Tag Archives: Personal

Hanson Loves Moose Caca

In the movie “My Big Fat Greek Wedding,” when Toula was a little girl, she sat alone in the school cafeteria, frizzy haired, big nosed, and unpopular. The blonde girls at the next table asked her what she was eating, and Toula quietly said “moussaka.” The popular girls laughed cruelly, saying “Ewwww, ”moose caca!”” (more)

Imagine that those cruel girls had gone on to tell other kids “Toula says she loves to eat moose caca!” That is how I feel when Noah Smith says:

Why is it that the sciences look like a feminist nirvana compared with the economics profession, which seems to have a built-in bias that prevents women from advancing?

Consider this 2011 blog post by George Mason University economist Robin Hanson. Hanson writes that “gentle, silent rape” of a woman by a man causes less harm than a wife cuckolding her husband:

I [am puzzled] over why our law punishes rape far more than cuckoldry…[M]ost men would rather be raped than cuckolded…Imagine a woman was drugged into unconsciousness and then gently raped, so that she suffered no noticeable physical harm nor any memory of the event, and the rapist tried to keep the event secret…Now compare the two cases, cuckoldry and gentle silent rape.

There was no outcry whatsoever over these remarks, nor any retraction that I could find. (more)

Now I’ve admitted as far back as 2006 that academia, economics included, is biased against women. (Having been in both physics and computer science before, I doubt the situation is much worse in econ.) This one post of mine that Smith points to did induce many negative responses in comments and elsewhere, and of my thousands of blog posts I’d be surprised if much more than a dozen had induced any blog responses by economists whatsoever. And I suggested that we consider that the harms of rape and cuckoldry might be similar; I didn’t claim I knew one to be definitely larger.

But more fundamentally, Noah Smith is plenty smart enough to understand that I was not at all minimizing the harm of rape when I used rape as a reference to ask if other harms might be even bigger. Just as people who accuse others of being like Hitler do not usually intend to praise Hitler, people who compare other harms to rape usually intend to emphasize how big are those other harms, not how small is rape.

But I’m pretty sure Smith knows that. Yet, like the girls who taunted Toula, Smith finds it suits him better to pretend to misunderstand.

Added noon: Steve Sailer weighs in.

Added 2p: Noah Smith and I have been having a twitter conversation on this.

Added 4p: My topic was the relative harm of cuckoldry & rape. Noah Smith says that this topic itself is innately offensive to most women, who think cuckoldry to be of such low harm that comparing it with rape suggests rape to be low harm. He is further offended that I would talk on a topic if I knew it might offend in this way. I said his presuming cuckoldry is of very low harm offends the many men who think it very high harm. He disagrees that there are many such men, and would bet on a poll on the subject, but thinks it offensive to make such a poll, and won’t help with that.

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This Time Isn’t Different

~1983 I read two articles that inspired me to change my career. One was by Ted Nelson on hypertext publishing, and the other by Doug Lenat on artificial intelligence. So I quit my U. of Chicago physics Ph.D. program and headed to Silicon Valley, for a job doing AI at Lockheed, and a hobby doing hypertext with Nelson’s Xanadu group.

A few years later, ~1986, I penned the following parable on AI research:

COMPLETE FICTION by Robin Hanson

Once upon a time, in a kingdom nothing like our own, gold was very scarce, forcing jewelers to try and sell little tiny gold rings and bracelets. Then one day a PROSPECTOR came into the capitol sporting a large gold nugget he found in a hill to the west. As the word went out that there was “gold in them thar hills”, the king decided to take an active management role. He appointed a “gold task force” which one year later told the king “you must spend lots of money to find gold, lest your enemies get richer than you.”

So a “gold center” was formed, staffed with many spiffy looking Ph.D types who had recently published papers on gold (remarkably similar to their earlier papers on silver). Experienced prospectors had been interviewed, but they smelled and did not have a good grasp of gold theory.

The center bought a large number of state of the art bulldozers and took them to a large field they had found that was both easy to drive on and freeway accessible. After a week of sore rumps, getting dirty, and not finding anything, they decided they could best help the gold cause by researching better tools.

