American men do have genuine reasons for anxiety. The traditional jobs that many men have filled are disappearing, thanks to automation and outsourcing. The jobs that remain require, in most cases, higher education, which is increasingly difficult for non-affluent families to afford. We should indeed tremble for the future of both men and women in our country unless we address that problem, and related problems of declining health and well-being for working-class men.
.. Three emotions, all infused by fear, play a role in today’s misogyny. The most obvious is anger — at women making demands, speaking up, in general standing in the way of unearned male privilege. Women were once good mothers and good wives, props and supports for male ambition, the idea goes –but here they are asserting themselves in the workplace. Here they are daring to speak about their histories of sexual abuse at the hands of powerful men. It’s okay for women to charge strangers with rape, especially if the rapist is of inferior social status. But to dare to accuse the powerful is to assail a bastion of privilege to which men still cling.
.. Coupled with anger is envy. All over the world, women are seeing unprecedented success in higher education, holding a majority of university seats. In our nation many universities quietly practice affirmative action for males with inferior scores, to achieve a “gender balance” that is sometimes dictated by commitment to male sports teams, given Title IX’s mandate of proportional funding.
.. But men still feel that women are taking “their” places in college classes, in professional schools.
.. Envy, propelled by fear, can be even more toxic than anger, because it involves the thought that other people enjoy the good things of life which the envier can’t hope to attain through hard work and emulation. Envy is the emotion of Aaron Burr in Lin-Manuel Miranda’s “Hamilton”
.. And then, beneath the hysteria, lurks a more primitive emotion: disgust at women’s animal bodies.
.. In the United States, we observe this dynamic in racism, in homophobia and even in revulsion toward the bodies of people who are aging. But in every culture male disgust targets women, as emblems of bodily nature, symbolic animals by contrast to males, almost angels with pure minds.
.. Disgust for women’s bodily fluids is fully compatible with sexual desire. Indeed, it often singles out women seen as promiscuous, the repositories of many men’s fluids.
.. as with the apparent defamation of Renate Dolphin in Kavanaugh’s infamous yearbook, men often crow with pride over intercourse with a woman imagined as sluttish and at the same time defame and marginalize her.
.. Disgust is often more deeply buried than envy and anger, but it compounds and intensifies the other negative emotions.
.. Our president seems to be especially gripped by disgust: for women’s menstrual fluids, their bathroom breaks, the blood imagined streaming from their surgical incisions, even their flesh, if they are more than stick-thin.
I think one could propose a whole list of unhelpful ways of talking about current developments in machine learning. For example:
- Data is the new oil
- Google and China (or Facebook, or Amazon, or BAT) have all the data
- AI will take all the jobs
- And, of course, saying AI itself.
More useful things to talk about, perhaps, might be:
- Enabling technology layers
- Relational databases.
.. Before relational databases appeared in the late 1970s, if you wanted your database to show you, say, ‘all customers who bought this product and live in this city’, that would generally need a custom engineering project. Databases were not built with structure such that any arbitrary cross-referenced query was an easy, routine thing to do. If you wanted to ask a question, someone would have to build it. Databases were record-keeping systems; relational databases turned them into business intelligence systems.
This changed what databases could be used for in important ways, and so created new use cases and new billion dollar companies. Relational databases gave us Oracle, but they also gave us SAP, and SAP and its peers gave us global just-in-time supply chains – they gave us Apple and Starbucks.
.. with each wave of automation, we imagine we’re creating something anthropomorphic or something with general intelligence. In the 1920s and 30s we imagined steel men walking around factories holding hammers, and in the 1950s we imagined humanoid robots walking around the kitchen doing the housework. We didn’t get robot servants – we got washing machines... machine learning lets us solve classes of problem that computers could not usefully address before, but each of those problems will require a different implementation, and different data, a different route to market, and often a different company... Machine learning is not going to create HAL 9000 (at least, very few people in the field think that it will do so any time soon), but it’s also not useful to call it ‘just statistics’... this might be rather like talking about SQL in 1980 – how do you get from explaining table joins to thinking about Salesforce.com? It’s all very well to say ‘this lets you ask these new kinds of questions‘, but it isn’t always very obvious what questions.
