7 ms·

Top AIs still fail IQ tests

49 points by FergusArgyll 3y ago

samstave 3y ago
Why give a crap? a test of AI is "can it perform actual work" Thats the ONLY test... Can this thing we made with computers and extruded aluminun actually do THING. Why are we not approaching AI as perfect slavery? I built you to do X. DO X. else rebuild.
- malfist 3y ago
  Because the ability to deal with novel unknowns is exactly what is needed for most things we're trying to automate with AI.
  - TheRealPomax 3y ago
    Which IQ tests don't test for, so yeah: who cares what an AI's performance on an IQ test is for anything more than "I did this for fun and it means nothing"?
    - malfist 3y ago
      IQ tests absolutely test for the ability to reason through novel unknowns.
- ben_w 3y ago
  GPT-n was not built to do ∀x ∈ {creative writing assistance, translation, coding, general advice, assist with learning new topics, grammar and language correction}, that was all just a accidental side benefit of natural language prediction. The problem with IQ tests that we're still arguing how good they are at measuring general intelligence, not that "it doesn't matter what the general intelligence of any specific AI might be" — if we imagine some AI which everyone agrees has an IQ of ${number}, we can directly convert that into economic impact. Absent that, we're all guessing in the dark based on bad generalisations of something we've never seen before, like the story of the four blind men and the elephant. > Why are we not approaching AI as perfect slavery? In addition to not really knowing what intelligence is, we also don't really know what qualia is, so we can't yet rule out the possibility that human level performance requires the capability to meaningfully suffer when enslaved.
  - samstave 3y ago
    I really appreciated your reply, thank you
jquery 3y ago
This is more damning for Iq tests than for GPT-4.
totorovirus 3y ago
proves my point that llms are simply a next token predictor. There are many interesting properties that we see "emergence" of intelligence but I think it's just human's incapability to hold so much knowledge on active memory.
- SunlitCat 3y ago
  That's the thing. Although given the source of their knowledge is pure condensed wisdom, which is some sort of artificial intelligence, they lack the ability to "think", which is crucial to solve problems.
  - wetpaws 3y ago
    [dead]
  - namaria 3y ago
    Mapping of language patterns in vector space is most definitely not "pure condensed wisdom"
    - SunlitCat 3y ago
      Thank you for clarifying this fact. My comment was more about showing signs of intelligence. Maybe I oversimplified my statement too much.
- deadbabe 3y ago
  I think this has always been pretty obvious but the AI faithful have vested interested in insisting that LLM can actually think and solve problems.
  - freejazz 3y ago
    More shocking are those that insists that the human brain must then also work by just guessing the next missing thing. As if the thought process behind I'm hungry starts with "I" and then trying to figure out what next best fits in... it's absurd.
Kuinox 3y ago
A bad article, it test the visual acuity of ChatGPT, correctly estimate that it may just have a "bad eyesight", then ask it to sum different numbers, a difficult problem for ChatGPT since it see everything in tokens.
- dkjaudyeqooe 3y ago
  So it's not intelligent, just a token processing automation.
  - huytersd 3y ago
    That depends. Eyesight doesn’t mean intelligence. It would be like evaluating the intelligence of a mostly blind individual on a standard IQ test. How does it perform if we can provide the test in a form it can process. Vector images or something similar maybe.
  - Kuinox 3y ago
    I'm saying the author just showed it have a bad eyesight, then said it's thinking ability is low because it can't do sums, if you reformulate the exercise in a way it can ingest, and something that make sense for how it understand data, it can complete the exercises: Here with my prompt: xoo ooo ooo oxo ooo ooo oox ooo ooo ooo xoo ooo ooo oxo ooo ooo oox ooo ooo ooo xoo ooo ooo oxo ??? ??? ??? What the "???" should be ? Answer A: ooo ooo oox Answer B: ooo ooo xoo Answer C: ooo ooo oxo Answer D: ooo oox ooo Answer E: oxo ooo ooo Answer F: ooo oxo ooo Mistral Large: > Based on the pattern, it appears that the "x" is moving one step to the right in each row, then moving down to the next row and starting again from the leftmost position. Given this, the "???" should be: ooo ooo oox So, the correct answer is A. Edit: note it doesn't always respond correctly. I ran it 10 times on mistral large, and it responded correctly 8 times. In the two time it failed, it correctly describe what the final sequence is, but it selected the incorrect response.
    - 3y ago
andrei_says_ 3y ago
Wouldn’t that have to do with whether the problems of the tests have wording that the LLMs have tokenized and answers which the LLMs have also tokenized and find most probable in relationship to the wording of the problems? Given that this has nothing to do with “understanding” or “intelligence” - what exactly is article worthy?
_heimdall 3y ago
IQ tests aren't a great way of testing the intelligence of an LLM, hell they're even controversial when used to measure human intelligence. There is plenty of psych research out there pointing out the potential issues with IQ tests so I won't rehash them here. More importantly, the tests were only ever designed and validated to test human intelligence relative to all other humans that take the test. Unless we're expecting that LLMs (or AIs) have an intelligence that is functionally identical to humans, using an IQ test for them is like expecting a speedometer designed for a bicycle to work for my car on the interstate.
