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The Hanson-Yudkowsky AI-Foom Debate - Machine Intelligence Research Institute
How Deviant Recent AI Progress Lumpiness?:
I seem to disagree with most people working on artificial intelligence (AI) risk. While with them I expect rapid change once AI is powerful enough to replace most all human workers, I expect this change to be spread across the world, not concentrated in one main localized AI system. The efforts of AI risk folks to design AI systems whose values won’t drift might stop global AI value drift if there is just one main AI system. But doing so in a world of many AI systems at similar abilities levels requires strong global governance of AI systems, which is a tall order anytime soon. Their continued focus on preventing single system drift suggests that they expect a single main AI system.

The main reason that I understand to expect relatively local AI progress is if AI progress is unusually lumpy, i.e., arriving in unusually fewer larger packages rather than in the usual many smaller packages. If one AI team finds a big lump, it might jump way ahead of the other teams.

However, we have a vast literature on the lumpiness of research and innovation more generally, which clearly says that usually most of the value in innovation is found in many small innovations. We have also so far seen this in computer science (CS) and AI. Even if there have been historical examples where much value was found in particular big innovations, such as nuclear weapons or the origin of humans.

Apparently many people associated with AI risk, including the star machine learning (ML) researchers that they often idolize, find it intuitively plausible that AI and ML progress is exceptionally lumpy. Such researchers often say, “My project is ‘huge’, and will soon do it all!” A decade ago my ex-co-blogger Eliezer Yudkowsky and I argued here on this blog about our differing estimates of AI progress lumpiness. He recently offered Alpha Go Zero as evidence of AI lumpiness:


In this post, let me give another example (beyond two big lumps in a row) of what could change my mind. I offer a clear observable indicator, for which data should have available now: deviant citation lumpiness in recent ML research. One standard measure of research impact is citations; bigger lumpier developments gain more citations that smaller ones. And it turns out that the lumpiness of citations is remarkably constant across research fields! See this March 3 paper in Science:

I Still Don’t Get Foom:
All of which makes it look like I’m the one with the problem; everyone else gets it. Even so, I’m gonna try to explain my problem again, in the hope that someone can explain where I’m going wrong. Here goes.

“Intelligence” just means an ability to do mental/calculation tasks, averaged over many tasks. I’ve always found it plausible that machines will continue to do more kinds of mental tasks better, and eventually be better at pretty much all of them. But what I’ve found it hard to accept is a “local explosion.” This is where a single machine, built by a single project using only a tiny fraction of world resources, goes in a short time (e.g., weeks) from being so weak that it is usually beat by a single human with the usual tools, to so powerful that it easily takes over the entire world. Yes, smarter machines may greatly increase overall economic growth rates, and yes such growth may be uneven. But this degree of unevenness seems implausibly extreme. Let me explain.

If we count by economic value, humans now do most of the mental tasks worth doing. Evolution has given us a brain chock-full of useful well-honed modules. And the fact that most mental tasks require the use of many modules is enough to explain why some of us are smarter than others. (There’d be a common “g” factor in task performance even with independent module variation.) Our modules aren’t that different from those of other primates, but because ours are different enough to allow lots of cultural transmission of innovation, we’ve out-competed other primates handily.

We’ve had computers for over seventy years, and have slowly build up libraries of software modules for them. Like brains, computers do mental tasks by combining modules. An important mental task is software innovation: improving these modules, adding new ones, and finding new ways to combine them. Ideas for new modules are sometimes inspired by the modules we see in our brains. When an innovation team finds an improvement, they usually sell access to it, which gives them resources for new projects, and lets others take advantage of their innovation.


In Bostrom’s graph above the line for an initially small project and system has a much higher slope, which means that it becomes in a short time vastly better at software innovation. Better than the entire rest of the world put together. And my key question is: how could it plausibly do that? Since the rest of the world is already trying the best it can to usefully innovate, and to abstract to promote such innovation, what exactly gives one small project such a huge advantage to let it innovate so much faster?


In fact, most software innovation seems to be driven by hardware advances, instead of innovator creativity. Apparently, good ideas are available but must usually wait until hardware is cheap enough to support them.

