In a youtube video titled Why next-token prediction is enough for AGI - Ilya Sutskever (OpenAI Chief Scientist) Ilya explains pretty much what I've been also theorizing in private. So lets discuss theory on this.
I go further to expound on the reason Ilya's point might be the case If you assume that LLMs are frozen models, and in nature, living beings have continuous learning capabilities:
- I argue that in natural systems with multiple agents, the environment for each agent is the other agents. So in cellular systems, cells learn to predict what other cells will communicate with it and adjust it's own output communication as necessary, they form organs and organelles, which as a unit also learn to predict input from the environment and make an internal model so that they can choose how to behave within that environment.
- I argue that the act of learning to predict input and output by making internal models is everything ever does in natural systems.
- And I also argue that because of this, all of the phenomena like "intelligence", "Emotions", etc. are emergent properties of the process of creating these internal models at an "agent" level. The thing is that in the natural world, all agents are made up of other agents, so the effects of input and output are magnified by a very deep agential tree in which some output affects input of other levels of agents at any level.
Some Examples of this:
- One can argue that a human playing a role at a company is an agent in a multi-agent system which we denote as a company. The company has certain rules of behavior and protocols that humans in each role must follow. These rules, as communicated at a corporate level, are followed by the humans, and this even changes how they can and cannot interact with each-other, forming a model in the mind of each human to predict what humans in other roles might tell them, as well as generate an output in the form of task completion. The company might be large enough to divide into departments and teams, and those form another agential layer in the agent that is "the company"
- You can think about that same thing with cells in organs, organs in bodies, etc.
- Natural ecosystems and micro-ecosystems are agents and are made up of the agents at the level of flora and fauna
One might argue that in an LLM, you are just predicting tokens and everything is sequential so it can't be the case that our complex thought processes come from next-token prediction. To that, I say that the mind's predictions can be thought of as having a vocabulary of ever-changing tokens, and that time is what makes our "experiences" as living beings sequential, even though the modality of input is of much higher order than simply text, your brain can still take that all and encode it into a set of symbols which are easy to compress into memory and organize into a predictable sequence.
So then what is the point?
It could very well be that though the brain is a physical entity, the mind is only a dynamic emergent behavior of the predictions and consequential inference which the brain has learned to perform given it's environment. Consciousness, Intelligence, and basically every other concept we use as part of our communication are just observable emergent properties of a much larger model, but they only emerge because the environment prompts us to generate it, and we generate our actions token-by-token if you were to look at us from the perspective of the next agential level up.
What's more? Humans+LLM based agents idea is just getting really cooking this year... so you can think of us as all part of a larger multi-agent system which in itself is an agent. It's turtles all the way down. Holonic not Heirarchic.
For further Reading: https://arxiv.org/abs/2307.15936 is a paper from this year which goes into how Latent skills can be measured and quantified for large language models. or search for the youtube lecture on the same title on this paper.
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