Surprised I haven’t come across this analogy before. Thinking about it now, ‘Animal-like abilities’ is a much better analogy in the context of the general public. It also helps ground scientists in thinking deeply about the difference between human and animal intelligence (some animals can use tools).
This is vague but it was a while ago... some researcher was trying to make AI more accessible to the public by downplaying high expectations. She represented it as a cartoon dog, amiable, willing, but evidently none too bright. I believe it was no more than an experiment, though a clever one. Ring anyone's bell?
Intelligence isn't a single-dimensional thing, but it seems pretty absurd to claim that there's no general factor at all. Could it really be meaningless to say that someone's smart?
It's like saying a car is fast. Sure, the car is fast. Is it also reliable, efficient, aesthetically pleasing, comfortable, or affordable? How does it handle? How does it do off-road? Can it fly? Can it operate underwater?
Real world fitness landscapes are complex and multidimensional.
My comment was a little snarky because while I like this site quite a bit for many reasons I do get a little fatigued by the prevalence of the IQ cult. The notion that there is a single metric on which brains can be sorted is sort of a sacred dogma around here, and one which to me is obviously quite wrong. All it takes is a casual glance at intelligence as it actually manifests in the only real world example of an intelligent system we have: biology. It's clearly a multidimensional quality that can be optimized for a large range of things just like anything else.
If intelligence is multidimensional in all other living things, by what rationale would we conclude that human intelligence is one-dimensional?
... or ...
If virtually every engineered or evolved capability is subject to multi-dimensional optimization, then why not also intelligence?
Absolutely true, but I'm not sure that's really a point of confusion. Almost everyone would agree that smart people aren't automatically good at athletics, or poetry, or woodworking. If you've seen people say that kind of thing I agree they're being silly.
Yet we have a bunch of general attributes we expect when we say "car". Maybe "she's smart" is rather like "she has a car". You don't know whether it's a Porsche or a Truck, gas or electric, but you'll generally rely on her being able to cover larger distances than on foot, and at higher speeds, while also hauling some reasonable amount of luggage.
I agree with this sentiment - single-dimensional intelligence is usually reactive, instinctual, applies to a very specific event or context. I believe human-level/multi-dimensional intelligence is the ability to extrapolate/abstract ideas from many different single-dimensional understandings.
I think regardless of whether or not AI is or ever will be multi-dimensional, that will depend on its ability to consider and make conclusions from many different single-dimensions - libraries if you will.
It totally could be meaningless. I think there is this conflation of two thing. I don't think "General Intelligence" is a thing. We don't have it, AIs probably won't. We have a collection of specialty neurological adaptations that are useful but certainly not adapted for "any" use. Even if they can be composed to be more powerful, there are a lot of limits to it.
This doesn't mean that AI won't get smarter than us at everything we can do, but based on the fact we are not getting much smarter we have to ask: is that just a limit of biology? Or are there scaling limits to Intelligence? Organizations and societies are a great example of the scaling limits of "Intelligence", they bypass a lot of human limits to make gesalt intelligent agents more reliable and less prone to certain biases, but generally not meaningfully "smarter" than any individual, just more patient and consistent. There are clear limits on the scalability Intelligence, just throwing more computational power at the problem isn't enough for runaway-agi
Trollish comment above but I do wonder about this. I always hear people who think ‘g’ is bullshit giving some form of this argument:
“AI can’t even x, how would you expect it to ever possibly do y?”
It’s like saying:
“How could a baby, which can not even tie its own shoes, spell a simple word, or fill a cup with water, ever possibly grow up to write a book, manage a corporation, or formulate a strategy for fighting a war? Impossible!”
It’s almost as if they think babies can’t learn. The AI is not a baby though, they say. Yeah, it’s not. But it’s a constantly learning body of work that is not regressing and not slowing down. Why would it not get to ‘general’ eventually?
We have billions of examples of babies growing up to learn to spell and tie their shoes, therefore it's very reasonable to assume that that's a skill most babies will get to.