Yes, sometimes architectural choices have wider impacts. But I was an artificial intelligence researcher for nine years, ending twenty years ago, and I never saw an architecture choice make a huge difference, relative to other reasonable architecture choices. For most big systems, overall architecture matters a lot less than getting lots of detail right. Researchers have long wandered the space of architectures, mostly rediscovering variations on what others found before.

Some hope that a small project could be much better at innovation because it specializes in that topic, and much better understands new theoretical insights into the basic nature of innovation or intelligence. But I don’t think those are actually topics where one can usefully specialize much, or where we’ll find much useful new theory. To be much better at learning, the project would instead have to be much better at hundreds of specific kinds of learning. Which is very hard to do in a small project.

What does Bostrom say? Alas, not much. He distinguishes several advantages of digital over human minds, but all software shares those advantages. Bostrom also distinguishes five paths: better software, brain emulation (i.e., ems), biological enhancement of humans, brain-computer interfaces, and better human organizations. He doesn’t think interfaces would work, and sees organizations and better biology as only playing supporting roles.


Similarly, while you might imagine someday standing in awe in front of a super intelligence that embodies all the power of a new age, superintelligence just isn’t the sort of thing that one project could invent. As “intelligence” is just the name we give to being better at many mental tasks by using many good mental modules, there’s no one place to improve it. So I can’t see a plausible way one project could increase its intelligence vastly faster than could the rest of the world.

Takeoff speeds:
Futurists have argued for years about whether the development of AGI will look more like a breakthrough within a small group (“fast takeoff”), or a continuous acceleration distributed across the broader economy or a large firm (“slow takeoff”).

I currently think a slow takeoff is significantly more likely. This post explains some of my reasoning and why I think it matters. Mostly the post lists arguments I often hear for a fast takeoff and explains why I don’t find them compelling.

(Note: this is not a post about whether an intelligence explosion will occur. That seems very likely to me. Quantitatively I expect it to go along these lines. So e.g. while I disagree with many of the claims and assumptions in Intelligence Explosion Microeconomics, I don’t disagree with the central thesis or with most of the arguments.)
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april 2018 by nhaliday
What Peter Thiel thinks about AI risk - Less Wrong
TL;DR: he thinks its an issue but also feels AGI is very distant and hence less worried about it than Musk.

I recommend the rest of the lecture as well, it's a good summary of "Zero to One"  and a good QA afterwards.

For context, in case anyone doesn't realize: Thiel has been MIRI's top donor throughout its history.

other stuff:
nice interview question: "thing you know is true that not everyone agrees on?"
"learning from failure overrated"
cleantech a huge market, hard to compete
software makes for easy monopolies (zero marginal costs, network effects, etc.)
for most of history inventors did not benefit much (continuous competition)
ethical behavior is a luxury of monopoly
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february 2018 by nhaliday
Docta Ignorantia – quas lacrimas peperere minoribus nostris!
One minor advantage of cultural homogeneity is that it gives you tools to figure out exactly how ignorant a society’s authors and intellectuals truly were. In an era when the pool of books written on any given topic was small, then if someone says something quirky we can eventually, given enough time and coffee, figure out exactly where he got his quirky ideas from.


However it may be, Schelling was a genius, and his contemporaries recognized his genius at an early age and rewarded it. For us this may be slightly difficult to parse, at first: how can you recognize the intellectual talent of a man — of a boy, really — who is in fact deeply ignorant of his own field, philosophy? How can you make him a professor and expect him to lecture on what he has only just started to study?

