproduced and reinforced when we engage with computers using natural language that relies on Word2vec. It is not hard to imagine how this model could also be racially biased, or biased against other groups.

These biases can be amplified duringthe process of language learning. As the MIT Technology Review points out: If the phrase computer programmer is more closely associated with men than women, then a search for theterm computer programmer CVs might rank men more highly than women. When this kind of language learning has applications across fields including medicine, education, employment, policymaking and criminal justice, it is not hard to see how much damage such biases can cause.

Removing such gender bias is a challenge, in part because the problem is inherently political: Word2vec entrenches the world as it is, rather thanwhat it could or should be. But if we are to alter the models to reflect aspirations, how do we decide what kind of world we want to see?

Digital technology offers myriad waysto put these understandings to work. It is not bad, but we have to challenge the presumption that it is neutral. Its potential is being explored in ways that are sometimes promising, often frightening and amazing. To make the most of this moment, we need to imagine a future without the oppressions of the past. We need to allow women to reach their potential in workplaces where they feel safe and respected. But we also need to look into the black mirror of technology and find the cracks of light shining through.

Read more: https://www.theguardian.com/commentisfree/2017/jul/07/technology-sexist-society-even-worse-women-potential

Googles Tensor2Tensor makes it easier to conduct deep learning experiments

Googles brain team is open sourcing Tensor2Tensor, a new deep learning library designed to help researchers replicate results from recent papers in the field and push the boundaries of whats possible by trying new combinations of models, datasets and other parameters. The sheer number of variables in AI research combined with the fast pace of new developments makes it difficult for experiments run in two distinct settings to match. This is a pain for researchers and a drag on research progress.

The Tensor2Tensor library makes it easier to maintain best practices while conducting AI research. It comes equipped with key ingredients including hyperparameters, data-sets, model architectures and learning rate decay schemes.

The best part is that any of these components can be swapped in and out in a modular fashion without completely destroying everything. From a training perspective, this means that with Tensor2Tensor you can bring in new models and data sets at any time a much simpler process than would ordinarily be possible.

Google isnt alone in its pursuits to help make research more reproducible outside the lab. Facebook recently open sourced ParlAI, its tool to facilitate dialog research that comes prepackaged with commonly used datasets.

Similarly, Googles Tensor2Tensor comes with models from recent Google research projects like Attention Is All You Need and One Model to Learn Them All. Everything is available now on Github so you can start training your own deep learning-powered tools.

Read more: https://techcrunch.com/2017/06/19/tensor2tensor/

Meet the millennials making big money riding China’s bitcoin wave

The cryptocurrency may have no physical form but the returns from trading it can be very real and for some theyre worth giving up your job for

On a sunny afternoon in west Beijing, on the auspicious eighth floor of a nondescript concrete high-rise, Huai Yang sits with the curtains drawn in his apartment, making his own luck.

For the past six months, 27-year-old Yang has worked mainly from home, mainly from his sofa, tracking and trading bitcoin, and watching the money roll in. The flat itself is modestly sized; Yang moved in in his pre-bitcoin days when he worked variously for a crowdfunder start-up, a branding consultancy and dabbled in hedge-fund management, all of which he describes as creative financial work. Now, though, his main focus is bitcoin, which is much younger, more fun, and much more money. Yang claims to make up to 1m yuan (116,000) a month, under the radar of the taxman, purely from trading the online cryptocurrency.

Bitcoin has no physical form but the rewards are very tangible; Yangs home is packed full of expensive gadgetry, most prominently a mega-sized flat screen smart board, over a metre wide, which Yang uses to chart bitcoins rise and fall in HD.

Normally, the graphs on Yangs screen show bitcoins and his own fortunes going up and up. At the time of writing, one bitcoin is worth 6,600 yuan (768) recent months have seen the value hover well above 8,000 yuan. The global worth of bitcoin is over $14bn USD (11.3bn), of which over 90% is in yuan, and Yang and his peers are cashing in. I want a more splendid life, he says.

Huai
Huai Yang, who trades bitcoin from his sofa Photograph: Naomi Goddard for the Guardian

Theres certainly big money to be made in bitcoin, but it comes at a high risk. Bitcoin was designed to be a peer-to-peer currency, free from interference from government and central banks. Since the currency was launched in 2009, however, the Chinese market, where government interventions are common, has come to dwarf all others.

One such intervention took place in February this year, when the government warned that there would be serious violations for trading platforms that failed to abide by strict money-laundering regulations. In line with this, OKCoin and Huobi.com, the two biggest exchanges in China, announced that they would be suspending bitcoin withdrawals for one month.

Incidents like these, which Yang sees as not convenient, but not [a] problem, give Chenxing (who asked that I only use his first name) pause for thought. Chenxing, a boyish, skittish 35, has been trading bitcoin for the past four months, after giving up his too comfortable job as a geo-information engineer for the government. The governments pressure on bitcoin platforms is not so easy to understand, he tells me. Im not sure its really about money laundering they try to control [bitcoin], but they cannot.

For Chenxing, its the system itself that is vulnerable: Technology changes every day, he explains. Maybe tomorrow a hacker can find a way to crack bitcoin the security is from mathematics. If you can crack the mathematics, bitcoin is nothing. Thats why, even though Chenxing describes himself as a believer in bitcoin, he doesnt plan to stay involved for the long term.

Its really not a stable thing, he says, both in terms of fluctuating prices and the uncertain technological future of the cryptocurrency. That said, hes still making more money than in his previous government job. In a good month, Chenxing will pocket the cash value of around five bitcoin, which is close to 40,000 yuan, and which Chenxing prefers to have in cold, hard cash.

Chenxing is something of an anomaly in Chinese bitcoin circles, where the general mood is one of evangelical faith in the currencys potential, especially in an economy where the government often devalues the national currency.

Brendan Gibson, 32, is a United States national who has been in China for six years, trading bitcoin for three. Weve barely sat down to talk when Gibson takes my phone and downloads the BTC Wallet app onto it, before transferring me the seeds of my cryptocurrency fortune: 0.0027 bitcoin, worth 2.50, which is the amount that everyone in the world would have if the 21m bitcoin in existence were equally divided up between all 7.8 billion of us. He believes that everybodys aunt or grandma should be using bitcoin.

Brendan
Brendan Gibson: Im just kind of fed up with the system. Photograph: Naomi Goddard for the Guardian

For Gibson, bitcoin is a way of life. He hopes to be completely bank free in the near future. Hailing from the shady mortgage industry of corporate America, Gibson shares Chenxings distrustful attitude, but is more concerned about private banks than bitcoins technological vulnerability. Im just kind of fed up with the system, he tells me over coffee in a slick caf and co-working space from where Gibson does most of his work remotely.

I dont think economies should be built on inflated numbers, and I think its kind of ridiculous that everybody relies on this inflated number in their bank account when its definitely not there bitcoin and other cryptocurrencies are making it so that we are our own banks, and thats one less things we have to worry about. Gibson owns two companies in China, and as far as possible uses bitcoin for all his daily expenses, converting the personal profits he makes into bitcoin to avoid using banks.

One of the commonly cited weaknesses in the bitcoin system is that if you lose your private key to access your bitcoin wallet, the bitcoin within are lost forever. In 2015, it was estimated that up to 30% of all mined bitcoins had been lost, with a value of 625m. Unsurprisingly, plenty of people see this as an opportunity to make some money.

Sun Zeyu, 27, works at a tech start-up based near Beijings university district that specialises in bitcoin. His latest project is Coldlar, an offline, physical wallet that stores users bitcoin and can be accessed by scanning a QR code. Bitcoin security is a tough question, Sun tells me, which is why he and his colleagues designed a product that allows people to circumvent bitcoin platforms and have even greater control over their bitcoin. Now that the value [of bitcoin] is going up, he explains, people really realise the importance of security.

Before, when we just traded one or two coins, people didnt mind, [but] now the value of bitcoin is much bigger. Sun got involved with bitcoin while at university after attending a seminar run by Huobi, one of the biggest trading platforms in China. Like his flashier friend Yang, Sun wanted money, and lots of it. He wont tell me exactly how much he earns, but assures me that its hundreds or thousands times more than the 10,000 yuan per month he was earning when he first dabbled in bitcoin three years ago.

His money comes from both his trading activity and his company salary. With the growth of bitcoin and related products like his Coldlar wallet, Sun believes that in 10 years time, the value of the cryptocurrency will be one bitcoin, one house in Beijing. Minor shocks to the system, like the recent suspension of bitcoin withdrawals in China, are just like breathing, he insists, and the inhalations of profit dwarf any other bumps in the road.

Sun
Sun Zeyu at work. Photograph: Naomi Goddard for the Guardian

Despite the solitary nature of their work, Yang, Sun, Gibson and Chenxing are all sociable creatures. Gibson is connected to hundreds of bitcoin aficionados in China, and has introduced close to 1,000 new people to the technology (although how many are like me, with 2.50 lying dormant in an unused wallet, is unknown), such is his enthusiasm for the cryptocurrency. Chenxing cites the social side of the bitcoin scene in Beijing as one of the main attractions of staying in the industry and the city.

I can meet some fun people who really love bitcoin I think most of the people who like bitcoin are people who like freedom he says. Yang, however, takes a slightly harder-edged approach. He has little patience for sceptics: Yes, bitcoin is a risk. Why should I have to discuss these things with [people concerned about the security]? I earn my money, thats enough. I dont waste my time explaining bitcoin [if] youre not my client. In some ways, Yang concedes, the less people understand bitcoin, the better it is for him. At the moment, the industry is like an ATM for him and his peers, and hes perfectly happy for things to stay that way.

In the fast-changing world of the crypto-currency, nothing seems to stay the same for long. Whether its unpredictable government interventions, or debates within the community about how the industry can and should be scaled, general growth in value thus fair doesnt necessarily suggest anything about the future of bitcoin, despite the faith of its adherents. Gibson makes the point that bitcoin has only been around for nine years; it took PayPal at least 10 to properly catch on.

In Japan it has recently been recognised as legal tender. Its unlikely that the same could ever happen in China, no matter how much its popularity continues to balloon. Chenxing, who has years of insider experience, is sure that [the government] will never accept a thing thats not built by themselves. Many bitcoin traders in China are in it for the long haul, confident that they can ride out any governmental interferences, as long as they have access to the internet. Chenxing, however, is more paranoid. His final thoughts on bitcoin are: I never feel secure.

Read more: https://www.theguardian.com/technology/2017/apr/11/meet-the-millennials-making-big-money-riding-chinas-bitcoin-wave

Meet the millennials making big money riding China’s bitcoin wave

The cryptocurrency may have no physical form but the returns from trading it can be very real and for some theyre worth giving up your job for

On a sunny afternoon in west Beijing, on the auspicious eighth floor of a nondescript concrete high-rise, Huai Yang sits with the curtains drawn in his apartment, making his own luck.