There are 0 examples of a man made construct reaching "general" intelligence (whatever fuzzy meaning you may attach to it); there is no reason to believe that because we can reach some arbitrary human milestone today ("be a world class Go player"), any other human milestone ("start and run a company, and be a mentor and inspiration to others") is plausible.
This is a very different case from e.g. "we built a prototype plane that can lift 15 ft off the ground for 10 seconds, we should be able to build one that can lift 10,000 feet for 10 hours", because in the former case we were beginning to have an understanding of aerodynamics that made that path plausible. We do not have such an understanding backing current ML work. That's well illustrated by the fact that only 6 years elapsed between the first manned flight and crossing the Atlantic by plane - practice caught up to theory really fast, as it tends to do in those cases. We are about 6 years in the current ML revolution - what is its equivalent to crossing the Atlantic?
The logic you're advocating for here is more along the lines of "I taught my dog how to sit - shouldn't be too hard to get him to sit in front of a computer and start writing Python code now that I've got the first half figured out".
(or - I taught my dog how to fetch - shouldn't be too hard to train him to be a full time food delivery employee, etc).
> There are 0 examples of a man made construct reaching "general" intelligence (whatever fuzzy meaning you may attach to it); there is no reason to believe that because we can reach some arbitrary human milestone today ("be a world class Go player"), any other human milestone ("start and run a company, and be a mentor and inspiration to others") is plausible.
No reason? If you were an outside observer looking at the very first individuals of what you decided to call human, maybe. But after so many steps were made? You can argue that, before there was a plane, there was zero reason to thing that humans can ever create one. And you could've claimed that for pretty much anything else. And you'd generally see that they did get created. Why would artificial intelligence be fundamentally different? I'm not saying "we can do it in the next five years", but to say that there's no reason to believe that it will ever happen?
> We do not have such an understanding backing current ML work.
And we did not have the knowledge of aerodynamics before we started looking into throwing things, then keeping things in the air for longer than they would if they were thrown, and eventually flying them (yes, a bit simplified).
>“How could a baby, which can not even tie its own shoes, spell a simple word, or fill a cup with water, ever possibly grow up to write a book, manage a corporation, or formulate a strategy for fighting a war? Impossible!"
I don't think anyone has ever said that and meant it. Except to the very naive, it's pretty obvious that a child growing up into a functional adult is a function of training volume and adaptation. On the other hand, training volume for "AI" tends to be huge and yet there are definite strata that these machines seem confined to.
I agree that that form of argumentation is meaningless though. AI is obviously improved over time, but as far as I know that's due to external modification and not just more training.
It’s not bullshit to notice that a bunch of different abilities tend to be highly correlated. That’s all g is.
What is bullshit is the formal theory of multiple intelligences, which is largely PC-inspired pseudoscience and does not account for the correlation between separate areas.
Athletic ability isn't intelligence. Intelligence may be a part of it (c.f. Wayne Gretzky's quote "skate to where the puck is going, not where it's been") but strength, speed, and stamina aren't.
Burmese pythons are way stronger than humans yet I doubt anyone would call them more intelligent. Mantis shrimp have way faster reflexes and their vision is way more sophisticated than ours yet they're not more intelligent as well.
There is a lot of debate over the definition of intelligence but one thing most definitions have in common is that it is general. A human being can play the piano, solve an integral, write a novel, compose a photograph, and many many more things. We do all of these things using the same "muscle", our brain. It turns out that some people's brains are faster at being able to learn all of these myriad tasks. That's all there is to it.
I think that's the main point where we disagree. I think there's a hell of a lot more to it.
I remember noticing when I was in school that while I could learn a variety of things, there were definitely subjects that were a lot easier for me and others in which I'd struggle no matter how hard I worked. I also observed other people with very different profiles of easy and hard subjects.
