LINK

Yoshua Bengio, one the world's leading expert on machine learning, and neural networks in particular, explains his view on these issues in an interview. Relevant quotes:

There are people who are grossly overestimating the progress that has been made. There are many, many years of small progress behind a lot of these things, including mundane things like more data and computer power. The hype isn’t about whether the stuff we’re doing is useful or not—it is. But people underestimate how much more science needs to be done. And it’s difficult to separate the hype from the reality because we are seeing these great things and also, to the naked eye, they look magical

[ Recursive self-improvement ] It’s not how AI is built these days. Machine learning means you have a painstaking, slow process of acquiring information through millions of examples. A machine improves itself, yes, but very, very slowly, and in very specialized ways. And the kind of algorithms we play with are not at all like little virus things that are self-programming. That’s not what we’re doing.

Right now, the way we’re teaching machines to be intelligent is that we have to tell the computer what is an image, even at the pixel level. For autonomous driving, humans label huge numbers of images of cars to show which parts are pedestrians or roads. It’s not at all how humans learn, and it’s not how animals learn. We’re missing something big. This is one of the main things we’re doing in my lab, but there are no short-term applications—it’s probably not going to be useful to build a product tomorrow.

We ought to be talking about these things [ AI risks ]. The thing I’m more worried about, in a foreseeable future, is not computers taking over the world. I’m more worried about misuse of AI. Things like bad military uses, manipulating people through really smart advertising; also, the social impact, like many people losing their jobs. Society needs to get together and come up with a collective response, and not leave it to the law of the jungle to sort things out.

I think it's fair to say that Bengio has joined the ranks of AI researchers like his colleagues Andrew Ng and Yann LeCun who publicly express skepticism towards imminent human-extinction-level AI.

When we anticipate the future, we the opportunity to inhibit our behaviours which we anticipate will lead to counterfactual outcomes. Those of us with sufficiently low latencies in our decision cycles may recursively anticipate the consequences of counterfactuating (neologism) interventions to recursively intervene against our interventions.

This may be difficult for some. Try modelling that decision cycle as a nano-scale approximation of time travel. One relevant paradox from popular culture is the farther future paradox described in the tv cartoon called Family Guy.

Watch this clip: https://www.youtube.com/watch?v=4btAggXRB_Q

Relating the satire back to our abstraction of the decision cycle, one may ponder:

What is a satisfactory stopping rule for the far anticipation of self-referential consequence?

That is:

(1) what are the inherent harmful implications of inhibiting actions in and of themselves: stress?

(2) what are their inherent merits: self-determination?

and (3) what are the favourable and disfavourable consequences as x point into the future given y number of points of self reference at points z, a, b and c?

see no ready solution to this problem in terms of human rationality, and see no corresponding problem in artificial intelligence, where it would also apply. Given the relevance to MIRI (since CFAR doesn't seem work on open-problems in the same way)

I would like to also take this opportunity to open this as an experimental thread for the community to generate a list of ''open-problems'' in human rationality that are otherwise scattered across the community blog and wiki. 

You're an average person.

You don't know what diseases you'll get in the future.

You know people get diseases and certain populations get diseases more than others, enough to say certain things cause diseases.

You're not quite the average person.

You have a strong preference against sickness and a strong belief in your ability to mitigate deleterious circumstances.

You have access to preventative research. You know if you don't work in a coal mine, overtrain when running, and eat healthy, you can stay healthier than those who take those risks.

You know that some disease outcomes are less than predictable, so you want to work towards the available of treatments that fill gaps in the availability of therapeutics. For instance, you might want a treatment for HIV to be developed, in case you become HIV infected, since there is a risk of HIV exposure for almost anyone exposed to unprotected sex, since they won't necessarily know their sexual partners entire serohistory (noologism?)
However, you don't know which diseases you will get. So how do you prioritise?

Perhaps, medical device and pharmaceutical company strategies could be ported to your situation.

Most people, including non-epidemiologist researchers, don't have access to epidemiology data sets.

Most people, don't have the patience to read a book on medical market research

You don't have the funds or connections to employ the world's only specialist in the area of medical market forecasting.

