Good. It’s dangerous to view AI as magic. I’ve had to debate way too many people who think they LLMs are actually intelligent. It’s dangerous to overestimate their capabilities lest we use them for tasks they can’t perform safely. It’s very powerful but the fact that it’s totally non deterministic and unpredictable means we need to very carefully design systems that rely on LLMs with heavy guards rails.
Not being combative or even disagreeing with you - purely out of curiosity, what do you think are the necessary and sufficient conditions of intelligence?
A worldview simulation it can use as a scratch pad for reasoning. I view reasoning as a set of simulated actions to convert a worldview from state a to state b.
It depends on how you define intelligence though. Normally people define it as human like, and I think there are 3 primary sub types of intelligence needed for cognizance, being reasoning, awareness, and knowledge. I think the current Gen is figuring out the knowledge type, but it needs to be combined with the other two to be complete.
Thanks! I’m not clear on what you mean by a worldview simulation as a scratch pad for reasoning. What would be an example of that process at work?
For sure, defining intelligence is non trivial. What clear the bar of intelligence, and what doesn’t, is not obvious to me. So that’s why I’m engaging here, it sounds like you’ve put a lot of thought into an answer. But I’m not sure I understand your terms.
A worldview is your current representational model of the world around you, so for example you know you’re a human on earth in a physical universe when a set of rules, you have a mental representation of your body and it’s capabilities, your location and the physicality of the things in your location. It can also be abstract things too, like your personality and your relationships and your understanding of what’s capable in the world.
Basically, you live in reality, but you need a way to store a representation of that reality in your mind in order to be able to interact with and understand that reality.
The simulation part is your ability to imagine manipulating that reality to achieve a goal, and if you break that down, you’re trying to convert reality from your perceived current real state A, to a imagined desired state B. Reasoning is coming up with a plan to convert the worldview from state A to state B step by step, so let’s say you want to brush your teeth, you a want to convert your worldview of you having dirty teeth to you having clean teeth, and to do that you reason that you need to follow a few steps to achieve that, like moving your body to the bathroom, retrieving tools (toothbrush and toothpaste) and applying mechanical action to your teeth to clean them. You created a step by step plan to change the state of your worldview to a new desired state you came up with. It doesn’t need to be physical either, it could be an abstract goal, like calculating a tip for a bill. It can also be a grand goal, like going to college or creating a mathematical proof.
LLMs don’t have a representational model of the world, they don’t have a working memory or a world simulation to use as a scratchpad for testing out reasoning. They just take a sequence of words and retrieve the next word that is probabilistically and relationally likely to be a good next word based on its training data.
They could be a really important cortex that can assist in developing a worldview model, but in their current granular state of being a single task AI model, they cannot do reasoning on their own.
Knowledge retrieval is an important component that assists in reasoning though, so it can still play a very important role in reasoning.
Interesting. I’m curious to know more about what you think of training datasets. Seems like they could be described as a stored representation of reality that maybe checks the boxes you laid out. It’s a very different structure of representation than what we have as animals, but I’m not sure it can be brushed off as trivial. The way an AI interacts with a training dataset is mechanistic, but as you describe, human worldviews can be described in mechanistic terms as well (I do X because I believe Y).
You haven’t said it, so I might be wrong, but are you pointing to freewill and imagination as somehow tied to intelligence in some necessary way?
I think worldview is all about simulation and maintaining state, it’s not really about making associations, but rather maintaining some kind of up to date and imaginary state that you can simulate on top of, to represent the world. I think it needs to be a very dynamic thing which is a pretty different paradigm to the ML training methodology.
Yes, I view these things as foundational to freewill and imagination, but I’m trying to think more low level than that. Simulation facilities imagination and reasoning facilities motivation which facilities free will.
Are those things necessary for intelligence? Well it depends on your definition and everyone has a different definition ranging from reciting information to full blown consciousness. Personally, I don’t really care about coming up with a rigid definition for it, it’s just a word, I care more about the attributes. I think LLMs are a good knowledge engine and knowledge is a component of intelligence.
I have a counter argument. From an evolutionary standpoint, if you keep doubling computer capacity exponentially isn’t it extraordinarily arrogant of humans to assume that their evolutionarily stagnant brains will remain relevant for much longer?
