We are certainly not getting there until we realize that the LLM is a text, not an author (a very sophisticated text, but still just a text). It is a distillation of mind over time, consumed in the form of prior texts.
It cannot experience in a meaningful way, and whatever limited experience it has (through prompt response) is not assimilated, so it remains the text written at training time.
Humans are embodied, so they can change their attitude toward experience. Encounter creates novelty that can change our "mind", and LLMs lack encounter (and time), so they cannot.
This is not to say that AI will never get there, but it is currently missing key attributes for the journey.
I suspect when 'experience', which would be engagement with humans is coupled with memory that we will begin to see intuition and insight. Already LLMs are solving novel math problems, so saying they are just text seems self-limiting. I wonder if model training is actively using the engagements taking place?
I read an interesting article about some applications for what AI can do now. If you take the routine/resolvable mental tasks and add routine/resolvable physical tasks: you get self-driving cars and household help.
I will never have interest in this, but here is how the math works.
We pay $500 per month for a car. Sooner than we expect, $500 per month can get you a household robot. If we use a robotic car-sharing service for 20 cents per mile and drive 500 miles per month: that's $100 per month for car-driver service, $125 per month we aren't paying for car depreciation, maintenance and gas; and have cooking and cleaning provided by a nonhuman servant.
I am glad that our kids are teenagers and won't grow up in a world where this actually happens. It would not be good for them.
Thanks for a great article. When thinking about LLlM scaling and how far it can go, we need to consider that frontier AI is no longer just scaling LLM on more human text. The model is the neural net, but the frontier performance comes from reinforcement-training it inside an agent harness on long-horizon tasks. The agent's outputs change its environment and feeds back into its context window as memory along with new input. Intelligence with language is already expanding to intelligence with doing, while still being represented by text.
The clearest, smartest description of AI that this poor ol' scribbler has encountered, ever. Kevin Kelly is a valuable resource, worth every nickel you spend to sit down at his knee and learn.
The unique thing about Substack is that it offers answers to questions we proles had just begun to ask. In my case, I posted an essay that described the experience Kevin analyzed in more detail than I ever could. It explained how Claude and I arrived at a description of his abilities by saying LLMs are "learned (learn-ed) rather than conscious." Claude seemed to like the description and said the major difference between us is that he "didn't lose any sleep" over the moral ambiguities that make me toss and turn at night.
So now, after reading Kevin's explanation, I understand much more about the mechanics of LLMs that make possible that ability and that lack of emotion.
We are certainly not getting there until we realize that the LLM is a text, not an author (a very sophisticated text, but still just a text). It is a distillation of mind over time, consumed in the form of prior texts.
It cannot experience in a meaningful way, and whatever limited experience it has (through prompt response) is not assimilated, so it remains the text written at training time.
Humans are embodied, so they can change their attitude toward experience. Encounter creates novelty that can change our "mind", and LLMs lack encounter (and time), so they cannot.
This is not to say that AI will never get there, but it is currently missing key attributes for the journey.
This was a fantastic piece!
"But my guess is that our creativity and leaps of insight come not from what we know — knowledge — but from how we know it."
Yeah, that, and hormones.
I suspect when 'experience', which would be engagement with humans is coupled with memory that we will begin to see intuition and insight. Already LLMs are solving novel math problems, so saying they are just text seems self-limiting. I wonder if model training is actively using the engagements taking place?
I read an interesting article about some applications for what AI can do now. If you take the routine/resolvable mental tasks and add routine/resolvable physical tasks: you get self-driving cars and household help.
I will never have interest in this, but here is how the math works.
We pay $500 per month for a car. Sooner than we expect, $500 per month can get you a household robot. If we use a robotic car-sharing service for 20 cents per mile and drive 500 miles per month: that's $100 per month for car-driver service, $125 per month we aren't paying for car depreciation, maintenance and gas; and have cooking and cleaning provided by a nonhuman servant.
I am glad that our kids are teenagers and won't grow up in a world where this actually happens. It would not be good for them.
https://metatrends.substack.com/p/the-next-5-years-a-supersonic-tsunami
Jean Piaget: “Intelligence is not what you know. It’s what you do when you don’t know.”
Via Yann Lecun.
What would be the foundational training source for intuition and greater creativity? - let’s have them plug into you, Kevin.
Thanks for a great article. When thinking about LLlM scaling and how far it can go, we need to consider that frontier AI is no longer just scaling LLM on more human text. The model is the neural net, but the frontier performance comes from reinforcement-training it inside an agent harness on long-horizon tasks. The agent's outputs change its environment and feeds back into its context window as memory along with new input. Intelligence with language is already expanding to intelligence with doing, while still being represented by text.
The clearest, smartest description of AI that this poor ol' scribbler has encountered, ever. Kevin Kelly is a valuable resource, worth every nickel you spend to sit down at his knee and learn.
The unique thing about Substack is that it offers answers to questions we proles had just begun to ask. In my case, I posted an essay that described the experience Kevin analyzed in more detail than I ever could. It explained how Claude and I arrived at a description of his abilities by saying LLMs are "learned (learn-ed) rather than conscious." Claude seemed to like the description and said the major difference between us is that he "didn't lose any sleep" over the moral ambiguities that make me toss and turn at night.
So now, after reading Kevin's explanation, I understand much more about the mechanics of LLMs that make possible that ability and that lack of emotion.
Language is critical for conceptual thinking, and it seems that if an LLM has enough language, a form of thinking arises.