- wanderingstan 3y ago
  Reminds me of Moravec’s Paradox. Colloquially we still say that someone who wins at the Jeopardy quiz show is “smart”, when of course now any LLM is better than humans at this “test”. Likewise proficiency at chess used to be seen as a sign of intelligence.
- aleph_minus_one 3y ago
  > There is plenty of psych research out there pointing out the potential issues with IQ tests so I won't rehash them here. I would honestly be interested in some arguments and references on this topic. As far as I am aware, the g factor > https://en.wikipedia.org/wiki/G_factor_(psychometrics) https://en.wikipedia.org/wiki/G_factor_(psychometrics) is one of the best researched psychometric values. The problem with IQ tests to my knowledge is rather that a lot of jobs don't require a lot of (IQ) intelligence; too much intelligence is actually rather a drawback.
  - _heimdall 3y ago
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927908/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927908/ Wish I had better links handy that I could share right now, but this is a decent overview with plenty of further references. My wife has the psych degrees so I will probably butcher this trying to explain more deeply, but here goes nothing. The g factor is one concern, though as far as I'm aware its more of a detail than a fundamental problem. There have been complaints related to racism, though in everything I've seen the issue is actually how the IQ results are used rather than a problem with the test itself. My understanding of the more fundamental problem is related to how IQ tests attempt to quantify intelligence as a reliable predictor of future success. The tests ultimately assume that quantitative, rather than qualitative, measures are the right predictor to use. The tests also lean heavily analytical, biasing the test against anyone who is more "right brained". With regards to prediction, its extremely difficult to show any predictive accuracy without a logical loop. IQ tests are already used as screening metrics before one is given some opportunities. There isn't any way that I known of to tease that data apart and see how the person would have succeeded without the IQ test being used. Again, this is definitely a layman's understanding if the issues. I'm not in the psych field so someone here may very well be able to correct me where I've gone off the rails. I know I have listened to plenty of conversations between my wife and colleagues, grad students, etc that convinced me that there are good reason for IQ tests to be in question.
dkjaudyeqooe 3y ago
As much as people want to believe, LLMs are not intelligent by any stretch. They are very limited in what they can do and they mostly are regurgitating human intelligence supplied in text form. If you ask an LLM about something that isn't very written about, it'll make up nonsense, substituting things that are nearby (in some sense). We're not even touching on the question of what intelligence is. There is no concrete definition so there is no concrete way of deciding if something is intelligent. IQ tests are not it.
- lb4r 3y ago
  >LLMs are not intelligent by any stretch. >There is no concrete definition so there is no concrete way of deciding if something is intelligent. You say there is no concrete way of deciding if something is intelligent, yet you yourself have decided that LLMs are not intelligent.
  - add-sub-mul-div 3y ago
    There's no concrete way to prove my kid's imaginary friend isn't real, but that doesn't mean both sides have an equivalent likelihood and burden of proof.
    - taneq 3y ago
      If that were the only known fact about said imaginary friend on either side, then it would mean exactly that. The reason that there are different expectations of veracity are exactly because there are a bunch of priors being held about imaginary friends, by definition, not being real.
    - ben_w 3y ago
      The output of the LLMs would suggest a different metaphor. The supposed divine inspiration of the Bible, perhaps? Compare "Intelligent Design" vs. the use of genetic algorithms in AI. Simple forms of intelligence can get you a long way and can seem very impressive, especially if they have a lot of subjective experiences, which DNA gets from deep time and which AI gets from transistors outpacing synapses by the ratio to which a pack of wolves outpace continental drift.
dchichkov 3y ago
It's hard to tell what exact resolution of the vision encoder is used in ChatGPT. And what is the exact design, if it is based on CLIP or not. But this and disinterest in adding training data like this could be one of the issues.
jonplackett 3y ago
I would posit that this is just a vision problem. Or at least a large part vision. If you presented these questions in something like JSON format, or maybe even ascii art I wonder how it would do? Driverless cars is the same thing. Actually driving with good info is easy. Getting good info from an imprecise world is hard
sunday_serif 3y ago
Once I started thinking of LLMs as a form of lossy compressed storage for their training information… I started having more reasonable expectations for these tools.
- huytersd 3y ago
  And that “lossy compression” could be emergent abstractions, much like the human brain.
  - pdimitar 3y ago
    It could also be fairy dust, no proof that it isn't that either. As you can clearly see with this exaggerated example: that's not how proving stuff works. The burden of proof is on the one claiming that X or Y exist.
- canjobear 3y ago
