Here's a piece by Mark Piesing in Wired UK about the difficulty and challenges in predicting AI. It covers a lot of our (Stuart Armstrong, Kaj Sotala and Seán Óh Éigeartaigh) research into AI prediction, along with Robin Hanson's response. It will hopefully cause people to look more deeply into our work, as published online, in the Pilsen Beyond AI conference proceedings, and forthcoming as "The errors, insights and lessons of famous AI predictions and what they mean for the future".

This brief post is written on behalf of Kaj Sotala, due to deadline issues.

The results of our prior analysis suggested that there was little difference between experts and non-experts in terms of predictive accuracy. There were suggestions, though, that predictions published by self-selected experts would be different from those elicited from less selected groups, e.g. surveys at conferences.

We have no real data to confirm this, but a single datapoint suggests the idea might be worth taking seriously. Michie conducted an opinion poll of experts working in or around AI in 1973. The various experts predicted adult-level human AI in:

• 5 years: 0 experts
• 10 years: 1 expert
• 20 years: 16 experts
• 50 years: 20 experts
• More than 50 years: 26 experts

On a quick visual inspection, these results look quite different from the distribution in the rest of the database giving a much more pessimistic prediction than the more self-selected experts:

But that could be an artifact from the way that the graph on page 12 breaks the predictions down to 5 year intervals while Michie breaks them down into intervals of 10, 20, 50, and 50+ years. Yet there seems to remain a clear difference once we group the predictions in a similar way [1]:

This provides some support for the argument that "the mainstream of expert opinion is reliably more pessimistic than the self-selected predictions that we keep hearing about".

[1] Assigning each prediction to the closest category, so predictions of <7½ get assigned to 5, 7½<=X<15 get assigned to 10, 15<=X<35 get assigned to 20, 35<=X<50 get assigned to 50, and 50< get assigned to over fifty.

I've just been through the proposal for the Dartmouth AI conference of 1956, and it's a surprising read. All I really knew about it was its absurd optimism, as typified by the quote:

An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.

But then I read the rest of the document, and was... impressed. Go ahead and read it, and give me your thoughts. Given what was known in 1955, they were grappling with the right issues, and seemed to be making progress in the right directions and have plans and models for how to progress further. Seeing the phenomenally smart people who were behind this (McCarthy, Minsky, Rochester, Shannon), and given the impressive progress that computers had been making in what seemed very hard areas of cognition (remember that this was before we discovered Moravec's paradox)... I have to say that had I read this back in 1955, I think the rational belief would have been "AI is probably imminent". Some overconfidence, no doubt, but no good reason to expect these prominent thinkers to be so spectacularly wrong on something they were experts in.

Excerpts from literature on robotic/self-driving/autonomous cars with a focus on legal issues, lengthy, often tedious; some more SI work. See also Notes on Psychopathy.

Having read through all this material, my general feeling is: the near-term future (1 decade) for autonomous cars is not that great. What's been accomplished, legally speaking, is great but more limited than most people appreciate. And there are many serious problems with penetrating the elaborate ingrown rent-seeking tangle of law & politics & insurance. I expect the mid-future (+2 decades) to look more like autonomous cars completely taking over many odd niches and applications where the user can afford to ignore those issues (eg. on private land or in warehouses or factories), with highways and regular roads continuing to see many human drivers with some level of automated assistance. However, none of these problems seem fatal and all of them seem amenable to gradual accommodation and pressure, so I am now more confident that in the long run we will see autonomous cars become the norm and human driving ever more niche (and possibly lower-class). On none of these am I sure how to formulate a precise prediction, though, since I expect lots of boundary-crossing and tertium quids. We'll see.

This post goes along with this one, which was merely summarising the results of the volunteer assessment. Here we present the further details of the methodology and results.

Kurzweil's predictions were decomposed into 172 separate statements, taken from the book "The Age of Spiritual Machines" (published in 1999). Volunteers were requested on Less Wrong and on reddit.com/r/futurology. 18 people initially volunteered to do varying amounts of assessment of Kurzweil's predictions; 9 ultimately did so.

