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Right for the Wrong Reasons

14 katydee 24 January 2013 12:02AM

One of the few things that I really appreciate having encountered during my study of philosophy is the Gettier problem. Paper after paper has been published on this subject, starting with Gettier's original "Is Justified True Belief Knowledge?" In brief, Gettier argues that knowledge cannot be defined as "justified true belief" because there are cases when people have a justified true belief, but their belief is justified for the wrong reasons.

For instance, Gettier cites the example of two men, Smith and Jones, who are applying for a job. Smith believes that Jones will get the job, because the president of the company told him that Jones would be hired. He also believes that Jones has ten coins in his pocket, because he counted the coins in Jones's pocket ten minutes ago (Gettier does not explain this behavior). Thus, he forms the belief "the person who will get the job has ten coins in his pocket."

Unbeknownst to Smith, though, he himself will get the job, and further he himself has ten coins in his pocket that he was not aware of-- perhaps he put someone else's jacket on by mistake. As a result, Smith's belief that "the person who will get the job has ten coins in his pocket" was correct, but only by luck.

While I don't find the primary purpose of Gettier's argument particularly interesting or meaningful (much less the debate it spawned), I do think Gettier's paper does a very good job of illustrating the situation that I refer to as "being right for the wrong reasons." This situation has important implications for prediction-making and hence for the art of rationality as a whole.

Simply put, a prediction that is right for the wrong reasons isn't actually right from an epistemic perspective.

If I predict, for instance, that I will win a 15-touch fencing bout, implicitly believing this will occur when I strike my opponent 15 times before he strikes me 15 times, and I in fact lose fourteen touches in a row, only to win by forfeit when my opponent intentionally strikes me many times in the final touch and is disqualified for brutality, my prediction cannot be said to have been accurate.

Where this gets more complicated is with predictions that are right for the wrong reasons, but the right reasons still apply. Imagine the previous example of a fencing bout, except this time I score 14 touches in a row and then win by forfeit when my opponent flings his mask across the hall in frustration and is disqualified for an offense against sportsmanship. Technically, my prediction is again right for the wrong reasons-- my victory was not thanks to scoring 15 touches, but thanks to my opponent's poor sportsmanship and subsequent disqualification. However, I likely would have scored 15 touches given the opportunity.

In cases like this, it may seem appealing to credit my prediction as successful, as it would be successful under normal conditions. However, I  we have to resist this impulse and instead simply work on making more precise predictions. If we start crediting predictions that are right for the wrong reasons, even if it seems like the "spirit" of the prediction is right, this seems to open the door for relying on intuition and falling into the traps that contaminate much of modern philosophy.

What we really need to do in such cases seems to be to break down our claims into more specific predictions, splitting them into multiple sub-predictions if necessary. My prediction about the outcome of the fencing bout could better be expressed as multiple predictions, for instance "I will score more points than my opponent" and "I will win the bout." Some may notice that this is similar to the implicit justification being made in the original prediction. This is fitting-- drawing out such implicit details is key to making accurate predictions. In fact, this example itself was improved by tabooing[1] "better" in the vague initial sentence "I will fence better than my opponent."

In order to make better predictions, we must cast out those predictions that are right for the wrong reasons. While it may be tempting to award such efforts partial credit, this flies against the spirit of the truth. The true skill of cartography requires forming both accurate and reproducible maps; lucking into accuracy may be nice, but it speaks ill of the reproducibility of your methods.

 

[1] I greatly suggest that you make tabooing a five-second skill, and better still recognizing when you need to apply it to your own processes. It pays great dividends in terms of precise thought.

Assessing Kurzweil: the results

42 Stuart_Armstrong 16 January 2013 04:51PM

Predictions of the future rely, to a much greater extent than in most fields, on the personal judgement of the expert making them. Just one problem - personal expert judgement generally sucks, especially when the experts don't receive immediate feedback on their hits and misses. Formal models perform better than experts, but when talking about unprecedented future events such as nanotechnology or AI, the choice of the model is also dependent on expert judgement.

Ray Kurzweil has a model of technological intelligence development where, broadly speaking, evolution, pre-computer technological development, post-computer technological development and future AIs all fit into the same exponential increase. When assessing the validity of that model, we could look at Kurzweil's credentials, and maybe compare them with those of his critics - but Kurzweil has given us something even better than credentials, and that's a track record. In various books, he's made predictions about what would happen in 2009, and we're now in a position to judge their accuracy. I haven't been satisfied by the various accuracy ratings I've found online, so I decided to do my own assessments.

I first selected ten of Kurzweil's predictions at random, and gave my own estimation of their accuracy. I found that five were to some extent true, four were to some extent false, and one was unclassifiable 

But of course, relying on a single assessor is unreliable, especially when some of the judgements are subjective. So I started a call for volunteers to get assessors. Meanwhile Malo Bourgon set up a separate assessment on Youtopia, harnessing the awesome power of altruists chasing after points.

The results are now in, and they are fascinating. They are...