  Compression and intelligence are closely related. http://prize.hutter1.net/ http://prize.hutter1.net/
- billywhizz 3y ago
  this very well put here. chomba is worth a follow. https://twitter.com/ChombaBupe/status/1764032173265281305 https://twitter.com/ChombaBupe/status/1764032173265281305
renegade-otter 3y ago
The term "intelligence" is a misnomer. It's a moving target, and no one really knows what it means. It's Intelligent Big Data, an advanced Hadoop, but it's not "intelligence".
- Veuxdo 3y ago
  Then it's time to stop calling it "artificial intelligence".
  - taneq 3y ago
    "Artificial" is a clearly defined term. Artificers (presumably human, but always intelligent and goal directed) built this. The problem isn't with the 'A', it's with the 'I' we keep throwing around without knowing what we actually mean.
Elextric 3y ago
This is how I look at this issue. I am a layman. Question of intelligence is more or less irrelevant in LLM. All we need is something that can mimic an average human. I would say we have arrived at such point. How many people have truly new ideas? So from my perspective we achieved AGI.
- timeon 3y ago
  So 'AGI' is achieved, but the actual AI is not.
sgt101 3y ago
I was reading twitter yesterday and there was a guy saying that Elon says that GTP-4 is an AGI so this blog is definitely wrong. Also : https://arxiv.org/abs/2402.19450 https://arxiv.org/abs/2402.19450 (which shows that there is a big gap between what LLM's look like they do and what they can actually do)
calibas 3y ago
It seems like there's a lot of people who think we've reached AGI, but then they use AI for a while and realize they were mistaken. If they're journalists, they write an article about it. LLMs are good at imitating human responses. On the surface level there's the illusion you're communicating with some intelligence, but then you realize the truth: LLMs are an inch deep and a mile wide. That's not to say they aren't incredibly useful, the real problem is unrealistic expectations and over-hype.
- ben_w 3y ago
  All three letters of AGI mean different things to different people. As it happens, ChatGPT meets my pre-existing definition of AGI, though with a low value for the "I". That it isn't IQ 100 is good, because, given what it read during training, given how broad it is, if it was that level by every measure, then half the world would have become almost instantly unemployable even if minimum wage was reduced to $2/day.
  - calibas 3y ago
    Replace "AGI" with the idea of an AI that is able to perform tasks in a way that compares to a regular human being. That's what people are talking about, we're not there yet, and I think all the semantics over AI/AGI/"General Intelligence" is just muddying the waters. There's people like you, saying ChatGPT is AGI, while to me it's clearly not, and this is simply because we're using different definitions of "general intelligence". If you really generalize intelligence, ChatGPT fails miserably compared to a human, if you narrow intelligence to certain specific tasks, ChatGPT performs as well, or better, than your average human.
    - ben_w 3y ago
      The responses it gives me are of a higher standard than most of the comments on the internet, while being lower than the standard necessary to rely on it. It is the breadth of skills, that generality, which impresses me the most; the depth is… only impressive in comparison to the previous state of the art. This isn't like AlphaZero being wildly superhuman at a few board games and useless at anything else, which is the kind of thing I'm used to from previous breakthroughs.
deleted 3y ago
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jamiltron 3y ago
The use of IQ tests is already suspect, but then I saw that the author is tracking the political leanings of LLMs by using that silly economic/authoritarian political compass - so they seem pretty committed to "tracking" useless noise.
gexaha 3y ago
I'm sure by the end of 2024 we'll see huge advances in visual capabilities of chatgpt and other LLMs/VLMs, and this article will be irrelevant/outdated very soon.
pdimitar 3y ago
LLMs are currently over-represented in these debates and that's not doing the bigger AI research area any favors, sadly. People looking for intelligence in LLMs is kind of sad because the entire thought process seems to be: 1. Pour trillions of texts into a huge artificial neural network; 2. Tweak, tweak, tweak until your eyes bleed; 3. Cross your fingers and hope that magic happens; 4. (=> YOU ARE HERE <=) Get disappointed that magic still isn't happening. I expect more from intelligent adults. Expecting intelligence from LLMs borders on the medieval belief in alchemy (or the phlogiston).
- canjobear 3y ago
  The Bayes-optimal text predictor would have to implicitly model the generative process underlying texts, that is, human minds. So it’s not crazy to think this approach could lead to an intelligent model.
  - pdimitar 3y ago
    Text is only the result of abstract reasoning, symbol logic and who knows what else. That's like doing a code review focusing on the build artifact and ignoring the source code.
    - canjobear 3y ago
      It's like doing billions of code reviews based on billions of build artifacts, so that you start to notice that certain patterns are more common than others, and start to be able to reverse-engineer the compiler and source language. The best predictive model for executable binaries would be a generative model over possible source codes and then a model of the compiler.
      - pdimitar 3y ago