At least he's broken down the field into best practice questions:

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-seventh section in the reading guide: Pathways and enablers.

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: “Pathways and enablers” from Chapter 14

Summary

1. Is hardware progress good?
   1. Hardware progress means machine intelligence will arrive sooner, which is probably bad.
   2. More hardware at a given point means less understanding is likely to be needed to build machine intelligence, and brute-force techniques are more likely to be used. These probably increase danger.
   3. More hardware progress suggests there will be more hardware overhang when machine intelligence is developed, and thus a faster intelligence explosion. This seems good inasmuch as it brings a higher chance of a singleton, but bad in other ways:
      1. Less opportunity to respond during the transition
      2. Less possibility of constraining how much hardware an AI can reach
      3. Flattens the playing field, allowing small projects a better chance. These are less likely to be safety-conscious.
   4. Hardware has other indirect effects, e.g. it allowed the internet, which contributes substantially to work like this. But perhaps we have enough hardware now for such things.
   5. On balance, more hardware seems bad, on the impersonal perspective.
2. Would brain emulation be a good thing to happen?
   1. Brain emulation is coupled with 'neuromorphic' AI: if we try to build the former, we may get the latter. This is probably bad.
   2. If we achieved brain emulations, would this be safer than AI? Three putative benefits:
      1. "The performance of brain emulations is better understood"
         1. However we have less idea how modified emulations would behave
         2. Also, AI can be carefully designed to be understood
      2. "Emulations would inherit human values"
         1. This might require higher fidelity than making an economically functional agent
         2. Humans are not that nice, often. It's not clear that human nature is a desirable template.
      3. "Emulations might produce a slower take-off"
         1. It isn't clear why it would be slower. Perhaps emulations would be less efficient, and so there would be less hardware overhang. Or perhaps because emulations would not be qualitatively much better than humans, just faster and more populous of them
         2. A slower takeoff may lead to better control
         3. However it also means more chance of a multipolar outcome, and that seems bad.
   3. If brain emulations are developed before AI, there may be a second transition to AI later.
      1. A second transition should be less explosive, because emulations are already many and fast relative to the new AI. 
      2. The control problem is probably easier if the cognitive differences are smaller between the controlling entities and the AI.
      3. If emulations are smarter than humans, this would have some of the same benefits as cognitive enhancement, in the second transition.
      4. Emulations would extend the lead of the frontrunner in developing emulation technology, potentially allowing that group to develop AI with little disturbance from others.
      5. On balance, brain emulation probably reduces the risk from the first transition, but added to a second  transition this is unclear.
   4. Promoting brain emulation is better if:
      1. You are pessimistic about human resolution of control problem
      2. You are less concerned about neuromorphic AI, a second transition, and multipolar outcomes
      3. You expect the timing of brain emulations and AI development to be close
      4. You prefer superintelligence to arrive neither very early nor very late
3. The person affecting perspective favors speed: present people are at risk of dying in the next century, and may be saved by advanced technology

Another view

I talked to Kenzi Amodei about her thoughts on this section. Here is a summary of her disagreements:

Bostrom argues that we probably shouldn't celebrate advances in computer hardware. This seems probably right, but here are counter-considerations to a couple of his arguments.

The great filter

A big reason Bostrom finds fast hardware progress to be broadly undesirable is that he judges the state risks from sitting around in our pre-AI situation to be low, relative to the step risk from AI. But the so called 'Great Filter' gives us reason to question this assessment.

The argument goes like this. Observe that there are a lot of stars (we can detect about ~10^22 of them). Next, note that we have never seen any alien civilizations, or distant suggestions of them. There might be aliens out there somewhere, but they certainly haven't gone out and colonized the universe enough that we would notice them (see 'The Eerie Silence' for further discussion of how we might observe aliens). 

This implies that somewhere on the path between a star existing, and it being home to a civilization that ventures out and colonizes much of space, there is a 'Great Filter': at least one step that is hard to get past. 1/10^22 hard to get past. We know of somewhat hard steps at the start: a star might not have planets, or the planets may not be suitable for life. We don't know how hard it is for life to start: this step could be most of the filter for all we know.