I barely passed organic chemistry. I studied hard, but it just would not "take." I had a friend who breezed through it. Anything that involved writing though? I'd breeze through it. I could get As in any class that involved understanding concepts and writing about them with almost no effort. This same friend struggled to write an essay.
I actually dropped out of vertebrate anatomy. Tons and tons of clades and names for discrete things. I found a way to adjust my major requirements to avoid taking it. Otherwise I would have had to switch majors. There is no way I could have passed that class with any amount of tutoring.
It's like I have a photographic memory for concepts, but can't recall discrete facts. I kept looking for the underlying patterns in organic chemistry but it turns out we don't understand it well enough to have those... we just know a ton of reactions that do things. I flounder badly in subjects whose facts can't be attached to an abstract conceptual framework, but I can read about a concept once and I understand it forever.
As a programmer I really love IDEs with "code insight" and other features that basically consult the documentation for you in real time. I get everything about the concepts and algorithms but can never remember the order of arguments on memcpy() even after using it for 20 years. I know memcpy() is there and what it does conceptually though, so it's fine since the IDE will show me which argument goes first.
My point is that different people who are "smart" can differ radically in their ability profile.
Yeah I think the notion of a scalar intelligence metric won't stand the test of time. Some people are born with golden hands and excel at everything they touch while others are over-achievers in narrow subjects only. Just think what a scalar intelligence concept would mean: there is only one single gene that gives rise to all of the variance in intelligence among humans. This can't be it, there are likely dozens if not hundreds of genes involved and each gene regulates a different aspect of brain function.
Even if you say that intelligence is a vector, well, vectors have norms. The perception I'm trying to counter here is that intelligence involves tradeoffs which somehow result in a fair outcome for all. That Bobby's writing ability is offset by Billy's chess-playing. That's not the case at all.
Take someone like John Von Neumann. One of the greatest mathematicians of the 20th century (and perhaps all time), he could also converse in a multitude of languages (native Hungarian, English, French, German, Italian, Ancient Greek). He was also an avid history buff, reading through Wilhelm Oncken's 46-volume Allgemeine Geschichte in Einzeldarstellungen at the age of 8 [1].
Later, he studied and became a chemical engineer while at the same time studying for his Ph.D in mathematics at the age of 20!
there is only one single gene that gives rise to all of the variance in intelligence among humans.
This does not follow. The claim is that some brains are better at learning than others. This means they can model and describe the complex relationship between ideas across a multitude of subject domains. It doesn't say anything about a single gene being responsible.
> The perception I'm trying to counter here is that intelligence involves tradeoffs which somehow result in a fair outcome for all.
Yeah a zero cost sum model is totally wrong. As are models which assume that everyone is born equally smart and all differences just come from training or whatever. Some people have better abilities and it doesn't imply that they are worse in other areas. But they might only have extraordinary performance in specific tasks. And others, like I've mentioned above, have golden hands, von Neumann being an example.
I've done some searching and the model of intelligence seems to be an open area of debate, but it seems that research has moved on from the "not much more than g" era. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6526477/
> For several decades, the question of whether measures of specific cognitive ability contributed anything meaningful to the prediction of performance on the job or performance in training once measures of general mental ability were taken into account appeared to be settled, and a consensus developed that there was little value in using specific ability measures in contexts where more general measures were available. It now appears that this consensus was premature, and that measures of specific abilities can make important contributions even if general measures are taken into account.
'g' is a scalar that tries to measure the relatively large chunk of correlation that tends to show up on aptitude tests.
On average, individuals who do well on one type of intellectual task tend to do well on most other types of intellectual tasks. Maybe someone is good at all forms of pattern recognition but just can't manage 3d-rotation tasks. It happens.
So an IQ test isn't perfect. There isn't one true value that exists in the world that is 'g'. Many tests now break out 'verbal' and 'mathematical' IQ, but I suspect even those have a correlation > 0.5
At school with me were 2 guys who were not that academic but they were natural sportsmen. They had something very evidently we didn't, and it's hard to put a finger on it. One became a semi-pro fooballer (football, UK). Their competence on the pitch or elsewhere was extraordinary.