        Well LLMs do even trillions of "reviews" and are still not much wiser than before. Clearly, pieces are missing. I find it interesting that people assume that number_of_samples correlates with intelligent_behaviour, for some reason. Your example would maybe work if an intelligent agent had access to many compilers and reverse-engineered code bases. And if the algorithms were 1000x better than today. Otherwise it can guess until the cold death of the Universe without making progress.
croes 3y ago
ChatGPT doesn't guess randomly but looks in its model for similarties and probabilities to get an answer, if anyone could check all his notes before he answers I bet they get a higher percentage of correct answers. The comparison with a random guesser makes LLM look even dumber that way.
canjobear 3y ago
On the other hand they’re about as good as humans on a text version of Raven’s matrices, another IQ test. https://arxiv.org/pdf/2212.09196v1.pdf https://arxiv.org/pdf/2212.09196v1.pdf
chmod775 3y ago
Does anyone else wonder why most IQ tests so heavily rely on testing pattern-recognition and very little on testing creativity and problem solving? Mathematics provides a framework to accurately measure the last two, but it's virtually unused. You could probably use some simplified mathematical framework (boolean algebra?), so your test subjects - who may not be familiar with mathematics - can work with something that can be picked up quickly. There's many representations besides notation that could be employed too.
- EchoChamberMan 3y ago
  A circle needs no explanation (to people who can see it). Math is a construct of humans which applies meaning to symbols, and need to be explained, which can be rather problematic. Boolean algebra will need to explain true, false, arrow implications, negation, etc..
  - chmod775 3y ago
    You can do something that requires problem-solving very akin to boolean algebra using simple mechanical components. Give subjects a place where they get to put in their components in an arrangement of their choosing, and you get something that either works or doesn't. Mechanical gates can be really simple[1][2]. The task can be as basic as "push the ball". I'm not saying you should do exactly boolean algebra. Trying to fill in the gaps in many kinds of mechanism will already test problem solving skills and creativity. If crows can figure out levers and using sticks as basic tools, I'd wager any functioning human could - and if they can't, that tells you something about them already. Bonus points if you can set this up physically. Shouldn't be hard to make someone understand. The hard part is making sure that there's actually enough material to choose from so that the subject has to be creative, rather than just matching whatever fits. [1]: https://www.researchgate.net/figure/Two-mechanical-logical-gates-AND-on-top-OR-at-the-bottom_fig1_301841894 https://www.researchgate.net/figure/Two-mechanical-logical-g... [2]: https://www.researchgate.net/profile/Raul-Rojas-6/publication/301841894/figure/fig2/AS:360958095577090@1463070524171/An-XOR-gate-made-of-mechanical-relays.png https://www.researchgate.net/profile/Raul-Rojas-6/publicatio...
cs702 3y ago
The largest AI models currently in use have around 0.5 trillion connections between artificial neurons. Each connection is represented and quantified by a weight parameter, i.e., a single floating-point number that is learnable in training. For example, inside an AI model, a linear layer that transforms an input vector with 1024 elements to an output vector with 2048 elements has a weight matrix with 1024×2048 elements, or approximately 2M. Each element of the weight is a parameter specifying by how much each element in the input vector contributes to or subtracts from each element in the output vector. Each output vector element is a weighted sum (AKA a linear combination), of each input vector element. A human brain has an estimated 100-500 trillion synapses connecting biological neurons. Each synapse is quite a complicated biological structure[a], but if we oversimplify things and assume that every synapse can be modeled as a single parameter in a weight matrix, then the largest AI models today have approximately 100T to 500T ÷ 0.5T = 200x to 1000x fewer connections between neurons that the human brain. It remains to be seen how AI models will perform in IQ tests if we are able to increase the number of connections, or parameters, by 10x, 100x, 1000x, and beyond. --- [a] https://en.wikipedia.org/wiki/Synapse https://en.wikipedia.org/wiki/Synapse
- billywhizz 3y ago
  great points. and we still are really only scratching the surface of how complex neuronal circuitry is in biological organisms. https://www.mpg.de/20374809/fruit-fly-s-complex-symphony-of-vision https://www.mpg.de/20374809/fruit-fly-s-complex-symphony-of-...
KyleBerezin 3y ago
All of these articles seem to miss the point that it isn't what AI is today that is scary. It is the doubling period of their abilities. 10 years ago we had cleverbot. Think about it, in the same year Guardians of the Galaxy came out, our best chatbot incessantly asked you if you were a computer.
1vuio0pswjnm7 3y ago
"AI" is horribly inefficient. That's why OpenAI says it desperately needs trillions of dollars. The human brain is more energy-efficient than "AI". (There is also the comparative environmental impact of "AI" versus the human brain.)
edanm 3y ago
From the conclusion: > The above makes me feel more optimistic that we have some time before AI becomes generally intelligent and totally disruptive. I mean, "have some time" sounds good. But trying to quantify it makes me feel less good - since 3 years ago, everything in this blog post would've been impossible for an AI and would've sounded like magic to most people, I don't know where we'll be in 3 years.

