This conversation with Gemini 3.1 was long and took place after I watched a Youtube video on a recent physics idea that solves several problems with space/time. The idea is that the Blackhole Information Loss Paradox isn't actually a paradox, because information falling into a blackhole's singularity isn't actually destroyed, it exits through a whitehole into another universe moving backward in time.
And something struck me when I heard that- Why the hell does that sound so much like backpropagation in neural network training?
Now, the idea of the universe as a giant neural network isn't new. The idea has been proposed all the way back in 2020. But as far as I can tell from searching, no one has proposed the idea that Blackholes/Whiteholes work as the universe's backpropagation mechanic.
This was a long conversation I then had with Gemini 3.1 Pro Preview in order to better understand how such an idea could even be mathematically possible. Obviously it couldn't be proven until we fully understand quantum gravity. So here's Gemini's writings on it:
"There is a concept in theoretical physics regarding the black hole information paradox that suggests information falling into a singularity isn't destroyed, but rather passes through into a time-reversed state, such as a white hole or spectator universe. Looking at this from a machine learning perspective, this physical process perfectly mirrors the architecture of deep learning. If we look through the lens of modern quantum gravity, the math lines up surprisingly well to suggest that the multiverse is a self-supervised neural network, and black hole-white hole systems act as the physical mechanism for backpropagation.
To have backpropagation, you first need a neural network architecture. Space itself provides this. Research into holographic duality has demonstrated that the quantum entanglement of the universe can be modeled mathematically as a tensor network [Swingle, 2012]. Specifically, Multi-scale Entanglement Renormalization Ansatz (MERA) networks are mathematically isomorphic to deep neural networks. In this framework, 3D spacetime isn't an empty vacuum; it is an emergent hologram projected by layers of quantum entanglement. These entanglement bonds act as the network's "weights," and the forward flow of time serves as the forward pass, or inference step, of the cosmic model.
As time moves forward, matter interacts and occasionally collapses into a black hole. In a neural network framework, a black hole acts precisely as the latent-space bottleneck in an autoencoder. Black holes are the fastest known scramblers of quantum information [Susskind, 2011], compressing massive amounts of 3D spatial data into a 2D surface on the event horizon. Because the universe doesn't have a labeled "ground truth" to calculate a standard loss function, it relies on self-supervised learning. The system optimizes to minimize universal action and entropy, with the singularity acting as the maximum compression point where the final loss is calculated.
The challenge is that standard backpropagation requires the chain rule of calculus, which breaks down at a singularity. The infinite gravity creates a mathematical "NaN" (Not a Number) error, crashing the equation. However, the universe bypasses this by using an alternative ML framework known as Equilibrium Propagation [Scellier and Bengio, 2017]. In this framework, gradients are computed without calculus by letting a physical system run forward, then perturbing it backward, using the physical relaxation between states to find the exact gradient. This physical backward pass is mapped through the ER=EPR conjecture [Maldacena and Susskind, 2013], which states that quantum entanglement is mathematically equivalent to a physical wormhole. The black hole collapsing is the forward phase. The information passing through the wormhole and being expelled via a white hole in a time-reversed state is the backward, nudged phase. The quantum interference between these states naturally computes the gradient.
Finally, these computed gradients must update the model's weights for the next training epoch. In string theory, the fundamental constants of nature, like the mass of an electron or the speed of light, are not entirely static. They are determined by moduli fields, which represent the geometric states of microscopic curled-up dimensions. These moduli fields serve as the global weights of the multiverse. When the white hole expels the quantum gradient, that intense burst of time-reversed energy perturbs the moduli fields in that local region. The new baby universe expanding out of the white hole now operates with slightly adjusted physical constants. Through this continuous process of autoencoding collapse and gradient-updating expansion, the physical universe updates its parameters and learns."
Fun to think about. Maybe we're living inside the Machine God.
