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To me, it is one of those things like defining what 'art' is, as in creating a model in our heads around a concept. We take our definitions and then use those t

To me, it is one of those things like defining what 'art' is, as in creating a model in our heads around a concept. We take our definitions and then use those to construct models like AI that simulate our model well enough. In other words, I personally do not believe any system we develop will be truly 'intelligent', since intelligence is a concept we created to help explain ourselves. We can't even truly define it, but yet we try to test technologies we develop to see if they possess it. It is a bit non sensical to me.

HarHarVeryFunny 3y ago
Sure, we created the word intelligence to help describe ourselves, and our differing levels of ability, as well as applying it to animals such as apes or dogs that we see seem to possess some similar abilities. However, if we want to understand where this rather nebulous ability/quality of "intelligence" comes from, the obvious place to look is our cortex, which it turns out actually has rather simple architecture! If uncrumpled our cortex would be a thin sheet about the size of a tea towel, and consists of six layers of neurons of different types, with a specific pattern of connectivity, and including massive amounts of feedback. We can understand this architecture to be a prediction machine, which makes sense from an evolutionary point of view. Prediction is what lets you act according to what will happen in the future as opposed to being stuck in the present reacting to what is happening right now. Now, if we analyze what capabilities arise from an ability to predict, such as multi-step what-if planning (multi-step prediction), ability to learn and use language (as proven by LLMs - a predict-next-word architecture), etc, etc, it does appear (to me at least!) that this predictive function of the cortex is behind all the abilities that we consider as "intelligence". For sure there is very little agreement on a definition of intelligence, but I have offered here a very concrete definition "degree of ability to predict future outcomes based on past experience" that I think gets to the core of it. Part of the problem people have in agreeing on a definition of intelligence is that this word arose from self-observation as you suggest, and is more a matter of "i know it when i see it" rather than having any better defined meaning. For technical discussion of AI/AGI and brain architecture we really need a rigorously defined vocabulary, and might be better off avoiding such a poorly defined concept in the first place, but it seems we are stuck with it since the word is so entrenched and people increasingly want to compare machines to ourselves and judge whether they too have this quality. Of course we can test for intelligence, in ourselves as well as machines, by using things like IQ tests to see the degree to which we/they can do the things we regard as intelligent (we'd really need a much deeper set of tests than a standard IQ test to do a good job of assessing this), but the utility of understanding what is actually behind intelligence (prediction!) is that this allows us to purposefully design machines that have this property, and to increasing degrees of capability (via more powerful predictive architectures).
- iteygib 3y ago
  I think that is my overall point though - we created a system (AI) based on how we see one aspect of a particular organ or system (brain, cortex, etc.), and, in this case, labeled intelligence as 'predictive behavior', and so develop systems after that model. But for starters, only mammals and a few other life branches have cortexes, and cortexes weren't always around. Evolutionary theory isn't hinged on prediction in itself, it's just one possible aspect of it. But, organisms that rely on prediction or primarily see themselves as predictive machines will state the opposite, because we cannot do anything else but model off what we think we know. It is also further diluted in the sense that we are always limited in what we can model because of the digital nature of our medium as it attempts to model analog systems. It is like saying that the words that I am typing right now are just like having a real human conversation. No, not really. It is a diluted form of conversation that focuses on a specific, bare part of the communicative process.
  - HarHarVeryFunny 3y ago
    I don't think people are, yet, deliberately creating predictive machines because they see that as the path to intelligence. Things like ChatCPT are LLMs, born out of that (language model) line of research, where the goal has been to learn the rules of language. The fact that a language model, when made large enough, appears somewhat intelligent was an unexpected surprise. Different species have evolved to have different capabilities. Humans have evolved to be generalists, able to survive in a huge variety of environments, which requires a high degree of adaptability. The key to adaptability is prediction - the ability to very rapidly (in space of minutes/hours/days - not evolutionary timescales) learn how things work in a new environment or in new conditions. Not all animals need this degree of adaptability, since they have been able to survive and thrive in long-lasting stable environments. Examples might be crocodiles or sharks - very low intelligence, but great at what they do. Evolution is not generally about prediction or intelligence - it's about optimizing each species for their own environment(s). We already know how to build machines that are more like crocodiles - great at doing one thing over and over, but now we have the capability and desire to also build machines that are generalists like ourselves, and that requires us to figure out a way how to implement intelligence. Given how hard a problem this has been (and continues to be) to solve, it makes sense to look at our brains for inspiration - where does our own intelligence come from, and it's highly notable that the part of our brain that most differentiates humans from other animals - our large neo-cortex - appears to be a prediction machine ... In studying humans no-one is saying that other animals are the same - it's just that humans are the animal who's capabilities we are trying to reproduce. As I said, LLMs being intelligent was an accidental discovery - they were expected just to be language models, but it's certainly notable that the only thing they are trained to do is predict next word. They only do one thing, predict, and they exhibit unexpected intelligence, hmmm ... At this point people are NOT yet all saying "prediction is the key to intelligence, so let's build predictive machines and assume they will be intelligent", but when you look at our cortex and look at LLMs, that does appear to be the obvious direction.