Each volunteer was given a separate randomised list of the numbers 1 to 172, with instructions to go through the statements in the order given by the list and give their assessment of the correctness of the prediction (the exact instructions are at the end of this post). They were to assess the predictions on the following five point scale:

• 1=True, 2=Weakly True, 3=Cannot decide, 4=Weakly False, 5=False

They assessed a varying amount of predictions, giving 531 assessments in total, for an average of 59 assessments per volunteer (the maximum attempted was all 172 predictions, the minimum was 10). They generally followed the randomised order correctly - there were three out of order assessments (assessing prediction 36 instead of 38, 162 instead of a 172, and missing out 75). Since the number of errors was very low, and seemed accidental, I decided that this would not affect the randomisation and kept those answers in.

The assessments (anonymised) can be found here.

Here's a link to a review, by The Economist, of a book about prediction, some of the common ways in which people make mistakes and some of the methods by which they could improve:

Looking ahead : How to look ahead—and get it right

One paragraph from that review:

A guiding light for Mr Silver is Thomas Bayes, an 18th-century English churchman and pioneer of probability theory. Uncertainty and subjectivity are inevitable, says Mr Silver. People should not get hung up on this, and instead think about the future the way gamblers do: “as speckles of probability”. In one surprising chapter, poker, a game from which Mr Silver once earned a living, emerges as a powerful teacher of the virtues of humility and patience.

An over-simplification, but an evocative one:

• The social sciences are contentious, their predictions questionable.
• And yet social sciences use the scientific method; AI predictions generally don't.
• Hence predictions involving human-level AI should be treated as less certain than any prediction in the social sciences.

Drazen Prelec's Bayesian truth serum ¹ is becoming well-known on LW as a means of divining truth from biased opinions. The method exploits the tendency for opinions to correlate with predictions of the proportion of others with the same opinion, known as the false consensus effect or the typical mind fallacy. ² Even though BTS seems appealing and I've seen a couple people hope for an online implementation, it can fail badly when monetary transfers aren't present. I'm going to present a better method that operates on weaker assumptions and doesn't require money to change hands.

Original Bayesian Truth Serum

Suppose n people are asked a question with m possible answers. Each person will answer the question and predict the proportion of other people giving each answer. If the i-th person gives answer k, let x_ik = 1 and otherwise x_ik = 0. Let the prediction by person i of the proprotion of others answering k be y_ik. For each answer k, use these to compute the actual proportions  and the geometric mean of predictions :

Then, compute individual payments s_i as

The first sum is the information score, rewarding a choice of a surprisingly common answer. The second sum is the prediction score, rewarding accurate predictions of others' answers. With a sufficiently large number of participants, honest reporting is a Bayes-Nash equilibrium. The people with the correct opinion will tend to have the highest scores on average, even if they are in the minority, so it's possible to learn the truth even in the face of bias.

Some potential issues with this procedure:

• The participants should be Bayesians with a common prior. The common prior assumption is mostly for convenience in proving equilibrium, and it's unclear how necessary it is for truth-telling. Without assuming common priors, tricky issues arise with higher-order beliefs about the priors of others. Setting aside incentives for honesty, a common prior isn't necessary to distinguish the truth in simulations.
• The number of respondents has to be sufficiently large to guarentee truthfulness, but this number depends on the unknown common prior of the participants. I find this the least troubling assumption, since it's not obvious how to extract extra profit on this basis alone. Witkowski and Parkes (2012) construct a similar mechanism that is incentive compatible with as few as three participants, but appears to be more sensitive to the common prior assumption.
• Participants must care only about maximizing their score. In particular, participants don't care about which answer ends up being favored by the mechanism. This is the really troubling assumption, particularly when money isn't involved. If payments are trivial or not present, here's a simple manipulation: if you want answer k to win, pick some other answer at random and give your prediction y_k close to zero. Your own score will be very negative, but anyone who gave answer k will have a huge score.
• Participants can't be too exposed to the opinions of others. Keeping a public tally of answers makes this useless.