You can make the same argument about humans that you do AI, but from a biological and societal standpoint. Barring any jokes about certain political or geographical stereotypes, humans have gotten “smarter” that we used to be. We are very adaptable, and with improvements to diet and education, we have managed to stay ahead of the curve. We didn’t peak at hunter-gatherer. We didn’t stop at the Renaissance. And we blew right past the industrial revolution. I’m not going to channel my “Humanity, Fuck Yeah” inner wolf howl, but I have to give our biology props. The body is an amazing machine, and even though we can look at things like the current crop of AI and think, “Welp, that’s it, humans are done for,” I’m sure a lot of people thought the same at other pivotal moments in technological and societal advancement. Here I am, though, farting taco bell into my office chair and typing about it.
You can compare human intelligence to centuries ago on a simple linear scale. Neural density has not increased by any stretch of the imagination in the way that transistor density has. But I’m not just talking density I’m talking about scalability that is infinite. Infinite scale of knowledge and data.
Let’s face it people are already not that intelligent, we are smart enough to use the technology of other smarter people. And then there are computers, they are growing intelligently with an artificial evolutionary pressure being exerted on their development, and you’re telling me that that’s not going to continue to surpass us in every way? There is very little to stop computers from being intelligent on a galactic scale.
Computer power doesn’t scale infinitely, unless you mean building a world mind and powering if off of the spinning singularity at the center of the galaxy like a type 3 civilization, and that’s sci-fi stuff. We still have to worry about bandwidth, power, cooling, coding and everything else that going into running a computer. It doesn’t just “scale”. There is a lot that goes into it, and it does have a ceiling. Quantum computing may alleviate some of that, but I’ll hold my applause until we see some useful real world applications for it.
Furthermore, we still don’t understand how the mind works, yet. There are still secrets to unlock and ways to potentially augment and improve it. AI is great, and I fully support the advancement in technology, but don’t count out humans so quickly. We haven’t even gotten close to human level intelligence and GOFAI, and maybe we never will.
You can believe whatever you want, but I don’t think it’s arrogant to say what I did. You are basing your view of humanity on what you think humanity has done, and basing your view on AI based on what you think it will do. Those are fundamentally different and not comparable. If you want to talk about the science fiction future of AI, we should talk about the science fiction future of humanity as well. Let’s talk about augmenting ourselves, extending lifespans, and all of the good things that people think we’ll do in the coming centuries. If you want to look at humans and say that we haven’t evolved at all in the last 3000 years, then we should look at computers the same way. Computers haven’t “evolved” at all. They still do the same thing they always have. They do a lot more of it, but they don’t do anything “new”. We have found ways to increase the processing power, and the storage capacity, but a computer today has the same limits that the one that sent us to the moon had. It’s a computer, and incapable of original thought. You seem to believe that just because we throw more ram and processors at it that somehow that will change things, but it doesn’t. It just means we can do the same things, but faster. Eventually we’ll run out of things to process and data to store, but that won’t bring AI any closer to reality. We are climbing the mountain, but you speak like we have already crested. We’ve barely left base camp in the grand scheme of artificial intelligence.
Apart from your use of infinite I agree, there is no reason we shouldn’t be able to surpass nature with synthetic intelligence. The time computers have existed is a mere blip on a historic scale, and computers has surpassed us at logic games like Chess and at math already long ago.
Modern LLM models are just the current stage, before that it could be said it was pattern recognition. We had OCR in the 80’s as probably the most practical example. It may seem there is long between the breakthroughs, but 40 years is nothing compared to evolution.
I have no doubt strong AI will be achieved eventually, and when we do, I have no doubt AI will surpass our intelligence in every way very quickly.
As a counter argument against that, companies are trying to make self driving cars work for 20 years. Processing power has increased by a million and the things still get stuck. Pure processing power isn’t everything.
The masses have been treating it like actual magic since the early stages and are only slowly warming up to the idea it‘s calculations. Calculations of things that are often more than the sum of it‘s parts as people start to realize. Well some people anyway.
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I found this graph very clear
Good. It’s dangerous to view AI as magic. I’ve had to debate way too many people who think they LLMs are actually intelligent. It’s dangerous to overestimate their capabilities lest we use them for tasks they can’t perform safely. It’s very powerful but the fact that it’s totally non deterministic and unpredictable means we need to very carefully design systems that rely on LLMs with heavy guards rails.
