HuShifang

I think this is a great explanation.

The "Ladder of Causation" proposed by Judea Pearl covers similar ground - "Rung 1” reasoning is the purely predictive work of ML models, "Rung 2" is the interactive optimization of reinforcement learning, and "Rung 3" is the counterfactual and casual reasoning / DGP construction and work of science. LLMs can parrot Rung 3 understanding from ingested texts but it can't generate it.

Of course, the hypothesis that they are sufficiently aware of everything -- existing and being nurtured solely to be slaughtered, having already seen many friends die, if female having had many children stolen from them -- to be traumatized to the point of utter emotional breakdown and unfeeling is also consistent with said observation. Humans who have survived war crimes and horrific battles tend to go pretty dead-eyed too. Death might even be welcomed, as a deliverance.

I'd just add, these three can be obtained from veg-friendly supplements too.

I'm not an expert in this, but...

> BNNs bring the following advantages over GPs: First, training large GPs is computationally expensive, and traditional training algorithms scale as the cube of the number of data points in the time series. In contrast, for a fixed width, training a BNN will often be approximately linear in the number of data points. Second, BNNs lend themselves better to GPU and TPU hardware acceleration than GP training operations.

If I'm not mistaken Hilbert Space Gaussian Processes (HSGPs) are O(mn+m) (where m is the number of basis functions, often something like m=30, m=60, or m=100), which is also a huge improvement over conventional GPs' O(n^3). I know that there are some constraints on HSGPs (e.g. they work best with stationary time series, and they're not quite as accurate, flexible, or readily interpretable or tunable as conventional GPs), but what would be the argument for an AutoBNN over an HSGP? Is it mainly about the lack of a need for domain expert input?

As an outsider to the "tech" scene, who did a PhD generals field on modern European intellectual history (which included a dollop of recent continental philosophy), it's endlessly fascinating to me how people in tech have fixated upon Girard. While a few of his ilk do come up in standard reading lists, he (generally) doesn't -- he is far more prominent vis-a-vis his peers in this discourse than in his "native" one. I suspect that this owes to path dependency and his metaphysics' compatibility with the industry's participants' socio-intellectual priors (so to speak).

I think one further bit of horror is, it has the potential to erode your memory of the lost person, piece by piece regressing your recollection of them to the mean (so to speak) of the model's training set. At some point, will the bereaved -- to use the example given here -- just passively accept that the lost loved one actually was the sort of person to write about quilting, even when they never did in life?

John Eliot Gardner's "Bach: Music in the Castle of Heaven" is a sort of musical biography featuring a (very) deep dive on the cantatas.

Yes, but as I said, I was talking about early Confucians.