Statistical model of IQ test beats humans at the same IQ test | Hacker News Reader

Statistical model of IQ test beats humans at the same IQ test

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81 pointsjacquesm41 comments

Here is the most up to date version of the paper: http://arxiv.org/pdf/1505.07909.pdf

The questions on the IQ test are very specific. Word analogies, identifying antonyms, synonyms, and which word isn't like the others.

The method they use is similar to word2vec. That is, it does statistics on millions of words and how they occur in sentences to try to create a vector representation of it. These vector representations include a lot of semantic information about the words and what they mean, which makes it possible for the computer to do these tests automatically.

They then compared it's performance on these questions with people they paid on Mechanical Turk. And the computer did better.

This is really interesting that it can beat humans, and shows some understanding of the relationships between words. However it doesn't mean it's intelligent, or even close.

It also doesn't mean IQ tests are worthless. What they measure is variation between humans. People that do better on these questions, also tend to do better on other unrelated exercises. Like puzzles, memory challenges, or even reaction times. That these things are correlated suggests there is some single underlying factor which determines some of the variation of mental ability in humans, and we call that IQ, and can measure it.

However computers have a totally different set of factors that determine their strengths and weaknesses, and applying human tests tells us nothing. It doesn't tell us that the tests are worthless because dumb computers can pass them, because it still correlates with intelligence in humans. It doesn't mean the computer is intelligent, because some of the tests might be beatable with clever algorithms like this, that obviously aren't very general.

g includes things like special reasoning and learning to dance not just word games.

Which is why many actual IQ tests involve 3d puzzles or images. So, this has very little to do with IQ tests as a subject just types of questions that might show up on one. Further, they use 'People' to mean a specific set of people instead of people who would do well on these tests making the paper link bait.

AKA, chess programs beat some people fairly early on, it was only really news when they could beat top players.

PS: Some validated IQ tests involve zero written words. Due to issues with language bias.

Thanks for the link to the paper. I think the older criticisms still stand:

- They created their own data set. Instead of a general Q-A system, they may have overfit to this particular task and question types.

- They set the human intelligence benchmark by using Mechanical Turk. This may not be representative of true human intelligence (given the lower quality of Mechanical Turkers).

As a future work, I hope they look at the work the Allen AI institute is doing with Aristo [1]

There is a current challenge [2] to beat 8th graders on a standardized 8th grade science exam. Here the data set is made by someone else, the questions are closer to real-life questions (vs. simple analogies), progress and results can be compared to other research teams and the human benchmark is set by the actual performance of 8th graders.

For human intelligence, next to accuracy and speed, we also care about simplicity. This system nails accuracy and speed, but it may be beaten by someone who has never read the entirety of Wikipedia. A deep net trained on millions of words and their relations may be too complex for this task (it uses up a lot of energy to train).

As to computer intelligence being different than human intelligence: I once nearly aced an aptitude test where I had access to a search engine. The test involved programming languages I never programmed a single line in. Yet, searching for keywords in the question, combined with keywords in the answers, I could give correct answers, merely by comparing page count statistics. Like the robot in Searle's room, I was merely pattern matching, without a real understanding or insight into the questions asked. The result of my test leaked out on the workfloor, and for weeks I was a headline wonder (having beaten all the senior engineers' scores), without really deserving it.

[1] http://allenai.org/aristo.html [2] https://www.kaggle.com/c/the-allen-ai-science-challenge

These general QA datasets wouldn't work, because their system can only do word analogies. And some similar tasks like antonyms. It can't understand a sentence or anything.

To add to your second point, Andrew Gelman had some blog posts earlier this year detailing the challenges of doing online surveys, where he used simple questions that respondents "should" have been able to answer in a survey and found that a fair percentage >10% answered some of these simple questions wrong. I am assuming there is no incentive for answering more questions correctly so it's possible that some respondents may have answered blindly to finish quickly leading to lower scores.

"The researchers had the deep learning machine and 200 human subjects at Amazon’s Mechanical Turk crowdsourcing facility answer the same verbal questions. The result: their system performed better than the average human."

Mechanical Turk represents the average human?

And did they just ask the Turk subjects whether they had a bachelor's or master's degree or not as proof that they had bachelor's or master's degrees? Could you count on that being accurate?

It's what most employers do.

I don't think they rated it against the Turk subjects, I think the Turk subjects were used to train the AI and it got a score of between 115 and 120 which is what you should have on average if you graduate with a bachelors degree.

If you match up the best AI systems against the bottom 25% of the U.S. population, the humans won't come out looking too smart. AI might still fail the Turing test at least some of the time, but it's getting a little scary.

The point of an IQ test is to measure g, or general mental ability. In other words, if a computer can test well on one IQ test, it may also test well on other IQ tests, since it should be general. Otherwise, it's a domain-specific ability for a specific IQ test, which wouldn't be as interesting.

If a system could perform generally well, then the implications are astounding. It could imply general analogizing capability. It would also mean AI came way faster than anyone thought.

...or, it could indicate a system optimized for the goal of performing well on the metric of taking a specific type of test...