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Checking Kurzweil's track record

12 Stuart_Armstrong 30 October 2012 11:07AM

Predictions are cheap and easy; verification is hard, essential, and rare. For things like AI, we seem to be restricted to nothing but expert predictions - but expert predictions on AI are not very good, either in theory or in practice. If we are some experts who stand out, we would really want to identify them - and there is nothing better than a track record for identifying true experts.

So we're asking for help to verify the predictions of one of the most prominent futurists of this century: Ray Kurzweil, from his book "The Age of Spiritual Machines". By examining his predictions for times that have already come and gone, we'll be able to more appropriately weight his predictions for times still to come. By taking part, by lending your time to this, you will be directly helping us understand and predict the future, and will get showered in gratitude and kudos and maybe even karma.

I've already made an attempt at this (if you are interested in taking part in this project, avoid clicking on that link for now!). But you cannot trust a single person's opinions, and that was from a small (albeit random) sample of the predictions. For this project, I've transcribed his predictions into 172 separate (short) statements, and any volunteers would be presented with a random selection among these. The volunteers would then do some Google research (or other) to establish whether the prediction had come to pass, and then indicate their verdict. More details on what exactly will be measured, and how to interpret ambiguous statements, will be given to the volunteers once the project starts.

If you are interested, please let me know at stuart.armstrong@philosophy.ox.ac.uk (or in the comment thread here), indicating how many of the 172 questions you would like to attempt. The exercise will probably happen in late November or early December.

This will be done unblinded, because Kurzweil's predictions are so well known that it would be infeasible to find large numbers of people who are technologically aware but ignorant of them. Please avoid sharing your verdicts with others; it is entirely your own individual assessment that we are interested in having.

Counterfactual resiliency test for non-causal models

20 Stuart_Armstrong 30 August 2012 05:30PM

Non-causal models

Non-causal models are quite common in many fields, and can be quite accurate. Here predictions are made, based on (a particular selection of) past trends, and it is assumed that these trends will continue in future. There is no causal explanation offered for the trends under consideration: it's just assumed they will go on as before. Non-causal models are thus particularly useful when the underlying causality is uncertain or contentious. To illustrate the idea, here are three non-causal models in computer development:

  1. Moore's laws about the regular doubling of processing speed/hard disk size/other computer related parameter.
  2. Robin Hanson's model where the development of human brains, hunting, agriculture and the industrial revolution are seen as related stages of accelerations of the underlying economic rate of growth, leading to the conclusion that there will be another surge during the next century (likely caused by whole brain emulations or AI).
  3. Ray Kurzweil's law of time and chaos, leading to his law of accelerating returns. Here the inputs are the accelerating evolution of life on earth, the accelerating 'evolution' of technology, followed by the accelerating growth in the power of computing across many different substrates. This leads to a consequent 'singularity', an explosion of growth, at some point over the coming century.

Before anything else, I should thank Moore, Hanson and Kurzweil for having the courage to publish their models and put them out there where they can be critiqued, mocked or praised. This is a brave step, and puts them a cut above most of us.

That said, though I find the first argument quite convincing, I find have to say I find the other two dubious. Now, I'm not going to claim they're misusing the outside view: if you accuse them of shoving together unrelated processes into a single model, they can equally well accuse you of ignoring the commonalities they have highlighted between these processes. Can we do better than that? There has to be a better guide to the truth that just our own private impressions.

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AI timeline predictions: are we getting better?

52 Stuart_Armstrong 17 August 2012 07:07AM

EDIT: Thanks to Kaj's work, we now have more rigorous evidence on the "Maes-Garreau law" (the idea that people will predict AI coming before they die). This post has been updated with extra information. The original data used for this analysis can now be found through here.

Thanks to some sterling work by Kaj Sotala and others (such as Jonathan Wang and Brian Potter - all paid for by the gracious Singularity Institute, a fine organisation that I recommend everyone look into), we've managed to put together a databases listing all AI predictions that we could find. The list is necessarily incomplete, but we found as much as we could, and collated the data so that we could have an overview of what people have been predicting in the field since Turing.

We retained 257 predictions total, of various quality (in our expanded definition, philosophical arguments such as "computers can't think because they don't have bodies" count as predictions). Of these, 95 could be construed as giving timelines for the creation of human-level AIs. And "construed" is the operative word - very few were in a convenient "By golly, I give a 50% chance that we will have human-level AIs by XXXX" format. Some gave ranges; some were surveys of various experts; some predicted other things (such as child-like AIs, or superintelligent AIs).

Where possible, I collapsed these down to single median estimate, making some somewhat arbitrary choices and judgement calls. When a range was given, I took the mid-point of that range. If a year was given with a 50% likelihood estimate, I took that year. If it was the collection of a variety of expert opinions, I took the prediction of the median expert. If the author predicted some sort of AI by a given date (partial AI or superintelligent AI), I took that date as their estimate rather than trying to correct it in one direction or the other (there were roughly the same number of subhuman AIs as suphuman AIs in the list, and not that many of either). I read extracts of the papers to make judgement calls when interpreting problematic statements like "within thirty years" or "during this century" (is that a range or an end-date?).

