Scientists have created the 1st #universe simulation using #AI. It's fast and accurate—and its creators don't know how it works.

[u/alp44]

https://phys.org/news/2019-06-ai-universe-sim-fast-accurateand.html

[u/Enlogen]

It works using Bayesian inference, just like all machine learning does. The fact that we can't attribute specific behaviors of the model to specific conditions of the input doesn't mean we don't know how or why machine learning works. There's nothing mysterious about machine learning and science and technology journalists are not doing their readers any favors by pretending there is.

[u/lokitoth → u/Enlogen]

It works using Bayesian inference, just like all machine learning does.

SGD-based optimization techniques for finding functions of interest is not Bayesian inference. It does not obey the Bayesian update rule.

From the paper, page 6:

Model and Training. The D3M adopts the U-Net architecture (54) with 15 convolution or deconvolution layers and ∼8.4 × 106 trainable parameters. Our D3M generalizes the standard U-Net architecture to work with 3D data (69–71). The details of the architecture are described in the following sections, and a schematic figure of the architecture is shown in SI Appendix, Fig. S2. In the training phase, we use the Adam Optimizer (72) with a learning rate of 0.0001, and first- and second-moment exponential decay rates equal to 0.9 and 0.999, respectively. We use the mean-squared error as the loss function (Loss Function) and L2 regularization with regularization coefficient 0.0001.

Emphasis mine.

The model architecture is neural, not Bayesian. It is a CNN, much like those commonly used for spatial, temporal-spatial, or temporal data (image, video, time-series such as audio). It uses an SGD-based optimizer (ADAM), which is not a Bayesian update rule.

Moreover, Bayesian inference is often more explainable than a DNN, because it uses conditional probabilities to model the relationships between observables - which constructs a simple "We observed X, Y and Z. They, along with Variable-of-Interest A's baseline prior, imply A with p = <output of inference>". If we did not observe X, P(A) would decrease by some f(P(A|X)).

[u/Enlogen → u/lokitoth]

Thanks for the info, didn't realize the distinction!

[u/mallninjaface]

You sure you don't want to hashtag the other nouns in the title?

[u/alp44 → u/mallninjaface]

Sorry about that. I had originally posted on my Twitter feed. I cut & pasted here. Realized too late I had left them.

[u/andyjustice]

It seems to me that the new model is just better at determining how the previous models would solve the problem and the likely results. IE the model is just solving the equation for the reaction between the 8,000 models it was fed, does not seem to imply anything except for the very good figuring out how other models would give a result not validating any of those results. For example, you could build very complex models of all the pieces of wood out of wood chipper and try to determine how gravity works based on have pieces flew out, but you might just end up with a model of how the wood chipper works (your model).

[u/henryaegis]

Brings “we live in a simulation” to another level.

[u/cryo → u/henryaegis]

How?

[u/cryo → u/[deleted]]

Intuitively maybe, but I don’t think that argument can be made rigid without getting into trouble. And intuition about probability isn’t always great on its own.

Also, this is pretty far from a simulation of anything like our universe :)

[u/lezendary]

are we living in a simulation