With a little prodding and tweaking, I can imagine a search engine doing decent on many classes of standardized tests; and so can test administrators, leading (partially) to why smart-phones/computers/connected-devices are generally forbidden when taking standardized tests.

..but let's not overlook the fact that standardized tests aren't exactly stellar at determining "intelligence". I've had tests that allowed all manners of resources to be used during the test and it was still a difficult test.

In their paper they mention that they only did the verbal reasoning part. I'm pretty sure an AI algorithm that solves all three parts ( logic questions, mathematical questions and verbal reasoning) without some hard coded heuristics for the mathematical and logic part doesn't exist just yet.

IQ tests may be designed to measure g, but that doesn't mean they actually do. The best thing we can hope for is that a test correlates with g. But for every finite test it will be possible to find an algorithm that will produce high marks without requiring general intelligence. In the specific case outlined in the article it's clear that they only found a method to answer a very specific type of test.

(And all that's assuming something like g even exists.)

> But for every finite test it will be possible to find an algorithm that will produce high marks without requiring general intelligence.

if you didnt know what the test was, then this procedure of using a "specific" algorithm is not distinguishable from general intelligence!

This is only for certain, verbal questions like antonym of, synonym of, X is to Y as Z to what; for each of them a separate, specific model was built (not that the AI read the question and printed relevant answer).

To sum up, the paper should be titled "Dictionary is better than MechTurk users on look-in-dictionary tasks."

Link to the original paper: http://arxiv.org/pdf/1505.07909v1.pdf

> In other words, if a computer can test well on one IQ test, it may also test well on other IQ tests, since it should be general.

> If a system could perform generally well, then the implications are astounding. It could imply general analogizing capability.

There is no reason to expect this at all. IQ tests are designed to measure $g$, which is a strong, profoundly important, but imperfect correlation among humans in how they perform across many cognitive tasks. The fact that all these different tasks (even reaction time!) are correlated for humans is weak evidence that they are all influenced by a single biological factor. However, there is this very little reason to think that all these tasks will be correlated for arbitrary reasoning systems, especially a system specifically trained to perform on one specific test.

An analogy: the lengths of various body parts in humans (finger length, leg length, etc.) are all very correlated, and point to a general factor of size, i.e., it is reasonable to talk about "tall" humans and "short" humans, which generally have all their body parts longer or shorter, respectively. But if we're designing humanoid robots from scratch, there is no reason to expect there to be such a general factor of size. Some robots will be designed for welding car doors, and will have arms but no legs!

In short, the fact that IQ tests measure g, which is then predictive across many cognitive tasks, is crucial based on the fact that the sample set is humans, for which a small number of causal mechanisms (genetics, nutrician, etc.) are responsible for the correlation.

The point of an IQ test is to measure IQ, or "Intelligence Quotient", or how intelligent someone is, except what it really measures is how good people are at doing IQ tests.

Nobody agrees what Intelligence is, and people with a high IQ are not always clever.

It was amusing to see the quotations from Wikipedia articles (which I have worked on) that appear in the authors' paper without any citation to Wikipedia. They have made some effort to look up the background of IQ testing, to the extent of cribbing from some Wikipedia articles, but their citation of the actual peer-reviewed psychology literature on IQ testing is all but nonexistent (they cite mostly machine-learning papers), and as several astute comments point out, training a machine (or, for that matter, a human being) to perform specifically well on the specific item content of a test does NOT raise the inference that the specially trained human or machine is generally smart at whatever else the test-taking-trained human or machine does.

Another serious objection to the study, also brought up in some previous comments, is that IQ scores are norm-based scores (they compare test-takers to a general population, not to criteria of correct or incorrect answers), and it is very likely here that the Mechanical Turk recruits found for the study are NOT a representative sample of the relevant population of human beings for comparison to the machine's performance.

But my most serious objection to this study, among several, is that the item content chosen for this study was not even items professional developed and pains-takingly tested item by item by psychologists for inclusion in an IQ test battery, but rather the kind of dreck usually found in unvalidated online IQ tests, which are simply parlor games and should not be taken seriously. The study to a very great degree demonstrates the well known computer science principle "garbage in, garbage out," and does regrettably little to advance the literature on artificial intelligence.

For readers who would like to read some of the legitimate psychology research literature on IQ testing, I strongly recommend the Wikipedia article "IQ classification," which has a better bibliography (by far) than any other Wikipedia article about IQ testing.

https://en.wikipedia.org/wiki/IQ_classification

I don't think machines will ever achieve human like intelligence in the current paradigm. A very basic property of the human intelligence is that there's just one program (learning algorithm, if you please) that is capable of learning all activities, such as playing chess, playing the guitar, solving math problems etc. We are born with this program and our life is a learning experience. On the other hand, computers have separate programs for different activities. A chess playing computer can only play chess. A machine capable of verbal reasoning (as in this article) is capable of only verbal reasoning. I do not think human like intelligence can be achieved by simply assembling these separate programs together, but this is just my humble opinion or intuition.

Your general sentiment was true with old-school AI that was just exploring trees of options and intelligently pruning branches. However, for quite some time now we've moved beyond that and into areas that are promising for generalization.