Whatever that special thing is, once you see it in action you realise it's a genuine facet of something. You may not want to call it an intelligence, but I'm ok with doing so. It wasn't a physical thing, they weren't physically special, ergo it was a mental thing. "Intelligence" covers that quite well, I think.
I think the distinction being drawn by other commenters is that this physical learning ability is impressive and desirable, but not generally applicable to arbitrary problems to the same degree as whatever "g" might be.
ISWYM but I did carefully call it "an intelligence". I strongly agree what you say about it not being like, or obviously related to, or an aspect of, general intelligence, but I'm happy to call it 'an' intelligence. There's a risk you're defining something out of existence so as not to encroach on an existing definition. Or maybe you're right. This isn't my field.
> There is a lot of debate over the definition of intelligence but one thing most definitions have in common is that it is general. A human being can play the piano, solve an integral, write a novel, compose a photograph, and many many more things.
I believe generality in human intelligence is overrated. We can't do large multiplication in our heads, we can't solve the Salesman problem in our heads, and many other things we can't do because our intelligence is not in fact general. It has a set of skills and can combine them in many ways.
There are some direct biological 'g' factors that you can think of. One would be sleep quality, another brain volume. Also, brain damage events like concussions, and probably anesthesia, affect both athletic and mathematical performance. Thus, you should also expect there to be kinds of genetic and congenital developmental deficiencies that do the same.
Looking at relationships of reaction time/IQ as well as target practice/ASVAB score, they have a stronger relationship at the low end of the spectrum. Among kids in the 90% decile, you shouldn't see much of a relationship! They've got low difference in 'g' factor, I guess. But if you go down to the 20-50% range, you get, to increasing degrees, all those malcoordinated kids on the playground who seem like they were born with a pre-concussed brain.
Obviously, parental mating patterns affect some of the overall correlation, but the way the relationship is stronger at the lower end indicates the presence of true 'g' factors.
The theory of multiple intelligences is the work of Howard Gardner [1]. The core of Gardner's thesis is that there are weak correlations between these different abilities. Research shows the opposite: there are strong correlations between them [2]. This indicates that there is an underlying meta-ability that explains all of these different abilities. This meta-ability is usually called g and it's tested with IQ tests.
> This indicates that there is an underlying meta-ability that explains all of these different abilities.
It suggests that such a meta-ability might exist, but doesn't prove anything. It's also possible that these abilities are discrete but are correlated because they're subject to selection as a package... sort of like how physical coordination and endurance are probably selected for at the same time.
In any case the existence of such a meta-ability doesn't mean intelligence is one dimensional unless you re-define intelligence so as to exclude everything that can't be tied to that ability.
It's also possible that these abilities are discrete but are correlated because they're subject to selection as a package
It is also possible, but there's no evidence for it. There's no evidence for any of Gardner's work. The biggest criticism of his work is that there's no empirical basis for the classification of his "intelligences." Gardner even admits to this, saying the choices are the work of an "artistic judgment" [1].
"Chimps have better short term memory than humans, even on human oriented tasks... Short term memory is an important dimension of intelligence. By Kelly's argument, humans are not smarter than chimps; indeed, he would claim that "smarter than chimpanzee" is a meaningless concept. This is cold comfort to the chimps whose species survive only because we deign to allow it. It is colder comfort still to all those species we have already wiped out. It's also cold comfort to humans who might be worried about being wiped out by machines." -- Human Compatible by Stuart Russell, page 148
And then you keep thinking about how fast cereal is disappearing in your house, wondering whether you're just so sleepy in the morning you don't remember eating more and more of it - until one night you spot a string of AInts stealing your cereal, piece by piece. By the time you've realized your mistake, the colony has grown 100x in size and already spread to the two neighboring houses, being held back by the front line of an ongoing war with an AInt colony of your friend one street down.