In our already-degenerate culture, talent has become synonymous with grinding. Having no common standards for the good, the beautiful, and the true, we have no easy way to judge whether someone who disagrees with us is far-sighted or short-sighted. (Imagine looking at Monet’s haystacks for the very first time.) With no consensus on the questions that matter, to seek standards for expertise we have no choice but to turn to the things that don’t matter: the raw mass of (relatively) uncontroversial background material that anyone hoping to become an expert on a certain subject would find useful.
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january 2018 by nhaliday
Darwinian medicine - Randolph Nesse
The Dawn of Darwinian Medicine:
TABLE 1 Examples of the use of the theory of natural selection to predict the existence of phenomena otherwise unsuspected
TABLE 2 A classification of phenomena associated with infectious disease
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november 2017 by nhaliday
An investigation of the unexpectedly high fertility of secular, native-born Jews in Israel: Population Studies: Vol 70, No 2
Secular, native-born Jews in Israel enjoy the socio-economic status of many affluent populations living in other democratic countries, but have above-replacement period and cohort fertility. This study revealed a constellation of interrelated factors which together characterize the socio-economic, cultural, and political environment of this fertility behaviour and set it apart from that of other advanced societies. The factors are: a combination of state and family support for childbearing; a dual emphasis on the social importance of women's employment and fertility; policies that support working mothers within a conservative welfare regime; a family system in which parents provide significant financial and caregiving aid to their adult children; relatively egalitarian gender-role attitudes and household behaviour; the continuing importance of familist ideology and of marriage as a social institution; the role of Jewish nationalism and collective behaviour in a religious society characterized by ethno-national conflict; and a discourse which defines women as the biological reproducers of the nation.
Fertility trends in Israel and Palestinian territories
my impression is the evidence actually favors propaganda effects over tax credits and shit. but I need to gather it all together at some pt
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october 2017 by nhaliday
Can Europe Run Greece? Lessons from U.S. Fiscal Receiverships in Latin America, 1904-31 by Noel Maurer, Leticia Arroyo Abad :: SSRN
In 2012 and again in 2015, the German government proposed sending German administrators to manage Greece’s tax and privatization authorities. The idea was that shared governance would reduce corruption and root out inefficient practices. (In 2017 the Boston Globe proposed a similar arrangement for Haiti.) We test a version of shared governance using eight U.S. interventions between 1904 and 1931, under which American officials took over management of Latin American fiscal institutions. We develop a stylized model in which better monitoring by incorruptible managers does not lead to higher government revenues. Using a new panel of data on fiscal revenues and the volume and terms of trade, we find that revenue fell under receiverships. Our results hold under instrumental variables estimation and with counterfactual specifications using synthetic controls.
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september 2017 by nhaliday
Living with Ignorance in a World of Experts
Another kind of track record that we might care about is not about the expert’s performance, qua expert, but about her record of epistemic integrity. This will be important for helping provide reasonably well supported answers to (Q3) and (Q4) in particular. Anderson (2011) offers some related ideas in her discussion of “criteria for judging honesty” and “criteria for judging epistemic responsibility.” Things we might be interested include the following:
• evidence of previous expert-related dishonesty (e.g. plagiarism, faking data)
• evidence of a record of misleading statements (e.g. cherry-picking data, quotations out of context)
• evidence of a record of misrepresenting views of expert opponents
• evidence of evasion of peer-review or refusal to allow other experts to assess work
• evidence of refusal to disclose data, methodology, or detailed results
• evidence of refusal to disclose results contrary to the expert’s own views
• evidence of “dialogic irrationality”: repeating claims after they have been publicly refuted, without responding to the refutations
• evidence of a record of “over-claiming” of expertise: claiming expertise beyond the expert’s domain of expertise
• evidence of a record of “lending” one’s expertise to support other individuals or institutions that themselves lack epistemic integrity in some of the above ways
• evidence of being an “opinion for hire”—offering expert testimony for pay, perhaps particularly if that testimony conflicts with other things the expert has said
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september 2017 by nhaliday
Europa, Enceladus, Moon Miranda | West Hunter
A lot of ice moons seem to have interior oceans, warmed by tidal flexing and possibly radioactivity.  But they’re lousy candidates for life, because you need free energy; and there’s very little in the interior oceans of such system.

It is possible that NASA is institutionally poor at pointing this out.
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september 2017 by nhaliday
The Once and Future Khan | West Hunter
Razib Khan managed to get himself hired and fired by the New York Times over the course of a single day, an enviable record.  Having the Times look upon you with favor is a dubious honor in the first place, something like having a leper ask you out on a date – so a quick hire-and-fire is optimal. Something for the CV, but you never had to actually hang out with the slimebags.  Not as cool as ‘refused the Fields Medal’,  but pretty cool.