For the past six months, 27-year-old Yang has worked mainly from home, mainly from his sofa, tracking and trading bitcoin, and watching the money roll in. The flat itself is modestly sized; Yang moved in in his pre-bitcoin days when he worked variously for a crowdfunder start-up, a branding consultancy and dabbled in hedge-fund management, all of which he describes as creative financial work. Now, though, his main focus is bitcoin, which is much younger, more fun, and much more money. Yang claims to make up to 1m yuan (116,000) a month, under the radar of the taxman, purely from trading the online cryptocurrency.

Bitcoin has no physical form but the rewards are very tangible; Yangs home is packed full of expensive gadgetry, most prominently a mega-sized flat screen smart board, over a metre wide, which Yang uses to chart bitcoins rise and fall in HD.

Normally, the graphs on Yangs screen show bitcoins and his own fortunes going up and up. At the time of writing, one bitcoin is worth 6,600 yuan (768) recent months have seen the value hover well above 8,000 yuan. The global worth of bitcoin is over $14bn USD (11.3bn), of which over 90% is in yuan, and Yang and his peers are cashing in. I want a more splendid life, he says.

Huai
Huai Yang, who trades bitcoin from his sofa Photograph: Naomi Goddard for the Guardian

Theres certainly big money to be made in bitcoin, but it comes at a high risk. Bitcoin was designed to be a peer-to-peer currency, free from interference from government and central banks. Since the currency was launched in 2009, however, the Chinese market, where government interventions are common, has come to dwarf all others.

One such intervention took place in February this year, when the government warned that there would be serious violations for trading platforms that failed to abide by strict money-laundering regulations. In line with this, OKCoin and Huobi.com, the two biggest exchanges in China, announced that they would be suspending bitcoin withdrawals for one month.

Incidents like these, which Yang sees as not convenient, but not [a] problem, give Chenxing (who asked that I only use his first name) pause for thought. Chenxing, a boyish, skittish 35, has been trading bitcoin for the past four months, after giving up his too comfortable job as a geo-information engineer for the government. The governments pressure on bitcoin platforms is not so easy to understand, he tells me. Im not sure its really about money laundering they try to control [bitcoin], but they cannot.

For Chenxing, its the system itself that is vulnerable: Technology changes every day, he explains. Maybe tomorrow a hacker can find a way to crack bitcoin the security is from mathematics. If you can crack the mathematics, bitcoin is nothing. Thats why, even though Chenxing describes himself as a believer in bitcoin, he doesnt plan to stay involved for the long term.

Its really not a stable thing, he says, both in terms of fluctuating prices and the uncertain technological future of the cryptocurrency. That said, hes still making more money than in his previous government job. In a good month, Chenxing will pocket the cash value of around five bitcoin, which is close to 40,000 yuan, and which Chenxing prefers to have in cold, hard cash.

Chenxing is something of an anomaly in Chinese bitcoin circles, where the general mood is one of evangelical faith in the currencys potential, especially in an economy where the government often devalues the national currency.

Brendan Gibson, 32, is a United States national who has been in China for six years, trading bitcoin for three. Weve barely sat down to talk when Gibson takes my phone and downloads the BTC Wallet app onto it, before transferring me the seeds of my cryptocurrency fortune: 0.0027 bitcoin, worth 2.50, which is the amount that everyone in the world would have if the 21m bitcoin in existence were equally divided up between all 7.8 billion of us. He believes that everybodys aunt or grandma should be using bitcoin.

Brendan
Brendan Gibson: Im just kind of fed up with the system. Photograph: Naomi Goddard for the Guardian

For Gibson, bitcoin is a way of life. He hopes to be completely bank free in the near future. Hailing from the shady mortgage industry of corporate America, Gibson shares Chenxings distrustful attitude, but is more concerned about private banks than bitcoins technological vulnerability. Im just kind of fed up with the system, he tells me over coffee in a slick caf and co-working space from where Gibson does most of his work remotely.

I dont think economies should be built on inflated numbers, and I think its kind of ridiculous that everybody relies on this inflated number in their bank account when its definitely not there bitcoin and other cryptocurrencies are making it so that we are our own banks, and thats one less things we have to worry about. Gibson owns two companies in China, and as far as possible uses bitcoin for all his daily expenses, converting the personal profits he makes into bitcoin to avoid using banks.

One of the commonly cited weaknesses in the bitcoin system is that if you lose your private key to access your bitcoin wallet, the bitcoin within are lost forever. In 2015, it was estimated that up to 30% of all mined bitcoins had been lost, with a value of 625m. Unsurprisingly, plenty of people see this as an opportunity to make some money.

Sun Zeyu, 27, works at a tech start-up based near Beijings university district that specialises in bitcoin. His latest project is Coldlar, an offline, physical wallet that stores users bitcoin and can be accessed by scanning a QR code. Bitcoin security is a tough question, Sun tells me, which is why he and his colleagues designed a product that allows people to circumvent bitcoin platforms and have even greater control over their bitcoin. Now that the value [of bitcoin] is going up, he explains, people really realise the importance of security.

Before, when we just traded one or two coins, people didnt mind, [but] now the value of bitcoin is much bigger. Sun got involved with bitcoin while at university after attending a seminar run by Huobi, one of the biggest trading platforms in China. Like his flashier friend Yang, Sun wanted money, and lots of it. He wont tell me exactly how much he earns, but assures me that its hundreds or thousands times more than the 10,000 yuan per month he was earning when he first dabbled in bitcoin three years ago.

His money comes from both his trading activity and his company salary. With the growth of bitcoin and related products like his Coldlar wallet, Sun believes that in 10 years time, the value of the cryptocurrency will be one bitcoin, one house in Beijing. Minor shocks to the system, like the recent suspension of bitcoin withdrawals in China, are just like breathing, he insists, and the inhalations of profit dwarf any other bumps in the road.

Sun
Sun Zeyu at work. Photograph: Naomi Goddard for the Guardian

Despite the solitary nature of their work, Yang, Sun, Gibson and Chenxing are all sociable creatures. Gibson is connected to hundreds of bitcoin aficionados in China, and has introduced close to 1,000 new people to the technology (although how many are like me, with 2.50 lying dormant in an unused wallet, is unknown), such is his enthusiasm for the cryptocurrency. Chenxing cites the social side of the bitcoin scene in Beijing as one of the main attractions of staying in the industry and the city.

I can meet some fun people who really love bitcoin I think most of the people who like bitcoin are people who like freedom he says. Yang, however, takes a slightly harder-edged approach. He has little patience for sceptics: Yes, bitcoin is a risk. Why should I have to discuss these things with [people concerned about the security]? I earn my money, thats enough. I dont waste my time explaining bitcoin [if] youre not my client. In some ways, Yang concedes, the less people understand bitcoin, the better it is for him. At the moment, the industry is like an ATM for him and his peers, and hes perfectly happy for things to stay that way.

In the fast-changing world of the crypto-currency, nothing seems to stay the same for long. Whether its unpredictable government interventions, or debates within the community about how the industry can and should be scaled, general growth in value thus fair doesnt necessarily suggest anything about the future of bitcoin, despite the faith of its adherents. Gibson makes the point that bitcoin has only been around for nine years; it took PayPal at least 10 to properly catch on.

In Japan it has recently been recognised as legal tender. Its unlikely that the same could ever happen in China, no matter how much its popularity continues to balloon. Chenxing, who has years of insider experience, is sure that [the government] will never accept a thing thats not built by themselves. Many bitcoin traders in China are in it for the long haul, confident that they can ride out any governmental interferences, as long as they have access to the internet. Chenxing, however, is more paranoid. His final thoughts on bitcoin are: I never feel secure.

Read more: https://www.theguardian.com/technology/2017/apr/11/meet-the-millennials-making-big-money-riding-chinas-bitcoin-wave

Your animal life is over. Machine life has begun. The road to immortality

In California, radical scientists and billionaire backers think the technology to extend life by uploading minds to exist separately from the body is only a few years away

Heres what happens. You are lying on an operating table, fully conscious, but rendered otherwise insensible, otherwise incapable of movement. A humanoid machine appears at your side, bowing to its task with ceremonial formality. With a brisk sequence of motions, the machine removes a large panel of bone from the rear of your cranium, before carefully laying its fingers, fine and delicate as a spiders legs, on the viscid surface of your brain. You may be experiencing some misgivings about the procedure at this point. Put them aside, if you can.

Youre in pretty deep with this thing; theres no backing out now. With their high-resolution microscopic receptors, the machine fingers scan the chemical structure of your brain, transferring the data to a powerful computer on the other side of the operating table. They are sinking further into your cerebral matter now, these fingers, scanning deeper and deeper layers of neurons, building a three-dimensional map of their endlessly complex interrelations, all the while creating code to model this activity in the computers hardware. As thework proceeds, another mechanical appendage less delicate, less careful removes the scanned material to a biological waste container for later disposal. This is material you will no longer be needing.

At some point, you become aware that you are no longer present in your body. You observe with sadness, or horror, or detached curiosity the diminishing spasms of that body on the operating table, the last useless convulsions of a discontinued meat.

The animal life is over now. The machine life has begun.

This, more or less, is the scenario outlined by Hans Moravec, a professor of cognitive robotics at Carnegie Mellon, in his 1988 book Mind Children: The Future of Robot and Human Intelligence. It is Moravecs conviction that the future of the human species will involve a mass-scale desertion of our biological bodies, effected by procedures of this kind. Its a belief shared by many transhumanists, a movement whose aim is to improve our bodies and minds to the point where we become something other and better than the animals we are. Ray Kurzweil, for one, is a prominent advocate of the idea of mind-uploading. An emulation of the human brain running on an electronic system, he writes in The Singularity Is Near, would run much faster than our biological brains. Although human brains benefit from massive parallelism (on the order of 100 trillion interneuronal connections, all potentially operating simultaneously), the rest time of the connections is extremely slow compared to contemporary electronics. The technologies required for such an emulation sufficiently powerful and capacious computers and sufficiently advanced brainscanning techniques will be available, he announces, by the early 2030s.

And this, obviously, is no small claim. We are talking about not just radically extended life spans, but also radically expanded cognitive abilities. We are talking about endless copies and iterations of the self. Having undergone a procedure like this, you would exist to the extent you could meaningfully be said to exist at all as an entity of unbounded possibilities.