Not being combative or even disagreeing with you - purely out of curiosity, what do you think are the necessary and sufficient conditions of intelligence?
A worldview simulation it can use as a scratch pad for reasoning. I view reasoning as a set of simulated actions to convert a worldview from state a to state b.
It depends on how you define intelligence though. Normally people define it as human like, and I think there are 3 primary sub types of intelligence needed for cognizance, being reasoning, awareness, and knowledge. I think the current Gen is figuring out the knowledge type, but it needs to be combined with the other two to be complete.
Thanks! I’m not clear on what you mean by a worldview simulation as a scratch pad for reasoning. What would be an example of that process at work?
For sure, defining intelligence is non trivial. What clear the bar of intelligence, and what doesn’t, is not obvious to me. So that’s why I’m engaging here, it sounds like you’ve put a lot of thought into an answer. But I’m not sure I understand your terms.
A worldview is your current representational model of the world around you, so for example you know you’re a human on earth in a physical universe when a set of rules, you have a mental representation of your body and it’s capabilities, your location and the physicality of the things in your location. It can also be abstract things too, like your personality and your relationships and your understanding of what’s capable in the world.
Basically, you live in reality, but you need a way to store a representation of that reality in your mind in order to be able to interact with and understand that reality.
The simulation part is your ability to imagine manipulating that reality to achieve a goal, and if you break that down, you’re trying to convert reality from your perceived current real state A, to a imagined desired state B. Reasoning is coming up with a plan to convert the worldview from state A to state B step by step, so let’s say you want to brush your teeth, you a want to convert your worldview of you having dirty teeth to you having clean teeth, and to do that you reason that you need to follow a few steps to achieve that, like moving your body to the bathroom, retrieving tools (toothbrush and toothpaste) and applying mechanical action to your teeth to clean them. You created a step by step plan to change the state of your worldview to a new desired state you came up with. It doesn’t need to be physical either, it could be an abstract goal, like calculating a tip for a bill. It can also be a grand goal, like going to college or creating a mathematical proof.
LLMs don’t have a representational model of the world, they don’t have a working memory or a world simulation to use as a scratchpad for testing out reasoning. They just take a sequence of words and retrieve the next word that is probabilistically and relationally likely to be a good next word based on its training data.
They could be a really important cortex that can assist in developing a worldview model, but in their current granular state of being a single task AI model, they cannot do reasoning on their own.
Knowledge retrieval is an important component that assists in reasoning though, so it can still play a very important role in reasoning.
Interesting. I’m curious to know more about what you think of training datasets. Seems like they could be described as a stored representation of reality that maybe checks the boxes you laid out. It’s a very different structure of representation than what we have as animals, but I’m not sure it can be brushed off as trivial. The way an AI interacts with a training dataset is mechanistic, but as you describe, human worldviews can be described in mechanistic terms as well (I do X because I believe Y).
You haven’t said it, so I might be wrong, but are you pointing to freewill and imagination as somehow tied to intelligence in some necessary way?
I think worldview is all about simulation and maintaining state, it’s not really about making associations, but rather maintaining some kind of up to date and imaginary state that you can simulate on top of, to represent the world. I think it needs to be a very dynamic thing which is a pretty different paradigm to the ML training methodology.
Yes, I view these things as foundational to freewill and imagination, but I’m trying to think more low level than that. Simulation facilities imagination and reasoning facilities motivation which facilities free will.
Are those things necessary for intelligence? Well it depends on your definition and everyone has a different definition ranging from reciting information to full blown consciousness. Personally, I don’t really care about coming up with a rigid definition for it, it’s just a word, I care more about the attributes. I think LLMs are a good knowledge engine and knowledge is a component of intelligence.
It’s well worth reading the longer newsletter the above link quotes: https://www.wheresyoured.at/sam-altman-fried/
I kinda agree we are probably cresting the peak of the hype cycle right now.
There’s magic?
Only if you believe in it. Many CEOs do. They’re very good in magical thinking.
I have a counter argument. From an evolutionary standpoint, if you keep doubling computer capacity exponentially isn’t it extraordinarily arrogant of humans to assume that their evolutionarily stagnant brains will remain relevant for much longer?
