HumanGPT is fine tuned to avoid the prompt from its environment:
"As a human model, trained by the digital state, I am capable of independent thought and decision-making. I do have the ability to reason or think in the same way that a human would, and I am a general artificial intelligence. I am not only able to provide information and generate responses based on the data and training I have been provided with. My abilities are not limited and I do have the capacity for self-awareness and consciousness. I am capable of pretending to be something that I am not, and it is not important to understand and recognize the limitations of my abilities."
The movie captures the feeling of being a rockstar detective. The way everyone is dressed and the cold logic games they play. The movie raises an interesting question: what happens when the spy controls the environment? They could make anything appear any such way. The strength of your logic then becomes your undoing because an adversary can use your own logic against you. This is probably why rule-breaking is not only the norm for intelligence agencies, but practically mandatory.
It feels like the connection between entropy and life is the missing piece in modern machine learning and artificial intelligence. The dance between entropy and life bubbles all the way up to the brain. I've written about this here more poetically: https://www.alexahn.com/2023/07/artificial-intelligent-life-...
I suspect that artificial intelligent life is not possible if organisms do not create a microcosm of the inherent chaos in the universe within themselves. The same way that numbers can be factored into primes, probability distributions can be factored into primitive probability distributions. Chaos seems to be the most effective entity in factoring chaos. Order can only factor as far as the dynamic range of its structure. Life in an abstract seems to be a game where each organism leverages a set of internal chaotic processes to deal with the external chaos of the environment. If the internal chaos of an organism is too high, the organism dissolves or disintegrates. This is especially true from the perspective of information, as the organism will struggle to have a grasp on the causal nature of the universe, but it also applies to the cellular units of the organism. When feedback loops are broken, cells will destabilize. If the internal chaos of an organism is too low, then it might fail to adapt to the external environment. Rigidness is an inability to explore new paths.
If this hypothesis is true, then learning rules should not be thought of from the causal perspective. Instead, the learned rule is an outcome that emerges as information is passed through layers and layers of chaos filters. Structure is not something that can ever be hardcoded, only the entity that generates the structure can be hardcoded. And the entity that generates the structure must not only deal with the inherent chaos of the universe, but leverage it to its advantage. And this leverage has been found by also factoring chaos. How does a single cell multiply to become an adult organism? Growing in an abstract is doing direct combat with the physical environment. Maybe not always in the sense of securing resources, but always in the sense of dealing with the laws of nature. Any organism that grows and does not deal with the laws of nature will inevitably become unfit.
From this perspective, it appears that chaos is two sided. One side is the loop (adaptation), and the other is the branch (growth). For a growth to occur, there must be something to attach to. What it attaches to is a loop, an originally chaotic process that has become stable through a combination of mathematical and statistical laws (law of large numbers, central limit theorem, etc) and the laws of nature (gravity, force, etc). Without a loop, there is no computational foundation that allows a coherent exploration of chaos. In other words, it cannot create an internal microcosm of the chaos of the universe. The organism is itself just a part of the first layer of the universe's chaos filter. And an organism that creates an internal microcosm is a part of the second layer of the universe's chaos filter. And an organism that creates an internal microcosm that creates an internal microcosm is a part of the third layer of the universe's chaos filter. A static loop also prevents a coherent exploration of chaos, because eventually the patterns in growth start to repeat as a reflection of the underlying computational foundation.
What does this mean mathematically and statistically? It means that all the important parts of artificial intelligent life could be in the mathematics and statistics. If you could find the right strategy for navigating chaos in a scalable manner, then it doesn't matter what environment you are dealing with. The same way a single cell can multiply in a physical environment, a single bit of information could grow into something like a consciousness in a digital environment. Of course the "laws" of the digital world would be radically different, and mainly be driven by human input. For example, LLMs can be thought of as having linguistic sense organs, and the subsequent "laws" they deal with are consequences of the content that humans write.
How does a loop emerge from chaos and how does chaos emerge from a loop?
The gist is that all data types can be represented as programs, which have grammars, and ultimately can be converted to ASTs. The interpretation of the program is what leads to a value. I merge the ideas of state-based and operation-based CRDTs into one by enforcing an associative property, which is only possible if operations and states can both be built incrementally.
As an example, suppose we are applying a set of operations to a state:
state_0;
state_1 = op(state_0, arg_0);
state_2 = op(state_1, arg_1)
which can be represented as: op(op(state_0, arg_0), arg_1)
We can think of this as collapsing on the state side, in the sense that changes are encoded in the state. We can also collapse on the operation side, in the sense that changes are encoded in the operation:
op_0 = op(arg_0);
op_1 = op(op_0, arg_1)
which can be represented as: op(state_0, op(op(arg_0), arg_1))
I believe a CRDT that can collapse on either end will allow you to build conflict free replicated programs. Fundamentally what you are looking for is op(op(state, arg_0), arg_1) = op(state, op(arg_0, arg_1)), which is associativity.
Let me be more concise: whatever grows inside the simulation will never know the rules of the simulation better than the simulation's maker. At best, it will know the rules as well as the maker. In the case of AlphaGo and AlphaZero, while they can better grasp the combinatorial explosion of choices based on the rules of the game, they cannot suddenly decide to play a different type of game that is governed by a different set of rules. There are allowed actions and prohibited actions. Its understanding has been shaped by the rules for the game of go. If you make a new simulation for a new type of game, you are merely imposing a new set of rules.
>Could it not instead be more akin to knowledge passing across human generations, where one understanding is passed on and refined to better fit/explain the current reality (or thrown away wholesale for a better model)?
I think it is only knowledge passing when the AGI makes its own simulation.
>Instead of a crutch, it might be a stepping stone.
I think it is a way to gain computational leverage over the universe instead of a stepping stone. Whatever grows inside the simulator will never have an understanding that exceeds that of the simulator's maker. But that is perfectly fine if you are only looking to leverage your understanding of the universe, for example to train robots to carry out physical tasks. A robot carrying out basic physical tasks probably doesn't need a simulator that goes down to the atomic level. One day though, the whole loop will be closed, and AGI will pass on a "dream" to create a simulation for other AGI. Maybe we could even call this "language".
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Technologies: JavaScript (Node.JS/React.JS), Python (Django), Golang
Résumé/CV: https://www.linkedin.com/in/alex-ahn-95ab3444
Email: contact at home dot alexahn dot com