Robust Bayesian truth serum

Suppose participants care about influencing the final result rather than their score. Then the mechanism has to be constructed in such a way that influence is maximized by being honest. For yes/no questions, majority vote has this property. You can't do better than giving your true opinion. As discussed above, Prelec's BTS does not have this property. Instead, I'm going to rely on asymmetric polynomial scoring rules.

Suppose there are two answers, a and b. Ask people for their opinion and the proportion y_i of others they expect to answer a. Let n_a be the number of people answering a and n_b be the number of b answers. Then, the scores for a endorsers are:

and the scores for b endorsers are:

where t is some positive integer. Finally, average the scores for each answer. The answer with the highest mean score is most likely to be correct.

Unlike BTS, this works with any number of participants, does not depend on a common prior, and can't be directly manipulated. Of course, it is still susceptible to false-name attacks or inside knowledge of the answers of others. In simulations, this performs about as well or better than BTS for t = 5 or 6. I've only tested this for binary questions, but I have in mind a generalization to multiple answers.

Why exactly does this work? I'm still trying to figure that out. My results are primarily numerical, not analytical. More details can be found in my working paper. The source for the paper and simulations ³ is here, if you want to dig in even further. An online implementation is in progress, although going slowly with my scanty web development skills.

A while ago I wrote briefly on why the Singularity might not be near and my estimates badly off. I saw it linked the other day, and realized that pessimism seemed to be trendy lately, which meant I ought to work on why one might be optimistic instead: http://www.gwern.net/Mistakes#counter-point

(Summary: long-sought AI goals have been recently achieved, global economic growth & political stability continues, and some resource crunches have turned into surpluses - all contrary to long-standing pessimistic forecasts.)

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.

I searched for articles on the topic and couldn't find any.

It seems to me that intelligence explosion makes human annihilation much more likely, since superintelligences will certainly be able to outwit humans, but that a human-level intelligence that could process information much faster than humans would certainly be a large threat itself without any upgrading. It could still discover programmable nanomachines long before humans do, gather enough information to predict how humans will act, etc. We already know that a human-level intelligence can "escape from the box." Not 100% of the time, but a real AI will have the opportunity for many more trials, and its processing abilities should make it far more quick-witted than we are.

I think a non-friendly AI would only need to be 20 years or so more advanced than the rest of humanity to pose a major threat, especially if self-replicating nanomachines are possible. Skeptics of intelligence explosion should still be worried about the creation of computers with unfriendly goal systems. What am I missing?

The magazine has a bunch of articles dealing with what the world may be like 98,000 years hence. What with the local interest in the distant future, and with prediction itself, I thought I'd bring it to your attention.

http://www.newscientist.com/special/deep-future?cmpid=NLC|NSNS|2012-0503-GLOBAL|deepfuture&utm_medium=NLC&utm_source=NSNS&utm_content=deepfuture

If you wanted to check whether your creations were prone to misbehaving, one way would be to leave a big juicy apple hanging tantalisingly from a very visible tree, turn your back for a while, and see what they get up to.

That would work if you were God and your creations were essentially children. But if the creator is us and the creation is a super-intelligent AI, such 'honey-pots' and unlikely to work. They are tricks, and we shouldn't rely on being able to trick a being more intelligent than ourselves - unless its intelligence doesn't matter.

We can use utility indifference to construct such traps. Utility indifference can be informally summarised as:

"There is a stochastic event X which can output 0 or 1. We wish the AI act as if it believed that X=0, with certainty. It's problematic for the AI to have incorrect beliefs, so the result is achieved through the definition of the AI's utility function."