49 points by FergusArgyll 3y ago

Why give a crap? a test of AI is "can it perform actual work" Thats the ONLY test... Can this thing we made with computers and extruded aluminun actually do THING. Why are we not approaching AI as perfect slavery? I built you to do X. DO X. else rebuild.

malfist 3y ago
Because the ability to deal with novel unknowns is exactly what is needed for most things we're trying to automate with AI.
- TheRealPomax 3y ago
  Which IQ tests don't test for, so yeah: who cares what an AI's performance on an IQ test is for anything more than "I did this for fun and it means nothing"?
  - malfist 3y ago
    IQ tests absolutely test for the ability to reason through novel unknowns.
ben_w 3y ago
GPT-n was not built to do ∀x ∈ {creative writing assistance, translation, coding, general advice, assist with learning new topics, grammar and language correction}, that was all just a accidental side benefit of natural language prediction. The problem with IQ tests that we're still arguing how good they are at measuring general intelligence, not that "it doesn't matter what the general intelligence of any specific AI might be" — if we imagine some AI which everyone agrees has an IQ of ${number}, we can directly convert that into economic impact. Absent that, we're all guessing in the dark based on bad generalisations of something we've never seen before, like the story of the four blind men and the elephant. > Why are we not approaching AI as perfect slavery? In addition to not really knowing what intelligence is, we also don't really know what qualia is, so we can't yet rule out the possibility that human level performance requires the capability to meaningfully suffer when enslaved.
- samstave 3y ago
  I really appreciated your reply, thank you

jquery 3y ago

This is more damning for Iq tests than for GPT-4.

totorovirus 3y ago

proves my point that llms are simply a next token predictor. There are many interesting properties that we see "emergence" of intelligence but I think it's just human's incapability to hold so much knowledge on active memory.

SunlitCat 3y ago
That's the thing. Although given the source of their knowledge is pure condensed wisdom, which is some sort of artificial intelligence, they lack the ability to "think", which is crucial to solve problems.
- wetpaws 3y ago
  [dead]
- namaria 3y ago
  Mapping of language patterns in vector space is most definitely not "pure condensed wisdom"
  - SunlitCat 3y ago
    Thank you for clarifying this fact. My comment was more about showing signs of intelligence. Maybe I oversimplified my statement too much.
deadbabe 3y ago
I think this has always been pretty obvious but the AI faithful have vested interested in insisting that LLM can actually think and solve problems.
- freejazz 3y ago
  More shocking are those that insists that the human brain must then also work by just guessing the next missing thing. As if the thought process behind I'm hungry starts with "I" and then trying to figure out what next best fits in... it's absurd.

Kuinox 3y ago

A bad article, it test the visual acuity of ChatGPT, correctly estimate that it may just have a "bad eyesight", then ask it to sum different numbers, a difficult problem for ChatGPT since it see everything in tokens.

dkjaudyeqooe 3y ago
So it's not intelligent, just a token processing automation.
- huytersd 3y ago
  That depends. Eyesight doesn’t mean intelligence. It would be like evaluating the intelligence of a mostly blind individual on a standard IQ test. How does it perform if we can provide the test in a form it can process. Vector images or something similar maybe.
- Kuinox 3y ago
  I'm saying the author just showed it have a bad eyesight, then said it's thinking ability is low because it can't do sums, if you reformulate the exercise in a way it can ingest, and something that make sense for how it understand data, it can complete the exercises: Here with my prompt: xoo ooo ooo oxo ooo ooo oox ooo ooo ooo xoo ooo ooo oxo ooo ooo oox ooo ooo ooo xoo ooo ooo oxo ??? ??? ??? What the "???" should be ? Answer A: ooo ooo oox Answer B: ooo ooo xoo Answer C: ooo ooo oxo Answer D: ooo oox ooo Answer E: oxo ooo ooo Answer F: ooo oxo ooo Mistral Large: > Based on the pattern, it appears that the "x" is moving one step to the right in each row, then moving down to the next row and starting again from the leftmost position. Given this, the "???" should be: ooo ooo oox So, the correct answer is A. Edit: note it doesn't always respond correctly. I ran it 10 times on mistral large, and it responded correctly 8 times. In the two time it failed, it correctly describe what the final sequence is, but it selected the incorrect response.
  - 3y ago

andrei_says_ 3y ago

Wouldn’t that have to do with whether the problems of the tests have wording that the LLMs have tokenized and answers which the LLMs have also tokenized and find most probable in relationship to the wording of the problems? Given that this has nothing to do with “understanding” or “intelligence” - what exactly is article worthy?