I was introduced to Randal Koene at a Bay Area transhumanist conference. He wasnt speaking at the conference, but had come along out of personal interest. A cheerfully reserved man in his early 40s, he spoke in the punctilious staccato of a non-native English speaker who had long mastered the language. As we parted, he handed me his business card and much later that evening Iremoved it from my wallet and had a proper look at it. The card was illustrated with a picture of a laptop, on whose screen was displayed a stylised image of a brain. Underneath was printed what seemed to me an attractively mysterious message: Carboncopies: Realistic Routes to Substrate Independent Minds. Randal A Koene, founder.

I took out my laptop and went to the website of Carboncopies, which I learned was a nonprofit organisation with a goal of advancing the reverse engineering of neural tissue and complete brains, Whole Brain Emulation and development of neuroprostheses that reproduce functions of mind, creating what we call Substrate Independent Minds. This latter term, I read, was the objective to be able to sustain person-specific functions of mind and experience in many different operational substrates besides the biological brain. And this, I further learned, was a process analogous to that by which platform independent code can be compiled and run on many different computing platforms.

It seemed that I had met, without realising it, a person who was actively working toward the kind of brain-uploading scenario that Kurzweil had outlined in The Singularity Is Near. And this was a person I needed to get to know.

Randal
Randal Koene: It wasnt like I was walking into labs, telling people I wanted to upload human minds to computers.

Koene was an affable and precisely eloquent man and his conversation was unusually engaging for someone so forbiddingly intelligent and who worked in so rarefied a field as computational neuroscience; so, in his company, I often found myself momentarily forgetting about the nearly unthinkable implications of the work he was doing, the profound metaphysical weirdness of the things he was explaining to me. Hed be talking about some tangential topic his happily cordial relationship with his ex-wife, say, or the cultural differences between European and American scientific communities and Id remember with a slow, uncanny suffusion of unease that his work, were it to yield the kind of results he is aiming for, would amount to the most significant event since the evolution of Homo sapiens. The odds seemed pretty long from where I was standing, but then again, I reminded myself, the history of science was in many ways an almanac of highly unlikely victories.

One evening in early spring, Koene drove down to San Francisco from the North Bay, where he lived and worked in a rented ranch house surrounded by rabbits, to meet me for dinner in a small Argentinian restaurant on Columbus Avenue. The faint trace of an accent turned out to be Dutch. Koene was born in Groningen and had spent most of his early childhood in Haarlem. His father was a particle physicist and there were frequent moves, including a two-year stint in Winnipeg, as he followed his work from one experimental nuclear facility to the next.

Now a boyish 43, he had lived in California only for the past five years, but had come to think of it as home, or the closest thing to home hed encountered in the course of a nomadic life. And much of this had to do with the culture of techno-progressivism that had spread outward from its concentrated origins in Silicon Valley and come to encompass the entire Bay Area, with its historically high turnover of radical ideas. It had been a while now, he said, since hed described his work to someone, only for them to react as though he were making a misjudged joke or simply to walk off mid-conversation.

In his early teens, Koene began to conceive of the major problem with the human brain in computational terms: it was not, like a computer, readable and rewritable. You couldnt get in there and enhance it, make it run more efficiently, like you could with lines of code. You couldnt just speed up a neuron like you could with a computer processor.

Around this time, he read Arthur C Clarkes The City and the Stars, a novel set a billion years from now, in which the enclosed city of Diaspar is ruled by a superintelligent Central Computer, which creates bodies for the citys posthuman citizens and stores their minds in its memory banks at the end of their lives, for purposes of reincarnation. Koene saw nothing in this idea of reducing human beings to data that seemed to him implausible and felt nothing in himself that prevented him from working to bring it about. His parents encouraged him in this peculiar interest and the scientific prospect of preserving human minds in hardware became a regular topic of dinnertime conversation.

Computational neuroscience, which drew its practitioners not from biology but from the fields of mathematics and physics, seemed to offer the most promising approach to the problem of mapping and uploading the mind. It wasnt until he began using the internet in the mid-1990s, though, that he discovered a loose community of people with an interest in the same area.

As a PhD student in computational neuroscience at Montreals McGill University, Koene was initially cautious about revealing the underlying motivation for his studies, for fear of being taken for a fantasist or an eccentric.

I didnt hide it, as such, he said, but it wasnt like I was walking into labs, telling people I wanted to upload human minds to computers either. Id work with people on some related area, like the encoding of memory, with a view to figuring out how that might fit into an overall road map for whole brain emulation.

Having worked for a while at Halcyon Molecular, a Silicon Valley gene-sequencing and nanotechnology startup funded by Peter Thiel, he decided to stay in the Bay Area and start his own nonprofit company aimed at advancing the cause to which hed long been dedicated: carboncopies

Koenes decision was rooted in the very reason he began pursuing that work in the first place: an anxious awareness of the small and diminishing store of days that remained to him. If hed gone the university route, hed have had to devote most of his time, at least until securing tenure, to projects that were at best tangentially relevant to his central enterprise. The path he had chosen was a difficult one for a scientist and he lived and worked from one small infusion of private funding to the next.

But Silicon Valleys culture of radical techno-optimism had been its own sustaining force for him, and a source of financial backing for a project that took its place within the wildly aspirational ethic of that cultural context. There were people there or thereabouts, wealthy and influential, for whom a future in which human minds might be uploaded to computers was one to be actively sought, a problem to be solved, disruptively innovated, by the application of money.

Transcendence
Brainchild of the movies: in Transcendence (2014), scientist Will Caster, played by Johnny Depp, uploads his mind to a computer program with dangerous results.

One such person was Dmitry Itskov, a 36-year-old Russian tech multimillionaire and founder of the 2045 Initiative, an organisationwhose stated aim was to create technologies enabling the transfer of an individuals personality to a more advanced nonbiological carrier, and extending life, including to the point of immortality. One of Itskovs projects was the creation of avatars artificial humanoid bodies that would be controlled through brain-computer interface, technologies that would be complementary with uploaded minds. He had funded Koenes work with Carboncopies and in 2013 they organised a conference in New York called Global Futures 2045, aimed, according to its promotional blurb, at the discussion of a new evolutionary strategy for humanity.

When we spoke, Koene was working with another tech entrepreneur named Bryan Johnson, who had sold his automated payment company to PayPal a couple of years back for $800m and who now controlled a venture capital concern called the OS Fund, which, I learned from its website, invests in entrepreneurs working towards quantum leap discoveries that promise to rewrite the operating systems of life. This language struck me as strange and unsettling in a way that revealed something crucial about the attitude toward human experience that was spreading outward from its Bay Area centre a cluster of software metaphors that had metastasised into a way of thinking about what it meant to be a human being.

And it was the sameessential metaphor that lay at the heart of Koenes project: the mind as a piece of software, an application running on the platform of flesh. When he used the term emulation, he was using it explicitly to evoke the sense in which a PCs operating system could be emulated on a Mac, as what he called platform independent code.

The relevant science for whole brain emulation is, as youd expect, hideously complicated, and its interpretation deeply ambiguous, but if I can risk a gross oversimplification here, I will say that it is possible to conceive of the idea as something like this: first, you scan the pertinent information in a persons brain the neurons, the endlessly ramifying connections between them, the information-processing activity of which consciousness is seen as a byproduct through whatever technology, or combination of technologies, becomes feasible first (nanobots, electron microscopy, etc). That scan then becomes a blueprint for the reconstruction of the subject brains neural networks, which is then converted into a computational model. Finally, you emulate all of this on a third-party non-flesh-based substrate: some kind of supercomputer or a humanoid machine designed to reproduce and extend the experience of embodiment something, perhaps, like Natasha Vita-Mores Primo Posthuman.

The whole point of substrate independence, as Koene pointed out to me whenever I asked him what it would be like to exist outside of a human body, and I asked him many times, in various ways was that it would be like no one thing, because there would be no one substrate, no one medium of being. This was the concept transhumanists referred to as morphological freedom the liberty to take any bodily form technology permits.

You can be anything you like, as an article about uploading in Extropy magazine put it in the mid-90s. You can be big or small; you can be lighter than air and fly; you can teleport and walk through walls. You can be a lion or an antelope, a frog or a fly, a tree, a pool, the coat of paint on a ceiling.

What really interested me about this idea was not how strange and far-fetched it seemed (though it ticked those boxes resolutely enough), but rather how fundamentally identifiable it was, how universal. When talking to Koene, I was mostly trying to get to grips with the feasibility of the project and with what it was he envisioned as a desirable outcome. But then we would part company I would hang up the call, or I would take my leave and start walking toward the nearest station and I would find myself feeling strangely affected by the whole project, strangely moved.

Because there was something, in the end, paradoxically and definitively human in this desire for liberation from human form. I found myself thinking often of WB Yeatss Sailing to Byzantium, in which the ageing poet writes of his burning to be free of the weakening body, the sickening heart to abandon the dying animal for the manmade and immortal form of a mechanical bird. Once out of nature, he writes, I shall never take/ My bodily form from any natural thing/ But such a form as Grecian goldsmiths make.

One evening, we were sitting outside a combination bar/laundromat/standup comedy venue in Folsom Street a place with the fortuitous name of BrainWash when I confessed that the idea of having my mind uploaded to some technological substrate was deeply unappealing to me, horrifying even. The effects of technology on my life, even now, were something about which I was profoundly ambivalent; for all I had gained in convenience and connectedness, I was increasingly aware of the extent to which my movements in the world were mediated and circumscribed by corporations whose only real interest was in reducing the lives of human beings to data, as a means to further reducing us to profit.

The content we consumed, the people with whom we had romantic encounters, the news we read about the outside world: all these movements were coming increasingly under the influence of unseen algorithms, the creations of these corporations, whose complicity with government, moreover, had come to seem like the great submerged narrative of our time. Given the world we were living in, where the fragile liberal ideal of the autonomous self was already receding like a half-remembered dream into the doubtful haze of history, wouldnt a radical fusion of ourselves with technology amount, in the end, to a final capitulation of the very idea of personhood?

Koene nodded again and took a sip of his beer.

Hearing you say that, he said, makes it clear that theres a major hurdle there for people. Im more comfortable than you are with the idea, but thats because Ive been exposed to it for so long that Ive just got used to it.

Dmitry
Russian billionaire Dmitry Itskov wants to create technologies enabling the transfer of an individuals personality to a more advanced nonbiological carrier. Photograph: Mary Altaffer/AP

In the weeks and months after I returned from San Francisco, I thought obsessively about the idea of whole brain emulation. One morning, I was at home in Dublin, suffering from both a head cold and a hangover. I lay there, idly considering hauling myself out of bed to join my wife and my son, who were in his bedroom next door enjoying a raucous game of Buckaroo. I realised that these conditions (head cold, hangover) had imposed upon me a regime of mild bodily estrangement. As often happens when Im feeling under the weather, I had a sense of myself as an irreducibly biological thing, an assemblage of flesh and blood and gristle. I felt myself to be an organism with blocked nasal passages, a bacteria-ravaged throat, a sorrowful ache deep within its skull, its cephalon. I was aware of my substrate, in short, because my substrate felt like shit.