If the filter is a step we have passed, there is nothing to worry about. But if it is a step in our future, then probably we will fail at it, like everyone else. And things that stop us from visibly colonizing the stars are may well be existential risks.

At least one way of understanding anthropic reasoning suggests the filter is much more likely to be at a step in our future. Put simply, one is much more likely to find oneself in our current situation if being killed off on the way here is unlikely.

So what could this filter be? One thing we know is that it probably isn't AI risk, at least of the powerful, tile-the-universe-with-optimal-computations, sort that Bostrom describes. A rogue singleton colonizing the universe would be just as visible as its alien forebears colonizing the universe. From the perspective of the Great Filter, either one would be a 'success'. But there are no successes that we can see.

What's more, if we expect to be fairly safe once we have a successful superintelligent singleton, then this points at risks arising before AI.

So overall this argument suggests that AI is less concerning than we think and that other risks (especially early ones) are more concerning than we think. It also suggests that AI is harder than we think.

Which means that if we buy this argument, we should put a lot more weight on the category of 'everything else', and especially the bits of it that come before AI. To the extent that known risks like biotechnology and ecological destruction don't seem plausible, we should more fear unknown unknowns that we aren't even preparing for.

How much progress is enough?

Bostrom points to positive changes hardware has made to society so far. For instance, hardware allowed personal computers, bringing the internet, and with it the accretion of an AI risk community, producing the ideas in Superintelligence. But then he says probably we have enough: "hardware is already good enough for a great many applications that could facilitate human communication and deliberation, and it is not clear that the pace of progress in these areas is strongly bottlenecked by the rate of hardware improvement."

This seems intuitively plausible. However one could probably have erroneously made such assessments in all kinds of progress, all over history. Accepting them all would lead to madness, and we have no obvious way of telling them apart.

In the 1800s it probably seemed like we had enough machines to be getting on with, perhaps too many. In the 1800s people probably felt overwhelmingly rich. If the sixties too, it probably seemed like we had plenty of computation, and that hardware wasn't a great bottleneck to social progress.

If a trend has brought progress so far, and the progress would have been hard to predict in advance, then it seems hard to conclude from one's present vantage point that progress is basically done.

Notes

1. How is hardware progressing?

I've been looking into this lately, at AI Impacts. Here's a figure of MIPS/$ growing, from Muehlhauser and Rieber.

(Note: I edited the vertical axis, to remove a typo)

2. Hardware-software indifference curves

It was brought up in this chapter that hardware and software can substitute for each other: if there is endless hardware, you can run worse algorithms, and vice versa. I find it useful to picture this as indifference curves, something like this: 

(Image: Hypothetical curves of hardware-software combinations producing the same performance at Go (source).)

I wrote about predicting AI given this kind of model here.

3. The potential for discontinuous AI progress

While we are on the topic of relevant stuff at AI Impacts, I've been investigating and quantifying the claim that AI might suddenly undergo huge amounts of abrupt progress (unlike brain emulations, according to Bostrom). As a step, we are finding other things that have undergone huge amounts of progress, such as nuclear weapons and high temperature superconductors:

(Figure originally from here)

4. The person-affecting perspective favors speed less as other prospects improve

I agree with Bostrom that the person-affecting perspective probably favors speeding many technologies, in the status quo. However I think it's worth noting that people with the person-affecting view should be scared of existential risk again as soon as society has achieved some modest chance of greatly extending life via specific technologies. So if you take the person-affecting view, and think there's a reasonable chance of very long life extension within the lifetimes of many existing humans, you should be careful about trading off speed and risk of catastrophe.

5. It seems unclear that an emulation transition would be slower than an AI transition. 

One reason to expect an emulation transition to proceed faster is that there is an unusual reason to expect abrupt progress there.

6. Beware of brittle arguments

This chapter presented a large number of detailed lines of reasoning for evaluating hardware and brain emulations. This kind of concern might apply.