You may want to explore the work that DeepMind[1] has done. They're starting with simple universes (Atari games), but have developed a single program that can learn to play dozens of different games without having ever been told the rules. They learn by trial and error (specifically, Q-learning[2]) and rely only on reading pixels and knowing the current score. They learn fairly sophisticated behaviors and ultimately learn to play these games at levels far superior to what humans can achieve.

Many people are now trying to generalize these results to more realistic worlds, such as 3D games. And ultimately to agents that interact with the real world.

[1] http://deepmind.com/ [2] https://en.wikipedia.org/wiki/Q-learning

I am aware of DeepMind. It is surely a step in the right direction. However, while model independent reinforcement learning is one part of the puzzle, the other (and equally important) part is transfer of knowledge between agents (most human learning happens via interaction between a teacher and a student). I would really love to see some progress in that area.

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This one serves up a malicious advertisement to mobile users in certain regions (whatsapop browser hijacker with vibration and redirect).

For the security people at ad networks: The URL was

  whatsapops.com,

the mobile device was Android latest, the region was The Netherlands. It's not a virus on the device itself:

- No app installs since setting up the device a year ago.

- A lot of internet forum chatter with people using clean factory resets and getting the same problem.

Next to mining for phone numbers, they try to sucker you into installing a 12 Euro a week app. Very similar to the porn-redirects ads from DoubleClick that were plaguing mobile devices and tablets around 2012. This page also abuses the vibrate API and was successful in disabling the back key by working with a redirect-chain.

"but the machine built for this study actually outperformed the average human on these questions."

A subtle but key statement in the article. If the models are trained for the test, we're essentially looking at a standard machine learning problem, albeit with very modern techniques (word vectors, deep nets, etc). The point is that all of these are optimized towards a goal. In this case, the goal is the IQ test.

This is not close to being an intelligent being the way humans are. Candidate optimizations you can say humans are 'trained' for might be survival, finding meaning, reproduction, etc. All of these goals are extremely broad and abstract, especially in the context of computers.

I'm not saying this article is sensationalist, but it may be perceived sensationally. This article merely notes a predictable progression in artificial intelligence.

"intelligence level between people with bachelor degrees and people with master degrees"

This is pure nonsense. Average IQ raises with years spent in education[1], but the distribution is still bell-shaped, just shifted to the right ever so slightly.

It sounds like the editor feels real good that they belong to the class of educated = high IQ people, or real bad, that they didn't get the next-level degree, and got stuck stupid. Disastrous belief either way.

[1] https://brainsize.wordpress.com/2014/06/02/iq-years-of-educa...

I'm uncomfortable with research that merely states "we threw some Turkers at it". In my experience they are overall reliable, but there's no data on what qualifications (if any) the jobs were assigned, how much was offered, how long was given to complete the test, etc. It could be that some of these humans were actually by bots themselves, if insufficient controls were put in place.

http://turkernation.com/showthread.php?21352-The-Myth-of-Low...

An interesting paper at NIPS aims to combat the lower quality of Turkers with gamification. Instead of yes/no answers they offer a choice of: absolutely sure, yes, don't know, no, absolutely not. Monetary reward is doubled if they answer a question correctly with a high certainty, and it is zero'd when they are wrong.

http://arxiv.org/abs/1408.1387 "Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing"

I wonder if IQ tests (already discredited as far as I'm aware?) become considered less interesting/relevant in the same way chess has lost some of its lustre once computers exceeded human abilities? Eventually the only things humans will have to distinguish themselves is religion and incompetence. I'd love it to happen in our lifetimes to be able to see what real AI does. Maybe it's just as likely as judgement day is that we simply ignore each other...

Headline: AI outperforms humans on IQ test

Reality: AI scores >100 on IQ test

It's technically correct because it does beat humans on average, but it reads very differently.

Outperforming the median human on an IQ test and outperforming every human is probably a very small step for AI.

Actually, it's even less dramatic:

Headline: AI outperforms humans on IQ test

Reality: Trained machine learning algorithm scores better than the average untrained human.

If the Mechanical Turks were allowed to take versions of the test as many times as the AI was before the final test, the human average would be significantly higher.

Word analogies are the simplest problems on an aptitude or iq test, and are really just vocabulary questions in disguise. I think this result shows the limited usefulness of such questions in measuring intelligence rather than showing that the researchers' approach is a way to create AGI.

Until AI can move the boundaries of science/art/philosophy etc without massive direction from humans some of our jobs are still safe.

I would not be so smug however as I expect programming to be one of those jobs AI will become better than the human at within the next decade.

I think that if you had a person with average IQ spend time studying vocabulary, they would get a significant boost on an IQ test as well.

Watched Ex-Machina recently and I've got to drop it here:

I would not be wooed until the AI successfully seduce the human subject(s) involved.

And I would add, "human subject that knows they're talking to a machine."

Also hopefully a machine that's being treated humanely so as not to start the robot uprising, or some skynet biz.

If people are trained with the correct answers of IQ tests, they will get better scores.

suggested title: 'statistical model of IQ test beats humans at the same IQ test'

Ok, we'll go with that for now. Thanks!

"IQ-test-taking computer outperforms a sample of humans at IQ test."

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