And I was gripped by a sudden curiosity as to what, precisely, that substrate consisted of, as to what I myself happened, technically speaking, to be. I reached across for the phone on my nightstand and entered into Google the words What is the human… The first three autocomplete suggestions offered What is The Human Centipede about, and then: What is the human body made of, and then: What is the human condition.

It was the second question I wanted answered at this particular time, as perhaps a back door into the third. It turned out that I was 65% oxygen, which is to say that I was mostly air, mostly nothing. After that, I was composed of diminishing quantities of carbon and hydrogen, of calcium and sulphur and chlorine, and so on down the elemental table. I was also mildly surprised to learn that, like the iPhone I was extracting this information from, I also contained trace elements of copper and iron and silicon.

What a piece of work is a man, I thought, what a quintessence of dust.

Some minutes later, my wife entered the bedroom on her hands and knees, our son on her back, gripping the collar of her shirt tight in his little fists. She was making clip-clop noises as she crawled forward, he was laughing giddily and shouting: Dont buck! Dont buck!

With a loud neighing sound, she arched her back and sent him tumbling gently into a row of shoes by the wall and he screamed in delighted outrage, before climbing up again. None of this, I felt, could be rendered in code. None of this, I felt, could be run on any other substrate. Their beauty was bodily, in the most profound sense, in the saddest and most wonderful sense.

I never loved my wife and our little boy more, I realised, than when I thought of them as mammals. I dragged myself, my animal body, out of bed to join them.

To Be a Machine by Mark OConnell is published by Granta (12.99). To order a copy for 11.04 go to bookshop.theguardian.com or call 0330 333 6846. Free UK p&p over 10, online orders only. Phone orders min p&p of 1.99

Read more: https://www.theguardian.com/science/2017/mar/25/animal-life-is-over-machine-life-has-begun-road-to-immortality

Robert Mercer: the big data billionaire waging war on mainstream media

With links to Donald Trump, Steve Bannon and Nigel Farage, the rightwing American computer scientist is at the heart of a multimillion-dollar propaganda network

Just over a week ago, Donald Trump gathered members of the worlds press before him and told them they were liars. The press, honestly, is out of control, he said. The public doesnt believe you any more. CNN was described as very fake news story after story is bad. The BBC was another beauty.

That night I did two things. First, I typed Trump in the search box of Twitter. My feed was reporting that he was crazy, a lunatic, a raving madman. But that wasnt how it was playing out elsewhere. The results produced a stream of Go Donald!!!!, and You show em!!! There were star-spangled banner emojis and thumbs-up emojis and clips of Trump laying into the FAKE news MSM liars!

Trump had spoken, and his audience had heard him. Then I did what Ive been doing for two and a half months now. I Googled mainstream media is And there it was. Googles autocomplete suggestions: mainstream media is dead, dying, fake news, fake, finished. Is it dead, I wonder? Has FAKE news won? Are we now the FAKE news? Is the mainstream media we, us, I dying?

I click Googles first suggested link. It leads to a website called CNSnews.com and an article: The Mainstream media are dead. Theyre dead, I learn, because they we, I cannot be trusted. How had it, an obscure site Id never heard of, dominated Googles search algorithm on the topic? In the About us tab, I learn CNSnews is owned by the Media Research Center, which a click later I learn is Americas media watchdog, an organisation that claims an unwavering commitment to neutralising leftwing bias in the news, media and popular culture.

Another couple of clicks and I discover that it receives a large bulk of its funding more than $10m in the past decade from a single source, the hedge fund billionaire Robert Mercer. If you follow US politics you may recognise the name. Robert Mercer is the money behind Donald Trump. But then, I will come to learn, Robert Mercer is the money behind an awful lot of things. He was Trumps single biggest donor. Mercer started backing Ted Cruz, but when he fell out of the presidential race he threw his money $13.5m of it behind the Trump campaign.

Its money hes made as a result of his career as a brilliant but reclusive computer scientist. He started his career at IBM, where he made what the Association for Computational Linguistics called revolutionary breakthroughs in language processing a science that went on to be key in developing todays AI and later became joint CEO of Renaissance Technologies, a hedge fund that makes its money by using algorithms to model and trade on the financial markets.

One of its funds, Medallion, which manages only its employees money, is the most successful in the world generating $55bn so far. And since 2010, Mercer has donated $45m to different political campaigns all Republican and another $50m to non-profits all rightwing, ultra-conservative. This is a billionaire who is, as billionaires are wont, trying to reshape the world according to his personal beliefs.

Donald
Donald Trumps presidential campaigned received $13.5m from Robert Mercer. Photograph: Timothy A Clary/AFP/Getty Images

Robert Mercer very rarely speaks in public and never to journalists, so to gauge his beliefs you have to look at where he channels his money: a series of yachts, all called Sea Owl; a $2.9m model train set; climate change denial (he funds a climate change denial thinktank, the Heartland Institute); and what is maybe the ultimate rich mans plaything the disruption of the mainstream media. In this he is helped by his close associate Steve Bannon, Trumps campaign manager and now chief strategist. The money he gives to the Media Research Center, with its mission of correcting liberal bias is just one of his media plays. There are other bigger, and even more deliberate strategies, and shining brightly, the star at the centre of the Mercer media galaxy, is Breitbart.

It was $10m of Mercers money that enabled Bannon to fund Breitbart a rightwing news site, set up with the express intention of being a Huffington Post for the right. It has launched the careers of Milo Yiannopoulos and his like, regularly hosts antisemitic and Islamophobic views, and is currently being boycotted by more than 1,000 brands after an activist campaign. It has been phenomenally successful: the 29th most popular site in America with 2bn page views a year. Its bigger than its inspiration, the Huffington Post, bigger, even, than PornHub. Its the biggest political site on Facebook. The biggest on Twitter.

Prominent rightwing journalist Andrew Breitbart, who founded the site but died in 2012, told Bannon that they had to take back the culture. And, arguably, they have, though American culture is only the start of it. In 2014, Bannon launched Breitbart London, telling the New York Times it was specifically timed ahead of the UKs forthcoming election. It was, he said, the latest front in our current cultural and political war. France and Germany are next.

But there was another reason why I recognised Robert Mercers name: because of his connection to Cambridge Analytica, a small data analytics company. He is reported to have a $10m stake in the company, which was spun out of a bigger British company called SCL Group. It specialises in election management strategies and messaging and information operations, refined over 25 years in places like Afghanistan and Pakistan. In military circles this is known as psyops psychological operations. (Mass propaganda that works by acting on peoples emotions.)

Cambridge Analytica worked for the Trump campaign and, so Id read, the Leave campaign. When Mercer supported Cruz, Cambridge Analytica worked with Cruz. When Robert Mercer started supporting Trump, Cambridge Analytica came too. And where Mercers money is, Steve Bannon is usually close by: it was reported that until recently he had a seat on the board.

Last December, I wrote about Cambridge Analytica in a piece about how Googles search results on certain subjects were being dominated by rightwing and extremist sites. Jonathan Albright, a professor of communications at Elon University, North Carolina, who had mapped the news ecosystem and found millions of links between rightwing sites strangling the mainstream media, told me that trackers from sites like Breitbart could also be used by companies like Cambridge Analytica to follow people around the web and then, via Facebook, target them with ads.

On its website, Cambridge Analytica makes the astonishing boast that it has psychological profiles based on 5,000 separate pieces of data on 220 million American voters its USP is to use this data to understand peoples deepest emotions and then target them accordingly. The system, according to Albright, amounted to a propaganda machine.

A few weeks later, the Observer received a letter. Cambridge Analytica was not employed by the Leave campaign, it said. Cambridge Analytica is a US company based in the US. It hasnt worked in British politics.

Which is how, earlier this week, I ended up in a Pret a Manger near Westminster with Andy Wigmore, Leave.EUs affable communications director, looking at snapshots of Donald Trump on his phone. It was Wigmore who orchestrated Nigel Farages trip to Trump Tower the PR coup that saw him become the first foreign politician to meet the president elect.

Wigmore scrolls through the snaps on his phone. Thats the one I took, he says pointing at the now globally famous photo of Farage and Trump in front of his golden elevator door giving the thumbs-up sign. Wigmore was one of the bad boys of Brexit a term coined by Arron Banks, the Bristol-based businessman who was Leave.EUs co-founder.

Cambridge Analytica had worked for them, he said. It had taught them how to build profiles, how to target people and how to scoop up masses of data from peoples Facebook profiles. A video on YouTube shows one of Cambridge Analyticas and SCLs employees, Brittany Kaiser, sitting on the panel at Leave.EUs launch event.

Facebook was the key to the entire campaign, Wigmore explained. A Facebook like, he said, was their most potent weapon. Because using artificial intelligence, as we did, tells you all sorts of things about that individual and how to convince them with what sort of advert. And you knew there would also be other people in their network who liked what they liked, so you could spread. And then you follow them. The computer never stops learning and it never stops monitoring.

Steve
Steve Bannon, Donald Trumps chief strategist, is an associate of Robert Mercer. Photograph: Evan Vucci/AP

It sounds creepy, I say.

It is creepy! Its really creepy! Its why Im not on Facebook! I tried it on myself to see what information it had on me and I was like, Oh my God! Whats scary is that my kids had put things on Instagram and it picked that up. It knew where my kids went to school.

They hadnt employed Cambridge Analytica, he said. No money changed hands. They were happy to help.

Why?

Because Nigel is a good friend of the Mercers. And Robert Mercer introduced them to us. He said, Heres this company we think may be useful to you. What they were trying to do in the US and what we were trying to do had massive parallels. We shared a lot of information. Why wouldnt you? Behind Trumps campaign and Cambridge Analytica, he said, were the same people. Its the same family.

There were already a lot of questions swirling around Cambridge Analytica, and Andy Wigmore has opened up a whole lot more. Such as: are you supposed to declare services-in-kind as some sort of donation? The Electoral Commission says yes, if it was more than 7,500. And was it declared? The Electoral Commission says no. Does that mean a foreign billionaire had possibly influenced the referendum without that influence being apparent? Its certainly a question worth asking.

In the last month or so, articles in first the Swiss and the US press have asked exactly what Cambridge Analytica is doing with US voters data. In a statement to the Observer, the Information Commissioners Office said: Any business collecting and using personal data in the UK must do so fairly and lawfully. We will be contacting Cambridge Analytica and asking questions to find out how the company is operating in the UK and whether the law is being followed.