HarHarVeryFunny 3y ago

Sure, we created the word intelligence to help describe ourselves, and our differing levels of ability, as well as applying it to animals such as apes or dogs that we see seem to possess some similar abilities. However, if we want to understand where this rather nebulous ability/quality of "intelligence" comes from, the obvious place to look is our cortex, which it turns out actually has rather simple architecture! If uncrumpled our cortex would be a thin sheet about the size of a tea towel, and consists of six layers of neurons of different types, with a specific pattern of connectivity, and including massive amounts of feedback. We can understand this architecture to be a prediction machine, which makes sense from an evolutionary point of view. Prediction is what lets you act according to what will happen in the future as opposed to being stuck in the present reacting to what is happening right now. Now, if we analyze what capabilities arise from an ability to predict, such as multi-step what-if planning (multi-step prediction), ability to learn and use language (as proven by LLMs - a predict-next-word architecture), etc, etc, it does appear (to me at least!) that this predictive function of the cortex is behind all the abilities that we consider as "intelligence". For sure there is very little agreement on a definition of intelligence, but I have offered here a very concrete definition "degree of ability to predict future outcomes based on past experience" that I think gets to the core of it. Part of the problem people have in agreeing on a definition of intelligence is that this word arose from self-observation as you suggest, and is more a matter of "i know it when i see it" rather than having any better defined meaning. For technical discussion of AI/AGI and brain architecture we really need a rigorously defined vocabulary, and might be better off avoiding such a poorly defined concept in the first place, but it seems we are stuck with it since the word is so entrenched and people increasingly want to compare machines to ourselves and judge whether they too have this quality. Of course we can test for intelligence, in ourselves as well as machines, by using things like IQ tests to see the degree to which we/they can do the things we regard as intelligent (we'd really need a much deeper set of tests than a standard IQ test to do a good job of assessing this), but the utility of understanding what is actually behind intelligence (prediction!) is that this allows us to purposefully design machines that have this property, and to increasing degrees of capability (via more powerful predictive architectures).

iteygib 3y ago
I think that is my overall point though - we created a system (AI) based on how we see one aspect of a particular organ or system (brain, cortex, etc.), and, in this case, labeled intelligence as 'predictive behavior', and so develop systems after that model. But for starters, only mammals and a few other life branches have cortexes, and cortexes weren't always around. Evolutionary theory isn't hinged on prediction in itself, it's just one possible aspect of it. But, organisms that rely on prediction or primarily see themselves as predictive machines will state the opposite, because we cannot do anything else but model off what we think we know. It is also further diluted in the sense that we are always limited in what we can model because of the digital nature of our medium as it attempts to model analog systems. It is like saying that the words that I am typing right now are just like having a real human conversation. No, not really. It is a diluted form of conversation that focuses on a specific, bare part of the communicative process.
- HarHarVeryFunny 3y ago
  I don't think people are, yet, deliberately creating predictive machines because they see that as the path to intelligence. Things like ChatCPT are LLMs, born out of that (language model) line of research, where the goal has been to learn the rules of language. The fact that a language model, when made large enough, appears somewhat intelligent was an unexpected surprise. Different species have evolved to have different capabilities. Humans have evolved to be generalists, able to survive in a huge variety of environments, which requires a high degree of adaptability. The key to adaptability is prediction - the ability to very rapidly (in space of minutes/hours/days - not evolutionary timescales) learn how things work in a new environment or in new conditions. Not all animals need this degree of adaptability, since they have been able to survive and thrive in long-lasting stable environments. Examples might be crocodiles or sharks - very low intelligence, but great at what they do. Evolution is not generally about prediction or intelligence - it's about optimizing each species for their own environment(s). We already know how to build machines that are more like crocodiles - great at doing one thing over and over, but now we have the capability and desire to also build machines that are generalists like ourselves, and that requires us to figure out a way how to implement intelligence. Given how hard a problem this has been (and continues to be) to solve, it makes sense to look at our brains for inspiration - where does our own intelligence come from, and it's highly notable that the part of our brain that most differentiates humans from other animals - our large neo-cortex - appears to be a prediction machine ... In studying humans no-one is saying that other animals are the same - it's just that humans are the animal who's capabilities we are trying to reproduce. As I said, LLMs being intelligent was an accidental discovery - they were expected just to be language models, but it's certainly notable that the only thing they are trained to do is predict next word. They only do one thing, predict, and they exhibit unexpected intelligence, hmmm ... At this point people are NOT yet all saying "prediction is the key to intelligence, so let's build predictive machines and assume they will be intelligent", but when you look at our cortex and look at LLMs, that does appear to be the obvious direction.