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. Investigate in more depth how hardware progress affects factors of interest
2. Assess in more depth the likely implications of whole brain emulation 
3. Measure better the hardware and software progress that we see (e.g. some efforts at AI Impacts, MIRI, MIRI and MIRI)
4. Investigate the extent to which hardware and software can substitute (I describe more projects here)
5. Investigate the likely timing of whole brain emulation (the Whole Brain Emulation Roadmap is the main work on this)

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about how collaboration and competition affect the strategic picture. To prepare, read “Collaboration” from Chapter 14 The discussion will go live at 6pm Pacific time next Monday 23 March. Sign up to be notified here.

How to see into the future, by Tim Harford

The article may be gated. (I have a subscription through my school.)

It is mainly about two things: the differing approaches to forecasting taken by Irving Fisher, John Maynard Keynes, and Roger Babson; and Philip Tetlock's Good Judgment Project.

Key paragraph:

So what is the secret of looking into the future? Initial results from the Good Judgment Project suggest the following approaches. First, some basic training in probabilistic reasoning helps to produce better forecasts. Second, teams of good forecasters produce better results than good forecasters working alone. Third, actively open-minded people prosper as forecasters.

 

But the Good Judgment Project also hints at why so many experts are such terrible forecasters. It’s not so much that they lack training, teamwork and open-mindedness – although some of these qualities are in shorter supply than others. It’s that most forecasters aren’t actually seriously and single-mindedly trying to see into the future. If they were, they’d keep score and try to improve their predictions based on past errors. They don’t.

FHI has released a new tech report:

Armstrong, Bostrom, and Shulman. Racing to the Precipice: a Model of Artificial Intelligence Development.

Abstract:

This paper presents a simple model of an AI arms race, where several development teams race to build the first AI. Under the assumption that the first AI will be very powerful and transformative, each team is incentivized to finish first — by skimping on safety precautions if need be. This paper presents the Nash equilibrium of this process, where each team takes the correct amount of safety precautions in the arms race. Having extra development teams and extra enmity between teams can increase the danger of an AI-disaster, especially if risk taking is more important than skill in developing the AI. Surprisingly, information also increases the risks: the more teams know about each others’ capabilities (and about their own), the more the danger increases.

The paper is short and readable; discuss it here!

But my main reason for posting is to ask this question: What is the most similar work that you know of? I'd expect people to do this kind of thing for modeling nuclear security risks, and maybe other things, but I don't happen to know of other analyses like this.

Cross-posted from MIRI's blog.

MIRI aims to do research now that increases humanity's odds of successfully managing important AI-related events that are at least a few decades away. Thus, we'd like to know: To what degree can we take actions now that will predictably have positive effects on AI-related events decades from now? And, which factors predict success and failure in planning for decades-distant events that share important features with future AI events?

Or, more generally: How effectively can humans plan for future decades? Which factors predict success and failure in planning for future decades?

To investigate these questions, we asked Jonah Sinick to examine historical attempts to plan for future decades and summarize his findings. We pre-committed to publishing our entire email exchange on the topic (with minor editing), just as Jonah had done previously with GiveWell on the subject of insecticide-treated nets. The post below is a summary of findings from our full email exchange (.docx) so far.

We decided to publish our initial findings after investigating only a few historical cases. This allows us to gain feedback on the value of the project, as well as suggestions for improvement, before continuing. It also means that we aren't yet able to draw any confident conclusions about our core questions.

The most significant results from this project so far are:

1. Jonah's initial impressions about The Limits to Growth (1972), a famous forecasting study on population and resource depletion, were that its long-term predictions were mostly wrong, and also that its authors (at the time of writing it) didn't have credentials that would predict forecasting success. Upon reading the book, its critics, and its defenders, Jonah concluded that many critics and defenders had  seriously misrepresented the book, and that the book itself exhibits high epistemic standards and does not make significant predictions that turned out to be wrong.
2. Svante Arrhenius (1859-1927) did a surprisingly good job of climate modeling given the limited information available to him, but he was nevertheless wrong about two important policy-relevant factors. First, he failed to predict how quickly carbon emissions would increase. Second, he predicted that global warming would have positive rather than negative humanitarian impacts. If more people had taken Arrhenius' predictions seriously and burned fossil fuels faster for humanitarian reasons, then today's scientific consensus on the effects of climate change suggests that the humanitarian effects would have been negative.
3. In retrospect, Norbert Wiener's concerns about the medium-term dangers of increased automation appear naive, and it seems likely that even at the time, better epistemic practices would have yielded substantially better predictions.
4. Upon initial investigation, several historical cases seemed unlikely to shed substantial light on our  core questions: Norman Rasmussen's analysis of the safety of nuclear power plants, Leo Szilard's choice to keep secret a patent related to nuclear chain reactions, Cold War planning efforts to win decades later, and several cases of "ethically concerned scientists."
5. Upon initial investigation, two historical cases seemed like they might shed light on our  core questions, but only after many hours of additional research on each of them: China's one-child policy, and the Ford Foundation's impact on India's 1991 financial crisis.
6. We listed many other historical cases that may be worth investigating.