Cambridge Analytica said last Friday they are in touch with the ICO and are completely compliant with UK and EU data laws. It did not answer other questions the Observer put to it this week about how it built its psychometric model, which owes its origins to original research carried out by scientists at Cambridge Universitys Psychometric Centre, research based on a personality quiz on Facebook that went viral. More than 6 million people ended up doing it, producing an astonishing treasure trove of data.

These Facebook profiles especially peoples likes could be correlated across millions of others to produce uncannily accurate results. Michal Kosinski, the centres lead scientist, found that with knowledge of 150 likes, their model could predict someones personality better than their spouse. With 300, it understood you better than yourself. Computers see us in a more robust way than we see ourselves, says Kosinski.

But there are strict ethical regulations regarding what you can do with this data. Did SCL Group have access to the universitys model or data, I ask Professor Jonathan Rust, the centres director? Certainly not from us, he says. We have very strict rules around this.

A scientist, Aleksandr Kogan, from the centre was contracted to build a model for SCL, and says he collected his own data. Professor Rust says he doesnt know where Kogans data came from. The evidence was contrary. I reported it. An independent adjudicator was appointed by the university. But then Kogan said hed signed a non-disclosure agreement with SCL and he couldnt continue [answering questions].

Kogan disputes this and says SCL satisfied the universitys inquiries. But perhaps more than anyone, Professor Rust understands how the kind of information people freely give up to social media sites could be used.

Nigel
Former Ukip leader Nigel Farage is a friend of the Mercers. Photograph: Oli Scarff/AFP/Getty Images

The danger of not having regulation around the sort of data you can get from Facebook and elsewhere is clear. With this, a computer can actually do psychology, it can predict and potentially control human behaviour. Its what the scientologists try to do but much more powerful. Its how you brainwash someone. Its incredibly dangerous.

Its no exaggeration to say that minds can be changed. Behaviour can be predicted and controlled. I find it incredibly scary. I really do. Because nobody has really followed through on the possible consequences of all this. People dont know its happening to them. Their attitudes are being changed behind their backs.

Mercer invested in Cambridge Analytica, the Washington Post reported, driven in part by an assessment that the right was lacking sophisticated technology capabilities. But in many ways, its what Cambridge Analyticas parent company does that raises even more questions.

Emma Briant, a propaganda specialist at the University of Sheffield, wrote about SCL Group in her 2015 book, Propaganda and Counter-Terrorism: Strategies for Global Change. Cambridge Analytica has the technological tools to effect behavioural and psychological change, she said, but its SCL that strategises it. It has specialised, at the highest level for Nato, the MoD, the US state department and others in changing the behaviour of large groups. It models mass populations and then it changes their beliefs.

SCL was founded by someone called Nigel Oakes, who worked for Saatchi & Saatchi on Margaret Thatchers image, says Briant, and the company had been making money out of the propaganda side of the war on terrorism over a long period of time. There are different arms of SCL but its all about reach and the ability to shape the discourse. They are trying to amplify particular political narratives. And they are selective in who they go for: they are not doing this for the left.

In the course of the US election, Cambridge Analytica amassed a database, as it claims on its website, of almost the entire US voting population 220 million people and the Washington Post reported last week that SCL was increasing staffing at its Washington office and competing for lucrative new contracts with Trumps administration. It seems significant that a company involved in engineering a political outcome profits from what follows. Particularly if its the manipulation, and then resolution, of fear, says Briant.

Its the database, and what may happen to it, that particularly exercises Paul-Olivier Dehaye, a Swiss mathematician and data activist who has been investigating Cambridge Analytica and SCL for more than a year. How is it going to be used? he says. Is it going to be used to try and manipulate people around domestic policies? Or to ferment conflict between different communities? It is potentially very scary. People just dont understand the power of this data and how it can be used against them.

There are two things, potentially, going on simultaneously: the manipulation of information on a mass level, and the manipulation of information at a very individual level. Both based on the latest understandings in science about how people work, and enabled by technological platforms built to bring us together.

Are we living in a new era of propaganda, I ask Emma Briant? One we cant see, and that is working on us in ways we cant understand? Where we can only react, emotionally, to its messages? Definitely. The way that surveillance through technology is so pervasive, the collection and use of our data is so much more sophisticated. Its totally covert. And people dont realise what is going on.

Public mood and politics goes through cycles. You dont have to subscribe to any conspiracy theory, Briant says, to see that a mass change in public sentiment is happening. Or that some of the tools in action are straight out of the militarys or SCLs playbook.

But then theres increasing evidence that our public arenas the social media sites where we post our holiday snaps or make comments about the news are a new battlefield where international geopolitics is playing out in real time. Its a new age of propaganda. But whose? This week, Russia announced the formation of a new branch of the military: information warfare troops.

Sam Woolley of the Oxford Internet Institutes computational propaganda institute tells me that one third of all traffic on Twitter before the EU referendum was automated bots accounts that are programmed to look like people, to act like people, and to change the conversation, to make topics trend. And they were all for Leave. Before the US election, they were five-to-one in favour of Trump many of them Russian. Last week they have been in action in the Stoke byelection Russian bots, organised by who? attacking Paul Nuttall.

Politics is war, said Steve Bannon last year in the Wall Street Journal. And increasingly this looks to be true.

Theres nothing accidental about Trumps behaviour, Andy Wigmore tells me. That press conference. It was absolutely brilliant. I could see exactly what he was doing. Theres feedback going on constantly. Thats what you can do with artificial intelligence. You can measure ever reaction to every word. He has a word room, where you fix key words. We did it. So with immigration, there are actually key words within that subject matter which people are concerned about. So when you are going to make a speech, its all about how can you use these trending words.

Wigmore met with Trumps team right at the start of the Leave campaign. And they said the holy grail was artificial intelligence.

Who did?

Jared Kushner and Jason Miller.

Later, when Trump picked up Mercer and Cambridge Analytica, the game changed again. Its all about the emotions. This is the big difference with what we did. They call it bio-psycho-social profiling. It takes your physical, mental and lifestyle attributes and works out how people work, how they react emotionally.

Bio-psycho-social profiling, I read later, is one offensive in what is called cognitive warfare. Though there are many others: recoding the mass consciousness to turn patriotism into collaborationism, explains a Nato briefing document on countering Russian disinformation written by an SCL employee. Time-sensitive professional use of media to propagate narratives, says one US state department white paper. Of particular importance to psyop personnel may be publicly and commercially available data from social media platforms.

Yet another details the power of a cognitive casualty a moral shock that has a disabling effect on empathy and higher processes such as moral reasoning and critical thinking. Something like immigration, perhaps. Or fake news. Or as it has now become: FAKE news!!!!

How do you change the way a nation thinks? You could start by creating a mainstream media to replace the existing one with a site such as Breitbart. You could set up other websites that displace mainstream sources of news and information with your own definitions of concepts like liberal media bias, like CNSnews.com. And you could give the rump mainstream media, papers like the failing New York Times! what it wants: stories. Because the third prong of Mercer and Bannons media empire is the Government Accountability Institute.

Bannon co-founded it with $2m of Mercers money. Mercers daughter, Rebekah, was appointed to the board. Then they invested in expensive, long-term investigative journalism. The modern economics of the newsroom dont support big investigative reporting staffs, Bannon told Forbes magazine. You wouldnt get a Watergate, a Pentagon Papers today, because nobody can afford to let a reporter spend seven months on a story. We can. Were working as a support function.

Welcome to the future of journalism in the age of platform capitalism. News organisations have to do a better job of creating new financial models. But in the gaps in between, a determined plutocrat and a brilliant media strategist can, and have, found a way to mould journalism to their own ends.

In 2015, Steve Bannon described to Forbes how the GAI operated, employing a data scientist to trawl the dark web (in the article he boasts of having access to $1.3bn worth of supercomputers) to dig up the kind of source material Google cant find. One result has been a New York Times bestseller, Clinton Cash: The Untold Story of How and Why Foreign Governments and Businesses Helped Make Bill and Hillary Rich, written by GAIs president, Peter Schweizer and later turned into a film produced by Rebekah Mercer and Steve Bannon.

This, Bannon explained, is how you weaponise the narrative you want. With hard researched facts. With those, you can launch it straight on to the front page of the New York Times, as the story of Hillary Clintons cash did. Like Hillarys emails it turned the news agenda, and, most crucially, it diverted the attention of the news cycle. Another classic psyops approach. Strategic drowning of other messages.

This is a strategic, long-term and really quite brilliant play. In the 1990s, Bannon explained, conservative media couldnt take Bill Clinton down becausethey wound up talking to themselves in an echo chamber.

As, it turns out, the liberal media is now. We are scattered, separate, squabbling among ourselves and being picked off like targets in a shooting gallery. Increasingly, theres a sense that we are talking to ourselves. And whether its Mercers millions or other factors, Jonathan Albrights map of the news and information ecosystem shows how rightwing sites are dominating sites like YouTube and Google, bound tightly together by millions of links.

Is there a central intelligence to that, I ask Albright? There has to be. There has to be some type of coordination. You can see from looking at the map, from the architecture of the system, that this is not accidental. Its clearly being led by money and politics.

Theres been a lot of talk in the echo chamber about Bannon in the last few months, but its Mercer who provided the money to remake parts of the media landscape. And while Bannon understands the media, Mercer understands big data. He understands the structure of the internet. He knows how algorithms work.

Robert Mercer did not respond to a request for comment for this piece. NickPatterson, a British cryptographer, who worked at Renaissance Technologies in the 80s and is now a computational geneticist at MIT, described to me how he was the one who talent-spotted Mercer. There was an elite group working at IBM in the 1980s doing speech research, speech recognition, and when I joined Renaissance I judged that the mathematics we were trying to apply to financial markets were very similar.

Read more: https://www.theguardian.com/politics/2017/feb/26/robert-mercer-breitbart-war-on-media-steve-bannon-donald-trump-nigel-farage

Is Snapchat the new Facebook?

As Snapchat plans the most eagerly anticipated technology IPO since its older rival floated in 2012, Rupert Neate examines the two companies striking similarities

Snapchat hopes its planned flotation in New York will value the five-year-old photo-sharing app company at up to $25bn (20bn) and turn its 26-year-old founder, Evan Spiegel, into the worlds youngest billionaire with a $5.5bn fortune. It is the most eagerly anticipated technology initial public offering (IPO) since Facebook floated in 2012 turning its then 28-year-old founder, Mark Zuckerberg, into the worlds richest man under 30. The similarities between Snap (the official name for the company that owns Snapchat) and Facebook are striking, and have got many financial analysts and advertising experts asking if Snapchat is the new Facebook.