You might need to extract ant pheromones and draw a circle with it, so that ants keep reinforcing that circular path attracting more ants, forming a death pit.
The title of the article does an awful job of summarising it. The main subject of the article is not about the level of intelligence of current or future AI, as the title suggsts. Instead, the article is a reflection on the progress in the field in the last few years and a discussion of the degree to which progress in deep learning has benefited, or harmed, AI research in general.
This is best summarised by the "Key Insights" box on top of the article:
> the recent successes of deep learning have revealed something very interesting about the structure of our world, yet this seems to be the least pursued and talked about topic today
> In AI, the key question today is not whether we should use model-based or function-based approaches but how to integrate and fuse them so we can realise their objective benefits
> We need a new generation of AI researchers who are well versed in an appreciate classical AI, machine learning, and computer science more broadly while also being informed about AI history.
The first "key point" refers to classes of functions that can be seen as "cognitive functions". For example, mapping a set of inputs to outputs can reasonably be considered as approximating some aspect of cognition when the inputs are regions of images and the subjects their labels, so that the function performs object recognition, a task that AI research has long considered an aspect of cognition. Understanding how such functions work has the potential to contribute to our understanding of human cognition, that has long been a major goal of AI research. Yet, in recent years, interest has shifted from understanding such results to applying them at the level of phone apps, etc.
The final key point is a call to arms. We can't make progress as a field by throwing out everything we've done before, everytime we achieve some success in a narrow range of tasks. The author witnessed this happenning in the 1980's with the rise and fall of expert systems -and the AI winter that followed. In modern times, the success of deep learning has all but eclipsed the deep knowledge that researchers in the field once possessed about symbolic logic and important avenues of research are impossible to follow because the younger generation of researchers simply don't have the necessary background - and are "bullied by the success" of neural networks into directing their careers towards neural network research, whatever their true interests.
On a personal level, not summarising the article anymore, the latter is the most disturbing development. Neural networks can perform "perceptual" tasks, but are wholly incapable of reasoning. Symbolic AI had reasoning down pat- and not in approximate fashion (as a recent trend in deep learning research attempts to perform it). Yet, we seem to have regressed and lost one ability to perform one set of cognitive tasks in the process of figuring out how to perform another.
In the past, AI researchers were well-rounded polymaths, versed in CS but also (continuous) mathematics, physics, psychology, linguistics... Nowadays, researchers seem to be optimising for a narrow band of knowledge and ignoring everything else. This cannot end well.
Francois Chollet tries to address some of these problems by tackling how intelligence is measured. Most AI systems are measured by task performance, but he argues that intelligence is about "skill-acquisition efficiency".
42 comments
[ 3.0 ms ] story [ 87.8 ms ] threadReal world fitness landscapes are complex and multidimensional.
My comment was a little snarky because while I like this site quite a bit for many reasons I do get a little fatigued by the prevalence of the IQ cult. The notion that there is a single metric on which brains can be sorted is sort of a sacred dogma around here, and one which to me is obviously quite wrong. All it takes is a casual glance at intelligence as it actually manifests in the only real world example of an intelligent system we have: biology. It's clearly a multidimensional quality that can be optimized for a large range of things just like anything else.
If intelligence is multidimensional in all other living things, by what rationale would we conclude that human intelligence is one-dimensional?
... or ...
If virtually every engineered or evolved capability is subject to multi-dimensional optimization, then why not also intelligence?
I think regardless of whether or not AI is or ever will be multi-dimensional, that will depend on its ability to consider and make conclusions from many different single-dimensions - libraries if you will.
This doesn't mean that AI won't get smarter than us at everything we can do, but based on the fact we are not getting much smarter we have to ask: is that just a limit of biology? Or are there scaling limits to Intelligence? Organizations and societies are a great example of the scaling limits of "Intelligence", they bypass a lot of human limits to make gesalt intelligent agents more reliable and less prone to certain biases, but generally not meaningfully "smarter" than any individual, just more patient and consistent. There are clear limits on the scalability Intelligence, just throwing more computational power at the problem isn't enough for runaway-agi
“AI can’t even x, how would you expect it to ever possibly do y?”