The project has also produced a chapter-by-chapter list of some key lessons from Nate Silver's The Signal and the Noise, available here.

Further details are given below. For sources and more, please see our full email exchange (.docx).

In general and across all instances I can think of so far, I do not agree with the part of your futurological forecast in which you reason, "After event W happens, everyone will see the truth of proposition X, leading them to endorse Y and agree with me about policy decision Z."

Example 1:  "After a 2-year-old mouse is rejuvenated to allow 3 years of additional life, society will realize that human rejuvenation is possible, turn against deathism as the prospect of lifespan / healthspan extension starts to seem real, and demand a huge Manhattan Project to get it done."  (EDIT:  This has not happened, and the hypothetical is mouse healthspan extension, not anything cryonic.  It's being cited because this is Aubrey de Grey's reasoning behind the Methuselah Mouse Prize.)

Alternative projection:  Some media brouhaha.  Lots of bioethicists acting concerned.  Discussion dies off after a week.  Nobody thinks about it afterward.  The rest of society does not reason the same way Aubrey de Grey does.

Example 2:  "As AI gets more sophisticated, everyone will realize that real AI is on the way and then they'll start taking Friendly AI development seriously."

Alternative projection:  As AI gets more sophisticated, the rest of society can't see any difference between the latest breakthrough reported in a press release and that business earlier with Watson beating Ken Jennings or Deep Blue beating Kasparov; it seems like the same sort of press release to them.  The same people who were talking about robot overlords earlier continue to talk about robot overlords.  The same people who were talking about human irreproducibility continue to talk about human specialness.  Concern is expressed over technological unemployment the same as today or Keynes in 1930, and this is used to fuel someone's previous ideological commitment to a basic income guarantee, inequality reduction, or whatever.  The same tiny segment of unusually consequentialist people are concerned about Friendly AI as before.  If anyone in the science community does start thinking that superintelligent AI is on the way, they exhibit the same distribution of performance as modern scientists who think it's on the way, e.g. Hugo de Garis, Ben Goertzel, etc.

Consider the situation in macroeconomics.  When the Federal Reserve dropped interest rates to nearly zero and started printing money via quantitative easing, we had some people loudly predicting hyperinflation just because the monetary base had, you know, gone up by a factor of 10 or whatever it was.  Which is kind of understandable.  But still, a lot of mainstream economists (such as the Fed) thought we would not get hyperinflation, the implied spread on inflation-protected Treasuries and numerous other indicators showed that the free market thought we were due for below-trend inflation, and then in actual reality we got below-trend inflation.  It's one thing to disagree with economists, another thing to disagree with implied market forecasts (why aren't you betting, if you really believe?) but you can still do it sometimes; but when conventional economics, market forecasts, and reality all agree on something, it's time to shut up and ask the economists how they knew.  I had some credence in inflationary worries before that experience, but not afterward...  So what about the rest of the world?  In the heavily scientific community you live in, or if you read econblogs, you will find that a number of people actually have started to worry less about inflation and more about sub-trend nominal GDP growth.  You will also find that right now these econblogs are having worry-fits about the Fed prematurely exiting QE and choking off the recovery because the elderly senior people with power have updated more slowly than the econblogs.  And in larger society, if you look at what happens when Congresscritters question Bernanke, you will find that they are all terribly, terribly concerned about inflation.  Still.  The same as before.  Some econblogs are very harsh on Bernanke because the Fed did not print enough money, but when I look at the kind of pressure Bernanke was getting from Congress, he starts to look to me like something of a hero just for following conventional macroeconomics as much as he did.

