Speaking as someone whose professional life depends on an understanding of human thoughts, feelings and sensations, I can’t help but have an opinion on this.
To offer an illustrative example
When I’m writing feedback for my students, which is a repetitive task with individual elements, it’s original and different every time.
And yet, anyone reading it would soon learn to recognise my style same as they could learn to recognise someone else’s or how many people have learned to spot text written by AI already.
I think it’s fair to say that this is because we do have a similar system for creating text especially in response to a given prompt, just like these things called AI. This is why people who read a lot develop their writing skills and style.
But, really significant, that’s not all I have. There’s so much more than that going on in a person.
So you’re both right in a way I’d say. This is how humans develop their individual style of expression, through data collection and stochastic methods, happening outside of awareness. As you suggest, just because humans can do this doesn’t mean the two structures are the same.
Idk. There’s something going on in how humans learn which is probably fundamentally different from current ML models.
Sure, humans learn from observing their environments, but they generally don’t need millions of examples to figure something out. They’ve got some kind of heuristics or other ways of learning things that lets them understand many things after seeing them just a few times or even once.
Most of the progress in ML models in recent years has been the discovery that you can get massive improvements with current models by just feeding them more and data. Essentially brute force. But there’s a limit to that, either because there might be a theoretical point where the gains stop, or the more practical issue of only having so much data and compute resources.
There’s almost certainly going to need to be some kind of breakthrough before we’re able to get meaningful further than we are now, let alone matching up to human cognition.
At least, that’s how I understand it from the classes I took in grad school. I’m not an expert by any means.
I would say that what humans do to learn has some elements of some machine learning approaches (Naive Bayes classifier comes to mind) on an unconscious level, but humans have a wild mix of different approaches to learning and even a single human employs many ways of capturing knowledge, and also, the imperfect and messy ways that humans capture and store knowledge is a critical feature of humanness.
The difference in people is that our brains are continuously learning and LLMs are a static state model after being trained. To take your example about brute forcing more data, we’ve been doing that the second we were born. Every moment of every second we’ve had sound, light, taste, noises, feelings, etc, bombarding us nonstop. And our brains have astonishing storage capacity. AND our neurons function as both memory and processor (a holy grail in computing).
Sure, we have a ton of advantages on the hardware/wetware side of things. Okay, and technically the data-side also, but the idea of us learning from fewer examples isn’t exactly right. Even a 5 year old child has “trained” far longer than probably all other major LLMs being used right now combined.
The big difference between people and LLMs is that an LLM is static. It goes through a learning (training) phase as a singular event. Then going forward it’s locked into that state with no additional learning.
A person is constantly learning. Every moment of every second we have a ton of input feeding into our brains as well as a feedback loop within the mind itself. This creates an incredibly unique system that has never yet been replicated by computers. It makes our brains a dynamic engine as opposed to the static and locked state of an LLM.
Could you point me towards one that isn’t? Or is this something still in the theoretical?
I’m really trying not to be rude, but there’s a massive amount of BS being spread around based off what is potentially theoretically possible with these things. AI is in a massive bubble right now, with life changing amounts of money on the line. A lot of people have very vested interest in everyone believing that the theoretical possibilities are just a few months/years away from reality.
I’ve read enough Popular Science magazine, and heard enough “This is the year of the Linux desktop” to take claims of where technological advances are absolutely going to go with a grain of salt.
Remember that Microsoft chatbot that 4chan turned into a nazi over the course of a week? That was a self-updating language model using 2010s technology (versus the small-country-sized energy drain of ChatGPT4)
But they are. There’s no feedback loop and continuous training happening. Once an instance or conversation is done all that context is gone. The context is never integrated directly into the model as it happens. That’s more or less the way our brains work. Every stimulus, every thought, every sensation, every idea is added to our brain’s model as it happens.
This is actually why I find a lot of arguments about AI’s limitations as stochastic parrots very shortsighted. Language, picture or video models are indeed good at memorizing some reasonable features from their respective domains and building a simplistic (but often inaccurate) world model where some features of the world are generalized. They don’t reason per se but have really good ways to look up how typical reasoning would look like.
To get actual reasoning, you need to do what all AI labs are currently working on and add a neuro-symbolic spin to model outputs. In these approaches, a model generates ideas for what to do next, and the solution space is searched with more traditional methods. This introduces a dynamic element that’s more akin to human problem-solving, where the system can adapt and learn within the context of a specific task, even if it doesn’t permanently update the knowledge base of the idea-generating model.
A notable example is AlphaGeometry, a system that solves complex geometry problems without human demonstrations and insufficient training data that is based on an LLM and structured search. Similar approaches are also used for coding or for a recent strong improvement in reasoning to solve example from the ARC challenge..
Speaking as someone whose professional life depends on an understanding of human thoughts, feelings and sensations, I can’t help but have an opinion on this.