So they set up some demo sand hills in clear view of the king’s castle and stuffed them with nicely polished gold bars. Then they split into various research projects, such as “bigger diggers”, for handling gold boulders if they found any, and “timber-gold alloys’, for making houses from the stuff when gold eventually became plentiful.

After a while the town barons complained loud enough and also got some gold research money. The lion’s share was allocated to the most politically powerful barons, who assigned it to looking for gold in places where it would be very convenient to find it, such as in rich jewelers’ backyards. A few bulldozers, bought from smiling bulldozer salespeople wearing “Gold is the Future” buttons, were time shared across the land. Searchers who, in their alloted three days per month of bulldozer time, could just not find anything in the backyards of “gold committed” jewelers were admonished to search harder next month.

The smart money understood that bulldozers were the best digging tool, even though they were expensive and hard to use. Some backward prospector types, however, persisted in panning for gold in secluded streams. Though they did have some success, gold theorists knew that this was due to dumb luck and the incorporation of advanced bulldozer research ideas in later pan designs.

After many years of little success, the king got fed up and cut off all gold funding. The center people quickly unearthed their papers which had said so all along. The end.

P.S. There really was gold in them thar hills. Still is.

As you can see, I had become disillusioned on academic research, but still suffered youthful over-optimism on near-term A.I. prospects.

I’ve since learned that we’ve seen “booms” like the one I was caught up in then every few decades for centuries. In each boom many loudly declare high expectations and concern regarding rapid near-term progress in automation. “The machines are finally going to soon put everyone out of work!” Which of course they don’t. We’ve instead seen a pretty slow & steady rate of humans displaced by machines on jobs.

Today we are in another such boom. For example, David Brooks recently parroted Kevin Kelley saying this time is different because now we have cheaper hardware, better algorithms, and more data. But those facts were also true in most of the previous booms; nothing has fundamentally changed! In truth, we remain a very long way from being able to automate all jobs, and we should expect the slow steady rate of job displacement to long continue.

One way to understand this is in terms of the distribution over human jobs of how good machines need to be to displace humans. If this parameter is distributed somewhat evenly over many orders of magnitude, then continued steady exponential progress in machine abilities should continue to translate into only slow incremental displacement of human jobs. Yes machines are vastly better than they were before, but they must get far more vastly better to displace most human workers.

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“I Robot, You Unemployed”

Tomorrow (Wednesday) at 7pm EST I’ll do a Learn Liberty Live! web presentation on “I, Robot. You, Unemployed” here. After a short ten minute presentation, I’ll lead ninety minutes of discussion. I expect to focus on em econ.

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Cyprus Mail Profile

From a wide-ranging profile of me in the Cyprus Mail (a newspaper):

I’ve learned a bit about his lifestyle – [Hanson] reads widely; he goes biking; he likes movies, and peruses ‘100 Best Films’ lists to check how many he’s seen – but not very much. A profile is supposed to be personal, I remind him. But he shakes his head.

When interviewers talk to a musician or an athlete (or indeed a well-known academic), he points out, they’re forever asking them to ‘tell me about the rest of your life’ – yet “the way people become famous musicians or athletes is to focus so much of their energy on this professional thing, [so] there usually isn’t much of a ‘rest of their life’. And that’s not a message people usually want to hear, so they make up silly things in order to seem personal.”

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Great Filter TEDx

This Saturday I’ll speak on the great filter at TEDx Limassol in Cyprus. Though I first wrote about the subject in 1996, this is actually the first time I’ve been invited to speak on it. It only took 19 years. I’ll post links here to slides and video when available.

Added 22Sep: A preliminary version of the video can be found here starting at minute 34.

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Boston Talks This Week

Monday: Why is Abstraction both Statusful and Silly? 7:00p, 98 Elm St Apt 1, Somerville.
Tuesday: Shall We Vote On Values, But Bet On Beliefs? noon, 206 Lake Hall, Northeastern Univ.
Wednesday: Factoring Geopolitical Risk Into Decision-Making, 12:20p, Global ICON Conf.

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Paul Carr Interviews Me

In this episode of the Wow! Signal Podcast. The topic is ems, starting about minute 35, after an interview with Heath Rezabek.