That issue is a hell of a lot more clear-cut than the medical science for human rejuvenation, which in turn is far more clear-cut ethically and policy-wise than issues in AI.

After event W happens, a few more relatively young scientists will see the truth of proposition X, and the larger society won't be able to tell a damn difference.  This won't change the situation very much, there are probably already some scientists who endorse X, since X is probably pretty predictable even today if you're unbiased.  The scientists who see the truth of X won't all rush to endorse Y, any more than current scientists who take X seriously all rush to endorse Y.  As for people in power lining up behind your preferred policy option Z, forget it, they're old and set in their ways and Z is relatively novel without a large existing constituency favoring it.  Expect W to be used as argument fodder to support conventional policy options that already have political force behind them, and for Z to not even be on the table.

Previous: http://lesswrong.com/lw/6ya/link_get_paid_to_train_your_rationality/

The IARPA-run forecasting contest remains ongoing. Season 1 has largely finished up, and groups are preparing for season 2. Season 1 participants like myself get first dibs, but http://goodjudgmentproject.com/ has announced in emails they have spots open for first-time participants! I assume the other groups may have openings as well.

I personally found the tournament a source of predictions to stick on PB.com and I even did pretty well in GJP. (When I checked a few weeks ago, I was ranked 28 of 203 in my experimental group.) I haven't been paid my honorarium yet, though.

When will AGI be created? When will WBE be possible? It would be nice to have somewhat reliable methods of long-term technological forecasting. Do we? Here's my own brief overview of the subject...

Nagy et al. (2010) is, I think, the best paper in the field. At first you might think it's basically supporting Kurzweillian conclusions about exponential curves for all technologies, but there are serious qualifications to make. The first is that the prediction laws they tested are linear regression models, which fit the data well but are not theoretically appropriate for modeling the data because the assumptions of independence and so on are not satisfied. A second and bigger qualification is that Nagy & company only used data from small time slices for most technologies examined in the paper. This latter problem becomes a larger source of worry when you note that we have reason to expect many technologies to follow a logistic rather than exponential growth pattern, and exponential and logistic growth patterns look the same for the first part of their curves — see Modis (2006). A third qualification is that Nagy's performance curves database is not representative of "technology in general" or anything like that. Fourth, Nagy's study is the first of its kind, not a summary of 20 years of careful work all leading to a shared set of conclusions we can be fairly confident about. The hedonic hotspots that fire in my brain when I engage in hyperbole want me to say that serious long-term technological forecasting is not summarized by Nagy but begins with Nagy. (But that, of course, compresses history too much.)

Williams (2011) demonstrates that prediction markets just aren't yet tested in the domain of long-term forecasting, and have several incentive-structure problems yet to be worked out. I bought the book, but it's probably not worth $125. If you go to the library and want to copy just one chapter, make it Croxson's.

I see basically no evidence that any expert elicitation method is reliable for long-term technological forecasting (e.g. see Rowe & Wright 2001). The first study to show positive results from expert elicitation (in this case, a particular version of the Delphi method) for long-term forecasting is a single paper from last year: this one.

So if you want to predict the future of technology, it's best not to tell detailed stories. Instead, you'll want to focus on "disjunctive" outcomes that, like the evolution of eyes or the emergence of markets, can come about through many different paths and can gather momentum once they begin. Humans actually tend to intuitively underestimate the likelihood of such convergent outcomes (Tversky and Kahneman 1974).

A tournament is currently being initiated by the Intelligence Advanced Research Project Activity (IARPA) with the goal of improving forecasting methods for global events of national (US) interest. One of the teams (The Good Judgement Team) is recruiting volunteers to have their forecasts tracked. Volunteers will receive an annual honorarium ($150), and it appears there will be ongoing training to improve one's forecast accuracy (not sure exactly what form this will take).

I'm registered, and wondering if any other LessWrongers are participating/considering it. It could be interesting to compare methods and results.

Extensive quotes and links below the fold.