Consider the following situation: a master AI that will create the source code of another, autonomous AI. The master AI is setup to produce the code of a Friendly AI, or a reduced impact AI - but we're not sure if the master AI is trustworthy. It would be good to go through the code very carefully before unleashing it (or erasing it). But if the master AI knows this (and we must assume it does), then it will either obfuscate the dangerous parts or put traps in it, making it risky for us to look too closely.

Then implement the following setup. The code travels through some stochastic event X, such that if X=0, the code is immediately transferred into a disciple AI, completely autonomous, which is at liberty to immediately modify its own code and erase any trace of what it was. If X=1, the code is instead copied to our hard drives for analysis. The AI is given some reason to keep the code short - maybe there is an increasing chance of the code getting erased the longer it is.

On top of this, we implement utility indifference: the master AI will act as if it believed X=0 with certainty, while we have setup X so that X=1 with near-certainty. Then the master AI will be motivated to produce the shortest code compatible with its utility function, in the 'knowledge' that this code will be copied into a powerful autonomous AI, and will never be seen by humans. It has no incentive to trap its code - quite the reverse, as it wants to keep it short.

And therefore we have all the time in the world to go through the code, looking for defection. Other ways of using utility indifference to trap AIs are no doubt possible, but this was the first setup that sprang to my mind.

Nate Silver (the NYT quantitative political analyst) and Dave Weigel (the Slate columnist) have started a good tradition, listing their worst predictions of 2011. (Silver also listed his best.)

If any other pundits are doing the same, link them here.

From Cafe Hayek (original): Two meteorologists have announced that they will stop using certain forecast methods, even though they've used them for 20 years.

There's a correction at the end of the article, too!

The topic and the problems associated with it are probably familiar to many of you already. But I think some may find this review by Freeman Dyson of the book Thinking, Fast and Slow by Daniel Kahneman interesting. 

In 1955, when Daniel Kahneman was twenty-one years old, he was a lieutenant in the Israeli Defense Forces. He was given the job of setting up a new interview system for the entire army. The purpose was to evaluate each freshly drafted recruit and put him or her into the appropriate slot in the war machine. The interviewers were supposed to predict who would do well in the infantry or the artillery or the tank corps or the various other branches of the army. The old interview system, before Kahneman arrived, was informal. The interviewers chatted with the recruit for fifteen minutes and then came to a decision based on the conversation. The system had failed miserably. When the actual performance of the recruit a few months later was compared with the performance predicted by the interviewers, the correlation between actual and predicted performance was zero.

Kahneman had a bachelor’s degree in psychology and had read a book, Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence by Paul Meehl, published only a year earlier. Meehl was an American psychologist who studied the successes and failures of predictions in many different settings. He found overwhelming evidence for a disturbing conclusion. Predictions based on simple statistical scoring were generally more accurate than predictions based on expert judgment.

A famous example confirming Meehl’s conclusion is the “Apgar score,” invented by the anesthesiologist Virginia Apgar in 1953 to guide the treatment of newborn babies. The Apgar score is a simple formula based on five vital signs that can be measured quickly: heart rate, breathing, reflexes, muscle tone, and color. It does better than the average doctor in deciding whether the baby needs immediate help. It is now used everywhere and saves the lives of thousands of babies. Another famous example of statistical prediction is the Dawes formula for the durability of marriage. The formula is “frequency of love-making minus frequency of quarrels.” Robyn Dawes was a psychologist who worked with Kahneman later. His formula does better than the average marriage counselor in predicting whether a marriage will last.

Having read the Meehl book, Kahneman knew how to improve the Israeli army interviewing system. His new system did not allow the interviewers the luxury of free-ranging conversations with the recruits. Instead, they were required to ask a standard list of factual questions about the life and work of each recruit. The answers were then converted into numerical scores, and the scores were inserted into formulas measuring the aptitude of the recruit for the various army jobs. When the predictions of the new system were compared to performances several months later, the results showed the new system to be much better than the old. Statistics and simple arithmetic tell us more about ourselves than expert intuition.