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Days Of Our Lives

Oedipus famously answered this riddle:

What goes on four feet in the morning, two feet at noon, and three feet in the evening?

The answer: people crawl when babies, walk as adults, and use a cane when old. It seems natural to divide lives into three parts: young, middle, and old. But where exactly should the boundaries fall? One tempting approach comes from the facts that in the US today lifespans average about 29000 days, and people typically marry and have kids at about 10000 days. So maybe we should split life into the first, second, and third 10000 days.

If we split life into 5000 days units, we get:

  • 0 days; 0 years – Birth
  • 5000 days; 13.7 years – Mid-puberty
  • 10000 days; 27.4 years – First marriage & kids
  • 15000 days; 41.1 years – Start to notice body decline
  • 20000 days; 54.8 years – Near kids’ first marriage & kids, own peak of relative income, productivity, 90% still alive
  • 25000 days; 68.5 years – Near when most retire, 75% still alive
  • 30000 days; 82.1 years – Typical death age, 42% still alive
  • 35000 days; 95.8 years – Only 4% still alive

Note that 5000 days is near the doubling time of the world economy.

In my life, I married at 10250, had my first kid at 11500, started grad school again at 12400, started at GMU at 14600, and was tenured at 16540. And today I am 20,000 days old, within a few days of all my kids being employed college graduates. So a lot happened to me in that third 5000 days, and I now enter the last third of a typical lifespan, with expected declining (but hardly zero) relative productivity. Of course if cryonics works I might live lots longer.

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Em Econ @ Yale Thursday

The Yale Technology & Ethics study group hosts about one talk a month on various futurist topics. Amazingly, I was their very first speaker when the group started in 2002. And this Thursday I’ll return to talk on the same subject:

The Age of Em: Social Implications of Brain Emulations

4:15-6:15pm, May 22, Yale ISPS, 77 Prospect St (corner of Prospect & Trumbull), Rm A002.

The three most disruptive transitions in history were the introduction of humans, farming, and industry. If another transition lies ahead, a good guess for its source is artificial intelligence in the form of whole brain emulations, or “ems,” sometime in the next century. I attempt a broad synthesis of standard academic consensus, including in business and social science, in order to outline a baseline scenario set modestly far into a post-em-transition world. I consider computer architecture, energy use, cooling infrastructure, mind speeds, body sizes, security strategies, virtual reality conventions, labor market organization, management focus, job training, career paths, wage competition, identity, retirement, life cycles, reproduction, mating, conversation habits, wealth inequality, city sizes, growth rates, coalition politics, governance, law, and war.

My ’02 talk was controversial; Thursday’s talk will likely be well. All are welcome.

Added 28May: Audio, slides.

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Sam Wilson Podcast

Sam Wilson and I did a podcast for his series, on near-far, em econ, and related topics.

One topic that came up briefly deserves emphasis: robustness can be very expensive.

Imagine I told you to pack a bag for a trip, but I wouldn’t tell you to where. The wider the set of possibilities you needed to handle, the bigger and more expensive your bag would have to be. You might not need a bag at all if you knew your destination was to stay inside one of the hundred largest airports. But you’d need a big bag if you might go anywhere on the surface of the Earth. You’d need a space-suit if you might go anywhere in the solar system, and if you might go anywhere within the Sun, well we have no bag for that.

Similarly, it sounds nice to say that because the future can be hard to predict, we should seek strategies that are robust to many different futures. But the wider the space of futures one seeks to be robust against, the most expensive that gets. For example, if you insist on being ready for an alien invasion by all possible aliens, we just have no bag for that. The situation is almost as bad if you say we need to give explicit up-front-only instructions to a computer that will overnight become a super-God and take over the world.

Of course if those are the actual situations you face, then you must do your best, and pay any price, even if extinction is your most likely outcome. But you should think carefully about whether these are likely enough bag-packing destinations to make it worth being robust toward them. After all, it can be very expensive to pack a spacesuit for a beach vacation.

(There is a related formal result in learning theory: it is hard to learn anything without some expectations about the kind of world you are learning about.)

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