Monthly Archives: September 2008

With today’s snapback, the Dow lost 777 and regained 485.

As of this evening, Intrade says the probability of a bailout bill passing by Oct 31st is 85%.

(777-485)/(1-.85) = 1,946.  So a bailout bill makes an expected difference of 2000 points on the Dow.

Of course this is a bogus calculation, but it’s an interesting one.  Not overwhelmingly on-topic for OB, but it involves prediction markets and I didn’t see anyone else pointing it out.  I hope the bailout fails decisively, so this calculation can be tested.

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Awww, a Zebra

This image recently showed up on Flickr (original is nicer):

With the caption:

“Alas for those who turn their eyes from zebras and dream of dragons!  If we cannot learn to take joy in the merely real, our lives shall be empty indeed.” — Eliezer S. Yudkowsky.

“Awww!”, I said, and called over my girlfriend over to look.

“Awww!”, she said, and then looked at me, and said,  “I think you need to take your own advice!”

Me:  “But I’m looking at the zebra!”
Her:  “On a computer!
Me:  (Turns away, hides face.)
Her:  “Have you ever even seen a zebra in real life?”
Me:  “Yes!  Yes, I have!  My parents took me to Lincoln Park Zoo!  …man, I hated that place.”

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The Magnitude of His Own Folly

Followup toMy Naturalistic Awakening, Above-Average AI Scientists

In the years before I met that would-be creator of Artificial General Intelligence (with a funded project) who happened to be a creationist, I would still try to argue with individual AGI wannabes.

In those days, I sort-of-succeeded in convincing one such fellow that, yes, you had to take Friendly AI into account, and no, you couldn’t just find the right fitness metric for an evolutionary algorithm.  (Previously he had been very impressed with evolutionary algorithms.)

And the one said:  Oh, woe!  Oh, alas!  What a fool I’ve been!  Through my carelessness, I almost destroyed the world!  What a villain I once was!

Now, there’s a trap I knew I better than to fall into –

– at the point where, in late 2002, I looked back to Eliezer1997‘s AI proposals and realized what they really would have done, insofar as they were coherent enough to talk about what they "really would have done".

When I finally saw the magnitude of my own folly, everything fell into place at once.  The dam against realization cracked; and the unspoken doubts that had been accumulating behind it, crashed through all together.  There wasn’t a prolonged period, or even a single moment that I remember, of wondering how I could have been so stupid.  I already knew how.

And I also knew, all at once, in the same moment of realization, that to say, I almost destroyed the world!, would have been too prideful.

It would have been too confirming of ego, too confirming of my own importance in the scheme of things, at a time when – I understood in the same moment of realization – my ego ought to be taking a major punch to the stomach.  I had been so much less than I needed to be; I had to take that punch in the stomach, not avert it.

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It is a strange fact that for many professions, the odds of success are extremely low, and so is the average pay.  For example, many people have dreams about getting into acting, but I’ve seen estimates that only 5% of people who call themselves actors make a living acting, as opposed waiting tables and whatnot.  Similar odds exist for would-be novelists, musicians.  There are the high profile people one thinks of for the categories that act like lotto jackpots, a focal point that overwhelms objective odds. It is somewhat unrealistic, if not cruel, to tell people that chasing their dreams is a waste of time.  People like to dream, and after all, ‘the experts’ are often wrong.  Yet I think this phenomenon suggests that perhaps people accept low average ‘return’, in exchange for the dreams these professions present.

The inequality in these fields, where incomes have a power law distribution, is not bug, it’s a feature.  The presence of superstars, whose income and status is so high, is the offsetting basis of the dreams for those in the industry, why they are willing to accept less ‘on average’.  In dreams without such opportunities, you need security, or some other offset to compensate. If this is true, it suggests there is a general hope premium in industries, and even assets.  Many financial assets that have the highest volatilities have below average returns, if not negative returns: out-of-the-money call options, Junk bonds, highly volatile stocks, extreme-odds at the racetrack.  We pay to dream, and it can be frivolous, as with the \$1 I recently spent on a \$206MM lotto ticket Saturday (odds: 1 in 130 million, I did not win), but it can also be a significant part of one’s investment portfolio (not wise, in my opinion, but real).