Reflecting fifty years later on his experience in the Israeli army, Kahneman remarks in Thinking, Fast and Slow that it was not unusual in those days for young people to be given big responsibilities. The country itself was only seven years old. “All its institutions were under construction,” he says, “and someone had to build them.” He was lucky to be given this chance to share in the building of a country, and at the same time to achieve an intellectual insight into human nature. He understood that the failure of the old interview system was a special case of a general phenomenon that he called “the illusion of validity.” At this point, he says, “I had discovered my first cognitive illusion.”

Cognitive illusions are the main theme of his book. A cognitive illusion is a false belief that we intuitively accept as true. The illusion of validity is a false belief in the reliability of our own judgment. The interviewers sincerely believed that they could predict the performance of recruits after talking with them for fifteen minutes. Even after the interviewers had seen the statistical evidence that their belief was an illusion, they still could not help believing it. Kahneman confesses that he himself still experiences the illusion of validity, after fifty years of warning other people against it. He cannot escape the illusion that his own intuitive judgments are trustworthy.

An episode from my own past is curiously similar to Kahneman’s experience in the Israeli army. I was a statistician before I became a scientist. At the age of twenty I was doing statistical analysis of the operations of the British Bomber Command in World War II. The command was then seven years old, like the State of Israel in 1955. All its institutions were under construction. It consisted of six bomber groups that were evolving toward operational autonomy. Air Vice Marshal Sir Ralph Cochrane was the commander of 5 Group, the most independent and the most effective of the groups. Our bombers were then taking heavy losses, the main cause of loss being the German night fighters.

Cochrane said the bombers were too slow, and the reason they were too slow was that they carried heavy gun turrets that increased their aerodynamic drag and lowered their operational ceiling. Because the bombers flew at night, they were normally painted black. Being a flamboyant character, Cochrane announced that he would like to take a Lancaster bomber, rip out the gun turrets and all the associated dead weight, ground the two gunners, and paint the whole thing white. Then he would fly it over Germany, and fly so high and so fast that nobody could shoot him down. Our commander in chief did not approve of this suggestion, and the white Lancaster never flew.

The reason why our commander in chief was unwilling to rip out gun turrets, even on an experimental basis, was that he was blinded by the illusion of validity. This was ten years before Kahneman discovered it and gave it its name, but the illusion of validity was already doing its deadly work. All of us at Bomber Command shared the illusion. We saw every bomber crew as a tightly knit team of seven, with the gunners playing an essential role defending their comrades against fighter attack, while the pilot flew an irregular corkscrew to defend them against flak. An essential part of the illusion was the belief that the team learned by experience. As they became more skillful and more closely bonded, their chances of survival would improve.

When I was collecting the data in the spring of 1944, the chance of a crew reaching the end of a thirty-operation tour was about 25 percent. The illusion that experience would help them to survive was essential to their morale. After all, they could see in every squadron a few revered and experienced old-timer crews who had completed one tour and had volunteered to return for a second tour. It was obvious to everyone that the old-timers survived because they were more skillful. Nobody wanted to believe that the old-timers survived only because they were lucky.

At the time Cochrane made his suggestion of flying the white Lancaster, I had the job of examining the statistics of bomber losses. I did a careful analysis of the correlation between the experience of the crews and their loss rates, subdividing the data into many small packages so as to eliminate effects of weather and geography. My results were as conclusive as those of Kahneman. There was no effect of experience on loss rate. So far as I could tell, whether a crew lived or died was purely a matter of chance. Their belief in the life-saving effect of experience was an illusion.

The demonstration that experience had no effect on losses should have given powerful support to Cochrane’s idea of ripping out the gun turrets. But nothing of the kind happened. As Kahneman found out later, the illusion of validity does not disappear just because facts prove it to be false. Everyone at Bomber Command, from the commander in chief to the flying crews, continued to believe in the illusion. The crews continued to die, experienced and inexperienced alike, until Germany was overrun and the war finally ended.