You can make the same argument about humans that you do AI, but from a biological and societal standpoint. Barring any jokes about certain political or geographical stereotypes, humans have gotten “smarter” that we used to be. We are very adaptable, and with improvements to diet and education, we have managed to stay ahead of the curve. We didn’t peak at hunter-gatherer. We didn’t stop at the Renaissance. And we blew right past the industrial revolution. I’m not going to channel my “Humanity, Fuck Yeah” inner wolf howl, but I have to give our biology props. The body is an amazing machine, and even though we can look at things like the current crop of AI and think, “Welp, that’s it, humans are done for,” I’m sure a lot of people thought the same at other pivotal moments in technological and societal advancement. Here I am, though, farting taco bell into my office chair and typing about it.
You can compare human intelligence to centuries ago on a simple linear scale. Neural density has not increased by any stretch of the imagination in the way that transistor density has. But I’m not just talking density I’m talking about scalability that is infinite. Infinite scale of knowledge and data.
Let’s face it people are already not that intelligent, we are smart enough to use the technology of other smarter people. And then there are computers, they are growing intelligently with an artificial evolutionary pressure being exerted on their development, and you’re telling me that that’s not going to continue to surpass us in every way? There is very little to stop computers from being intelligent on a galactic scale.
Computer power doesn’t scale infinitely, unless you mean building a world mind and powering if off of the spinning singularity at the center of the galaxy like a type 3 civilization, and that’s sci-fi stuff. We still have to worry about bandwidth, power, cooling, coding and everything else that going into running a computer. It doesn’t just “scale”. There is a lot that goes into it, and it does have a ceiling. Quantum computing may alleviate some of that, but I’ll hold my applause until we see some useful real world applications for it.
Furthermore, we still don’t understand how the mind works, yet. There are still secrets to unlock and ways to potentially augment and improve it. AI is great, and I fully support the advancement in technology, but don’t count out humans so quickly. We haven’t even gotten close to human level intelligence and GOFAI, and maybe we never will.
As I said that answer seems incredibly arrogant in the face of evolutionary pressure and logarithmic growth.
You can believe whatever you want, but I don’t think it’s arrogant to say what I did. You are basing your view of humanity on what you think humanity has done, and basing your view on AI based on what you think it will do. Those are fundamentally different and not comparable. If you want to talk about the science fiction future of AI, we should talk about the science fiction future of humanity as well. Let’s talk about augmenting ourselves, extending lifespans, and all of the good things that people think we’ll do in the coming centuries. If you want to look at humans and say that we haven’t evolved at all in the last 3000 years, then we should look at computers the same way. Computers haven’t “evolved” at all. They still do the same thing they always have. They do a lot more of it, but they don’t do anything “new”. We have found ways to increase the processing power, and the storage capacity, but a computer today has the same limits that the one that sent us to the moon had. It’s a computer, and incapable of original thought. You seem to believe that just because we throw more ram and processors at it that somehow that will change things, but it doesn’t. It just means we can do the same things, but faster. Eventually we’ll run out of things to process and data to store, but that won’t bring AI any closer to reality. We are climbing the mountain, but you speak like we have already crested. We’ve barely left base camp in the grand scheme of artificial intelligence.
Apart from your use of infinite I agree, there is no reason we shouldn’t be able to surpass nature with synthetic intelligence. The time computers have existed is a mere blip on a historic scale, and computers has surpassed us at logic games like Chess and at math already long ago.
Modern LLM models are just the current stage, before that it could be said it was pattern recognition. We had OCR in the 80’s as probably the most practical example. It may seem there is long between the breakthroughs, but 40 years is nothing compared to evolution.
I have no doubt strong AI will be achieved eventually, and when we do, I have no doubt AI will surpass our intelligence in every way very quickly.
As a counter argument against that, companies are trying to make self driving cars work for 20 years. Processing power has increased by a million and the things still get stuck. Pure processing power isn’t everything.
If only.
The masses have been treating it like actual magic since the early stages and are only slowly warming up to the idea it‘s calculations. Calculations of things that are often more than the sum of it‘s parts as people start to realize. Well some people anyway.
Trying to make real and good use of AI generative models are cracks in the magic.