Pinker on implementing world peace

From Better Angels of Our Nature, ch. 5:

In “Perpetual Peace,” Kant envisioned a “federation of free states” that would fall well short of an international Leviathan. It would be a gradually expanding club of liberal republics rather than a global megagovernment, and it would rely on the soft power of moral legitimacy rather than on a monopoly on the use of force. The modern equivalent is the intergovernmental organization or IGO — a bureaucracy with a limited mandate to coordinate the policies of participating nations in some area in which they have a common interest. The international entity with the best track record for implementing world peace is probably not the United Nations, but the European Coal and Steel Community, an IGO founded in 1950 by France, West Germany, Belgium, the Netherlands, and Italy to oversee a common market and regulate the production of the two most important strategic commodities. The organization was specifically designed as a mechanism for submerging historic rivalries and ambitions — especially West Germany’s — in a shared commercial enterprise. The Coal and Steel Community set the stage for the European Economic Community, which in turn begot the European Union.

Many historians believe that these organizations helped keep war out of the collective consciousness of Western Europe. By making national borders porous to people, money, goods, and ideas, they weakened the temptation of nations to fall into militant rivalries, just as the existence of the United States weakens any temptation of, say, Minnesota and Wisconsin to fall into a militant rivalry. By throwing nations into a club whose leaders had to socialize and work together, they enforced certain norms of cooperation. By serving as an impartial judge, they could mediate disputes among member nations. And by holding out the carrot of a vast market, they could entice applicants to give up their empires (in the case of Portugal) or to commit themselves to liberal democracy (in the case of former Soviet satellites and, perhaps soon, Turkey).

Richard Clarke and R.P. Eddy on AI risk

Richard Clarke and R.P. Eddy recently published Warnings, a book in which they try to identify “those rare people who… have accurate visions of looming disasters.” The opening chapter explains the aims of the book:

…this book will seek to answer these questions: How can we detect a real Cassandra among the myriad of pundits? What methods, if any, can be employed to better identify and listen to these prophetic warnings? Is there perhaps a way to distill the direst predictions from the surrounding noise and focus our attention on them?

…As we proceeded through these Cassandra Event case studies in a variety of different fields, we began to notice common threads: characteristics of the Cassandras, of their audiences, and of the issues that, when applied to a modern controversial prediction of disaster, might suggest that we are seeing someone warning of a future Cassandra Event. By identifying those common elements and synthesizing them into a methodology, we create what we call our Cassandra Coefficient, a score that suggests to us the likelihood that an individual is indeed a Cassandra whose warning is likely accurate, but is at risk of being ignored.

Having established this process for developing a Cassandra Coefficient based on past Cassandra Events, we next listen for today’s Cassandras. Who now among us may be accurately warning us of something we are ignoring, perhaps at our own peril?

Of the risks covered in the book, Clarke says he’s most worried about sea level rise, and Eddy says he’s most worried about superintelligence.

Below is a sampling of what they say in the chapter on risks from advanced AI systems. Note that I’m merely quoting from their take, not necessarily agreeing with it. (Indeed, there are significant parts I disagree with.)

[Read more…]

There was only one industrial revolution

Many people these days talk about an impending “fourth industrial revolution” led by AI, the internet of things, 3D printing, quantum computing, and more. The first three revolutions are supposed to be:

  • 1st industrial revolution (~1800-1870): the world industrializes for the first time via steam, textiles, etc.
  • 2nd industrial revolution (1870-1914): continued huge growth via steel, oil, other things, and especially electricity.
  • 3rd industrial revolution (1980-today): personal computers, internet, etc.

I think this is a misleading framing for the last few centuries, though, because one of these things is not remotely like the others. As far as I can tell, the major curves of human well-being and empowerment bent exactly once in recorded history, during the “1st” industrial revolution:

all curves, with events

(And yes, there’s still a sharp jump around 1800-1870 if you chart this on a log scale.)

The “2nd” and “3rd” industrial revolutions, if they are coherent notions at all, merely continued the new civilizational trajectory created by the “1st” industrial revolution.

I think this is important for thinking about how big certain future developments might be. For example, authors of papers at some top machine learning conference seem to think there’s a decent chance that “unaided machines [will be able to] accomplish every task better and more cheaply than human workers” sometime in the next few decades. There’s plenty of reason to doubt this aggregate forecast, but if that happens, I think the impact would likely be on the scale of the (original) industrial revolution, rather than that of e.g. the (so small it’s hard to measure?) impact of the “3rd” industrial revolution. But for some other technologies (e.g. “internet of things”), it’s hard to tell a story for how it could possibly be as big a deal as the original industrial revolution.

