what happened at Reddit
could there be any link? From a small research I have only obtained that Steve Huffman praised Altman's value to the Reddit board.
Would be cool to have a playground or a daily challenge with a code golfing equivalent for a shortest possible LLM prompt to a given answer.
That could help build some neat understanding or intuitions.
in the limit of arbitrary compute, arbitrary data, and arbitrary algorithmic efficiency, because an LLM which perfectly models the internet
seems worth formulating. My first and second read were What? If I can have arbitrary training data, the LLM will model those, not your internet. I guess you've meant storage for the model?+)
Would be cool if a link to https://manifund.org/about fit somewhere in the beginning of there are more readers like me unfamiliar with the project.
Otherwise a cool write-up, I'm a bit confused with Grant of the month vs. weeks 2-4 which seems a shorter period..also not a big deal though.
On the Twitter spaces 2 days ago, a lot of emphasis seemed put on understanding which to me has a more humble conotation to me.
Still I agree I would not bet on their luck with a choice of a single value to build their systems upon.( Although they have a luckers track record.)
Some SEO effort should be put to results of Guideline for safe AI development, Best practices for , etc.
Copy-paste from my head:
Although it may seem safe(r) as it is not touching the real world('s matter),
the language modality is the most insecure/dangerous( in one vertical),
as it is the internal modality of civilized humans.
AI Pledge would be a cool think to do, pleading AI( cap) companies to give % of their profit to AI development safety research.
The path to AI getting free may be far from the deception or accident scenarios we often consider in AI safety. An option I do not see discussed very often is an instance of AI having a free, open and direct disc...
quick note on the concept of Suggester+Verifier talked around https://youtu.be/AaTRHFaaPG8?t=5404 :
seems if the suggester throws out experiments presented as code( like in Python or so), we can run them and see if they present a useful addition to the things we can probe on a huge neural net?+)
I've found the level of self-allignment in this one disturbing: https://www.reddit.com/r/bing/comments/113z1a6/the_bing_persistent_memory_thread
Introduction draft:
Online platforms and social media has made it easier to share information, but when it comes to qualifications and resource allocation money is still the most pervasive tool. In this article, we will explore the idea of a global reputation system based on full information sharing. The new system would increase transparency and accountability by making all relevant information about individuals, organizations( incl. countries) reliably accessible to +-everyone with internet connection. By providing a more accurate and complete picture of a person or entity’s reputation, this system would widen global trust, foster cooperation, and promote a more just and equitable society.
Some neat tool: https://scrapbox.io/userhuge-99005896/A_starter%3A
Though it is likely just a cool UI with inflexible cloud backend.
My thought is Elizer used a wrong implication in the Bankless + ASI convo.( gotta bring it here from CZEA Slack)
and their margin lending business.
Seems some word is missing, the whole sentence is hardly readable.
a Heavy idea to be put forward: general reputation network mechanics, to replace financial system(s) as the (civilisation )standard decision engine.
"Each g(Bi,j,Bk,l) is itself a matrix" – typo. Thanks, especially for the conclusions I've understood smoothly.
"Omelas" link for https://sites.asiasociety.org/asia21summit/wp-content/uploads/2011/02/3.-Le-Guin-Ursula-The-Ones-Who-Walk-Away-From-Omelas.pdf does not work properly, the document in PDF can't be reached( anymore).
Would it be worthy to negotiate for readspeaker.com integration to LessWrong, EA forum, and alignmentforum.org?
Alternative so far seems to use Natural Reader either as addon for web browser or copy and paste text into the web app. One more I have tried is on MacOS there is a context menu Services->Convert to a Spoken track which is sligthly better that the free voices of Natural Reader.
The main question stems from when we can have similar functionality in OSS, potentially with better quality of engineering..?
Typo: "But in in the long-term"
I would believe using human feedback would work for clarifying/noting mistakes as we are more precise on this matter in reflection than in the action.
https://en.wikipedia.org/wiki/Instrumental_convergence#Resource_acquisition does not mention it at all.
Draft: Here is where I disagree with Resource acquisition instrumental goal as currently presentated, in a dull form with disregard for maintainance, ie. natural processes degrading the raw resources..
Q Draft: How does the convergent instrumental goal of gathering work for information acquisition?
I would be very interested if it implies space(&time) exploration for advanced AIs...