People take risk based on hope, and hope is a function of one’s dreams.  In 1961 Walter Gutman wrote a book You only have to get rich once, and noted that "growth stocks might better be called dream stocks", and that ‘dreams are real–we have them every day.  It’s a big mistake to think dreams are unreal and what is called real life is real’.   This is a simple, profound, model of the value effect, where stodgy, low beta firms without much upside generate a higher return than stocks that can be classified by some as the next ‘Yahoo!’ Dreams, in moderation of course, are paradoxically as real as anything, and assets that embolden dreams have extra value for investors, and they are willing to accept a lower ‘average’ return because in taking risk, because they are already ignoring the skepticism of the consensus.

Jonathan Alter recently wrote that we should perhaps pay teachers more, and pay their superstars a lot.  I think the data suggest that if we start paying really large salaries for superstar teachers, we could pay a few of them more, but pay them in aggregate less.  That is, we would have just as much supply if we offered them the hope of become very wealthy, with a lower average pay, than our current system, which pays them a rather solid pay but with very limited upside.  No one with really large ambitions goes into education precisely because they top has a low ceiling.  Hope is worth something, in terms of a higher price, and thus lower  return.

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Dangerous Species Warnings

For most species, officially declaring them "endangered" makes them worse off:

Ferraro, McIntosh, and Ospina (2007) find that … for a large majority of the species studied, listing under the [U.S. Endangered Species Act] has actually harmed the species’ chances of recovery.

Ferraro et al examine two different elements of the ESA’s operation: the impact of listing a species as being endangered, and the effects of species-specific government recovery expenditures. … For the 25 percent of the listed species that garner about 95 percent of all government recovery funding, the ESA seems to have produced improvements in the chances of recovery. But for the other 75 percent of species, those that are largely ignored by the funding process, the ESA has sharply reduced species’ viability, compared to unlisted species that are otherwise similar except for listing status. …

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Friedman’s “Prediction vs. Explanation”

We do ten experiments. A scientist observes the results, constructs a theory consistent with them, and uses it to predict the results of the next ten. We do them and the results fit his predictions. A second scientist now constructs a theory consistent with the results of all twenty experiments.

The two theories give different predictions for the next experiment. Which do we believe? Why?

One of the commenters links to Overcoming Bias, but as of 11PM on Sep 28th, David’s blog’s time, no one has given the exact answer that I would have given.  It’s interesting that a question so basic has received so many answers.

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Above-Average AI Scientists

Followup toThe Level Above Mine, Competent Elites

(Those who didn’t like the last two posts should definitely skip this one.)

I recall one fellow, who seemed like a nice person, and who was quite eager to get started on Friendly AI work, to whom I had trouble explaining that he didn’t have a hope.  He said to me:

"If someone with a Masters in chemistry isn’t intelligent enough, then you’re not going to have much luck finding someone to help you."

It’s hard to distinguish the grades above your own.  And even if you’re literally the best in the world, there are still electron orbitals above yours – they’re just unoccupied.  Someone had to be "the best physicist in the world" during the time of Ancient Greece.  Would they have been able to visualize Newton?

At one of the first conferences organized around the tiny little subfield of Artificial General Intelligence, I met someone who was heading up a funded research project specifically declaring AGI as a goal, within a major corporation.  I believe he had people under him on his project.  He was probably paid at least three times as much as I was paid (at that time).  His academic credentials were superior to mine (what a surprise) and he had many more years of experience.  He had access to lots and lots of computing power.

And like nearly everyone in the field of AGI, he was rushing forward to write code immediately – not holding off and searching for a sufficiently precise theory to permit stable self-improvement.