Storeable, convenient veg*n meal options

Ever since I wrapped up my animal consciousness report, I’ve been working to become a better reducetarian. As such, I’ve been hunting for storeable, convenient veg*n meal options. (Getting restaurants to carry tastier veg*n food is harder, but I’ve been enjoying my Impossible Burgers!)

A spreadsheet of my findings thus far is here. In my area, they’re available locally via Instacart. So far, my tastiest solution is “buy Vegetarian Plus meals.”

Most of these meals don’t satisfy me on their own, so I usually supplement with a banana or whatever.

Books, music, etc. from August-September 2017

Books

  • Tegmark, Life 3.0

Music

Music I most enjoyed discovering this month:

Movies/TV

Ones I “really liked” (no star), or “loved” (star):

  • Spicer: Ingrid Goes West (2017) ★
  • Ross: Captain Fantastic (2016)
  • Shults: It Comes at Night (2017)
  • Young: Hounds of Love (2016)
  • Alvarez: Don’t Breathe (2016)

Three wild speculations from amateur quantitative macrohistory

Note: As usual, these are my personal guesses and opinions, not those of my employer.

In How big a deal was the Industrial Revolution?, I looked for measures (or proxy measures) of human well-being / empowerment for which we have “decent” scholarly estimates of the global average going back thousands of years. For reasons elaborated at some length in the full report, I ended up going with:

  1. Physical health, as measured by life expectancy at birth.
  2. Economic well-being, as measured by GDP per capita (PPP) and percent of people living in extreme poverty.
  3. Energy capture, in kilocalories per person per day.
  4. Technological empowerment, as measured by war-making capacity.
  5. Political freedom to live the kind of life one wants to live, as measured by percent of people living in a democracy.

(I also especially wanted measures of subjective well-being and social well-being, and also of political freedom as measured by global rates of slavery, but these data aren’t available; see the report.)

Anyway, the punchline of the report is that when you chart these six measures over the past few millennia (data; zoomable), you get a chart like this (axes removed for space reasons): [Read more…]

Hillary Clinton on AI risk

From What Happened, p. 241:

Technologists like Elon Musk, Sam Altman, and Bill Gates, and physicists like Stephen Hawking have warned that artificial intelligence could one day pose an existential security threat. Musk has called it “the greatest risk we face as a civilization.” Think about it: Have you ever seen a movie where the machines start thinking for themselves that ends well? Every time I went out to Silicon Valley during the campaign, I came home more alarmed about this. My staff lived in fear that I’d start talking about “the rise of the robots” in some Iowa town hall. Maybe I should have. In any case, policy makers need to keep up with technology as it races ahead, instead of always playing catch-up.

Update 11/24/2017: Clinton said more about AI fears in an interview with Hugh Hewitt:

Bill Gates, Elon Musk, Stephen Hawking, a lot of really smart people are sounding an alarm that we’re not hearing. And their alarm is artificial intelligence is not our friend. It can assist us in many ways if it is properly understood and contained. But we are racing headfirst into a new era of artificial intelligence that is going to have dramatic effects on how we live, how we think, how we relate to each other. You know, what are we going to do when we get driverless cars? It sounds like a great idea. And how many millions of people, truck drivers and parcel delivery people and cab drivers and even Uber drivers, what do we do with the millions of people who will no longer have a job? We are totally unprepared for that. What do we do when we are connected to the internet of things and everything we know and everything we say and everything we write is, you know, recorded somewhere? And it can be manipulated against us. So I, you know, one thing I wanted to do if I had been president was to have a kind of blue ribbon commission with people from all kinds of expertise coming together to say what should America’s policy on artificial intelligence be?

But of course, the worries Gates & Musk & Hawking have expressed are not about self-driving cars.

Books, music, etc. from July 2017

Books

  • [none]

Music

Music I most enjoyed discovering this month:

Movies/TV

Ones I “really liked” (no star), or “loved” (star):

  • Nichols: Loving (2016)
  • Gray: The Lost City of Z (2016)
  • Various: Transparent, season 3 (2016)
  • Mangold: Logan (2017)
  • Showalter: The Big Sick (2017)
  • Peele: Get Out (2017) ★
  • Nolan: Dunkirk (2017) ★
  • Arnold: American Honey (2016)

Books, music, etc. from June 2017

Books

  • Allison: Destined for War (2017). Decent, but very one-sided in its arguments. Scary.