So some biases will certainly have crept in during the process. That said, it's still probably the best data we have. So keeping all that in mind, let's have a look at what these guys said (and it was mainly guys).

continue reading »

Kurzweil's predictions: good accuracy, poor self-calibration

30 Stuart_Armstrong 11 July 2012 09:55AM

Predictions of the future rely, to a much greater extent than in most fields, on the personal judgement of the expert making them. Just one problem - personal expert judgement generally sucks, especially when the experts don't receive immediate feedback on their hits and misses. Formal models perform better than experts, but when talking about unprecedented future events such as nanotechnology or AI, the choice of the model is also dependent on expert judgement.

Ray Kurzweil has a model of technological intelligence development where, broadly speaking, evolution, pre-computer technological development, post-computer technological development and future AIs all fit into the same exponential increase. When assessing the validity of that model, we could look at Kurzweil's credentials, and maybe compare them with those of his critics - but Kurzweil has given us something even better than credentials, and that's a track record. In various books, he's made predictions about what would happen in 2009, and we're now in a position to judge their accuracy. I haven't been satisfied by the various accuracy ratings I've found online, so I decided to do my own.

Some have argued that we should penalise predictions that "lack originality" or were "anticipated by many sources". But hindsight bias means that we certainly judge many profoundly revolutionary past ideas as "unoriginal", simply because they are obvious today. And saying that other sources anticipated the ideas is worthless unless we can quantify how mainstream and believable those sources were. For these reasons, I'll focus only on the accuracy of the predictions, and make no judgement as to their ease or difficulty (unless they say things that were already true when the prediction was made).

Conversely, I won't be giving any credit for "near misses": this has the hindsight problem in the other direction, where we fit potentially ambiguous predictions to what we know happened. I'll be strict about the meaning of the prediction, as written. A prediction in a published book is a form of communication, so if Kurzweil actually meant something different to what was written, then the fault is entirely his for not spelling it out unambiguously.

One exception to that strictness: I'll be tolerant on the timeline, as I feel that a lot of the predictions were forced into a "ten years from 1999" format. So I'll estimate the prediction accurate if it happened at any point up to the end of 2011, if data is available. 

The number of predictions actually made seem to vary from source to source; I used my copy of "The Age of Spiritual Machines", which seems to be the original 1999 edition. In the chapter "2009", I counted 63 prediction paragraphs. I then chose ten numbers at random between 1 and 63, and analysed those ten predictions for correctness (those wanting to skip directly to the final score can scroll down). Seeing Kurzweil's nationality and location, I will assume all prediction refer only to technologically advanced nations, and specifically to the United States if there is any doubt. Please feel free to comment on my judgements below; we may be able to build a Less Wrong consensus verdict. It would be best if you tried to reach your own conclusions before reading my verdict or anyone else's. Hence I present the ten predictions, initially without commentary:

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New Year's Prediction Thread (2012)

20 gwern 01 January 2012 09:35AM

Going through expiring predictions reminded me. Just as we did for 2010 and 2011, it's time for LessWrong to make its beliefs pay rent and give hostages to fortune in making predictions for events in 2012 and beyond.

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Amanda Knox: post mortem

22 gwern 20 October 2011 04:10PM

Continuing my interest in tracking real-world predictions, I notice that the recent acquittal of Knox & Sollecito offers an interesting opportunity - specifically, many LessWrongers gave probabilities for guilt back in 2009 in komponisto’s 2 articles:

Both were interesting exercises, and it’s time to do a followup. Specifically, there are at least 3 new pieces of evidence to consider:

  1. the failure of any damning or especially relevant evidence to surface in the ~2 years since (see also: the hope function)
  2. the independent experts’ report on the DNA evidence
  3. the freeing of Knox & Sollecito, and continued imprisonment of Rudy Guede (with reduced sentence)

Point 2 particularly struck me (the press attributes much of the acquittal to the expert report, an acquittal I had not expected to succeed), but other people may find the other 2 points or unmentioned news more weighty.

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1001 PredictionBook Nights

51 gwern 08 October 2011 04:04PM

I explain what I've learned from creating and judging thousands of predictions on personal and real-world matters: the challenges of maintenance, the limitations of prediction markets, the interesting applications to my other essays, skepticism about pundits and unreflective persons' opinions, my own biases like optimism & planning fallacy, 3 very useful heuristics/approaches, and the costs of these activities in general.

Plus an extremely geeky parody of Fate/Stay Night.

This essay exists as a large section of my page on predictions markets on gwern.net: http://www.gwern.net/Prediction%20markets#1001-predictionbook-nights

New Year's Predictions Thread (2011)

10 Kevin 02 January 2011 09:58AM

As we did last year, use this thread to make predictions for the next year and next decade, with probabilities attached when practical. 

Happy New Year, Less Wrong!

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