Martin Vlach

Wiki Contributions


As a variation of your thought experiment, I've pondered: How do you morally evaluate a life of a human who lives with some mental suffering during a day, but thrives in vivid and blissful dreams during their sleep time? 
  In a hypothetical adversary case one may even have dreams formed by their desires and the desires be made stronger by the daytime suffering. Intuitively it seems dissociative disorders might arise with a mechanism like this.

I'm fairly interested in that topic and wrote a short draft here explaining a few basic reasons to explicitly develop capabilities measuring tools as it would improve risk mitigations. What resonates from your question is that for 'known categories' we could start from what the papers recognise and dig deeper for more fine coarsed (sub-)capabilities.

Oh, good, I've contacted the owner and they responded it was necessary to get their IP address whitelisted by LW operators. That should resolve soon.

Draft for AI capabilities systematic evaluation development proposal:

The core idea here is that easier visibility of AI models' capabilities helps safety of development in multiple ways.

  1. Clearer situation awareness of safety research – Researchers can see where we are in various aspects and modalities, they get a track record/timeline of abilities developed which can be used as baseline for future estimates.
    • Division of capabilities can help create better models of components necessary for general intelligence. Perhaps a better understanding of cognitive abilities hierarchy can be extracted.
  2. Capabilities testing can be forced by regulatory policies to put most advanced systems under more scrutiny and/or safe(ty) design support. To state differently: better alignment of attention focus to emerging risk( of highly capable AIs).
    • Presumably smooth and well available testing infrastructure or tools are a prerequisite here.

The most obvious risks are:

  • Measure becoming a challenge and a goal, speeding up a furious developments of strong AI systems.
  • Technical difficulties of testing setup(s) and evaluation, especially handling the factor of randomness in mechanics(/output generation) of AI systems.

Q: Did anyone train an AI on video sequences where associated caption (descriptive, mostly) is given or generated from another system so that consequently, when the new system gets capable of:
+ describe a given scene accurately 

+ predict movements with both visual and/or textual form/representation

+ evaluate questions concerning the material/visible world, e.g. Does a fridge have wheels?  Which animals do we most likely to see on a flower?


"goal misgeneralization paper does does"–typo

"list of papers that it’s"→ that are

Can you please change/update the links for "git repo" and "Github repo"? One goes through a redirect which may possibly die out, the other points to tree/devel while the instructions in readme advise to build from and into master.

Shows only blank white page RN. Mind to update/delete it?

Maybe in cryptocurrency sector the Lunar-punk movement can be seen as a bet against national governments( which I use as proxy to civilisation here) being able to align the crypto-technologies with financial regulations.
This is a very convoluted area with high risks of investments though. I would look into ZK-rollups, ZCASH or XMR here considering such an investment.

Did you mis-edit? Anyway using that for mental visualisation might end up with structure \n__like \n____this \n______therefore…

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