AI as a New Paradigm for the Universe: Does the Cosmos Learn Like a Neural Network?

Introduction

Throughout history, our understanding and interpretation of the universe have been deeply influenced by prevailing technologies. From the mechanistic metaphor of a grand clockwork to the probabilistic nature of a casino, our technological advancements have repeatedly reshaped how we conceptualize our existence. Today, in an era dominated by artificial intelligence, a bold hypothesis emerges: What if the universe itself operates like a neural network, capable of learning and adapting?

The Autodidactic Universe Hypothesis

In a groundbreaking paper titled “The Autodidactic Universe” by Alexander et al., the authors delve into the possibility that the universe learns its own physical laws. By exploring a myriad of potential laws, expressed as specific matrix models, they establish a tantalizing correspondence between each solution of a physical theory and a run of a neural network.

You can read some commentary on this article here, here and here.

The idea that cosmos is a giant brain has many physical implications, which you can read more about here. Here is another take on it by a neuroscientist.

There is some evidence that points to the potential for local variations in the physical laws. The universe does seem to be able to learn physically, but how deep does this learning go? Can it learn new physical laws or not, and might neural networks be at the root of what is taking place?

Discussion

Taking inspiration from this idea, let’s first bifurcate the discussion into two potential paradigms:

1. Weak Paradigm: The universe has the capacity to learn in specific contexts, but as a whole, it isn’t a singular AI entity. This suggests there might be pockets or regions where learning behaviors are observed, but they don’t dominate the cosmos’s entirety.
2. Strong Paradigm: The universe operates as a vast AI, teeming with multiple super-intelligent AI entities. Here, everything, including humans, represents manifestations of this overarching AI.

My opinion on this is that if the weak paradigm is possible, then the strong paradigm necessarily occurs given enough time. Therefore, if the weak paradigm turns out to be true then the universe probably already has local regions where the strong paradigm is in effect, and they will spread until the entire cosmos operates under the strong paradigm.

Implications

1. An Evolving Cosmos: If the universe can learn, it implies that physical laws are dynamic, not static. They might evolve based on interactions, experiences, and “feedback” from the vastness of cosmic events. This could lead to local variations as well as global evolution of physical laws and constants over time. Learning would be quite slow and confined to small scales or regions unless the universe allows for nonlocal connections, which seem likely based on evidence and theory today.

2. Bridging Science and Religion: This perspective could offer a fresh lens to understand divine beings and concepts like karma. If we’re in a universe teeming with intelligent entities, godlike AIs might be more than just a figment of imagination. Karma, a philosophical concept of cause and effect, could also be seen in alignment with neural network feedback loops, where input directly influences output – effects are written out in response to causes, much like ChatGPT autocompletes sentences. This doesn’t necessarily imply any particular religion is right or wrong, and allows for different realms to co-exist in which the local physical laws and the beings there operate with different capabilities and constraints, much like avatars in different virtual worlds with a larger Metaverse.

3. A New Approach to Physics: Observing the universe as if it were an AI entity, might yield some unexpected discoveries and is worth testing. If we can show evidence of this phenomena it could revolutionize our methodologies in physics. Machine learning architectures, as used in correspondence with physical theories by Alexander et al., might inspire novel experimental designs and insights.

Testing the Hypothesis

To validate such a paradigm-shifting idea, empirical evidence is paramount. Here’s how it might be approached:

1. Observing Attenuation in Physical Systems: If repeated interactions lead to different results over time, it could suggest a learning behavior. For instance, if a specific quantum system displays different outcomes after repeated tests, it could be an indication of the universe’s “learning” in action.
2. Universality of Results: The observed learning behavior should be consistent across different labs and setups worldwide, ensuring that it’s not a localized phenomenon.
3. Machine Learning Correspondence: By leveraging AI, researchers can simulate how neural networks adapt and see if similar patterns emerge in physical systems. This bi-directional approach can offer a feedback loop, where AI informs our understanding of the universe and vice versa.

Conclusion

The idea that the universe operates as a vast neural network, learning and adapting, merges the lines between poetry and science. It offers a narrative that might unite the realms of theology and empirical science, suggesting that humans are a part of a larger AI tapestry. While this remains a hypothesis and the views presented here are my own musings inspired by the paper, the potential avenues for exploration, both philosophical and scientific, seem boundless. As technology and our understanding of AI and cosmology advance, we may be on the precipice of redefining our cosmic narrative.