In short, he was just the sort of fellow that…  Well, many people, when they hear about Friendly AI, say:  "Oh, it doesn’t matter what you do, because [someone like this guy] will create AI first."  He’s the sort of person about whom journalists ask me, "You say that this isn’t the time to be talking about regulation, but don’t we need laws to stop people like this from creating AI?"

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Competent Elites

Followup toThe Level Above Mine

(Anyone who didn’t like yesterday’s post should probably avoid this one.)

I remember what a shock it was to first meet Steve Jurvetson, of the venture capital firm Draper Fisher Jurvetson.

Steve Jurvetson talked fast and articulately, could follow long chains of reasoning, was familiar with a wide variety of technologies, and was happy to drag in analogies from outside sciences like biology – good ones, too.

I once saw Eric Drexler present an analogy between biological immune systems and the "active shield" concept in nanotechnology, arguing that just as biological systems managed to stave off invaders without the whole community collapsing, nanotechnological immune systems could do the same.

I thought this was a poor analogy, and was going to point out some flaws during the Q&A.  But Steve Jurvetson, who was in line before me, proceeded to demolish the argument even more thoroughly.  Jurvetson pointed out the evolutionary tradeoff between virulence and transmission that keeps natural viruses in check, talked about how greater interconnectedness led to larger pandemics – it was very nicely done, demolishing the surface analogy by correct reference to deeper biological details.

I was shocked, meeting Steve Jurvetson, because from everything I’d read about venture capitalists before then, VCs were supposed to be fools in business suits, who couldn’t understand technology or engineers or the needs of a fragile young startup, but who’d gotten ahold of large amounts of money by dint of seeming reliable to other business suits.

One of the major surprises I received when I moved out of childhood into the real world, was the degree to which the world is stratified by genuine competence.

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Bank Politics Is Not About Bank Policy

Pundits are falling all over themselves to advise us on the banking crisis.  Politics Is Not About Policy predicts that politics in this crisis is also not about policy.  It says that since we will mainly pick the policy recommended by the highest status coalition of advisors, each pundit will rise in status if the policy we choose is similar to the one he suggested.  So pundits are eager to offer reasonable seeming advice.  We listen because we want to see who will win this status battle, and maybe shift the coalition we support a bit to better position ourselves in such conflicts.

You might say we are using status as a clue to policy, since higher status advisors on average suggest better policies.  But if we were serious about getting the most effective policy advice we’d be collecting pundit prediction track records.  Yet I doubt the few who actually did predict this crisis will be listened to more on what we should do about it.  And while pundits etc. are spending fortunes now to push their views, we still won’t spend the million it would take to create a good (e.g., prediction market) system to collect and track pundit banking predictions.

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The Level Above Mine

Followup toThe Proper Use of Humility, Tsuyoku Naritai

(At this point, I fear that I must recurse into a subsequence; but if all goes as planned, it really will be short.)

I once lent Xiaoguang "Mike" Li my copy of "Probability Theory: The Logic of Science".  Mike Li read some of it, and then came back and said:

"Wow… it’s like Jaynes is a thousand-year-old vampire."

Then Mike said, "No, wait, let me explain that -" and I said, "No, I know exactly what you mean."  It’s a convention in fantasy literature that the older a vampire gets, the more powerful they become.

I’d enjoyed math proofs before I encountered Jaynes.  But E.T. Jaynes was the first time I picked up a sense of formidability from mathematical arguments.  Maybe because Jaynes was lining up "paradoxes" that had been used to object to Bayesianism, and then blasting them to pieces with overwhelming firepower – power being used to overcome others.  Or maybe the sense of formidability came from Jaynes not treating his math as a game of aesthetics; Jaynes cared about probability theory, it was bound up with other considerations that mattered, to him and to me too.

For whatever reason, the sense I get of Jaynes is one of terrifying swift perfection – something that would arrive at the correct answer by the shortest possible route, tearing all surrounding mistakes to shreds in the same motion.  Of course, when you write a book, you get a chance to show only your best side.  But still.

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