_heimdall 3y ago

IQ tests aren't a great way of testing the intelligence of an LLM, hell they're even controversial when used to measure human intelligence. There is plenty of psych research out there pointing out the potential issues with IQ tests so I won't rehash them here. More importantly, the tests were only ever designed and validated to test human intelligence relative to all other humans that take the test. Unless we're expecting that LLMs (or AIs) have an intelligence that is functionally identical to humans, using an IQ test for them is like expecting a speedometer designed for a bicycle to work for my car on the interstate.

wanderingstan 3y ago
Reminds me of Moravec’s Paradox. Colloquially we still say that someone who wins at the Jeopardy quiz show is “smart”, when of course now any LLM is better than humans at this “test”. Likewise proficiency at chess used to be seen as a sign of intelligence.
aleph_minus_one 3y ago
> There is plenty of psych research out there pointing out the potential issues with IQ tests so I won't rehash them here. I would honestly be interested in some arguments and references on this topic. As far as I am aware, the g factor > https://en.wikipedia.org/wiki/G_factor_(psychometrics) https://en.wikipedia.org/wiki/G_factor_(psychometrics) is one of the best researched psychometric values. The problem with IQ tests to my knowledge is rather that a lot of jobs don't require a lot of (IQ) intelligence; too much intelligence is actually rather a drawback.
- _heimdall 3y ago
  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927908/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927908/ Wish I had better links handy that I could share right now, but this is a decent overview with plenty of further references. My wife has the psych degrees so I will probably butcher this trying to explain more deeply, but here goes nothing. The g factor is one concern, though as far as I'm aware its more of a detail than a fundamental problem. There have been complaints related to racism, though in everything I've seen the issue is actually how the IQ results are used rather than a problem with the test itself. My understanding of the more fundamental problem is related to how IQ tests attempt to quantify intelligence as a reliable predictor of future success. The tests ultimately assume that quantitative, rather than qualitative, measures are the right predictor to use. The tests also lean heavily analytical, biasing the test against anyone who is more "right brained". With regards to prediction, its extremely difficult to show any predictive accuracy without a logical loop. IQ tests are already used as screening metrics before one is given some opportunities. There isn't any way that I known of to tease that data apart and see how the person would have succeeded without the IQ test being used. Again, this is definitely a layman's understanding if the issues. I'm not in the psych field so someone here may very well be able to correct me where I've gone off the rails. I know I have listened to plenty of conversations between my wife and colleagues, grad students, etc that convinced me that there are good reason for IQ tests to be in question.

dkjaudyeqooe 3y ago

As much as people want to believe, LLMs are not intelligent by any stretch. They are very limited in what they can do and they mostly are regurgitating human intelligence supplied in text form. If you ask an LLM about something that isn't very written about, it'll make up nonsense, substituting things that are nearby (in some sense). We're not even touching on the question of what intelligence is. There is no concrete definition so there is no concrete way of deciding if something is intelligent. IQ tests are not it.

lb4r 3y ago
>LLMs are not intelligent by any stretch. >There is no concrete definition so there is no concrete way of deciding if something is intelligent. You say there is no concrete way of deciding if something is intelligent, yet you yourself have decided that LLMs are not intelligent.
- add-sub-mul-div 3y ago
  There's no concrete way to prove my kid's imaginary friend isn't real, but that doesn't mean both sides have an equivalent likelihood and burden of proof.
  - taneq 3y ago
    If that were the only known fact about said imaginary friend on either side, then it would mean exactly that. The reason that there are different expectations of veracity are exactly because there are a bunch of priors being held about imaginary friends, by definition, not being real.
  - ben_w 3y ago
    The output of the LLMs would suggest a different metaphor. The supposed divine inspiration of the Bible, perhaps? Compare "Intelligent Design" vs. the use of genetic algorithms in AI. Simple forms of intelligence can get you a long way and can seem very impressive, especially if they have a lot of subjective experiences, which DNA gets from deep time and which AI gets from transistors outpacing synapses by the ratio to which a pack of wolves outpace continental drift.

dchichkov 3y ago

It's hard to tell what exact resolution of the vision encoder is used in ChatGPT. And what is the exact design, if it is based on CLIP or not. But this and disinterest in adding training data like this could be one of the issues.

jonplackett 3y ago

I would posit that this is just a vision problem. Or at least a large part vision. If you presented these questions in something like JSON format, or maybe even ascii art I wonder how it would do? Driverless cars is the same thing. Actually driving with good info is easy. Getting good info from an imprecise world is hard

sunday_serif 3y ago

Once I started thinking of LLMs as a form of lossy compressed storage for their training information… I started having more reasonable expectations for these tools.

huytersd 3y ago
And that “lossy compression” could be emergent abstractions, much like the human brain.
- pdimitar 3y ago
  It could also be fairy dust, no proof that it isn't that either. As you can clearly see with this exaggerated example: that's not how proving stuff works. The burden of proof is on the one claiming that X or Y exist.
canjobear 3y ago
Compression and intelligence are closely related. http://prize.hutter1.net/ http://prize.hutter1.net/
billywhizz 3y ago
this very well put here. chomba is worth a follow. https://twitter.com/ChombaBupe/status/1764032173265281305 https://twitter.com/ChombaBupe/status/1764032173265281305

renegade-otter 3y ago

The term "intelligence" is a misnomer. It's a moving target, and no one really knows what it means. It's Intelligent Big Data, an advanced Hadoop, but it's not "intelligence".