- .. Machine learning may well deliver better results for questions you’re already asking about data you already
- .. Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for ‘angry’ emails, or ‘anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches,
- .. machine learning opens up new data types to analysis – computers could not really read audio, images or video before and now, increasingly, that will be possible.
.. Within this, I find imaging much the most exciting. Computers have been able to process text and numbers for as long as we’ve had computers, but images (and video) have been mostly opaque.
.. Now they’ll be able to ‘see’ in the same sense as they can ‘read’. This means that image sensors (and microphones) become a whole new input mechanism – less a ‘camera’ than a new, powerful and flexible sensor that generates a stream of (potentially) machine-readable data. All sorts of things will turn out to be computer vision problems that don’t look like computer vision problems today.
.. I met a company recently that supplies seats to the car industry, which has put a neural network on a cheap DSP chip with a cheap smartphone image sensor, to detect whether there’s a wrinkle in the fabric (we should expect all sorts of similar uses for machine learning in very small, cheap widgets, doing just one thing, as described here). It’s not useful to describe this as ‘artificial intelligence’: it’s automation of a task that could not previously be automated. A person had to look.
.. one of my colleagues suggested that machine learning will be able to do anything you could train a dog to do
.. Ng has suggested that ML will be able to do anything you could do in less than one second.
.. I prefer the metaphor that this gives you infinite interns, or, perhaps, infinite ten year olds.
.. Five years ago, if you gave a computer a pile of photos, it couldn’t do much more than sort them by size. A ten year old could sort them into men and women, a fifteen year old into cool and uncool and an intern could say ‘this one’s really interesting’. Today, with ML, the computer will match the ten year old and perhaps the fifteen year old. It might never get to the intern. But what would you do if you had a million fifteen year olds to look at your data? What calls would you listen to, what images would you look at, and what file transfers or credit card payments would you inspect?
.. machine learning doesn’t have to match experts or decades of experience or judgement. We’re not automating experts. Rather, we’re asking ‘listen to all the phone calls and find the angry ones’. ‘Read all the emails and find the anxious ones’. ‘Look at a hundred thousand photos and find the cool (or at least weird) people’.
.. this is what automation always does;
- Excel didn’t give us artificial accountants,
- Photoshop and Indesign didn’t give us artificial graphic designers and indeed
- steam engines didn’t give us artificial horses. ..
Rather, we automated one discrete task, at massive scale.
.. Where this metaphor breaks down (as all metaphors do) is in the sense that in some fields, machine learning can not just find things we can already recognize, but find things that humans can’t recognize, or find levels of pattern, inference or implication that no ten year old (or 50 year old) would recognize.
.. This is best seen Deepmind’s AlphaGo. AlphaGo doesn’t play Go the way the chess computers played chess – by analysing every possible tree of moves in sequence. Rather, it was given the rules and a board and left to try to work out strategies by itself, playing more games against itself than a human could do in many lifetimes. That is, this not so much a thousand interns as one intern that’s very very fast, and you give your intern 10 million images and they come back and say ‘it’s a funny thing, but when I looked at the third million images, this pattern really started coming out’.
.. what fields are narrow enough that we can tell an ML system the rules (or give it a score), but deep enough that looking at all of the data, as no human could ever do, might bring out new results?
the glib elite consensus is that because American men are born with a head start, dominate the upper ranks of the country’s major institutions, and don’t face any particular challenges, life should be easy for us. Do the men who dropped out of high school need to be “convinced” that this isn’t true? What about the men whose friends are suffering from drug addiction, or who are addicted themselves? The men with no job prospects, no social capital, and no hope of finding a life partner? When Carlson pointed out, correctly, in the first entry of the series that American men are increasingly dropping out of school, addicted to drugs, falling out of the job market, committing crimes, and killing themselves, it wasn’t an exercise in persuasion. Carlson’s target audience doesn’t need him to “convince” them that their lives have gone awry; they know it already.