It’s like saying:
“How could a baby, which can not even tie its own shoes, spell a simple word, or fill a cup with water, ever possibly grow up to write a book, manage a corporation, or formulate a strategy for fighting a war? Impossible!”
It’s almost as if they think babies can’t learn. The AI is not a baby though, they say. Yeah, it’s not. But it’s a constantly learning body of work that is not regressing and not slowing down. Why would it not get to ‘general’ eventually?
There are 0 examples of a man made construct reaching "general" intelligence (whatever fuzzy meaning you may attach to it); there is no reason to believe that because we can reach some arbitrary human milestone today ("be a world class Go player"), any other human milestone ("start and run a company, and be a mentor and inspiration to others") is plausible.
This is a very different case from e.g. "we built a prototype plane that can lift 15 ft off the ground for 10 seconds, we should be able to build one that can lift 10,000 feet for 10 hours", because in the former case we were beginning to have an understanding of aerodynamics that made that path plausible. We do not have such an understanding backing current ML work. That's well illustrated by the fact that only 6 years elapsed between the first manned flight and crossing the Atlantic by plane - practice caught up to theory really fast, as it tends to do in those cases. We are about 6 years in the current ML revolution - what is its equivalent to crossing the Atlantic?
The logic you're advocating for here is more along the lines of "I taught my dog how to sit - shouldn't be too hard to get him to sit in front of a computer and start writing Python code now that I've got the first half figured out".
(or - I taught my dog how to fetch - shouldn't be too hard to train him to be a full time food delivery employee, etc).
No reason? If you were an outside observer looking at the very first individuals of what you decided to call human, maybe. But after so many steps were made? You can argue that, before there was a plane, there was zero reason to thing that humans can ever create one. And you could've claimed that for pretty much anything else. And you'd generally see that they did get created. Why would artificial intelligence be fundamentally different? I'm not saying "we can do it in the next five years", but to say that there's no reason to believe that it will ever happen?
> We do not have such an understanding backing current ML work.
And we did not have the knowledge of aerodynamics before we started looking into throwing things, then keeping things in the air for longer than they would if they were thrown, and eventually flying them (yes, a bit simplified).
I don't think anyone has ever said that and meant it. Except to the very naive, it's pretty obvious that a child growing up into a functional adult is a function of training volume and adaptation. On the other hand, training volume for "AI" tends to be huge and yet there are definite strata that these machines seem confined to.
I agree that that form of argumentation is meaningless though. AI is obviously improved over time, but as far as I know that's due to external modification and not just more training.
What is bullshit is the formal theory of multiple intelligences, which is largely PC-inspired pseudoscience and does not account for the correlation between separate areas.
A car's speed and its ability to drive off-road are highly correlated?
Rocket thrust and specific impulse are highly correlated? (Hint: these are inversely correlated.)
I didn't come to this through PC. I came to it through a basic understanding of learning and optimization.
Burmese pythons are way stronger than humans yet I doubt anyone would call them more intelligent. Mantis shrimp have way faster reflexes and their vision is way more sophisticated than ours yet they're not more intelligent as well.
There is a lot of debate over the definition of intelligence but one thing most definitions have in common is that it is general. A human being can play the piano, solve an integral, write a novel, compose a photograph, and many many more things. We do all of these things using the same "muscle", our brain. It turns out that some people's brains are faster at being able to learn all of these myriad tasks. That's all there is to it.
I think that's the main point where we disagree. I think there's a hell of a lot more to it.
I remember noticing when I was in school that while I could learn a variety of things, there were definitely subjects that were a lot easier for me and others in which I'd struggle no matter how hard I worked. I also observed other people with very different profiles of easy and hard subjects.