Music

Music I most enjoyed discovering this month:

  • [none]

Movies/TV

Ones I “really liked” (no star), or “loved” (star):

  • Various: Stranger Things, season 1 (2016)
  • Campos: Christine (2016)
  • Various: Fargo, season 3 (2017) ★
  • Various: Better Call Saul, season 3 (2017) ★
  • Various: The Handmaid’s Tale, season 1 (2017) ★

Media I’m looking forward to, July 2017 edition

Books

* = added this round
bold = especially excited

[Read more…]

Books, music, etc. from May 2017

Books

  • [none]

Music

Music I most enjoyed discovering this month:

  • Saagara: 2 (2017)
  • Perfume Genius: No Shape (2017)

Movies/TV

Ones I “really liked” (no star), or “loved” (star):

  • Birbiglia: Don’t Think Twice (2016)
  • Various: Master of None, season 2 (2017)

Media I’m looking forward to, June 2017 edition

Books

* = added this round
bold = especially excited

[Read more…]

Monkey classification errors

More Wynne & Udell (2013):

Michael D’Amato and Paul van Sant (1988) trained Cebus apella monkeys to discriminate slides containing people from those that did not. The monkeys readily learned to do this. Then the monkeys were presented with novel slides they had never seen before which contained either scenes with people or similar scenes with no people in them. Here also the monkeys spontaneously classified the majority of slides correctly. So far, so good – clear evidence that the monkeys had not just learned the particular slides they had been trained on but had abstracted a person concept from those slides that they then successfully applied to pictures they had never seen before.

Or had they? D’Amato and van Sant did not stop their analysis simply with the observation that the monkeys had successfully transferred their learning to novel slides – rather they went on to look carefully at the kinds of errors the monkeys had made. Although largely successful with the novel slides, the monkeys made some very puzzling mistakes. For example, one of the person slides that the monkeys had failed to recognize as a picture of a human being had been a head and shoulders portrait – which, to another human, is a classic image of a person. One of the slides that the monkeys had incorrectly classified as containing a human had actually been a shot of a jackal carrying a dead flamingo in its mouth; both the jackal and its prey were also reflected in the water beneath them. What person in her right mind could possible confuse a jackal with a flamingo in its mouth with another human being?

The explanation for both these mistakes is the same: the monkeys had generalized on the basis of the particular features contained in the slides they had been trained with rather than learning the more abstract concept that the experimenters had intended. The head and shoulders portrait of a person lacked the head-torso-arms-legs body shape that had been most common among the images that the monkeys had been trained with, and consequently, they had rejected it as not similar enough to the positive image they were looking for. Similarly, during training, the only slides that had contained flashes of red happened to be those of people. Three of the training slides had contained people wearing a piece of red clothing, whereas none of the nonperson slides had contained the color red. Consequently, when the jackal with prey slide came along during testing, it contained the color red, and so the monkeys classified it as a person slide.

Adversarial examples for pigeons

From Wynne & Udell (2013):

Michael Young and colleagues carried out experiments that add to a sense that the pigeon’s perception of pictures of objects is not identical to our own. They trained pigeons to peck in different locations on a computer-controlled touch screen, depending on which of four different objects was presented: an arch, a barrel, a brick, and a triangular wedge (Young et al., 2001). The objects were initially presented to the pigeons as images shaded to suggest light shining on them from one direction. Next, Young and colleagues tested the pigeons with pictures of the same objects, but this time illuminated from a different direction… To the experimenters’ surprise, the pigeons’ ability to recognize the objects was disturbed by changes in lighting that human observers were barely able to perceive… [see below]

pigeons study

Books, music, etc. from April 2017

Books

  • Callahan, The Givers. An interesting quick portrait of contemporary mega-philanthropy. I haven’t bothered to form opinions about the recommendations in the final chapter.
  • Walters, Feminism: A Very Short Introduction. Meh.

Music

Music I most enjoyed discovering this month:

Movies/TV

Ones I “really liked” (no star), or “loved” (star):

  • Ade: Toni Erdmann (2016)
  • Guadagnino: A Bigger Splash (2016)
  • Yeon: Train to Busan (2016)

Media I’m looking forward to, May 2017 edition

Books

* = added this round
bold = especially excited

[Read more…]

Books, music, etc. from March 2017

Books

  • Dormehl, Thinking Machines. This “history” of AI is mostly a quick survey of news stories about AI progress from the past three years.
  • Wood, The Way of the Strangers. Quite good, afaict.
  • Barrett, How Emotions Are Made. The book is a mixed bag, but fwiw I find this general approach more promising than Ekman/Panksepp/etc. Best Cliffs Notes is maybe this interview.

Music

Music I most enjoyed discovering this month:

Movies/TV

Ones I “really liked” (no star), or “loved” (star):

  • Birbiglia & Barrish, Sleepwalk with Me (2012)
  • Carloni & Nelson, Kung Fu Panda 3 (2016)
  • Blair, I Don’t Feel at Home in This World Anymore (2017)
  • Fukunaga, Beasts of No Nation (2015) ★
  • Audiard, Dheepan (2015)
  • Ferreras, Wrinkles (2011)