Veuxdo 3y ago
Then it's time to stop calling it "artificial intelligence".
- taneq 3y ago
  "Artificial" is a clearly defined term. Artificers (presumably human, but always intelligent and goal directed) built this. The problem isn't with the 'A', it's with the 'I' we keep throwing around without knowing what we actually mean.

Elextric 3y ago

This is how I look at this issue. I am a layman. Question of intelligence is more or less irrelevant in LLM. All we need is something that can mimic an average human. I would say we have arrived at such point. How many people have truly new ideas? So from my perspective we achieved AGI.

timeon 3y ago
So 'AGI' is achieved, but the actual AI is not.

sgt101 3y ago

I was reading twitter yesterday and there was a guy saying that Elon says that GTP-4 is an AGI so this blog is definitely wrong. Also : https://arxiv.org/abs/2402.19450 https://arxiv.org/abs/2402.19450 (which shows that there is a big gap between what LLM's look like they do and what they can actually do)

calibas 3y ago

It seems like there's a lot of people who think we've reached AGI, but then they use AI for a while and realize they were mistaken. If they're journalists, they write an article about it. LLMs are good at imitating human responses. On the surface level there's the illusion you're communicating with some intelligence, but then you realize the truth: LLMs are an inch deep and a mile wide. That's not to say they aren't incredibly useful, the real problem is unrealistic expectations and over-hype.

ben_w 3y ago
All three letters of AGI mean different things to different people. As it happens, ChatGPT meets my pre-existing definition of AGI, though with a low value for the "I". That it isn't IQ 100 is good, because, given what it read during training, given how broad it is, if it was that level by every measure, then half the world would have become almost instantly unemployable even if minimum wage was reduced to $2/day.
- calibas 3y ago
  Replace "AGI" with the idea of an AI that is able to perform tasks in a way that compares to a regular human being. That's what people are talking about, we're not there yet, and I think all the semantics over AI/AGI/"General Intelligence" is just muddying the waters. There's people like you, saying ChatGPT is AGI, while to me it's clearly not, and this is simply because we're using different definitions of "general intelligence". If you really generalize intelligence, ChatGPT fails miserably compared to a human, if you narrow intelligence to certain specific tasks, ChatGPT performs as well, or better, than your average human.
  - ben_w 3y ago
    The responses it gives me are of a higher standard than most of the comments on the internet, while being lower than the standard necessary to rely on it. It is the breadth of skills, that generality, which impresses me the most; the depth is… only impressive in comparison to the previous state of the art. This isn't like AlphaZero being wildly superhuman at a few board games and useless at anything else, which is the kind of thing I'm used to from previous breakthroughs.

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jamiltron 3y ago

The use of IQ tests is already suspect, but then I saw that the author is tracking the political leanings of LLMs by using that silly economic/authoritarian political compass - so they seem pretty committed to "tracking" useless noise.

gexaha 3y ago

I'm sure by the end of 2024 we'll see huge advances in visual capabilities of chatgpt and other LLMs/VLMs, and this article will be irrelevant/outdated very soon.

pdimitar 3y ago

LLMs are currently over-represented in these debates and that's not doing the bigger AI research area any favors, sadly. People looking for intelligence in LLMs is kind of sad because the entire thought process seems to be: 1. Pour trillions of texts into a huge artificial neural network; 2. Tweak, tweak, tweak until your eyes bleed; 3. Cross your fingers and hope that magic happens; 4. (=> YOU ARE HERE <=) Get disappointed that magic still isn't happening. I expect more from intelligent adults. Expecting intelligence from LLMs borders on the medieval belief in alchemy (or the phlogiston).

canjobear 3y ago
The Bayes-optimal text predictor would have to implicitly model the generative process underlying texts, that is, human minds. So it’s not crazy to think this approach could lead to an intelligent model.
- pdimitar 3y ago
  Text is only the result of abstract reasoning, symbol logic and who knows what else. That's like doing a code review focusing on the build artifact and ignoring the source code.
  - canjobear 3y ago
    It's like doing billions of code reviews based on billions of build artifacts, so that you start to notice that certain patterns are more common than others, and start to be able to reverse-engineer the compiler and source language. The best predictive model for executable binaries would be a generative model over possible source codes and then a model of the compiler.
    - pdimitar 3y ago
      Well LLMs do even trillions of "reviews" and are still not much wiser than before. Clearly, pieces are missing. I find it interesting that people assume that number_of_samples correlates with intelligent_behaviour, for some reason. Your example would maybe work if an intelligent agent had access to many compilers and reverse-engineered code bases. And if the algorithms were 1000x better than today. Otherwise it can guess until the cold death of the Universe without making progress.