I barely passed organic chemistry. I studied hard, but it just would not "take." I had a friend who breezed through it. Anything that involved writing though? I'd breeze through it. I could get As in any class that involved understanding concepts and writing about them with almost no effort. This same friend struggled to write an essay.
I actually dropped out of vertebrate anatomy. Tons and tons of clades and names for discrete things. I found a way to adjust my major requirements to avoid taking it. Otherwise I would have had to switch majors. There is no way I could have passed that class with any amount of tutoring.
It's like I have a photographic memory for concepts, but can't recall discrete facts. I kept looking for the underlying patterns in organic chemistry but it turns out we don't understand it well enough to have those... we just know a ton of reactions that do things. I flounder badly in subjects whose facts can't be attached to an abstract conceptual framework, but I can read about a concept once and I understand it forever.
As a programmer I really love IDEs with "code insight" and other features that basically consult the documentation for you in real time. I get everything about the concepts and algorithms but can never remember the order of arguments on memcpy() even after using it for 20 years. I know memcpy() is there and what it does conceptually though, so it's fine since the IDE will show me which argument goes first.
My point is that different people who are "smart" can differ radically in their ability profile.
Take someone like John Von Neumann. One of the greatest mathematicians of the 20th century (and perhaps all time), he could also converse in a multitude of languages (native Hungarian, English, French, German, Italian, Ancient Greek). He was also an avid history buff, reading through Wilhelm Oncken's 46-volume Allgemeine Geschichte in Einzeldarstellungen at the age of 8 [1].
Later, he studied and became a chemical engineer while at the same time studying for his Ph.D in mathematics at the age of 20!
there is only one single gene that gives rise to all of the variance in intelligence among humans.
This does not follow. The claim is that some brains are better at learning than others. This means they can model and describe the complex relationship between ideas across a multitude of subject domains. It doesn't say anything about a single gene being responsible.
[1] https://en.wikipedia.org/wiki/John_von_Neumann#Child_prodigy
Yeah a zero cost sum model is totally wrong. As are models which assume that everyone is born equally smart and all differences just come from training or whatever. Some people have better abilities and it doesn't imply that they are worse in other areas. But they might only have extraordinary performance in specific tasks. And others, like I've mentioned above, have golden hands, von Neumann being an example.
I've done some searching and the model of intelligence seems to be an open area of debate, but it seems that research has moved on from the "not much more than g" era. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6526477/
> For several decades, the question of whether measures of specific cognitive ability contributed anything meaningful to the prediction of performance on the job or performance in training once measures of general mental ability were taken into account appeared to be settled, and a consensus developed that there was little value in using specific ability measures in contexts where more general measures were available. It now appears that this consensus was premature, and that measures of specific abilities can make important contributions even if general measures are taken into account.
See also https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480721/ and https://doi.org/10.1111/j.1744-6570.2010.01182.x
'g' is a scalar that tries to measure the relatively large chunk of correlation that tends to show up on aptitude tests.
On average, individuals who do well on one type of intellectual task tend to do well on most other types of intellectual tasks. Maybe someone is good at all forms of pattern recognition but just can't manage 3d-rotation tasks. It happens.
So an IQ test isn't perfect. There isn't one true value that exists in the world that is 'g'. Many tests now break out 'verbal' and 'mathematical' IQ, but I suspect even those have a correlation > 0.5
Whatever that special thing is, once you see it in action you realise it's a genuine facet of something. You may not want to call it an intelligence, but I'm ok with doing so. It wasn't a physical thing, they weren't physically special, ergo it was a mental thing. "Intelligence" covers that quite well, I think.
There is a nice paper on this topic.
On the Measure of Intelligence - François Chollet https://arxiv.org/abs/1911.01547
I believe generality in human intelligence is overrated. We can't do large multiplication in our heads, we can't solve the Salesman problem in our heads, and many other things we can't do because our intelligence is not in fact general. It has a set of skills and can combine them in many ways.