Another theme of Kahneman’s book, proclaimed in the title, is the existence in our brains of two independent sytems for organizing knowledge. Kahneman calls them System One and System Two. System One is amazingly fast, allowing us to recognize faces and understand speech in a fraction of a second. It must have evolved from the ancient little brains that allowed our agile mammalian ancestors to survive in a world of big reptilian predators. Survival in the jungle requires a brain that makes quick decisions based on limited information. Intuition is the name we give to judgments based on the quick action of System One. It makes judgments and takes action without waiting for our conscious awareness to catch up with it. The most remarkable fact about System One is that it has immediate access to a vast store of memories that it uses as a basis for judgment. The memories that are most accessible are those associated with strong emotions, with fear and pain and hatred. The resulting judgments are often wrong, but in the world of the jungle it is safer to be wrong and quick than to be right and slow.

System Two is the slow process of forming judgments based on conscious thinking and critical examination of evidence. It appraises the actions of System One. It gives us a chance to correct mistakes and revise opinions. It probably evolved more recently than System One, after our primate ancestors became arboreal and had the leisure to think things over. An ape in a tree is not so much concerned with predators as with the acquisition and defense of territory. System Two enables a family group to make plans and coordinate activities. After we became human, System Two enabled us to create art and culture.

If you've made it this far read the rest of the review here. There is still some cool stuff after this.

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.

An improper prior is essentially a prior probability distribution that's infinitesimal over an infinite range, in order to add to one. For example, the uniform prior over all real numbers is an improper prior, as there would be an infinitesimal probability of getting a result in any finite range. It's common to use improper priors for when you have no prior information.

The mark of a good prior is that it gives a high probability to the correct answer. If I bet 1,000,000 to one that a coin will land on heads, and it lands on tails, it could be a coincidence, but I probably had a bad prior. A good prior is one that results in me not being very surprised.

With a proper prior, probability is conserved, and more probability mass in one place means less in another. If I'm less surprised when a coin lands on tails, I'm more surprised when it lands on heads. This isn't true with an improper prior. If I wanted to predict the value of a random real number, and used a normal distribution with a mean of zero and a standard deviation of one, I'd be pretty darn surprised if it doesn't end up being pretty close to zero, but I'd be infinitely surprised if I used a uniform distribution. No matter what the number is, it will be more surprising with the improper prior. Essentially, a proper prior is better in every way. (You could find exceptions for this, such as averaging a proper and improper prior to get an improper prior that still has finite probabilities and they just add up to 1/2, or by using a proper prior that has zero in some places, but you can always make a proper prior that's better in every way to a given improper prior).

Dutch books also seems to be a popular way of showing what works and what doesn't, so here's a simple Dutch argument against improper priors: I have two real numbers: x and y. Suppose they have a uniform distribution. I offer you a bet at 1:2 odds that x has a higher magnitude. They're equally likely to be higher, so you take it. I then show you the value of x. I offer you a new bet at 100:1 odds that y has a higher magnitude. You know y almost definitely has a higher magnitude than that, so you take it again. No matter what happens, I win.

You could try to get out of it by using a different prior, but I can just perform a transformation on it to get what I want. For example, if you choose a logarithmic prior for the magnitude, I can just take the magnitude of the log of the magnitude, and have a uniform distribution.

There are certainly uses for an improper prior. You can use it if the evidence is so great compared to the difference between it and the correct value that it isn't worth worrying about. You can also use it if you're not sure what another person's prior is, and you want to give a result that is at least as high as they'd get no matter how much there prior is spread out. That said, an improper prior is never actually correct, even in things that you have literally no evidence for.

People Neglect Who They Really Are When Predicting Their Own Future Happiness (article @ ScienceDaily)

The scientists who conducted this interesting study...

found that our natural sunny or negative dispositions might be a more powerful predictor of future happiness than any specific event. They also discovered that most of us ignore our own personalities when we think about what lies ahead -- and thus miscalculate our future feelings.