" (...) the term technical is a red flag for me, as it is many times used not for the routine business of implementing ideas but for the parts, ideas and all, which are just hard to understand and many times contain the main novelties."
- Saharon Shelah
"A little learning is a dangerous thing ;
Drink deep, or taste not the Pierian spring" - Alexander Pope
As a true-born Dutchman I endorse Crocker's rules.
For my most of my writing see my short-forms (new shortform, old shortform)
Twitter: @FellowHominid
Personal website: https://sites.google.com/view/afdago/home
It seems nobody outside Ukraine/Russia is actually at the leading edge of where the reality of military technology is. That includes Hamas. Even using the drone doctrine from two years ago would be devastating to the Israelis. Probably they don't have the resources, organization to do so.
[Even Ukraine itself is not really there - there are clearly many simple ways drones and drone manufacturing could be improved they haven't had the time and resources to focus on yet. ]
Expect terror/resistance groups to start utilizing drones en masse in the next few years.
What about the latent adversarial training papers?
What about the Mechanistically Elicitating Latent Behaviours?
I've been told a Bayes net is "just" a functor from a free Cartesian category to a category of probability spaces /Markov Kernels.
I was intrigued by your claim that FFS is already subsumed by work on academia. I clicked the link you provided but from a quick skim it doesn't seem to do FFS or anything beyond the usual pearl causality story as far as I can tell. Maybe I am missing something - could you provide an specific page where you think FFS is being subsumed?
Great stuff Jeremy!
Two basic comments:
1. Classical Learning Theory is flawed and predicts that neural networks should overfit when they don't.
The correct way to understand this is through the lens of singular learning theory.
2. Quantilizing agents can actually be reflectively stable. There's work by Diffractor (Alex Appel) on this topic that should become public soon.
Yeah follow-up posts will definitely get into that!
To be clear: (1) the initial posts won't be about Crutchfield work yet - just introducing some background material and overarching philosophy (2) The claim isn't that standard measures of information theory are bad. To the contrary! If anything we hope these posts will be somewhat of an ode to information theory as a tool for interpretability.
Adam wanted to add a lot of academic caveats - I was adamant that we streamline the presentation to make it short and snappy for a general audience but it appears I might have overshot ! I will make an edit to clarify. Thank you!
I agree with you about the importance of Kolmogorov complexity philosophically and would love to read a follow-up post on your thoughts about Kolmogorov complexity and LLM interpretability:)
There is a general phenomena in mathematics [and outside maths as well!] where in a certain context/ theory we have two equivalent definitions of a concept that become inequivalent when we move to a more general context/theory . In our case we are moving from the concept of probability distributions to the concept of an imprecise distribution (i.e. a convex set of probability distributions, which in particular could be just one probability distribution). In this case the concepts of 'independence' and 'invariant under group action' will splinter into inequivalent concepts.
Example (splintering of Indepence) In classical probability theory there are three equivalent ways to state that a distribution is independent
1.
2.
3.
In imprecise probability these notions split into three inequivalent notions. The first is 'strong independence' or 'aleatoric independence'. The second and third are called 'irrelevance', i.e. knowing does not tell us anything about [or for 3 knowing does not tell us anything about ].
Example (splintering of invariance). There are often debates in foundations of probability, especially subjective Bayesian accounts about the 'right' prior. An ultra-Jaynesian point of view would argue that we are compelled to adopt a prior invariant under some symmetry if we do not posses subjective knowledge that breaks that symmetry ['epistemic invariance'], while a more frequentist or physicalist point of view would retort that we would need evidence that the system in question is in fact invariant under said symmetry ['aleatoric invariance']. In imprecise probability the notion of invariance under a symmetry splits into a weak 'epistemic' invariance and a strong 'aleatoric' invariance. Roughly spreaking, latter means that each individual distribution in the convex set , is invariant under the group action while the former just means that the convex set is closed under the action
The point isn't about goal misalignment but capability generalisation. It is surprising to some degree that just selecting on reproductive fitness through its proxies of being well-fed, social status etc humans have obtained the capability to go to the moon. It points toward a coherent notion & existence of 'general intelligence' as opposed to specific capabilities.
Drone countermeasures are an idle hope. The only real counter to drones is more drones.
Lasers, shotguns, tank redesign [no holes!], nets, counter-drones, flak etc will all be part of the arsenal surely but thinking drone countermeasures are going to restore the previous generation's war doctrine is as silly as thinking that metallurgy innovations will reverse the gunpowder age.
The
futurepresent of warfare is drones, drones, drones.