There are some direct biological 'g' factors that you can think of. One would be sleep quality, another brain volume. Also, brain damage events like concussions, and probably anesthesia, affect both athletic and mathematical performance. Thus, you should also expect there to be kinds of genetic and congenital developmental deficiencies that do the same.
Looking at relationships of reaction time/IQ as well as target practice/ASVAB score, they have a stronger relationship at the low end of the spectrum. Among kids in the 90% decile, you shouldn't see much of a relationship! They've got low difference in 'g' factor, I guess. But if you go down to the 20-50% range, you get, to increasing degrees, all those malcoordinated kids on the playground who seem like they were born with a pre-concussed brain.
Obviously, parental mating patterns affect some of the overall correlation, but the way the relationship is stronger at the lower end indicates the presence of true 'g' factors.
[1] https://en.wikipedia.org/wiki/Theory_of_multiple_intelligenc...
[2] https://en.wikipedia.org/wiki/Theory_of_multiple_intelligenc...
It suggests that such a meta-ability might exist, but doesn't prove anything. It's also possible that these abilities are discrete but are correlated because they're subject to selection as a package... sort of like how physical coordination and endurance are probably selected for at the same time.
In any case the existence of such a meta-ability doesn't mean intelligence is one dimensional unless you re-define intelligence so as to exclude everything that can't be tied to that ability.
It is also possible, but there's no evidence for it. There's no evidence for any of Gardner's work. The biggest criticism of his work is that there's no empirical basis for the classification of his "intelligences." Gardner even admits to this, saying the choices are the work of an "artistic judgment" [1].
[1] https://en.wikipedia.org/wiki/Theory_of_multiple_intelligenc...
https://news.ycombinator.com/newsguidelines.html
Sometimes they fall into infinite loops killing the entire colony: Ant death spirals.
https://www.youtube.com/watch?v=N0HoqjxfvJ4
This is best summarised by the "Key Insights" box on top of the article:
> the recent successes of deep learning have revealed something very interesting about the structure of our world, yet this seems to be the least pursued and talked about topic today
> In AI, the key question today is not whether we should use model-based or function-based approaches but how to integrate and fuse them so we can realise their objective benefits
> We need a new generation of AI researchers who are well versed in an appreciate classical AI, machine learning, and computer science more broadly while also being informed about AI history.
The first "key point" refers to classes of functions that can be seen as "cognitive functions". For example, mapping a set of inputs to outputs can reasonably be considered as approximating some aspect of cognition when the inputs are regions of images and the subjects their labels, so that the function performs object recognition, a task that AI research has long considered an aspect of cognition. Understanding how such functions work has the potential to contribute to our understanding of human cognition, that has long been a major goal of AI research. Yet, in recent years, interest has shifted from understanding such results to applying them at the level of phone apps, etc.
The final key point is a call to arms. We can't make progress as a field by throwing out everything we've done before, everytime we achieve some success in a narrow range of tasks. The author witnessed this happenning in the 1980's with the rise and fall of expert systems -and the AI winter that followed. In modern times, the success of deep learning has all but eclipsed the deep knowledge that researchers in the field once possessed about symbolic logic and important avenues of research are impossible to follow because the younger generation of researchers simply don't have the necessary background - and are "bullied by the success" of neural networks into directing their careers towards neural network research, whatever their true interests.
On a personal level, not summarising the article anymore, the latter is the most disturbing development. Neural networks can perform "perceptual" tasks, but are wholly incapable of reasoning. Symbolic AI had reasoning down pat- and not in approximate fashion (as a recent trend in deep learning research attempts to perform it). Yet, we seem to have regressed and lost one ability to perform one set of cognitive tasks in the process of figuring out how to perform another.
In the past, AI researchers were well-rounded polymaths, versed in CS but also (continuous) mathematics, physics, psychology, linguistics... Nowadays, researchers seem to be optimising for a narrow band of knowledge and ignoring everything else. This cannot end well.
https://arxiv.org/abs/1911.01547