I am not Razib, and I disagree with him on some things of importance: but I look forward to the day, a few years hence, when Razib is still cursing the ignorant commenters, while the New York Times is one with the dust of Nineveh and Tyre.
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september 2017 by nhaliday
Of Mice and Men | West Hunter
It’s not always easy figuring out how a pathogen causes disease. There is an example in mice for which the solution was very difficult, so difficult that we would probably have failed to discover the cause of a similarly obscure infectious disease in humans.

Mycoplasma pulmonis causes a chronic obstructive lung disease in mice, but it wasn’t easy to show this. The disease was first described in 1915, and by 1940, people began to suspect Mycoplasma pulmonis might be the cause. But then again, maybe not. It was often found in mice that seemed healthy. Pure cultures of this organism did not consistently produce lung disease – which means that it didn’t satisfy Koch’s postulates, in particular postulate 1 (The microorganism must be found in abundance in all organisms suffering from the disease, but should not be found in healthy organisms.) and postulate 3 (The cultured microorganism should cause disease when introduced into a healthy organism.).

Well, those postulates are not logic itself, but rather a useful heuristic. Koch knew that, even if lots of other people don’t.

This respiratory disease of mice is long-lasting, but slow to begin. It can take half a lifetime – a mouse lifetime, that is – and that made finding the cause harder. It required patience, which means I certainly couldn’t have done it.

Here’s how they solved it. You can raise germ-free mice. In the early 1970s, researchers injected various candidate pathogens into different groups of germ-free mice and waited to see which, if any, developed this chronic lung disease. It was Mycoplasma pulmonis , all right, but it had taken 60 years to find out.

It turned out that susceptibility differed between different mouse strains – genetic susceptibility was important. Co-infection with other pathogens affected the course of the disease. Microenvironmental details mattered – mainly ammonia in cages where the bedding wasn’t changed often enough. But it didn’t happen without that mycoplasma, which was a key causal link, something every engineer understands but many MDs don’t.

If there was a similarly obscure infectious disease of humans, say one that involved a fairly common bug found in both the just and the unjust, one that took decades for symptoms to manifest – would we have solved it? Probably not.

Cooties are everywhere.

gay germ search:
It’s hard to say, depends on how complicated the path of causation is. Assuming that I’m even right, of course. Some good autopsy studies might be fruitful – you’d look for microanatomical brain differences, as with nartcolepsy. Differences in gene expression, maybe. You could look for a pathogen – using the digital version of RDA (representational difference analysis), say on discordant twins. Do some old-fashioned epidemiology. Look for marker antibodies, signs of some sort of immunological event.

Do all of the above on gay rams – lots easier to get started, much less whining from those being vivisected.

Patrick Moore found the virus causing Kaposi’s sarcoma without any funding at all. I’m sure Peter Thiel could afford a serious try.
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september 2017 by nhaliday
The Complacent Intellectual Class | David V. Johnson
I WOULD LIKE TO COIN A PHRASE, the complacent intellectual class, to describe the overwhelming number of pundits, thought leaders, and policy wonks who accept, welcome, or even enforce slovenly scholarship. These people might, in the abstract, like research that maintains the highest standards, they might even consider themselves academics or bona fide researchers, when in fact they have lost the capacity of maintaining even the most basic standards of rigor.

I am motivated to do so after reading Tyler Cowen’s new book The Complacent Class: The Self-Defeating Quest for the American Dream. I propose the term with some trepidation. Cowen—a George Mason University economist, libertarian theorist, and “legendary blogger” (to quote the book’s inset)—is often a smart commentator who puts his finger on a lot of interesting social phenomena, introduces novel ideas, and proves worth reading from time to time.

But books are different from blog posts and op-eds. And this book fails so glaringly that it makes me despair for this country’s literary culture and intellectual life in general. So let me use Cowen’s latest venture to illustrate what we should all demand from the work of our intellectual class, lest our nation continue to vegetate in the pretend-thinking of #AspenIdeas pseudo-academia.
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september 2017 by nhaliday
Town square test - Wikipedia
In his book The Case for Democracy, published in 2004, Sharansky explains the term: If a person cannot walk into the middle of the town square and express his or her views without fear of arrest, imprisonment, or physical harm, then that person is living in a fear society, not a free society. We cannot rest until every person living in a "fear society" has finally won their freedom.[1]

Heckler's veto:
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august 2017 by nhaliday

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