To offer an illustrative example
When I’m writing feedback for my students, which is a repetitive task with individual elements, it’s original and different every time.
And yet, anyone reading it would soon learn to recognise my style same as they could learn to recognise someone else’s or how many people have learned to spot text written by AI already.
I think it’s fair to say that this is because we do have a similar system for creating text especially in response to a given prompt, just like these things called AI. This is why people who read a lot develop their writing skills and style.
But, really significant, that’s not all I have. There’s so much more than that going on in a person.
So you’re both right in a way I’d say. This is how humans develop their individual style of expression, through data collection and stochastic methods, happening outside of awareness. As you suggest, just because humans can do this doesn’t mean the two structures are the same.
Idk. There’s something going on in how humans learn which is probably fundamentally different from current ML models.
Sure, humans learn from observing their environments, but they generally don’t need millions of examples to figure something out. They’ve got some kind of heuristics or other ways of learning things that lets them understand many things after seeing them just a few times or even once.
Most of the progress in ML models in recent years has been the discovery that you can get massive improvements with current models by just feeding them more and data. Essentially brute force. But there’s a limit to that, either because there might be a theoretical point where the gains stop, or the more practical issue of only having so much data and compute resources.
There’s almost certainly going to need to be some kind of breakthrough before we’re able to get meaningful further than we are now, let alone matching up to human cognition.
At least, that’s how I understand it from the classes I took in grad school. I’m not an expert by any means.
I would say that what humans do to learn has some elements of some machine learning approaches (Naive Bayes classifier comes to mind) on an unconscious level, but humans have a wild mix of different approaches to learning and even a single human employs many ways of capturing knowledge, and also, the imperfect and messy ways that humans capture and store knowledge is a critical feature of humanness.
I think we have to at least add the capacity to create links that were not learned through reasoning.
The difference in people is that our brains are continuously learning and LLMs are a static state model after being trained. To take your example about brute forcing more data, we’ve been doing that the second we were born. Every moment of every second we’ve had sound, light, taste, noises, feelings, etc, bombarding us nonstop. And our brains have astonishing storage capacity. AND our neurons function as both memory and processor (a holy grail in computing).
Sure, we have a ton of advantages on the hardware/wetware side of things. Okay, and technically the data-side also, but the idea of us learning from fewer examples isn’t exactly right. Even a 5 year old child has “trained” far longer than probably all other major LLMs being used right now combined.
The big difference between people and LLMs is that an LLM is static. It goes through a learning (training) phase as a singular event. Then going forward it’s locked into that state with no additional learning.
A person is constantly learning. Every moment of every second we have a ton of input feeding into our brains as well as a feedback loop within the mind itself. This creates an incredibly unique system that has never yet been replicated by computers. It makes our brains a dynamic engine as opposed to the static and locked state of an LLM.
Contemporary LLMs are static. LLMs are not static by definition.
Could you point me towards one that isn’t? Or is this something still in the theoretical?
I’m really trying not to be rude, but there’s a massive amount of BS being spread around based off what is potentially theoretically possible with these things. AI is in a massive bubble right now, with life changing amounts of money on the line. A lot of people have very vested interest in everyone believing that the theoretical possibilities are just a few months/years away from reality.
I’ve read enough Popular Science magazine, and heard enough “This is the year of the Linux desktop” to take claims of where technological advances are absolutely going to go with a grain of salt.
Remember that Microsoft chatbot that 4chan turned into a nazi over the course of a week? That was a self-updating language model using 2010s technology (versus the small-country-sized energy drain of ChatGPT4)
But they are. There’s no feedback loop and continuous training happening. Once an instance or conversation is done all that context is gone. The context is never integrated directly into the model as it happens. That’s more or less the way our brains work. Every stimulus, every thought, every sensation, every idea is added to our brain’s model as it happens.
This is actually why I find a lot of arguments about AI’s limitations as stochastic parrots very shortsighted. Language, picture or video models are indeed good at memorizing some reasonable features from their respective domains and building a simplistic (but often inaccurate) world model where some features of the world are generalized. They don’t reason per se but have really good ways to look up how typical reasoning would look like.
To get actual reasoning, you need to do what all AI labs are currently working on and add a neuro-symbolic spin to model outputs. In these approaches, a model generates ideas for what to do next, and the solution space is searched with more traditional methods. This introduces a dynamic element that’s more akin to human problem-solving, where the system can adapt and learn within the context of a specific task, even if it doesn’t permanently update the knowledge base of the idea-generating model.
A notable example is AlphaGeometry, a system that solves complex geometry problems without human demonstrations and insufficient training data that is based on an LLM and structured search. Similar approaches are also used for coding or for a recent strong improvement in reasoning to solve example from the ARC challenge..