165
points
flaghacker
Joined 108 karma
https://github.com/KarelPeeters/
- flaghackerUsing this sampling-based approach you get correct covariance modeling for free. You have to only sample leaf values that are used in multiple places once per evaluation, but it looks like they do just that: https://github.com/mattt/Uncertain/blob/962d4cc802a2b179685d...
- Yes, the `regex` crate is also the regex engine used by ripgrep, both were developed by https://github.com/burntsushi.
- Out of curiosity, why are you still using 32-bit architectures?
- That switch-case gets optimized and compiled down to logic gates by the synthesis tools. It'll be a different set of gates from the original netlist (which might also have used a more regular grid structure for this), but it won't be _that_ different. It's not somehow running this switch-case in software emulation on a different CPU instantiated in this design.
- Matlab is one language that chooses the second. Every assignment a=b where b is a matrix creates a distinct copy of the matrix.
The interpreter is slightly more clever and instead implements this as a copy-on-writr mechanism, but that's just an implementation detail .
- Look like this was generated by an LLM.
- It means that instead of (only) doing convolution in spatial dimensions, it also(/instead) happens in the temporal dimension.
A good resource for the "instead" case: https://unit8.com/resources/temporal-convolutional-networks-...
The "also" case is an example of 3D convolution, an example of a paper that uses it: https://www.cv-foundation.org/openaccess/content_iccv_2015/p...
- Could you share the paper(s) about "backdoors" in physical constants? That doesn't make much sense at first sight.
- Sure, it's all slightly fuzzy. I don't think that detracts much from the my point though.
- For all emails sent to/from any Seattle owned email address in 2017, please provide the following information:
1. From address 2. To address 3. bcc addresses 4. cc addresses 5. Time 6. Date
Is this really a reasonable request that the government is expected to answer? Doesn't this expose a bunch of private information about government employees and the people they interact with? I understand this post (and apparently the law) takes this as completely normal thing, but it seems really weird to me.
Some examples:
* exact times people are getting in/out the office (eg. the time in the morning when a person first answers an email from their boss)
* full information about holidays taken by all employees (eg. days/weeks during which no emails are sent)
* friendships or relationships (eg. any communication between employees that doesn't follow from the hierarchy or from team delineations)
* information from criminal investigations (eg. an investigator sending an email to the parking fine department probably means one of the cases they're working on is related to parking fines)
This all seems a huge privacy leak? Should this stuff even be called "metadata" if so much can be derived from it?
- Interesting, what could power companies use this for? Checking for overgrown power lines? Checking for roofs to place solar panels one?
- Could you share the repo? I've been curious what nontrivial parsers look like woth nom, I always lose the overview pretty quickly.
- It's important to note that these probe models are very simple - they're "trained" by just doing a linear regression between the hidden activations and the desired output. This means that the probes can barely do any computation themselves, so if they work at all this is a strong indication that the signal they predict was already in the hidden activations.
For even more proof, see "Figure 5: Space and time neurons in Llama-2 models" for single neurons in LLMs that already encode this information, without even having to use a probe model to extract it.
- What value does this label have for English localization?
- That sounds interesting, can you give some examples? Is this limited to classical RL Q-learning type stuff for turn based games, or full AlphaStar-level agents for RTS games?
- I'm not an expert, but some thoughts:
* The problem with large vectors is that they have large dot products with every other vector, which would imply that they are more similar to everything which doesn't make sense.
* Adding the requirement that "length==1" doesn't matter much in high-dimensional spaces, since that only removes one degree of freedom. Don't try to use too much 3D intuition here.
* It might be intuitive to think that "large" should have implications for the size of the vectors, but that really only applies to a couple of examples. We want vectors to represent thousands of unrelated concepts, so this one case is really not that relevant or important.
* In reality what ends up happening is partially the "very" dimension you're suggesting, but also just a "largeness" dimension. Individual dimensions can still have a scale!
- What scene in the expanse is that?
- That sounds interesting, what does the JVM do for tree structures and linked lists? Is that just about compacting memory?
- Tokio is using the Rust async features, which are not green threads. In the former code has to explicitly mark potential yield points, in the latter green threads can be scheduled by the runtime without any help from the code itself.
As a historical note, Rust used to have green threads but they were abandoned a long time ago. This is a good talk about both the differences between different forms of concurrency/async and Rusts history with them: https://www.infoq.com/presentations/rust-2019/ (includes a transcript)