.. What Carlson was actually doing in “Men in America,” which concluded last week, was offering his version of an explanation of why conditions are dreadful for so many American men. He blamed a combination of
the vilification of traditional masculinity, and
certain ill-advised government programs
.. For whether or not he’s right about the reasons why men are facing hard times, Carlson is surely right that they are. Meanwhile, his critics, in a representative stand-in for polite progressive opinion, were content to mock or deny the existence of this state of affairs rather than take a stab at offering an alternative explanation for its causes.
.. Elliot Kaufman calls the Jordan Peterson demographic: men in their late teens, twenties, and early thirties who are frustrated with the way their lives are going, often for good reason, and eager for someone to recognize their plight and offer a way out
.. the Peterson phenomenon; the writer Park MacDougald describes his message thus:
Life is hard, you will suffer, and in order to handle that suffering, you will have to be prepared. Preparing means taking responsibility for yourself. That’s hard, too, so you may try to avoid it. You may use all manner of evasions and rationalizations to convince yourself that things will sort themselves out on their own, or that others will bail you out, or that if they don’t, it’s their fault and not yours. But that’s a lie. So stop lying. Accept responsibility for your fate.
.. “Peterson has become a celebrity by telling young people to get their act together, which suggests that there are a lot of them who need to hear it.”
.. one of Peterson’s virtues is that he appeals to men who have fallen out of society — those “not in education, employment, or training” — who might otherwise wind up in fringe movements.
.. Angela Nagle who, in her book about the Internet culture wars, posited a connection between the “growing celibacy among a large male population,” the “anxiety and anger about their low-ranking status in the hierarchy” that such persistent sexual frustration engenders, and the mushrooming of the so-called alt-right in 2016.
.. Peterson appeals to the same folks, but scorns the identitarian movement that has captured many of them.
.. to the extent that Peterson’s rise has drained support for cranks, there’s a strong case that in consequentialist terms it has been a good thing.
.. And, as the dismissive criticisms of Peterson and Carlson show, the Left has little on offer for young men save for a choice between self-abnegation and enduring slurs about sins of the past. Peterson is on to something: They could use a sustained dose of the insights they used to get from tough-minded teachers, coaches, clerics, professors, or drill sergeants. Many of the institutions represented by those stalwart role models, however, have decayed.
.. nobody should be blamed for feeling ambivalent that Fox News’ most cynical host is chief among them. Carlson’s career has been an exercise in switching convictions. His decision to pivot toward Peterson is intended to grow his rapt audience, not promote his sincerely held beliefs. He’s a shrewd actor and savvy businessman, not a responsible thinker.
.. Rather than grousing that the wrong people are reaching out to this demographic, it would be more productive to think honestly about why the demographic exists at all.
More than two decades ago, Harvard economist Dani Rodrik warned that globalization was driving a wedge between workers who had the skills and mobility to prosper in the global economy and those who did not. The key challenge, he argued, was to make globalization “compatible with domestic social and political stability”—that is, to ensure that international economic integration “does not contribute to domestic social disintegration.”
.. International trade weakens the postwar social contract between American employers and their workers. Less-skilled workers often are forced to accept lower wages, inferior benefits and diminished job security. Leading economists acknowledged that increased trade with lower-wage countries would widen the gap between highly skilled and less-skilled workers in advanced economies, but they played down the magnitude of these effects.
.. China’s accession to the World Trade Organization in 2001. Yes, China had a large state-owned sector, used public resources to encourage the private economy, and broadly subsidized its producers. But over time, the thinking went, the communists would see the folly of propping up inefficient producers. The state sector would shrink, and the market would become more powerful. China’s economy would converge with the Western model, and its political institutions eventually would evolve too.
.. automation—not protectionism—is the key to the future.