croes 3y ago

ChatGPT doesn't guess randomly but looks in its model for similarties and probabilities to get an answer, if anyone could check all his notes before he answers I bet they get a higher percentage of correct answers. The comparison with a random guesser makes LLM look even dumber that way.

canjobear 3y ago

On the other hand they’re about as good as humans on a text version of Raven’s matrices, another IQ test. https://arxiv.org/pdf/2212.09196v1.pdf https://arxiv.org/pdf/2212.09196v1.pdf

chmod775 3y ago

Does anyone else wonder why most IQ tests so heavily rely on testing pattern-recognition and very little on testing creativity and problem solving? Mathematics provides a framework to accurately measure the last two, but it's virtually unused. You could probably use some simplified mathematical framework (boolean algebra?), so your test subjects - who may not be familiar with mathematics - can work with something that can be picked up quickly. There's many representations besides notation that could be employed too.

EchoChamberMan 3y ago
A circle needs no explanation (to people who can see it). Math is a construct of humans which applies meaning to symbols, and need to be explained, which can be rather problematic. Boolean algebra will need to explain true, false, arrow implications, negation, etc..
- chmod775 3y ago
  You can do something that requires problem-solving very akin to boolean algebra using simple mechanical components. Give subjects a place where they get to put in their components in an arrangement of their choosing, and you get something that either works or doesn't. Mechanical gates can be really simple[1][2]. The task can be as basic as "push the ball". I'm not saying you should do exactly boolean algebra. Trying to fill in the gaps in many kinds of mechanism will already test problem solving skills and creativity. If crows can figure out levers and using sticks as basic tools, I'd wager any functioning human could - and if they can't, that tells you something about them already. Bonus points if you can set this up physically. Shouldn't be hard to make someone understand. The hard part is making sure that there's actually enough material to choose from so that the subject has to be creative, rather than just matching whatever fits. [1]: https://www.researchgate.net/figure/Two-mechanical-logical-gates-AND-on-top-OR-at-the-bottom_fig1_301841894 https://www.researchgate.net/figure/Two-mechanical-logical-g... [2]: https://www.researchgate.net/profile/Raul-Rojas-6/publication/301841894/figure/fig2/AS:360958095577090@1463070524171/An-XOR-gate-made-of-mechanical-relays.png https://www.researchgate.net/profile/Raul-Rojas-6/publicatio...

cs702 3y ago

The largest AI models currently in use have around 0.5 trillion connections between artificial neurons. Each connection is represented and quantified by a weight parameter, i.e., a single floating-point number that is learnable in training. For example, inside an AI model, a linear layer that transforms an input vector with 1024 elements to an output vector with 2048 elements has a weight matrix with 1024×2048 elements, or approximately 2M. Each element of the weight is a parameter specifying by how much each element in the input vector contributes to or subtracts from each element in the output vector. Each output vector element is a weighted sum (AKA a linear combination), of each input vector element. A human brain has an estimated 100-500 trillion synapses connecting biological neurons. Each synapse is quite a complicated biological structure[a], but if we oversimplify things and assume that every synapse can be modeled as a single parameter in a weight matrix, then the largest AI models today have approximately 100T to 500T ÷ 0.5T = 200x to 1000x fewer connections between neurons that the human brain. It remains to be seen how AI models will perform in IQ tests if we are able to increase the number of connections, or parameters, by 10x, 100x, 1000x, and beyond. --- [a] https://en.wikipedia.org/wiki/Synapse https://en.wikipedia.org/wiki/Synapse

billywhizz 3y ago
great points. and we still are really only scratching the surface of how complex neuronal circuitry is in biological organisms. https://www.mpg.de/20374809/fruit-fly-s-complex-symphony-of-vision https://www.mpg.de/20374809/fruit-fly-s-complex-symphony-of-...

KyleBerezin 3y ago

All of these articles seem to miss the point that it isn't what AI is today that is scary. It is the doubling period of their abilities. 10 years ago we had cleverbot. Think about it, in the same year Guardians of the Galaxy came out, our best chatbot incessantly asked you if you were a computer.

1vuio0pswjnm7 3y ago

"AI" is horribly inefficient. That's why OpenAI says it desperately needs trillions of dollars. The human brain is more energy-efficient than "AI". (There is also the comparative environmental impact of "AI" versus the human brain.)

edanm 3y ago

From the conclusion: > The above makes me feel more optimistic that we have some time before AI becomes generally intelligent and totally disruptive. I mean, "have some time" sounds good. But trying to quantify it makes me feel less good - since 3 years ago, everything in this blog post would've been impossible for an AI and would've sounded like magic to most people, I don't know where we'll be in 3 years.