How did they start?

Zuckerberg and Spiegel hit upon the ideas for their companies at university and then dropped out. Zuckerberg, a computer science major, began knocking up a website called Facemash, loosely based on Hot or Not, in his Harvard dorm room. The site evolved intoFacebook but not without a legal challenge from the Winklevoss twins, who sued Zuckerberg claiming he stole their idea.

Evan
Evan Spiegel could become the worlds youngest billionaire. Photograph: Jae C. Hong/AP

Snapchat was born out of banter between Spiegel and his Stanford fraternity brothers Frank Reginald Brown and Bobby Murphy. In 2011 the trio were discussing sexting and the need for a way to send pictures that disappeared.

As with Facebook, the genesis of the idea was disputed, and Brown sued Spiegel and the company. They settled out of court, with Spiegel, who was studying product design, and Murphy, a mathematics and computational science major, remaining majority shareholders with a 22.4% stake each.

How many users do they have?

Facebook had 900m users as it prepared for its 2012 flotation. Since then the social network has grown to 1.86bn monthly active users more than half the worlds population that has access to the internet. About 1.2bn check their Facebook accounts every day.

Snapchat has far fewer users, but the company claims they are much more engaged than Facebooks. Snapchat had 158m daily users at the last count. Two-thirds of them check the app every day and the average daily user visits the app 18 times a day, spending an average of 25-30 minutes a day sending snaps and watching snaps from their friends, celebrities and advertising brands.

Snapchat is only accessible via mobile phone. Snapchat claims to reach 41% of all 18- to 34-year-olds in the US each day.

How much are they worth?

Facebook has a market value of $373bn more than twice that of IBM. Facebook was valued at $104bn when it floated at $38 a share on the NYSE on 18 May 2012. Today the shares are changing hands at $131.

Facebook tried to buy Snapchat several times, and Spiegel has said Zuckerberg tried to force him to sell up. It was basically like, Were going to crush you, Spiegel told Forbes magazine. Spiegel rejected Zuckerbergs last $3bn takeover in November 2013. Facebook, which also owns Instagram, has since developed versions of 15 of Snapchats features.

Snaps IPO filings show the company is planning to float its shares at a level that would value the companyat $20-25bn.

How much control do the founders have?

Snapchats flotation is unusual. The company is not selling any voting shares, so the founders will controversially keep total control of the firm even after raising public money. Facebook has a voting structure that gives the founders far more rights than other shareholders.

How much money do they make?

Facebook made a profit of $10.2bn in 2016, up 177% on 2015. Its total advertising income was almost $27bn. But Facebook only turned its first profit in 2009.

Snap, which is spending a lot of money on expanding its user base, made a net loss of $515m in 2016 up on the $373m it lost in 2015.

Where does the income come from?

Both companies make their money from advertising at the expense of traditional advertising markets such as newspapers and TV.

Sir Martin Sorrell, chief executive of WPP, the worlds largest advertising company, has said his clients spent $1.7bn advertising on Facebook last year. That compares to $5bn WPP clients spent on Google ads, but is vastly more than the $90m spent on Snapchat.

Neil Campling, head of global technology research at Northern Trust Capital Markets, told CNBC: Snapchat is likely on a faster growth path than either Google or Facebook. Their opportunity is enormous and just beginning.

Snapchat has tried to differentiate itself from Facebook by not allowing adverts targeted directly at users interests or browsing history. I got an ad this morning for something I was thinking about buying yesterday, and its really annoying. We care about not being creepy, Spiegel said in 2015.

Facebooks advertising is sold entirely by computer program. Advertisers can visit ads.facebook.com, plug in payment information and create an advert. Those ads can be targeted as narrowly or broadly as the advertiser desires, and can be billed in a variety of ways, from paying a flat fee for every thousand views to payments per click, per like and more.

Where are they based?

Facebooks huge headquarters in Silicon Valleys Menlo Park has a green roof the size of seven American football pitches. The company employs more than 17,000 people across the world.

Read more: https://www.theguardian.com/media/2017/feb/03/is-snapchat-the-new-facebook

Here’s just how early gender stereotypes start to affect kids

Image: Christopher Mineses/mashable

Who is “really, really smart?” Boys or girls?

A new study found that young U.S. girls are less likely than boys to believe their own gender is the most brilliant.

While all 5-year-olds tended to believe that members of their own gender were geniuses, by age 6 that preference had diminished for girls a difference the researchers attributed to the influence of gender stereotypes.

“We found it surprising, and also very heartbreaking, that even kids at such a young age have learned these stereotypes,” said Lin Bian, the study’s co-author and a doctoral candidate at the University of Illinois at Urbana-Champaign.

A girl looks through a microscope during the 2016 Russian Festival of Science in Moscow.

Image: Vladimir Trefilov/Sputnik via AP

“It’s possible that in the long run, the stereotypes will push young women away from the jobs that are perceived as requiring brilliance, like being a scientist or an engineer,” she told Mashable.

A growing field

The study, published Thursday in the journal Science, builds on a growing body of research that suggests gender stereotypes can shape children’s interest and career ambitions at a young age.

A global study by the Organization for Economic Cooperation and Development found that girls “lack self-confidence” in their ability to solve math and science problems and thus score worse than they would otherwise, which discourages them from pursuing science, engineering, technology and mathematics (STEM) fields.

A 2016 study suggested a “masculine culture” in computer science and engineering makes girls feel like they don’t belong.

Students work on a Youth Media project at a STEM-focused public school in Astoria, New York.

Image: AP Photo/The Christian Science Monitor, Ann Hermes

Thursday’s research looks not at specific skills but at the broader concept of high-level intellectual abilities. In short, can girls be geniuses, too?

Sapna Cheryan, a psychology professor at the University of Washington who was not involved in the study, said the results were “super important” because they’re among the first to show us how young children not adults or high-schoolers respond to gender stereotypes.

But she said the findings are just as revealing for young boys as for girls.

“It’s not that girls are underestimating their own gender it’s that boys are overestimating themselves,” she told Mashable. Cheryan was the lead author of last year’s masculine culture study.

What we want as a society is for people to say boys and girls are equal,” she added.

Stereotyping starts early

Andrei Cimpian, a co-author of Thursday’s study, said his earlier research with adults showed that the fields people associate with requiring a high level of smarts also tend to be overwhelmingly represented by men.

“Across the board, the more that people in a field believe you need to be brilliant, the fewer women you see in the field,” Cimpian, an associate professor of psychology at New York University, told Mashable.

This same idea burrows itself into our brains as children, the study suggests.

Researchers worked with 400 children ages 5, 6 and 7 in a series of four experiments for the new study. (Not every child participated in every experiment for the study.)

In the first experiment, the psychologists wanted to see whether children associate being “really, really smart” with men more than with women.

To answer that question, a researcher told each child an elaborate story about a person who was brilliant and quick to solve problems, without hinting at all at the person’s gender. Next, the children looked at a series of pictures of men and women and were asked to guess who from the line-up was the character in the story.

During a series of similar questions, researchers kept track of how often children chose members of their own gender as being brilliant.

Among 5-year-olds, boys picked boys a majority of the time, while girls picked girls.

“This is the heyday of the ‘cooties’ stage,” Cimpian said. “It’s consistent with what we know about in-group biases in this young age group.”

But among 6- and 7-year-olds, a divide emerged. Girls were significantly less likely to rate women as super smart than boys were to pick members of their own gender.

The age groups were similarly split in a second prompt. Researchers asked kids to pick from activities described as either suited for brilliant kids, or kids who try really hard.

Five-year-old boys and girls both showed interest in the smart-kid activities. But by age 6, girls expressed more interest in the games for hard workers, while boys kept on with the “brilliant” games.

Why is this happening?

Researchers said it’s not entirely clear how these stereotypes form. Certainly marketing towards children lab sets are for boys, dollhouses are for girls plays a role.

And history books are filled with the achievements of white men who, generally speaking, did not face the same systemic discrimination that kept women and people of color out of classrooms and laboratories.

Cimpian and Bian said they are planning a larger, longer-term study to explore how these stereotypes form and stick, and how we can correct them.

In the meantime, they suggested a few ways that parents and teachers of young children could work to dispel the biased idea that men are inherently more prone to brilliance than women.

Bian noted that previous research has shown that girls respond better to what psychologists call a “growth mindset” the idea that studying, learning and making an effort are the key ingredients for success, not a stroke of genetic luck.

“We should recommend the importance of hard work, as opposed to brilliance,” she said.

A news clipping for Katherine Johnson, a NASA mathematician who was the lead figure in the movie ‘Hidden Figures.’

Image: Joseph Rodriguez /News & Record via AP

Sharing and touting the achievements of women can also help counter the stereotypes that genius is reserved for men. Cimpian cited the book and movie Hidden Figures, about the women scientists who helped NASA astronauts get to space for the first time, as a prime example.

Cheryan, the UW psychologist, said including young boys in such efforts is critical.

“There’s a societal message that if there’s a gender gap, it’s the girls we need to fix,” she said. “We have to be careful with that message, because it just reinforces the similar hierarchy that the boys are always doing the right thing. In reality, there’s probably things that could happen on both sides.”

BONUS: 5 Gender Stereotypes That Used To Be The Opposite

Read more: http://mashable.com/2017/01/26/girls-gender-stereotype-study/

How statistics lost their power and why we should fear what comes next | William Davies

The Long Read: The ability of statistics to accurately represent the world is declining. In its wake, a new age of big data controlled by private companies is taking over and putting democracy in peril

In theory, statistics should help settle arguments. They ought to provide stable reference points that everyone no matter what their politics can agree on. Yet in recent years, divergent levels of trust in statistics has become one of the key schisms that have opened up in western liberal democracies. Shortly before the November presidential election, a study in the US discovered that 68% of Trump supporters distrusted the economic data published by the federal government. In the UK, a research project by Cambridge University and YouGov looking at conspiracy theories discovered that 55% of the population believes that the government is hiding the truth about the number of immigrants living here.

Rather than diffusing controversy and polarisation, it seems as if statistics are actually stoking them. Antipathy to statistics has become one of the hallmarks of the populist right, with statisticians and economists chief among the various experts that were ostensibly rejected by voters in 2016. Not only are statistics viewed by many as untrustworthy, there appears to be something almost insulting or arrogant about them. Reducing social and economic issues to numerical aggregates and averages seems to violate some peoples sense of political decency.