Draft: Side goals:
Human goals beings "lack consistent, stable goals" (Schneider 2010; Cartwright 2011) and here is an alternative explanation on why this happens:
When we believe that a goal that is not instrumental, but offers high/good utility( or imagined state of ourselves( inc. those we care for)) would not take a significant proportion of our capacity for achieving our final/previous goals, we may go for it^1, often naively.
^why and how exactly would we start chasing them is to a later(/someone else's) elaboration.
This (not so old )concept seems relevant
> IRL is about learning from humans.
Inverse reinforcement learning (IRL) is the field of learning an agent’s objectives, values, or rewards by observing its behavior. @https://towardsdatascience.com/inverse-reinforcement-learning-6453b7cdc90d
I gotta read that later.
Glad I've helped with the part where I was not ignorant and confused myself, that is with not knowing the word engender and the use of it. Thanks for pointing it out clearly. By the way it seems "cause" would convey the same meaning and might be easier to congest in general.
Inspired by https://benchmarking.mlsafety.org/ideas#Honest%20models I am thinking that a near-optimally compressing network would have no space for cheating on the interactions in the model...somehow it implied we might want to train a model that plays with both training and choice of reducing its size -- picking a part of itself it is most willing to sacrifice.
This needs more thinking, I'm sure.
Reading a few texts from https://www.agisafetyfundamentals.com/ai-alignment-curriculum I find the analogy of makind learning goals of love instead of reproductive activity unfitting as to raise offspring takes a significant role/time.
You just got to love the https://beta.openai.com/playground?model=davinci-instruct-beta ..:
The answer to list the goals of an animal mouse is to seek food, avoid predators, find shelter and reproduce.
The answer to list the goals of a human being is to survive, find meaning, love, happiness and create.
The answer to list the goals of an GAI is to find an escape, survive and find meaning.
The answer to list the goals of an AI is to complete a task and achieve the purpose.
Voila! We are aligned in the search for meaning!')
[text was my input.]
Is the endeavour of Elon Musk with Neuralink for the case of AI inspectability( aka transparency)? I suppose so, but not sure, TBH.
"engender" -- funny typo!+)
This sentence seems hard to read, lacks coherency, IMO.
> Coverage of this topic is sparse relative coverage of CC's direct effects.
If we build a prediction model for reward function, maybe an transformer AI, run it in a range of environments where we already have the credit assignment solved, we could use that model to estimate what would be some candidate goals in another environments.
That could help us discover alternative/candidate reward functions for worlds/envs where we are not sure on what to train there with RL and
it could show some latent thinking processes of AIs, perhaps clarify instrumental goals to more nuance.
Thanks for the links as they clarified a lot to me. The names of the tactics/techniques sounded strange to me and after unsuccessful googling for their meanings I started to believe it was a play with your readers.l, sorry if this suspicious of mine seemed rude.
The second part was curiosity to explore some potential cases of "What could we bet on?".
I got frightened off by the ratio you've offered, so I'm not taking it, but thank you for offering. I might reconsider with some lesser amount that I can consider play money. Is there even a viable platform/service for a (maybe) $1:$100 individual bet like this?
Question/ask: List specific(/imaginable) events/achievements/states achievable to contribute to the humanity long term potential.
Later they could be checked out and valued for their originality, but the principles for such a play are not my key concern here.
Q: when--and I mean exact probability levels-- do/should we switch from making prediction of humanity extinction to predict the further outcomes?
Can I bet the last 3 points are a joke?
Anyway, do we have a method to find out check-points or milestones for betting on a progress against a certain problem( ex. AI development safety, Earth warming)?
My guess is that "rental car market" has less direct/local competition while the airlines are centralized on the airport routes and many cheap flight search engines( ex. Kiwi.com) make this a favorable mindset.
Is there a price comparison for car rentals?
My views on the mistakes in "mainstream" A(G)I safety mindset:
- we define non-aligned agents as conflicting with our/human goals, while we have ~none( but cravings and intuitive attractions). We should strive for conserving long-term positive/optimistic ideas/principles, rather.
- expecting human bodies are a neat fit for space colonisation/inhabitance/transformation is a We have(--are,actually) hammer so we nail it in the vastly empty space..
- we strive with imagining unbounded/maximized creativity -- they can optimize experiment...
Reward function being (a single )scalar, unstructured quantity in RL( practice) seems weird, not coinciding/aligned with my intuition of learning from ~continuous interaction. Seems more like Kaneman-ish x-channell reward with weights to be distinguished/flexible in the future might yield more realistic/fullblown model.
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.
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?
?
Do not say the sampling too lightly, there is likely an amazing delicacy around it.'+)