Nowhere is this more vividly manifest than with immigration. The thinktank British Future has studied how best to win arguments in favour of immigration and multiculturalism. One of its main findings is that people often respond warmly to qualitative evidence, such as the stories of individual migrants and photographs of diverse communities. But statistics especially regarding alleged benefits of migration to Britains economy elicit quite the opposite reaction. People assume that the numbers are manipulated and dislike the elitism of resorting to quantitative evidence. Presented with official estimates of how many immigrants are in the country illegally, a common response is to scoff. Far from increasing support for immigration, British Future found, pointing to its positive effect on GDP can actually make people more hostile to it. GDP itself has come to seem like a Trojan horse for an elitist liberal agenda. Sensing this, politicians have now largely abandoned discussing immigration in economic terms.

All of this presents a serious challenge for liberal democracy. Put bluntly, the British government its officials, experts, advisers and many of its politicians does believe that immigration is on balance good for the economy. The British government did believe that Brexit was the wrong choice. The problem is that the government is now engaged in self-censorship, for fear of provoking people further.

This is an unwelcome dilemma. Either the state continues to make claims that it believes to be valid and is accused by sceptics of propaganda, or else, politicians and officials are confined to saying what feels plausible and intuitively true, but may ultimately be inaccurate. Either way, politics becomes mired in accusations of lies and cover-ups.

The declining authority of statistics and the experts who analyse them is at the heart of the crisis that has become known as post-truth politics. And in this uncertain new world, attitudes towards quantitative expertise have become increasingly divided. From one perspective, grounding politics in statistics is elitist, undemocratic and oblivious to peoples emotional investments in their community and nation. It is just one more way that privileged people in London, Washington DC or Brussels seek to impose their worldview on everybody else. From the opposite perspective, statistics are quite the opposite of elitist. They enable journalists, citizens and politicians to discuss society as a whole, not on the basis of anecdote, sentiment or prejudice, but in ways that can be validated. The alternative to quantitative expertise is less likely to be democracy than an unleashing of tabloid editors and demagogues to provide their own truth of what is going on across society.

Is there a way out of this polarisation? Must we simply choose between a politics of facts and one of emotions, or is there another way of looking at this situation?One way is to view statistics through the lens of their history.We need to try and see them for what they are: neither unquestionable truths nor elite conspiracies, but rather as tools designed to simplify the job of government, for better or worse. Viewed historically, we can see what a crucial role statistics have played in our understanding of nation states and their progress. This raises the alarming question of how if at all we will continue to have common ideas of society and collective progress, should statistics fall by the wayside.


In the second half of the 17th century, in the aftermath of prolonged and bloody conflicts, European rulers adopted an entirely new perspective on the task of government, focused upon demographic trends an approach made possible by the birth of modern statistics. Since ancient times, censuses had been used to track population size, but these were costly and laborious to carry out and focused on citizens who were considered politically important (property-owning men), rather than society as a whole. Statistics offered something quite different, transforming the nature of politics in the process.

Statistics were designed to give an understanding of a population in its entirety,rather than simply to pinpoint strategically valuable sources of power and wealth. In the early days, this didnt always involve producing numbers. In Germany, for example (from where we get the term Statistik) the challenge was to map disparate customs, institutions and laws across an empire of hundreds of micro-states. What characterised this knowledge as statistical was its holistic nature: it aimed to produce a picture of the nation as a whole. Statistics would do for populations what cartography did for territory.

Equally significant was the inspiration of the natural sciences. Thanks to standardised measures and mathematical techniques, statistical knowledge could be presented as objective, in much the same way as astronomy. Pioneering English demographers such as William Petty and John Graunt adapted mathematical techniques to estimate population changes, for which they were hired by Oliver Cromwell and Charles II.

The emergence in the late 17th century of government advisers claiming scientific authority, rather than political or military acumen, represents the origins of the expert culture now so reviled by populists. These path-breaking individuals were neither pure scholars nor government officials, but hovered somewhere between the two. They were enthusiastic amateurs who offered a new way of thinking about populations that privileged aggregates and objective facts. Thanks to their mathematical prowess, they believed they could calculate what would otherwise require a vast census to discover.

There was initially only one client for this type of expertise, and the clue is in the word statistics. Only centralised nation states had the capacity to collect data across large populations in a standardised fashion and only states had any need for such data in the first place. Over the second half of the 18th century, European states began to collect more statistics of the sort that would appear familiar to us today. Casting an eye over national populations, states became focused upon a range of quantities: births, deaths, baptisms, marriages, harvests, imports, exports, price fluctuations. Things that would previously have been registered locally and variously at parish level became aggregated at a national level.

New techniques were developed to represent these indicators, which exploited both the vertical and horizontal dimensions of the page, laying out data in matrices and tables, just as merchants had done with the development of standardised book-keeping techniques in the late 15th century. Organising numbers into rows and columns offered a powerful new way of displaying the attributes of a given society. Large, complex issues could now be surveyed simply by scanning the data laid out geometrically across a single page.

These innovations carried extraordinary potential for governments. By simplifying diverse populations down to specific indicators, and displaying them in suitable tables, governments could circumvent the need to acquire broader detailed local and historical insight. Of course, viewed from a different perspective, this blindness to local cultural variability is precisely what makes statistics vulgar and potentially offensive. Regardless of whether a given nation had any common cultural identity, statisticians would assume some standard uniformity or, some might argue, impose that uniformity upon it.

Not every aspect of a given population can be captured by statistics. There is always an implicit choice in what is included and what is excluded, and this choice can become a political issue in its own right. The fact that GDP only captures the value of paid work, thereby excluding the work traditionally done by women in the domestic sphere, has made it a target of feminist critique since the 1960s. In France, it has been illegal to collect census data on ethnicity since 1978, on the basis that such data could be used for racist political purposes. (This has the side-effect of making systemic racism in the labour market much harder to quantify.)

Despite these criticisms, the aspiration to depict a society in its entirety, and to do so in an objective fashion, has meant that various progressive ideals have been attached to statistics. The image of statistics as a dispassionate science of society is only one part of the story. The other part is about how powerful political ideals became invested in these techniques: ideals of evidence-based policy, rationality, progress and nationhood grounded in facts, rather than in romanticised stories.


Since the high-point of the Enlightenment in the late 18th century, liberals and republicans have invested great hope that national measurement frameworks could produce a more rational politics, organised around demonstrable improvements in social and economic life. The great theorist of nationalism, Benedict Anderson, famously described nations as imagined communities,but statistics offer the promise of anchoring this imagination in something tangible. Equally, they promise to reveal what historical path the nation is on: what kind of progress is occurring? How rapidly? For Enlightenment liberals, who saw nations as moving in a single historical direction, this question was crucial.

The potential of statistics to reveal the state of the nation was seized in post-revolutionary France. The Jacobin state set about imposing a whole new framework of national measurement and national data collection. The worlds first official bureau of statistics was opened in Paris in 1800. Uniformity of data collection, overseen by a centralised cadre of highly educated experts, was an integral part of the ideal of a centrally governed republic, which sought to establish a unified, egalitarian society.

From the Enlightenment onwards, statistics played an increasingly important role in the public sphere, informing debate in the media, providing social movements with evidence they could use. Over time, the production and analysis of such data became less dominated by the state. Academic social scientists began to analyse data for their own purposes, often entirely unconnected to government policy goals. By the late 19th century, reformers such as Charles Booth in London and WEB Du Bois in Philadelphia were conducting their own surveys to understand urban poverty.

Illustration
Illustration by Guardian Design

To recognise how statistics have been entangled in notions of national progress, consider the case of GDP. GDP is an estimate of the sum total of a nations consumer spending, government spending, investments and trade balance (exports minus imports), which is represented in a single number. This is fiendishly difficult to get right, and efforts to calculate this figure began, like so many mathematical techniques, as a matter of marginal, somewhat nerdish interest during the 1930s. It was only elevated to a matter of national political urgency by the second world war, when governments needed to know whether the national population was producing enough to keep up the war effort. In the decades that followed, this single indicator, though never without its critics, took on a hallowed political status, as the ultimate barometer of a governments competence. Whether GDP is rising or falling is now virtually a proxy for whether society is moving forwards or backwards.

Or take the example of opinion polling, an early instance of statistical innovation occurring in the private sector. During the 1920s, statisticians developed methods for identifying a representative sample of survey respondents, so as to glean the attitudes of the public as a whole. This breakthrough, which was first seized upon by market researchers, soon led to the birth of the opinion polling. This new industry immediately became the object of public and political fascination, as the media reported on what this new science told us about what women or Americans or manual labourers thought about the world.

Nowadays, the flaws of polling are endlessly picked apart. But this is partly due to the tremendous hopes that have been invested in polling since its origins. It is only to the extent that we believe in mass democracy that we are so fascinated or concerned by what the public thinks. But for the most part it is thanks to statistics, and not to democratic institutions as such, that we can know what the public thinks about specific issues. We underestimate how much of our sense of the public interest is rooted in expert calculation, as opposed to democratic institutions.

As indicators of health, prosperity, equality, opinion and quality of life have come to tell us who we are collectively and whether things are getting better or worse, politicians have leaned heavily on statistics to buttress their authority. Often, they lean too heavily, stretching evidence too far, interpreting data too loosely, to serve their cause. But that is an inevitable hazard of the prevalence of numbers in public life, and need not necessarily trigger the type of wholehearted rejections of expertise that we have witnessed recently.

In many ways, the contemporary populist attack on experts is born out of the same resentment as the attack on elected representatives. In talking of society as a whole, in seeking to govern the economy as a whole, both politicians and technocrats are believed to have lost touch with how it feels to be a single citizen in particular. Both statisticians and politicians have fallen into the trap of seeing like a state, to use a phrase from the anarchist political thinker James C Scott. Speaking scientifically about the nation for instance in terms of macroeconomics is an insult to those who would prefer to rely on memory and narrative for their sense of nationhood, and are sick of being told that their imagined community does not exist.

On the other hand, statistics (together with elected representatives) performed an adequate job of supporting a credible public discourse for decades if not centuries. What changed?


The crisis of statistics is not quite as sudden as it might seem. For roughly 450 years, the great achievement of statisticians has been to reduce the complexity and fluidity of national populations into manageable, comprehensible facts and figures. Yet in recent decades, the world has changed dramatically, thanks to the cultural politics that emerged in the 1960s and the reshaping of the global economy that began soon after. It is not clear that the statisticians have always kept pace with these changes. Traditional forms of statistical classification and definition are coming under strain from more fluid identities, attitudes and economic pathways. Efforts to represent demographic, social and economic changes in terms of simple, well-recognised indicators are losing legitimacy.

Consider the changing political and economic geography of nation states over the past 40 years. The statistics that dominate political debate are largely national in character: poverty levels, unemployment, GDP, net migration. But the geography of capitalism has been pulling in somewhat different directions. Plainly globalisation has not rendered geography irrelevant. In many cases it has made the location of economic activity far more important, exacerbating the inequality between successful locations (such as London or San Francisco) and less successful locations (such as north-east England or the US rust belt). The key geographic units involved are no longer nation states. Rather, it is cities, regions or individual urban neighbourhoods that are rising and falling.

The Enlightenment ideal of the nation as a single community, bound together by a common measurement framework, is harder and harder to sustain. If you live in one of the towns in the Welsh valleys that was once dependent on steel manufacturing or mining for jobs, politicians talking of how the economy is doing well are likely to breed additional resentment. From that standpoint, the term GDP fails to capture anything meaningful or credible.

When macroeconomics is used to make a political argument, this implies that the losses in one part of the country are offset by gains somewhere else. Headline-grabbing national indicators, such as GDP and inflation, conceal all sorts of localised gains and losses that are less commonly discussed by national politicians. Immigration may be good for the economy overall, but this does not mean that there are no local costs at all. So when politicians use national indicators to make their case, they implicitly assume some spirit of patriotic mutual sacrifice on the part of voters: you might be the loser on this occasion, but next time you might be the beneficiary. But what if the tables are never turned? What if the same city or region wins over and over again, while others always lose? On what principle of give and take is that justified?

In Europe, the currency union has exacerbated this problem. The indicators that matter to the European Central Bank (ECB), for example, are those representing half a billion people. The ECB is concerned with the inflation or unemployment rate across the eurozone as if it were a single homogeneous territory, at the same time as the economic fate of European citizens is splintering in different directions, depending on which region, city or neighbourhood they happen to live in. Official knowledge becomes ever more abstracted from lived experience, until that knowledge simply ceases to be relevant or credible.

The privileging of the nation as the natural scale of analysis is one of the inbuilt biases of statistics that years of economic change has eaten away at. Another inbuilt bias that is coming under increasing strain is classification. Part of the job of statisticians is to classify people by putting them into a range of boxes that the statistician has created: employed or unemployed, married or unmarried, pro-Europe or anti-Europe. So long as people can be placed into categories in this way, it becomes possible to discern how far a given classification extends across the population.

This can involve somewhat reductive choices. To count as unemployed, for example, a person has to report to a survey that they are involuntarily out of work, even if it may be more complicated than that in reality. Many people move in and out of work all the time, for reasons that might have as much to do with health and family needs as labour market conditions. But thanks to this simplification, it becomes possible to identify the rate of unemployment across the population as a whole.

Heres a problem, though. What if many of the defining questions of our age are not answerable in terms of the extent of people encompassed, but the intensity with which people are affected? Unemployment is one example. The fact that Britain got through the Great Recession of 2008-13 without unemployment rising substantially is generally viewed as a positive achievement. But the focus on unemployment masked the rise of underemployment, that is, people not getting a sufficient amount of work or being employed at a level below that which they are qualified for. This currently accounts for around 6% of the employed labour force. Then there is the rise of the self-employed workforce, where the divide between employed and involuntarily unemployed makes little sense.

This is not a criticism of bodies such as the Office for National Statistics (ONS), which does now produce data on underemployment. But so long as politicians continue to deflect criticism by pointing to the unemployment rate, the experiences of those struggling to get enough work or to live on their wages go unrepresented in public debate. It wouldnt be all that surprising if these same people became suspicious of policy experts and the use of statistics in political debate, given the mismatch between what politicians say about the labour market and the lived reality.

The rise of identity politics since the 1960s has put additional strain on such systems of classification. Statistical data is only credible if people will accept the limited range of demographic categories that are on offer, which are selected by the expert not the respondent. But where identity becomes a political issue, people demand to define themselves on their own terms, where gender, sexuality, race or class is concerned.

Opinion polling may be suffering for similar reasons. Polls have traditionally captured peoples attitudes and preferences, on the reasonable assumption that people will behave accordingly. But in an age of declining political participation, it is not enough simply to know which box someone would prefer to put an X in. One also needs to know whether they feel strongly enough about doing so to bother. And when it comes to capturing such fluctuations in emotional intensity, polling is a clumsy tool.

Statistics have faced criticism regularly over their long history. The challenges that identity politics and globalisation present to them are not new either. Why then do the events of the past year feel quite so damaging to the ideal of quantitative expertise and its role in political debate?


In recent years, a new way of quantifying and visualising populations has emerged that potentially pushes statistics to the margins, ushering in a different era altogether. Statistics, collected and compiled by technical experts, are giving way to data that accumulates by default, as a consequence of sweeping digitisation. Traditionally, statisticians have known which questions they wanted to ask regarding which population, then set out to answer them. By contrast, data is automatically produced whenever we swipe a loyalty card, comment on Facebook or search for something on Google. As our cities, cars, homes and household objects become digitally connected, the amount of data we leave in our trail will grow even greater. In this new world, data is captured first and research questions come later.

In the long term, the implications of this will probably be as profound as the invention of statistics was in the late 17th century. The rise of big data provides far greater opportunities for quantitative analysis than any amount of polling or statistical modelling. But it is not just the quantity of data that is different. It represents an entirely different type of knowledge, accompanied by a new mode of expertise.

First, there is no fixed scale of analysis (such as the nation) nor any settled categories (such as unemployed). These vast new data sets can be mined in search of patterns, trends, correlations and emergent moods. It becomes a way of tracking the identities that people bestow upon themselves (such as #ImwithCorbyn or entrepreneur) rather than imposing classifications upon them. This is a form of aggregation suitable to a more fluid political age, in which not everything can be reliably referred back to some Enlightenment ideal of the nation state as guardian of the public interest.

Second, the majority of us are entirely oblivious to what all this data says about us, either individually or collectively. There is no equivalent of an Office for National Statistics for commercially collected big data. We live in an age in which our feelings, identities and affiliations can be tracked and analysed with unprecedented speed and sensitivity but there is nothing that anchors this new capacity in the public interest or public debate. There are data analysts who work for Google and Facebook, but they are not experts of the sort who generate statistics and who are now so widely condemned. The anonymity and secrecy of the new analysts potentially makes them far more politically powerful than any social scientist.

A company such as Facebook has the capacity to carry quantitative social science on hundreds of billions of people, at very low cost. But it has very little incentive to reveal the results. In 2014, when Facebook researchers published results of a study of emotional contagion that they had carried out on their users in which they altered news feeds to see how it affected the content that users then shared in response there was an outcry that people were being unwittingly experimented on. So, from Facebooks point of view, why go to all the hassle of publishing? Why not just do the study and keep quiet?


What is most politically significant about this shift from a logic of statistics to one of data is how comfortably it sits with the rise of populism. Populist leaders can heap scorn upon traditional experts, such as economists and pollsters, while trusting in a different form of numerical analysis altogether. Such politicians rely on a new, less visible elite, who seek out patterns from vast data banks, but rarely make any public pronouncements, let alone publish any evidence. These data analysts are often physicists or mathematicians, whose skills are not developed for the study of society at all. This, for example, is the worldview propagated by Dominic Cummings, former adviser to Michael Gove and campaign director of Vote Leave. Physics, mathematics and computer science are domains in which there are real experts, unlike macro-economic forecasting, Cummings has argued.

Figures close to Donald Trump, such as his chief strategist Steve Bannon and the Silicon Valley billionaire Peter Thiel, are closely acquainted with cutting-edge data analytics techniques, via companies such as Cambridge Analytica, on whose board Bannon sits. During the presidential election campaign, Cambridge Analytica drew on various data sources to develop psychological profiles of millions of Americans, which it then used to help Trump target voters with tailored messaging.

This ability to develop and refine psychological insights across large populations is one of the most innovative and controversial features of the new data analysis. As techniques of sentiment analysis, which detect the mood of large numbers of people by tracking indicators such as word usage on social media, become incorporated into political campaigns, the emotional allure of figures such as Trump will become amenable to scientific scrutiny. In a world where the political feelings of the general public are becoming this traceable, who needs pollsters?

Few social findings arising from this kind of data analytics ever end up in the public domain. This means that it does very little to help anchor political narrative in any shared reality. With the authority of statistics waning, and nothing stepping into the public sphere to replace it, people can live in whatever imagined community they feel most aligned to and willing to believe in. Where statistics can be used to correct faulty claims about the economy or society or population, in an age of data analytics there are few mechanisms to prevent people from giving way to their instinctive reactions or emotional prejudices. On the contrary, companies such as Cambridge Analytica treat those feelings as things to be tracked.

But even if there were an Office for Data Analytics, acting on behalf of the public and government as the ONS does, it is not clear that it would offer the kind of neutral perspective that liberals today are struggling to defend. The new apparatus of number-crunching is well suited to detecting trends, sensing the mood and spotting things as they bubble up. It serves campaign managers and marketers very well. It is less well suited to making the kinds of unambiguous, objective, potentially consensus-forming claims about society that statisticians and economists are paid for.

In this new technical and political climate, it will fall to the new digital elite to identify the facts, projections and truth amid the rushing stream of data that results. Whether indicators such as GDP and unemployment continue to carry political clout remains to be seen, but if they dont, it wont necessarily herald the end of experts, less still the end of truth. The question to be taken more seriously, now that numbers are being constantly generated behind our backs and beyond our knowledge, is where the crisis of statistics leaves representative democracy.

On the one hand, it is worth recognising the capacity of long-standing political institutions to fight back. Just as sharing economy platforms such as Uber and Airbnb have recently been thwarted by legal rulings (Uber being compelled to recognise drivers as employees, Airbnb being banned altogether by some municipal authorities), privacy and human rights law represents a potential obstacle to the extension of data analytics. What is less clear is how the benefits of digital analytics might ever be offered to the public, in the way that many statistical data sets are. Bodies such as the Open Data Institute, co-founded by Tim Berners-Lee, campaign to make data publicly available, but have little leverage over the corporations where so much of our data now accumulates. Statistics began life as a tool through which the state could view society, but gradually developed into something that academics, civic reformers and businesses had a stake in. But for many data analytics firms, secrecy surrounding methods and sources of data is a competitive advantage that they will not give up voluntarily.

A post-statistical society is a potentially frightening proposition, not because it would lack any forms of truth or expertise altogether, but because it would drastically privatise them. Statistics are one of many pillars of liberalism, indeed of Enlightenment. The experts who produce and use them have become painted as arrogant and oblivious to the emotional and local dimensions of politics. No doubt there are ways in which data collection could be adapted to reflect lived experiences better. But the battle that will need to be waged in the long term is not between an elite-led politics of facts versus a populist politics of feeling. It is between those still committed to public knowledge and public argument and those who profit from the ongoing disintegration of those things.

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Read more: https://www.theguardian.com/politics/2017/jan/19/crisis-of-statistics-big-data-democracy